Every formula & key number you need, organised by session.
C
Glossary
📖 Key Terms
Searchable A–Z reference of every AM term. From alpha to yield spread.
G
Equity Research
📊 Live Reports
Real analyst notes - Tesla (UBS), Adidas (Morningstar), Renault (Jefferies).
R
🎯 Core Exam Concepts - Know These Cold
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Asset Management BasicsAsset managers earn fees on AUM - they do not carry credit risk. Revenue falls when markets fall but so do variable costs.
CAPM FormulaE(r) = rf + β × [E(rm) − rf] Beta = systematic risk only. Specific risk is diversified away.
Portfolio Volatility (2 assets)σp = √(wA²σA² + wB²σB² + 2wAwBρσAσB) At ρ=−1 with equal weights and vols → volatility = 0%.
Alpha vs BetaAlpha (α) = excess return above risk-adjusted benchmark. Beta (β) = market sensitivity. β=1 moves 1:1 with market.
Session 1 · Slides pp. 1–43
Introduction to Asset Management
The business of investing client money for a fee - how asset managers earn revenue, the vehicles they use, the structural trends shaping the industry, and how to evaluate a fund manager.
Asset management is the business of investing client money for a fee. Unlike a bank, an asset manager does not place client money on its own balance sheet - it acts as an agent, earning fees on AUM.
Asset ManagerRevenue = % fee × AUM Management fee (0.5–2%) on assets managed. May charge performance fee (20% above benchmark).
✓ Low capital requirements ✓ High operating leverage ⚠ Revenue falls when markets fall
Bank (comparison)Revenue = Spread (lend high – borrow low) Takes deposits, lends at higher rates. Carries credit risk on balance sheet. Subject to Basel III capital requirements.
🔑 A bank owns the assets. An asset manager does not.
📝
Exam Trap: Asset managers carry reputational and performance risk - NOT credit risk.
Retail ClientsHNWI - investible assets ≥ $100M Mass Market - via mutual funds, ETFs Family Offices - single or multi-family Ultra-HNWI - private banking & bespoke mandates
🧠 Comprehension Check
An asset manager holds client money on its own balance sheet and earns revenue from the spread between borrowing and lending rates. True or false?
(a) True - asset managers work like banks
(b) False - asset managers act as agents, earning fees on AUM without balance-sheet risk
(b) is correct. Asset managers are agents: they invest client money but do not own it. They earn management and performance fees on AUM. Banks, by contrast, carry credit risk on their own balance sheets and earn a spread. This is the fundamental distinction - an asset manager carries reputational and performance risk, not credit risk.
Session 1›1.2 NAV & Fund Distribution
Investment Vehicles & NAV S1 pp. 4–6
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Vehicle
Description
Who uses it?
Liquidity
Separate Accounts
Portfolio owned directly by client, managed by AM
Large institutions
High
Mandates
Discretionary portfolio management instruction
Pension funds, SWFs
High
Mutual Funds / UCITs
Pooled vehicle, open-ended, daily NAV pricing
Retail, institutions
Daily
Limited Partnerships
PE/HF structure. GP manages, LP invests
Sophisticated investors
Low
SICAV
Société d'Investissement à Capital Variable
European retail/inst.
Daily
ETF
Exchange Traded Fund - trades intraday
All types
Intraday
- NAV FORMULA -
NAV = Net AUM / Number of Shares
Example: $100M AUM, 10M shares → NAV = $10/share
Open-end: shares created/redeemed daily at NAV
Closed-end: fixed shares, trades at premium/discount to NAV
Session 1›1.3 Investment Styles & Funds
Mutual Funds & Investment Styles S1 pp. 7–8, 14
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✅
Mutual Fund Advantages • Economies of scale • Professional management • Diversification • Liquidity (daily redemptions) • Transparency (NAV daily)
⚠
Mutual Fund Disadvantages • Fees erode returns (TER) • Negative alpha - most active funds underperform • No control over stock selection • Dilution risk on large flows
Style
Description
Risk
Example
Long Only
Buy & hold, benchmark-oriented
Lower
Fidelity, Vanguard
Hedge Funds
Long/short, absolute return, leverage
Higher
Bridgewater, Citadel
Private Equity
Illiquid, control investments, LBO/VC
Illiquid
KKR, Blackstone
Boutiques
Focused, concentrated alpha
Medium
Ariel, Artisan
Value Investing
Buy cheap vs intrinsic value
Lower
Berkshire Hathaway
🧠 Comprehension Check
Which of these is a disadvantage of mutual funds for the investor?
(a) Diversification across many securities
(b) Professional portfolio management
(c) Most active managers underperform their benchmark after fees
(d) Daily NAV transparency
(c) is correct. The biggest drawback of actively managed mutual funds is that roughly 80% underperform their benchmark over a 10-year horizon after accounting for management fees (TER). Options (a), (b), and (d) are advantages, not disadvantages.
Session 1›1.4 Revenue & Fee Model
Revenue Model & Fees S1 pp. 9–10
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Management FeeAnnual % of AUM regardless of performance. Traditional funds: 0.05%–1.0% Hedge funds: ~2% (“2 and 20” model) Private equity: 1.5%–2.0% on committed capital
Performance FeeShare of profits above a hurdle rate or benchmark. Hedge funds: typically 20% of profits High-water mark: no fee until previous peak is recovered
Revenue Model Characteristics✓ Low capital requirements - no balance sheet risk ✓ High operating leverage - costs don’t scale 1:1 ⚠ AUM-linked - markets down = revenues down ⚠ High talent costs - PMs are expensive
⚠
Industry Risk: Generating alpha consistently is very difficult. Most active managers underperform benchmarks after fees over the long run.
Org Structure & The 4Ps Framework S1 pp. 12–13
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Typical Asset Manager Org Chart
CEO
CIO
Mkt & Sales
Risk Control
Legal
Back-office
The 4Ps - How to Evaluate a Fund Manager
4P Framework (Slide p.13)1. Philosophy - What is the investment belief? 2. Process - Systematic or discretionary decisions? 3. People - Team stability, track record, depth 4. Performance - Alpha vs benchmark, risk-adjusted returns
Session 1›1.5 Ten Trends in AM
Part 2 - 10 Structural Trends
Slides pp. 15–43
① Increase of AUM S1 pp. 15–18
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Global AUM Growth 2005–2023 ($ Trillions) · Source: BCG / Thinking Ahead Institute
2023 total: $128T (+12.5% YoY). North America ~61% ($77.8T). Europe ~26% ($33.9T).
Growth DriversDiversification · Expertise · Transparency · Flexibility AUM grew from $36T (2005) → $128T (2023) - a ~3.6× increase. Institutional demand (pension funds) and democratization via mutual funds & ETFs are the primary engines.
$128TGlobal AUM 2023
45.5%Top 20 Firms Share
+12.5%YoY Growth 2023
61%North America Share
📝
Exam note: The course's slide cites ~69% North America (Towers Watson). TAI 2024 data shows ~61%. Use the slide figure (69%) in exams.
② Passive Management / ETF Growth S1 pp. 19–22
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Why Passive Is WinningCost: ETF fees as low as 0.03% vs 0.5–1.5% active Performance: ~80% of active equity managers underperform over 10yr Asset Allocation: ETFs enable cheap tactical shifts
Key Data (TAI 2024)Passive = 33.7% of total AUM (up from 31.7% in 2022) Passive grew +20.7% in 2023 vs +10.0% for active Big 3: BlackRock (iShares), Vanguard, State Street
Top 10 Asset Managers by AUM (2023)
#
Manager
HQ
AUM ($B)
Type
1
BlackRock
U.S.
$10,009
Independent
2
Vanguard Group
U.S.
$8,593
Mutual (investor-owned)
3
Fidelity Investments
U.S.
$4,582
Private/Independent
4
State Street Global
U.S.
$4,128
Bank-affiliated
5
J.P. Morgan A.M.
U.S.
~$3,000
Bank-affiliated
③ Alternative Investments Growth S1 pp. 23–28
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Why Alternatives Are GrowingLow rates (2010–2022): Bonds offered near-zero yields → investors sought returns elsewhere. Diversification: Low correlation to public equity and FI. Illiquidity premium: Extra return for locking up capital. Inefficient markets: More alpha available vs. highly efficient public markets.
📝
Exam: In alternatives, boutiques dominate because alpha cannot be scaled. This is the exception to industry concentration.
④ Lower Fees in Traditional Products S1 pp. 29–32
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Fee Compression DriversETF competition: Passive fees as low as 3bps - active managers forced to justify higher costs Scale of large players: BlackRock & Vanguard use size to compress fees Revenue shifting to alternatives where performance fees remain high (2+20) MiFID II: Forces unbundling of research & execution costs in Europe
⑤ Risk Control & Compliance S1 p. 33
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Post-2008 Regulatory WaveMiFID II - transparency in fees, best execution (EU) AIFMD - alternative investment fund managers directive UCITS IV/V - harmonized mutual fund rules across EU Dodd-Frank - systemic risk oversight (U.S.)
⑥ Asset Allocation as a Discipline S1 p. 34
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Growing ImportanceBrinson, Hood & Beebower (1986): Asset allocation explains ~90% of portfolio return variability. SAA vs TAA: Strategic (3–5yr) vs Tactical (short-term tilts). Covered in Sessions 10–11.
⑦ Megatrends - Marketing Tool or Alpha Source? S1 pp. 36–37
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✅
Argument FOR: Structural shifts (AI, demographics, energy transition) create long-duration themes with genuine return opportunities.
⚠
Course note: “All is in the price.” Widely-known trends are already reflected in valuations - no alpha for late buyers.
⑧ ESG - Environmental, Social & Governance S1 pp. 38–39
Key exam question:“What is the future of ESG?” - Present both sides: institutional momentum vs regulatory/performance headwinds.
⑨ Open Architecture S1 p. 40
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What It MeansClosed architecture (old): Banks only sold in-house funds. Captive distribution, conflict of interest. Open architecture: Distributors offer competing managers’ products. More fee competition → better outcomes for end-investors.
⑩ Concentration of the Industry S1 pp. 41–42
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Winner-Takes-Most in Traditional Products2023: Top 20 firms = 45.5% of total AUM (from ~41% in 2015) Exception: Alternatives - boutiques dominate because alpha cannot be scaled.
📝
Exam angle: Large managers win in traditional (scale → lower fees → more AUM). Boutiques win in alternatives (alpha degrades with size).
🔭 The Future of Asset Management S1 p. 43
▼
The course's closing slide (Session 1, p.43)
Likely Trends✓ AUM will continue growing ✓ ETFs and Alternative Investments diverge further ✓ AI integration accelerating (43% of top 500 already using AI)
In the current industry landscape, which type of firm dominates in alternative investments (hedge funds, PE)?
(a) Large, scaled asset managers like BlackRock and Vanguard
(b) Bank-affiliated managers with global distribution networks
(c) Boutique managers, because alpha cannot be scaled
(d) Sovereign wealth funds with permanent capital
(c) is correct. In traditional products, large managers win through scale economics (lower fees, broader distribution). But in alternatives, performance degrades as fund size grows - so boutiques with concentrated, high-conviction strategies dominate. This is the key exception to the industry concentration trend.
Sessions 2–3 · Slides pp. 1–52
Introduction to Portfolio Management
Modern Portfolio Theory, CAPM, the Security Market Line, diversification, alpha & beta - with interactive simulators you can play with live.
CAPM formula, SML simulator, CML vs SML, challenges to CAPM, anomalies.
6
2.7
Expected & Historical Returns
P = EPS × P/E, nominal vs real, value investing challenge.
7
Sessions 2–3›2.1 Key Concepts
📖 Core Vocabulary - Every Term You Must Know S2–3 pp. 3–4 · Port. Arch. Slide 2
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Term
Definition
Exam Use
Benchmark
Reference index against which a portfolio is measured (e.g. S&P 500, MSCI World)
Always specify which benchmark
Alpha (α)
Return above what CAPM predicts given the portfolio's beta. Jensen's alpha = actual − expected return
+ α = outperforms
Beta (β)
Sensitivity of asset return to market return. β=1 moves 1:1, β=2 moves 2× market, β=0 = risk-free
Systematic risk measure
Tracking Error
Standard deviation of the difference between portfolio returns and benchmark returns
Active risk measure
Long
Buy and hold an asset expecting it to rise
Most funds are long-only
Short
Borrow and sell an asset expecting it to fall; profit if price drops
HFs use long/short
Absolute Return
Target positive returns regardless of market direction (benchmark-agnostic)
Hedge fund approach
Active Management
Attempt to outperform benchmark through stock selection or timing
Generates alpha (or destroys it)
Passive Management
Replicate a benchmark exactly; no stock picking; low fees
ETFs, index funds
Asset Allocation
Decision on how to distribute capital across asset classes (equities, bonds, alternatives, cash)
~90% of return variation
Sharpe Ratio
(Portfolio return − Risk-free rate) / Portfolio std dev. Risk-adjusted return per unit of total risk
Higher = better
Information Ratio
Alpha / Tracking Error. Measures active return per unit of active risk
Active manager evaluation
📝
Exam pattern: Alpha and beta are tested constantly. Remember: alpha is about excess return after adjusting for risk. Beta is about sensitivity to market. They measure different things.
⚔️ Active vs Passive Management S2–3 pp. 5–7 · Port. Arch. Slide 5
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Active Management
Goal: outperform a benchmark (generate alpha)
Methods: fundamental analysis, technical analysis, or combination
Cost: higher fees (0.5–2%+ TER)
Reality: ~80% of active equity funds underperform their index over 10yr after fees
When it works: illiquid/inefficient markets (HY, EM, alternatives)
Passive Management
Goal: replicate a benchmark exactly (no alpha, no tracking error)
Methods: full replication or optimised sampling of index
Cost: very low (0.03–0.20% TER for ETFs)
Advantage: certainty of market return minus small fee
Limitation: cannot avoid market downturns; no customisation
📝
Key distinction: Active management uses both Technical Analysis (price, volume, short-term) and Fundamental Analysis (financials, valuation, medium-term). Passive uses neither - it simply replicates.
🧠 Comprehension Check
A portfolio manager generates 2% return above their benchmark after adjusting for risk. What is this excess return called?
(a) Beta
(b) Tracking error
(c) Alpha
(d) Sharpe ratio
(c) Alpha. Alpha is the excess return above a risk-adjusted benchmark. Beta measures market sensitivity, tracking error measures deviation from the benchmark, and the Sharpe ratio measures return per unit of total risk.
Sessions 2–3›2.2 Diversification & Risk
📐 Risk, Return & Volatility - The Basics S2–3 pp. 16–19
Arithmetic Mean (AM) = Sum of returns / n Use for: single period expected return
Geometric Mean (GM) = (∏HPRᵢ)^(1/n) − 1 Use for: long-run compounded performance
GM < AM always (unless returns are identical)
Risk MeasuresVariance (σ²) = Σ Pᵢ(Rᵢ − E[R])² Average squared deviation from mean
Standard Deviation (σ) = √Variance Most used risk measure - same units as return
Coefficient of Variation = σ / E[R] Use when comparing assets with different expected returns
── EXPECTED RETURN ──
E[R] = Σ Pᵢ × Rᵢ
where Pᵢ = probability of scenario i, Rᵢ = return in scenario i── VARIANCE (Expected Returns) ──
σ² = Σ Pᵢ × [Rᵢ − E(R)]²
── VARIANCE (Historical Returns) ──
σ² = Σ [HPYᵢ − AM]² / n
── COEFFICIENT OF VARIATION ──
CV = σ / E[R] (lower = better risk/return tradeoff)
📝
Exam trap: Use Geometric Mean for long-run comparison, Arithmetic Mean for single-period expected return. AM always ≥ GM (unless zero volatility).
🔗 Portfolio Volatility - The Diversification Equation S2–3 pp. 20–21
▼
The Key Insight (Slide p.20)Portfolio volatility ≠ weighted sum of individual volatilities.
The correlation between assets determines how much risk is actually reduced. This is the mathematical basis of diversification.
Most tested formula in the course. Know all three special cases cold. The ρ=−1 case (zero portfolio volatility) is a favourite exam question.
⚡ Interactive: Portfolio Volatility Simulator S2–3 p. 21
▼
Adjust the sliders to see how correlation and weights affect portfolio risk in real-time.
Asset A Volatility (σ_A)15%
Asset B Volatility (σ_B)20%
Correlation (ρ)0.00
Weight in A (w_A)50%
Loading...
Portfolio Vol vs Correlation
🔢 Real-World Correlation Matrix S2–3 p. 23
▼
The course's correlation matrix (Slide p.23). Notice how bonds and equities have negative or near-zero correlations - the foundation of a classic 60/40 portfolio.
Asset
Global Bonds
US Bonds
HY Bonds
S&P 500
MSCI World
MSCI EM
Commodities
Global Bonds (WGBI)
1.00
0.62
0.06
−0.05
0.03
−0.04
0.14
US Bonds
0.62
1.00
0.05
−0.23
−0.24
−0.24
0.08
HY Bonds
0.06
0.05
1.00
0.59
0.64
0.74
0.08
S&P 500
−0.05
−0.23
0.59
1.00
0.96
0.74
−0.01
MSCI World
0.03
−0.24
0.64
0.96
1.00
0.81
0.02
MSCI EM
−0.04
−0.24
0.74
0.74
0.81
1.00
0.14
Commodities
0.14
0.08
0.08
−0.01
0.02
0.14
1.00
💡
Key observation: US Bonds vs S&P 500 correlation = −0.23. This negative correlation is why a bond allocation reduces portfolio risk - bonds tend to rise when stocks fall. This is the classic 60/40 portfolio rationale.
⚠️
Crisis behaviour: In the 2022 rate-shock environment, bonds and stocks fell simultaneously - the correlation turned positive. This breaks the diversification assumption and is a major challenge for traditional 60/40 portfolios.
🧠 Comprehension Check
Two assets each have 20% volatility and equal weights. If their correlation is −1, what is the portfolio volatility?
(a) 20%
(b) 10%
(c) 0%
(d) 40%
(c) 0%. With perfect negative correlation, equal weights, and equal volatilities, the gains of one asset exactly offset the losses of the other.
Sessions 2–3›2.3 Technical vs Fundamental
Fundamental vs Technical Analysis S2–3 Summary
▼
Fundamental AnalysisAnalyses intrinsic value using financial statements, economic data, and industry trends.
Tools: DCF, P/E, EV/EBITDA, ROE, DuPont Timeframe: Medium to long-term Philosophy: Price will eventually converge to intrinsic value
Technical AnalysisAnalyses price patterns and trading volume to predict future price movements.
Tools: Moving averages, RSI, MACD, Bollinger Bands Timeframe: Short to medium-term Philosophy: All information is reflected in price; history repeats
📝
Exam note: The course emphasises that professional asset managers primarily use fundamental analysis. Technical analysis is more common among traders.
Sessions 2–3›2.4 Asset Classes & Cycles
📦 Asset Classes & The Investment Spectrum S2–3 pp. 8–14 · Port. Arch. Slide 7
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Asset Class
Examples
Expected Return
Risk
Liquidity
Money Market
T-Bills, CDs, commercial paper
Low (~risk-free rate)
Very low
Very high
Bonds / Fixed Income
Govt bonds, IG corporates, HY, EM debt
Low–Medium
Low–Med
High
Equity
Listed stocks, equity ETFs, REITs
Medium–High (~7% real hist.)
Medium
High
Alternative Investments
Hedge funds, PE, infrastructure, real estate
High (target)
High
Low
Currencies / FX
FX forwards, currency ETFs
Varies
Medium
High
The Investment Hierarchy (Slide p.14)Security Picking → stock-level decisions within asset classes Tactical AA (TAA) → short-term tilts away from strategic weights Strategic AA (SAA) → long-run target weights across asset classes
Brinson, Hood & Beebower (1986): SAA explains ~90% of return variability across portfolios.
Historical Returns (UBS Yearbook 2025 - 125 years, 1900–2024)Global equities: real return ~5% p.a. | Bonds: ~0.9% real p.a. | Bills: ~0.4% real p.a. Equity Risk Premium vs bills: ~4.3% (since 2000), ~5.2% historically US inflation: 2.9% p.a. average 1900–2024
⚙
Interactive Tools for This Section The following interactive components are embedded directly in the relevant sub-pages: • Market vs. Economic Cycles chart • Multi-Asset Investment Clock (Citi Research) • Equity Style Cycle - 4 Phases • Callan Periodic Table of Investment Returns (2004–2023)
Sessions 2–3›2.5 SAA, TAA & Security Picking
🏛️ The Three Tiers of Asset Allocation Port. Arch. Slide 6
▼
Every portfolio decision happens at one of three levels. The foundation determines everything - SAA is set first, TAA adjusts around it, and security picking fills in the details.
Level 3 - Security Picking (micro)
Identifying the specific individual equities, bonds, or funds to fulfil the SAA and TAA requirements. The most granular decision - comes last.
Intentional, short-term deviations from the SAA baseline to exploit current market anomalies or economic cycle positioning. TAA is always bounded by the SAA.
The long-term baseline. Sets the fixed target percentages of asset classes based on policy constraints and long-term risk tolerance. The BHB (1986) finding - SAA explains ~90% of return variability - makes this the single most important investment decision.
📝
Exam trap: TAA moves around SAA - it does not replace it. A pension fund with 60% equity SAA doing TAA might temporarily go to 55% or 65% equity, but never abandons the strategic baseline.
🔽 The 4P Investment Funnel Port. Arch. Slide 4
▼
The 4P funnel shows how an investment process flows from the broadest constraints down to specific securities. Every layer must be consistent - strategy must serve process, process must serve philosophy, philosophy must fit within policy.
Policy- the overarching map and constraints (IPS, risk limits, regulations)
Philosophy- the core belief system about how markets work (value? momentum? quant?)
Process- the systematic approach to executing the philosophy
Strategy- the specific tactical implementation
↓ Output: Resulting Securities
💡
"If you don't know where you are going, you're liable to end up somewhere else." - Yogi Berra. Without a clear policy and philosophy, every market shock triggers ad-hoc decisions that destroy long-run performance.
🧠 Comprehension Check
According to Brinson, Hood & Beebower (1986), what percentage of portfolio return variability is explained by the asset allocation decision?
(a) ~50%
(b) ~70%
(c) ~90%
(d) ~100%
(c) ~90%. The seminal BHB (1986) study found that strategic asset allocation explains approximately 90% of the variability in portfolio returns over time.
Sessions 2–3›2.6 Portfolio Theory & CAPM
🏛️ Markowitz Model - Assumptions & Efficient Frontier S2–3 pp. 26–34 · Port. Arch. Slide 12
▼
Key Markowitz Assumptions (Slide p.30)
• Investors maximise expected return for a given variance (mean-variance optimisation)
• Asset returns are jointly normally distributed
• Correlations are fixed and constant forever
• No taxes or transaction costs; all assets infinitely divisible
• All investors are rational and risk-averse
• All investors have the same information at the same time
⚠️
Criticism (Slide p.26): "Every model is a simplification of reality." Markowitz assumptions break down in practice - correlations spike in crises (2008), returns are not normally distributed (fat tails), and investors are not always rational.
Efficient Frontier - Portfolio Risk vs Return
Each dot = a portfolio combination. The curve = efficient frontier. Optimal portfolio = tangency point with the Capital Market Line (highest Sharpe ratio).
What the Efficient Frontier ShowsDominated portfolios: any portfolio below/right of the frontier offers worse risk/return Efficient portfolios: on the frontier - maximum return for a given risk level Optimal portfolio: the tangency point between the CML (Capital Market Line) and the efficient frontier - maximises the Sharpe Ratio CML: Connects the risk-free rate to the market portfolio. All investors should hold a mix of the risk-free asset and the market portfolio.
📐 CAPM - Formula, Assumptions & Intuition S2–3 pp. 27–37
▼
CAPM Assumptions (Slide p.27)
All investors: maximise utility, are rational & risk-averse, broadly diversified, are price takers, can borrow/lend at risk-free rate, have no taxes or transaction costs, have homogeneous expectations and the same information simultaneously.
The course's critique (Slide p.35): "Sharpe assumes beta equals risk." But beta only captures systematic risk. Value investing and factor models show that other risk factors (size, value, momentum) also earn premia that CAPM misses.
⚙
Interactive SML Simulator - Plot stocks on the Security Market Line, adjust risk-free rate and market return, and see which are above/below the SML. Use the embedded SML tool above to explore.
📊 CML vs SML - Key Distinctions S2–3 pp. 39–40
▼
Feature
Capital Market Line (CML)
Security Market Line (SML)
X-axis
Total risk (σ, standard deviation)
Systematic risk (β, beta)
Applies to
Efficient portfolios only
All assets & portfolios
Slope
(E(r_m) − r_f) / σ_m = Sharpe ratio of market
E(r_m) − r_f = Equity Risk Premium
Y-intercept
r_f
r_f
Use
Portfolio construction, optimal portfolio
Pricing individual securities
Overvalued asset
Below the CML
Below the SML (too low return)
Undervalued asset
Above the CML
Above the SML (excess return)
⚠️ Indices Are Not Efficient Portfolios S2–3 pp. 43–44
▼
Course Point (Slide p.43)
CAPM assumes investors hold the market portfolio - all risky assets in proportion to their market cap. But real indices (S&P 500, MSCI World) are cap-weighted, which means:
• They overweight the most expensive stocks (momentum bias)
• They underweight cheap stocks (value bias against the index)
• They are concentrated: top 10 stocks = ~35% of S&P 500
• The "market portfolio" is theoretical - no one actually holds every asset
💡
This is why smart beta / factor investing emerged - alternatives to cap-weighting that tilt toward known return premia (value, size, momentum, quality, low volatility).
📈 Value Investing as a Challenge to CAPM S2–3 pp. 45–46 · Port. Arch. Slide 13
▼
CAPM Says (Sharpe / Slide p.35)
Beta is the only risk that matters and earns a premium. A low-beta portfolio should earn close to the risk-free rate. Any return above CAPM is "alpha."
Value Investors Say (Buffett / Graham)
You can earn superior returns by buying stocks cheaply relative to their intrinsic value. Beta is not the right risk measure - fundamental business risk is. Low P/E, low P/B stocks historically outperform (Fama-French 1992).
📝
Exam angle: The course challenges CAPM via value investing. The slide (p.45) implies that if value investing consistently outperforms, either the market is not efficient or value stocks carry a risk not captured by beta.
Historical Real Returns by Asset Class · Source: UBS Global Investment Returns Yearbook 2025 (1900–2024)
Real annualised returns over 125 years. Equities dramatically outperform bonds and bills over the long run.
🔍 Anomalies: The Illusion of Perfect Markets Port. Arch. Slide 13
▼
⚠️ The Risk Fallacy
Theory (CAPM): Beta is the sole measure of risk. Volatility is constant and fully captured by beta.
Reality: Volatility is not constant. Overvalued assets create severe, unmodelled drawdown risks that beta fails to capture. A stock with low historical beta can still crash 60% in a crisis.
🔗 Shifting Correlations
Theory (MPT): Asset correlations are fixed and constant forever.
Reality: During severe market crises (2008, 2020), correlations between historically unrelated assets converge toward 1.0 - everything drops together. Diversification fails precisely when you need it most.
💰 The Value Discrepancy
Theory (EMH): All information is priced in immediately; markets are perfectly efficient. No sustained alpha is possible.
Reality: Established disciplines like Value Investing (Graham, Buffett) consistently exploit psychological mispricing and opportunity costs over the long term. If markets were perfectly efficient, no one would bother analysing them - a self-defeating prophecy.
📝
The synthesis: These three anomalies are why the course doesn't just teach theory - it challenges it. Real portfolio management requires knowing when the models fail and building in buffers for the reality they cannot capture.
🧠 Comprehension Check
In the CAPM, a stock with β = 1.5 and E(rm) = 10%, rf = 2% should have an expected return of:
- THE EARNINGS IDENTITY -P = EPS × P/EStock price = earnings per share × the multiple investors will payTherefore expected stock return ≈ΔP/P ≈ ΔEPS/EPS + Δ(P/E)/(P/E)
Two Drivers of Equity Returns1. Earnings growth: Driven by revenue growth, margin expansion, buybacks. Relatively predictable over 3–5 years. 2. Multiple change: Driven by sentiment, rates, risk appetite. Highly unpredictable.
📝
Course note: Long-term equity returns are driven by earnings. Short-term returns are driven by sentiment (P/E changes). This is why strategic allocation matters more than tactical.
DMS Global Returns Yearbook 2025: Since 1900, global equities returned ~5.0% real p.a. and bonds ~1.7% real p.a. The equity risk premium has been ~3.3% over bonds.
Sessions 4–5 · Slides pp. 1–40
Equity Management
The full equity analysis framework - from macro context and Porter analysis through DCF valuation, investment styles, and the buy/sell side ecosystem.
5 Sub-sectionsDCF & MultiplesPorter & DuPontInvestment StylesBuy vs Sell Side
Sub-sections
Click to navigate
4.1
Analysis Frameworks
8-step process, Porter, DuPont, EVA, operational vs financial risk.
1
4.2
Valuation
DCF vs multiples, key ratios, projection framework, target price.
2
4.3
Company Selection
Outperformers, pitfalls, defensive vs cyclical, timing.
3
4.4
Buy Side vs Sell Side
Analyst roles, research ecosystem, conflicts of interest.
Course framework for analysing any equity investment follows a disciplined top-down/bottom-up sequence. Every step filters the investment case before moving to the next.
#
Step
Key Questions
1
Macro
GDP growth, rates, FX, inflation - which macro regime are we in? Cycle phase?
2
Industry (Porter's 5 Forces)
Barriers to entry, supplier/buyer power, substitutes, competitive intensity
Check providers, customers, competitors for consistency signals
📝
Exam tip: The course materials explicitly combines Technical Analysis (short-term: price + volume) with Fundamental Analysis (medium-term). Knowing when each applies is tested.
🔍 Porter’s Five Forces S4–5 Summary Topic 5
▼
Michael Porter’s framework for analysing an industry’s competitive structure and long-term profitability.
Force
High = Bad for Company
Low = Good for Company
1. Rivalry
Many competitors, price wars, commoditised products
Few competitors, differentiated products, brand loyalty
2. Threat of New Entrants
Low barriers to entry, easy to replicate
High barriers (capital, regulation, IP, scale)
3. Threat of Substitutes
Many alternatives, low switching costs
No alternatives, high switching costs
4. Supplier Power
Few suppliers, unique inputs, high dependency
Many suppliers, commoditised inputs
5. Buyer Power
Few large buyers, price-sensitive, low switching costs
Many fragmented buyers, brand loyalty
📝
Exam angle: Porter tells you about the industry, not the company. A great company in a bad industry (high rivalry, low barriers) may still be a poor investment.
Why It MattersTwo companies can have the same ROE through very different paths. DuPont reveals how returns are generated: High margin path: Luxury goods (LVMH) High turnover path: Retailers (Walmart) High leverage path: Banks - ⚠ fragile
⚠
Red flag: ROE driven primarily by leverage is risky. If margins compress, debt amplifies losses. Prefer ROE driven by margins or turnover.
💰 Economic Value Added (EVA) S4–5 Summary Topic 7
▼
- EVA -EVA = NOPAT − (Capital Invested × WACC)EVA > 0 → company creates value above its cost of capitalEVA < 0 → company destroys shareholder value
📚
Key insight: A company can be profitable (positive net income) but still have negative EVA if its return on capital is below WACC. Profit alone is not enough - you must earn above the cost of capital.
⚖ Operational Risk vs Financial Risk S4–5 Summary Topic 8
▼
Operational RiskRisk from the business itself: revenue volatility, input costs, competition, regulation, technology disruption.
A company with high operational risk should use less financial leverage (and vice versa).
🧠 Comprehension Check
DuPont analysis decomposes ROE into three components. A bank with ROE of 15% driven primarily by an equity multiplier of 12x is exhibiting:
(a) A healthy, margin-driven return profile
(b) High efficiency via asset turnover
(c) Leverage-driven returns, which are fragile if margins compress
(d) Strong competitive positioning per Porter
(c) is correct. An equity multiplier of 12x means assets are 12 times equity - very high leverage. If margins fall even slightly, the high leverage amplifies losses. Leverage-driven ROE is the riskiest path in DuPont analysis.
Sessions 4–5›4.2 Valuation
📐 Valuation: DCF vs. Multiples S4 pp. 5–8
▼
DCF - Intrinsic ValueEnterprise Value = Σ FCF / (1+WACC)^t + Terminal Value
Discount projected free cash flows at WACC
Subtract net debt → Equity Value
✅ Best for stable, visible cash flows
⚠️ Highly sensitive to WACC and terminal growth assumptions
Multiples - Relative ValueP/E = Price / EPS - most widely used EV/EBITDA - capital structure neutral P/BV - useful for banks/financials Dividend Yield = DPS / Price P/CF - avoids accounting distortions ROE, ROA - profitability quality
⚠️ Always compare within industry and vs. history
── KEY RELATIONSHIP ──
E(R) = D/P + gExpected return = Dividend yield + sustainable growth rate
What growth rate (g) justifies the current multiple?
How certain are you about that g? → this is the core valuation question.
💡
Economic Value Added (EVA):EVA = EBIT(1–t) − (D+E) × WACC. If positive: the company earns above its cost of capital. A key measure of true wealth creation.
📊 Key Valuation Ratios Reference Table S4 p. 6
▼
Ratio
Formula
Best Used For
Watch Out
P/E
Price / EPS
Most sectors - broad comparability
Earnings can be manipulated; negative for loss-makers
EV/EBITDA
Enterprise Value / EBITDA
Cross-sector, M&A, capital-intensive
Ignores capex & working capital; differs by capital structure
P/BV
Price / Book Value
Banks, financials, asset-heavy industries
Book value distorted by intangibles, write-downs
Dividend Yield
DPS / Price
Utilities, income stocks
High yield can signal distress, not generosity
P/CF
Price / Cash Flow per Share
Companies with large depreciation
Cash flow definition varies (CFFO vs FCF vs EBITDA)
ROE
Net Income / Equity
Profitability quality; compares business efficiency
High leverage inflates ROE artificially
Pay-out Ratio
DPS / EPS
Dividend sustainability analysis
>100% = unsustainable unless drawing down reserves
📝
Course rule of thumb: Cyclicals and utilities trade at lower multiples than tech and growth stocks. Higher multiples are justified just before a cyclical upturn. Lower at end of expansion or contraction. Higher with high inflation. Lower where growth is structurally low.
🧮 Projection Framework: Estimating Earnings S4 p. 14
▼
The course's 4-step earnings projection methodology - used to anchor any buy/sell recommendation:
Step 1 - Present & Projected Earnings
Only recurring earnings count. Normalise out non-recurring items.
Project 2–3 years into the future. Identify sustainable earnings power.
Step 2 - Growth in Earnings
Identify sustainable long-term growth rate (g).
Project sales: market growth + market share gain.
Forecast product pricing and cost base separately.
Step 3 - Valuation Anchor
Apply E(R) = D/P + g. Key question: what g justifies the current price?
Use shortcuts: P/E, PEG, P/CF, yield, P/BV. Compare vs. peers and history.
A company trades at P/E = 25x while peers average 18x. An analyst using relative valuation would likely conclude:
(a) The stock is undervalued and should be bought
(b) The stock is overvalued relative to peers, unless justified by higher growth
(c) P/E is irrelevant for equity valuation
(d) The company has higher financial risk
(b) is correct. A premium P/E vs peers requires justification - typically higher earnings growth, better margins, or lower risk. Without such justification, the stock appears expensive on a relative basis.
Sessions 4–5›4.3 Company Selection
🌟 Stocks That Tend to Outperform (Course List) S4 p. 18
▼
Structural Outperformers
1. Family-controlled businesses - long-term orientation, skin in the game
2. Holdings / conglomerates - discount to NAV, capital allocation discipline
3. Small caps - less coverage, more inefficiency, higher alpha potential
4. HQ ≠ operations geography - e.g. Wolters Kluwer (Dutch, global ops)
5. Cyclicals at the trough - maximum pessimism = maximum upside
6. Share classes with discount - e.g. BMW non-voting preference
7. Spin-offs - forced selling creates mispricing at separation
Stocks to Approach Carefully
1. Very high growth story (usually priced in)
2. Serial acquirers (goodwill, integration risk)
3. IPOs (often overpriced day one)
4. Short-term or fad business models
5. Creative accounting signals
6. Key-person dependency
7. High debt companies
8. Overvalued on all metrics
⚠️ The 7 Classic Equity Pitfalls S4 p. 17
▼
#
Pitfall
Description
1
Trend extrapolation
Assuming past growth rate continues. Trends change - mean reversion is powerful.
2
Growth rate convergence
Very high growth rates always slow down. Competition erodes exceptional returns.
3
Competitive advantage decay
Moats erode over time. Tech disruption, regulation, new entrants.
4
Value trap
Cheap stock that stays cheap because the business is genuinely bad.
5
Accounting risk
Financial reports may not be trustworthy. Fraud (Enron), aggressive accounting (banks), unreliable standards (EM).
6
Bad management
Capital allocation matters as much as the business. Bad management can destroy any business.
7
Leverage risk
Financial structure can be a huge risk. A good business with too much debt becomes fragile in recessions.
⏱️ Timing the Investment S4 p. 17
▼
Fundamental Timing Signals
Business cycle phase (early vs. late expansion)
Earnings surprise direction (analyst revisions)
Calendar effects (January effect, earnings season)
Unanticipated news (M&A, regulatory change, macro shock)
Cyclical: Autos, construction, luxury. Demand swings with GDP. Higher beta.
Regulated vs Unregulated
Regulated: Utilities, telecoms, banks. Predictable but capped returns. Government sets prices/rules.
Unregulated: Tech, retail, industrials. More pricing power but more competition.
📝
Exam tip: In a recession, rotate into defensive sectors. In a recovery, rotate into cyclicals. This connects directly to the Investment Clock (S2–3) and TAA (S10–11).
Sessions 4–5›4.4 Buy Side vs Sell Side
🏛️ Buy Side vs. Sell Side Analysts S4 p. 24
▼
Producers of ResearchInvestment Banks (Goldman, JPM, UBS) - sell-side, client-facing Brokers - execution + basic research Independent Research (Morningstar, CFRA) - no conflicts from banking Asset Managers - internal buy-side research Investor Relations (IR) - company's own materials
Consumers of ResearchAsset Managers (AM) - primary audience for sell-side Investor Relations - monitors consensus Retail Investors - access via platforms Prop Desks, Family Offices - sophisticated consumers
Buy Side
Sell Side
Who
Asset managers, hedge funds, pension funds
Investment banks, brokers
Goal
Generate alpha for own portfolios
Generate business/commissions from clients
Bias Risk
Performance pressure → overconfidence
Banking relationships → conflicts of interest
Output
Internal investment decisions
Published research reports (Buy/Hold/Sell + TP)
Part 4 - Timing, Pitfalls & Stock Characteristics
Slides pp. 17–20
🧠 Comprehension Check
A sell-side analyst publishes a research report with a “Buy” recommendation. What is the primary conflict of interest?
(a) The analyst personally owns the stock
(b) The analyst’s firm may have investment banking relationships with the company
(c) The analyst is paid by the company being covered
(d) Sell-side analysts never have conflicts of interest
(b) is correct. Sell-side research is often at banks that also do investment banking for the covered companies. A negative rating could jeopardise lucrative M&A or capital markets mandates. This is why sell-side “Buy” recommendations vastly outnumber “Sell” recommendations.
~80% of active equity funds underperform over 10 years.
Passive / Index Investing
Goal: replicate benchmark return at minimum cost.
ETF fees as low as 0.03%.
No stock selection - market-cap weighted exposure.
Growing to 33.7% of total AUM (TAI 2024).
ETFs dominate passive; increasingly used for tactical AA.
Absolute Return
Seeks positive return regardless of market direction. Common in hedge funds.
Not benchmark-relative - the benchmark is zero (or cash + spread).
Tools: long/short, derivatives, leverage, currency overlays.
Higher complexity, higher fees, but theoretically lower market correlation.
💎 Value Investing S4 pp. 31–33
▼
The Value Thesis
Buy securities trading well below intrinsic value.
Look for: low P/E, low P/BV, high dividend yield, low EV/EBITDA vs. peers & history.
Contrarian by nature - buy when others are selling.
Pioneer: Benjamin Graham. Champion: Warren Buffett (Berkshire Hathaway).
✅
When Value wins: Post-recession recoveries, rate normalisation cycles, and macro-driven broad market selloffs - all tend to favour deep value positions where the discount to intrinsic value is most extreme.
📝
Value Trap Warning: The course materials explicitly flags this - buying a very cheap "bad" company is a classic mistake. Cheapness alone is not enough; you need a catalyst or improving fundamentals.
📈 Growth Investing S4 pp. 36–38
▼
The Growth Thesis
Buy companies with "huge" projected increase in earnings over time.
Accept higher multiples - P/E, EV/EBITDA - because future earnings justify them today.
Pro-trend, momentum-oriented. Often concentrated in sectors with structural tailwinds (tech, biotech, renewables).
Risks: growth doesn't materialise; multiple compression kills even moderate earnings growth.
Growth vs. Value - Course VerdictNo clear winner in risk-adjusted returns.
Both have experienced long stretches of outperformance.
There is long-term autocorrelation in relative performance of small-cap vs. large-cap too.
Context (macro regime, rates, cycle phase) determines which wins.
Warning Signs in Growth Stocks
Very, very high projected growth → often priced in
Companies growing via acquisition → goodwill risk
IPOs → often overpriced on listing day
Short-term business model → fragile moat
Creative accounting → red flag
Key-man dependency → concentration risk
🏆 Quality Investing S4 p. 39–40
▼
The Quality Thesis
Seek businesses with sustainable competitive advantages (moats): brand, network effects, switching costs, cost leadership, regulatory protection.
Quality screens often include: high ROE (>15%), low leverage, consistent earnings, strong FCF conversion, superior management.
Quality tends to outperform over full cycles because: (1) losses in downturns are smaller, (2) compounding works in their favour over time.
🔄 Contrarian Investing S4 p. 34–35
▼
The Contrarian Thesis
Buy when consensus is bearish; sell when consensus is euphoric.
Exploit behavioural biases: herding, recency bias, overreaction to news.
Related to value investing but emphasises sentiment and positioning as signals.
Tools: put/call ratios, short interest, fund flows, sentiment surveys, analyst downgrades (a contrarian buy signal).
Part 3 - The Analysis Ecosystem: Who Makes & Uses Research
Size EffectHistorically, small-cap stocks have outperformed large caps over long horizons (the “small firm effect”). Fama & French include Size (SMB) as a priced factor.
Course exam question: “Smart beta is a: (a) passive strategy, (b) active strategy, (c) mix between passive and active, (d) wrong strategy.” - Answer: (c).
🧠 Comprehension Check
Which strategy typically buys companies with low P/E ratios?
(a) Growth investing
(b) Value investing
(c) Quality investing
(d) GARP
(b) Value investing. Value investors look for stocks trading below intrinsic value, typically identified by low P/E, low P/B, or high dividend yield. Growth investors pay higher multiples for faster earnings growth. This is a question directly from the course's example sheet.
Passive has structurally elevated mega-cap valuations: $1B in S&P 500 passive = ~$110M to NVIDIA at 11% weight. Mechanical demand creates upward pressure independent of fundamentals. Mid-caps get less passive flow → potential active management opportunity.
IPO underperformance: Underwriting firms maximise momentum on IPO day → overpricing. Early buyers profit from the first-day pop; later secondary-market buyers often suffer declines.
Sell-side conflict math: Buy recommendations vastly outnumber Sell because a negative rating could jeopardise lucrative M&A mandates. Buy-side research (BlackRock internal) avoids this conflict entirely.
Session 6 · Slides pp. 1–19
Fixed Income Management & Forex
Bond pricing, credit ratings, yield curve dynamics, duration mechanics, bond taxonomy, and the foreign exchange market - with an interactive duration simulator.
7 FX drivers, Impossible Trinity, currency impact on portfolio returns.
4
Session 6›6.1 Bond Fundamentals
💰 Bond Pricing: The DCF Framework S6 p.3
▼
A bond is simply a stream of future cash flows - coupon payments and face value at maturity - discounted at the appropriate yield. The same DCF logic used in equity valuation applies, but the cash flows are contractual (not estimated).
Bond Pricing Formula (Slide p.3)
Price = CF₁/(1+r)¹ + CF₂/(1+r)² + … + CFₙ/(1+r)ⁿ
CF = coupon payments (+ face value in final period) · r = discount rate (YTM)
💡
Key Inverse Relationship: When interest rates rise, bond prices fall - and vice versa. This is not a coincidence; it is the mechanics of the DCF formula. A higher discount rate (r) makes each cash flow worth less today.
The 7 Risk Dimensions of Fixed Income
Risk Category
What It Means
Management Tool
Illiquidity
Bonds, especially corporates, trade OTC and can be hard to sell quickly without a price impact. Unlike equities, no central exchange.
Liquidity buffer, spread premium
Conflict of Interest
Rating agencies (Moody's, S&P) are paid by issuers. Structured credit (pre-2008) showed how this conflict can lead to inflated ratings.
Independent analysis, CDS spreads
Macro
Interest rate risk driven by central bank policy, inflation expectations, and economic growth. The most systematic risk for government bonds.
Duration management, rate hedges
Spreads
Corporate bonds trade at a yield premium (spread) over government bonds to compensate for default risk. Spreads widen in recessions.
Credit quality selection, diversification
Time / Maturity
Longer maturities mean greater sensitivity to rate changes (higher duration). A 30-year bond loses far more value when rates rise than a 2-year bond.
Duration targeting, laddering
Credit Quality
The probability that the issuer defaults on coupon or principal payments. Captured by credit ratings (AAA → D).
Rating analysis, credit limits
Secondary Market
Where bonds trade after issuance. Prices fluctuate based on supply/demand, credit changes, and macro moves.
Mark-to-market, bid/ask monitoring
Primary Market
New bond issuance. Investors who participate in primary get the initial pricing; may capture new issue concession.
Book-building participation
Part 2 - Credit Ratings
Session 6 Slide p.4
🏦 The Rating Agency Scale S6 p.4
▼
The three major rating agencies - Moody's, S&P, and Fitch - each use their own notation but the underlying credit quality categories are equivalent. The Investment Grade / High Yield boundary (BBB−/Baa3) is the single most important threshold in fixed income investing.
Risk Category
Moody's (LT)
S&P (LT)
Fitch (LT)
Meaning
Prime
Aaa
AAA
AAA
Highest quality, minimal credit risk. US Treasury, German Bund.
High Grade
Aa1–Aa3
AA+, AA, AA−
AA+, AA, AA−
Very low default risk. Strong sovereigns, top-tier corporates (Apple, Microsoft).
Upper Medium Grade
A1–A3
A+, A, A−
A+, A, A−
Low risk. Good financial position. E.g. Volkswagen, Total Energies.
Lower Medium Grade
Baa1–Baa3
BBB+, BBB, BBB−
BBB+, BBB, BBB−
Lowest Investment Grade. Adequate protection. Many institutional mandates stop here. Telefónica example from slide was Baa1/BBB+.
⬇️ BELOW THIS LINE = HIGH YIELD ("Junk") ⬇️ - Institutional mandates typically cannot hold below BBB−
Non-Inv. Grade / Speculative
Ba1–B3
BB+…B−
BB+…B−
Significant speculative characteristics. Higher default risk. High-yield funds and CLOs hold these.
Distressed / Default
Caa–C
CCC+…D
CCC…D
Extreme speculative. Near or in default. Distressed debt / special situations territory.
📝
Exam Point - The Fallen Angel: A "fallen angel" is a bond downgraded from Investment Grade to High Yield. This triggers forced selling by institutional mandates (which cannot hold HY), creating a price dislocation and potential value opportunity for unconstrained buyers.
💡
Real Bloomberg Example (Slide p.11 - Telefónica): Telefónica EMIS bond (TELEF04, 4⅜ coupon, maturing 02/2016) was rated Moody's Baa1 / S&P BBB+ / Fitch BBB+. This sits in the Lower Medium Grade - comfortably Investment Grade, eligible for most institutional mandates. Issue spread: 112.80 bps over DBR.
Primary vs SecondaryPrimary market: Where new bonds are issued (IPO equivalent). Underwritten by investment banks. Pricing set via book-building. Secondary market: Where existing bonds trade. OTC (not exchange) for most bonds. Liquidity varies enormously by issuer.
Macro & BondsBond prices move inversely to interest rates. Key macro drivers: • Central bank policy (rates up → bond prices down) • Inflation expectations (higher → yields up) • GDP growth (stronger → yields up) • Flight to quality (crisis → govt bond prices up)
📚
Bondholder vs equity holder conflict: Bondholders want stability (low risk, steady cash flows). Equity holders want growth (which may require risk-taking). This creates tension around dividends, leverage, and M&A decisions.
🧠 Comprehension Check
A bond rated BBB− by S&P is significant because:
(a) It is the highest investment grade rating
(b) It is the lowest investment grade rating - one notch below is high yield
(c) It indicates the bond is risk-free
(d) It means the bond cannot be traded on secondary markets
(b) is correct. BBB− (S&P) / Baa3 (Moody’s) is the lowest investment grade rating. A downgrade to BB+ makes it “fallen angel” - high yield / junk. Many institutional investors are mandated to hold only investment grade, so this threshold triggers forced selling.
Session 6›6.2 Yield Curve & Duration
📈 Yield Curve Shapes & What They Signal S6 pp. 5–6
▼
The yield curve plots bond yields against their maturities. Its shape encodes the market's collective forecast about growth, inflation, and monetary policy. The course uses this to frame macro positioning decisions.
① Normal (Positively Sloped)
Long-term yields exceed short-term. The standard shape - investors demand a premium for longer maturities (liquidity preference). Indicates economic expansion expected.
② Flat
Short and long rates are equal. Typically a transition phase - seen when central banks are hiking short rates into a slowing economy. Uncertainty signal.
③ Inverted (Negatively Sloped)
Short-term yields exceed long-term. Classic recession predictor - the 2-year/10-year inversion has preceded every US recession since 1955. Market expects rate cuts ahead.
④ Humped (Bell-shaped)
Medium maturities yield more than both short and long. Relatively rare. Seen during uncertainty about medium-term policy vs long-run anchoring. Associated with the Butterfly spread trade.
🇪🇸
Real Data (Slide p.12–13 - SPGB 2¾ 10/31): The Spanish government bond shown on the Bloomberg chart peaked at ~116 in 2019 (deeply negative yields era) and crashed to ~103 by mid-2022 as the ECB hiked rates aggressively. The yield chart shows YTM going from negative territory (−0.57%) to +1.42% - a dramatic rate cycle in just 18 months.
↔️ Yield Curve Shifts - The 5 Moves That Matter S6 p.7
▼
A fixed income portfolio's P&L is driven by how the yield curve moves, not just its current level. Bond managers decompose risk using these five curve movements.
① Parallel Shift
All maturities move up or down by the same amount. Most common shift. Duration determines total price impact.
② Flattening Shift
Short rates rise more (or fall less) than long rates. Curve becomes less steep. Classic late-cycle / tightening pattern. Typical: ECB hikes short rates while long inflation expectations stay anchored.
③ Steepening Shift
Long rates rise more than short rates. Curve becomes steeper. Typical in early recoveries - central bank holds short rates near zero while long rates price in growth/inflation.
④ Twisted Shift
Short end moves opposite to long end. Curve rotates around the middle. Related to monetary vs. fiscal policy divergence.
⑤ Humpedness (Butterfly)
Positive Butterfly: belly (mid maturities) falls relative to wings. Negative Butterfly: belly rises. The Butterfly trade exploits this move - long wings, short belly.
Key Takeaway
Most real-world curve moves are combinations of these. A parallel shift + steepening is common in early recovery. A portfolio manager's job is to position the portfolio so its duration profile profits from the expected move - or at minimum, hedges against it.
Part 4 - The 6 Dimensions of Bond Portfolio Management
Session 6 Slide p.8
🧩 Managing a Fixed Income Portfolio S6 p.8
▼
The course slide (p.8) shows a 2×2 grid + 2 rows: six key levers a fixed income portfolio manager pulls. Every active bond decision falls into one of these dimensions.
① Duration
The single biggest driver of bond portfolio returns. Increasing duration = more rate sensitivity = bigger gains/losses on rate moves. Matching liability duration is the goal in pension/LDI mandates.
② Credit Quality
Choosing between Investment Grade and High Yield, or within IG between AAA and BBB, affects the spread earned. More credit risk = higher yield but higher default risk and spread volatility.
③ Geographic Area
US Treasuries vs. German Bunds vs. Spanish Bonos vs. EM sovereign debt. Different rate cycles, credit risks, and liquidity profiles. Also drives FX exposure.
④ FX (Currency)
Foreign currency bonds add FX risk on top of rate risk. A EUR fund holding USD bonds needs to decide whether to hedge the dollar exposure (FX overlay) or leave it open as an additional return driver.
⑤ Derivatives
Interest rate swaps, CDS, futures, and swaptions are used to hedge or express views without changing the physical bond portfolio. A rate swap can change the effective duration of a portfolio instantly and cheaply.
⑥ Indices / Benchmarking
Bloomberg Aggregate, ICE BofA, iBoxx - choosing the benchmark defines the investable universe and shapes how active bets are measured. Index replication via ETFs (iShares Core EUR Corporate Bond) is cheap and increasingly common.
Part 5 - Duration: The Core Risk Metric
Session 6 Slide p.9
⚖️ Macaulay Duration - The Balancing Point S6 p.9
▼
Duration is the weighted average time to receive all of a bond's cash flows, measured in years. The course slide uses the physical intuition of a balance / fulcrum - the duration is where all cash flows balance out on a timeline.
Duration Simulator - Adjust the sliders
5.0%
10 yrs
5.0%
Macaulay Duration
7.72 yrs
Modified Duration
7.35
Price (per €100)
€100.00
+1% rate shock
−7.35%
Cash flow timeline - bar height = PV weight of each payment
7.72 yrs
Year 0Year 10
▲ The triangle marks where cash flows balance - that is Macaulay Duration.
Try setting Coupon = 0% to see a zero-coupon bond: duration equals maturity exactly.
Macaulay Duration Formula
D = Σ [t × PV(CFₜ)] / Price
• t = time period
• PV(CFₜ) = present value of cash flow at time t
• Price = current bond price (sum of all PV cash flows)
A zero-coupon bond's duration = its maturity.
A coupon bond's duration is always < maturity.
Example: Bond with ModD = 7.5
If rates rise +1% → price falls ~7.5%
If rates fall −0.5% → price rises ~3.75%
This is the primary tool for rate risk management.
📝
Exam Formula - Memorise This: ΔP/P ≈ −Modified Duration × Δy. If a bond has Modified Duration of 8 and yields rise by 50 bps (0.5%), price falls approximately 8 × 0.5% = 4%. Convexity makes this estimate slightly optimistic - actual price fall is a bit less than 4% due to the curvature of the price-yield relationship.
Part 6 - Bond Taxonomy
Session 6 Slide p.10
🧠 Comprehension Check
A 10-year bond with duration 7.5 years. If yields rise by 100bp, the approximate price change is:
(a) −1.0%
(b) −7.5%
(c) −10.0%
(d) −75.0%
(b) is correct. ΔP/P ≈ −Duration × Δy = −7.5 × 1.00% = −7.5%. Duration provides a first-order linear approximation of price sensitivity to yield changes.
Session 6›6.3 Bond Types
📋 The 7 Bond Types (in Seniority Order) S6 p.10
▼
In the event of default, bondholders are paid in seniority order. The ranking below goes from most senior (first paid, lowest yield) to most subordinated (last paid, highest yield). The course materials listed these explicitly.
Rank
Bond Type
Security / Backing
Risk / Yield
Who Uses It
1
Repo (Repurchase Agreement)
Collateralised by securities. Technically not a bond but a secured short-term borrowing.
Very Low / Low yield
Banks, dealers for liquidity management. Central bank open market operations.
2
Senior (Secured/Unsecured)
First in line among regular bondholders. May have asset collateral (Senior Secured) or just first claim on unsecured assets.
Low–Moderate
Most corporate bonds fall here. Apple, Toyota, LVMH senior bonds.
3
Subordinated
Paid after senior bondholders in default. Higher yield to compensate. Common in banking (Tier 2 capital).
Moderate–High
Bank capital instruments. Hybrid between bond and equity risk.
4
ABS (Asset-Backed Security)
Backed by a pool of assets (mortgages = MBS, auto loans, credit card receivables). SPV structure. Cash flows from assets service the bond.
Varies by tranche
Structured finance. CLOs, CDOs. Pre-2008 crisis epicentre. Still major market.
5
Covered Bond (Cédulas)
Dual recourse - claim on issuer AND on a segregated cover pool (mortgages). Safest form of corporate bond. Spanish cédulas hipotecarias are a major market.
Low (near sovereign)
European banks. Major source of mortgage funding. ECB accepts as collateral.
6
Hybrid / CoCo
Contingent Convertibles or hybrid capital. Convert to equity or are written down at regulatory trigger. AT1 capital for banks. Highest spread among IG-adjacent instruments.
High
Banks (AT1/T2). Barclays, Santander, BBVA issue significant volumes. Credit Suisse AT1 wiped out in 2023 - major market event.
Popular in rising rate environments. Loans, CLO liabilities, bank Tier 2 sometimes.
💡
Note on Floaters: A Floating Rate Note has near-zero interest rate duration because the coupon adjusts with market rates. But it still has credit duration. In a credit crisis, FRN prices fall not because rates changed, but because the credit spread widened.
Part 7 - Forex in Asset Management
Session 6 Slides pp. 14–19
Session 6›6.4 Forex
💱 The 7 Drivers of Exchange Rates S6 p.15
▼
#
FX Driver
Mechanism
Example
1
Interest Rates
Higher rates attract capital inflows → currency appreciates. Fisher effect: nominal rate diff = expected FX change.
USD strengthened in 2022–23 as Fed hiked rates more aggressively than ECB.
US outperformed EU in 2024; USD remained strong vs EUR.
3
PPP (Purchasing Power Parity)
Long-run exchange rates adjust to equalise purchasing power across countries. Currencies of high-inflation countries depreciate over time.
The Economist's Big Mac Index. Turkish lira collapsed as inflation ran 60–80%.
4
Carry Trade
Borrow in low-rate currency, invest in high-rate currency. Unwinds violently when market stress hits - everyone exits at once.
Classic: borrow JPY at 0%, invest in AUD at 4–5%. Unwind in August 2024 caused major market volatility.
5
Monetary Policy
Central bank signals and decisions affect relative rates and currency expectations. QE typically weakens a currency.
ECB QE from 2015 weakened EUR. BOJ yield curve control kept JPY weak until 2024.
6
Export–Import Balance
Current account surplus (exports > imports) means more foreign demand for domestic currency → appreciation.
Germany / China: persistent surpluses support EUR / CNY. US: persistent deficit is structural USD weakness pressure.
7
Speculation / Flows
Short-term the FX market is driven by positioning and flows. Soros broke the Bank of England in 1992 by shorting GBP.
FX spot market: $7.5 trillion per day (BIS 2022). Speculation dwarfs trade volumes.
🔺 The Impossible Trinity S6 p.16
▼
One of the most elegant concepts in international economics (Mundell–Fleming). A country cannot simultaneously have all three of the following. It must sacrifice one. This shapes the entire global monetary architecture.
Country / Regime
Keeps
Sacrifices
Eurozone
Fixed rate (EUR), Free capital
❌ Independent monetary policy (ECB decides for all)
USA / UK / Japan
Independent monetary policy, Free capital
❌ Fixed exchange rate (floating FX)
China (pre-liberalisation)
Fixed rate (CNY peg), Independent monetary policy
❌ Free capital movements (capital controls)
📝
Exam Point: The Eurozone crisis (2010–2012) illustrated exactly this. Countries like Greece and Spain could not devalue their currency or independently cut rates - they were locked inside the Euro. The only adjustment mechanism was internal devaluation (wage cuts), which is socially and politically brutal.
📊 FX Impact on Portfolio Returns - Nominal vs. Real S6 pp. 17–19
▼
The course uses two striking Bloomberg examples to show how FX can dominate equity returns. The currency is not a side issue - it often IS the return.
⚠️ Swiss Franc "Frankenschock" (Jan 2015)
The SNB abruptly removed the EUR/CHF floor on 15 Jan 2015. CHF appreciated ~20% in one day. The SMI (Swiss stock index) fell ~10% in CHF terms - but in EUR terms, Swiss equities rose dramatically because the CHF gain more than offset the equity fall.
Bloomberg data from slide p.18: Over the period, SMI priced in CHF: +4.6% | SX5E in EUR: +17.4% | But SMI in EUR: +21.6% - the Swiss market beat the Eurozone market once FX effects were included despite the equity decline in local terms.
🇻🇪 Venezuela - Nominal vs. Real Return
Bloomberg slide p.19 shows the Venezuelan IBVC index returning an extraordinary +418.6% total return in VES (local currency) over the period - but only −42.9% in USD. The local stock market appeared to "surge" in hyperinflationary terms, but foreign investors lost half their money.
This is the core distinction between Nominal Return and Real Return. A nominal 100% gain in a currency losing 95% of its value delivers a deeply negative real return.
💡
Asset Management Implication: A EUR-based pension fund buying USD assets must decide: (1) leave USD exposure open - adding FX beta, (2) hedge to EUR using FX forwards/swaps - removing FX risk but paying the hedge cost (linked to the rate differential), or (3) use a partial hedge / options-based overlay. The FX hedging decision can add or subtract 2–5% p.a. from returns depending on rate differentials.
Session 6 - Formula Cheat Sheet
Exam Ready
Bond Pricing
Price = Σ CFₜ / (1+r)ᵗ
Macaulay Duration
D = Σ [t × PV(CFₜ)] / Price
Modified Duration
ModD = MacD / (1 + YTM)
Price Change Approximation
ΔP/P ≈ −ModD × Δy
Credit Spread
Spread = Corp YTM − Govt YTM
Real Return (simplified)
(1+Nominal) / (1+Inflation) − 1
🧠 Comprehension Check
A carry trade involves borrowing in a low-yield currency and investing in a high-yield currency. The primary risk is:
(a) The low-yield currency appreciates sharply, increasing repayment costs
(b) Interest rates in both countries converge to zero
(c) Inflation falls in the high-yield country
(d) The trade is risk-free if properly hedged
(a) is correct. Carry trades profit from interest rate differentials but are exposed to FX risk. If the funding currency (e.g. JPY) appreciates sharply, the cost of repaying the loan can wipe out all carry income and more. Carry trades famously unwind violently in crises.
Sessions 7–8 · Bloomberg Terminal
Bloomberg Terminal
Navigation, keyboard shortcuts, and the course's curated function reference for equity, fixed income, macro, and portfolio analysis.
2 Sub-sectionsTerminal BasicsFunction Reference
7.1
Terminal Basics
Command line, menu system, panels, keyboard shortcuts.
1
7.2
Function Reference
The course's curated function list by asset class.
2
Sessions 7–8›7.1 Terminal Basics
Accordions:
Part 1 - Terminal Navigation
Getting Started with the Bloomberg - pp. 3–15
🏗️ The Two Building Blocks: Functions & Securities Overview p.3
▼
The entire Bloomberg terminal is built on two concepts. Master these and the rest follows naturally.
Functions
Applications that provide targeted information and analysis - either on the broad financial markets or on a specific security. Every function has a mnemonic (short name) used to access it quickly.
Two types:
• Non-security functions - market-wide data, no security needed (e.g. WEI, TOP)
• Security-specific functions - analyse a loaded security (e.g. DES, FA, YAS). These are marked with an asterisk (*) in function lists.
Securities
The financial instruments you analyse. Bloomberg's databases hold millions of securities organised by market sector and ticker.
To load a security: type the ticker + yellow market sector key + <GO>
The logic: First you load a security, then you run functions on it. Bloomberg's menus are organised around this sequence - once a security is loaded, all relevant functions appear automatically.
⌨️ The Command Line - Your Primary Interface pp. 4–6
▼
The command line runs across the top of every Bloomberg panel. The entire system is discoverable from it. Three distinct modes:
Mode 1Find a Function by Keyword
Start typing a keyword - don't press <GO>. A list of suggested matches appears as you type.
Type: MER → shows MA (Mergers & Acquisitions), MERC, BUYP…
Type: MERGER ACQ → narrows further
Mode 2Find a Security by Keyword
Type the issuer name or partial ticker. Refine with yellow market sector keys.
Type: DIS 7 → shows Disney bonds with 7% coupon
Press: <Corp> → filters to corporate bonds only
Mode 3Find by Direct Command
If you know the mnemonic or ticker, type it directly then press <GO>.
WEI <GO> → World Equity Indices
IBM US <Equity> <GO> → loads IBM for analysis
Mode 4Find a Quick Answer
Type a question into the command line, then press <Help> (not GO). Opens the Help Search (HL) screen with the answer highlighted.
Type: IBM Q3 2013 revenue → then press <HELP>
Result: HL screen highlights the answer directly
📝
Key rule: Keyword search = type and wait (no GO). Direct command = type mnemonic + press GO. Quick answer = type question + press HELP. Mixing these up is the #1 beginner mistake.
📂 The Menu System - Three Ways to Browse pp. 7–8
▼
All Bloomberg functions are organised onto a hierarchy of menus by market sector and workflow. Three ways to access them:
🟡
Yellow Key
Hit a yellow market sector key + <GO> to browse that sector's top-level menu.
e.g. <Equity><GO>, <Corp><GO>, <Curncy><GO>
🔐
Load a Security
Load any security and its function menu appears automatically. All relevant analysis functions for that security are listed.
e.g. IBM US <Equity> <GO>
📋
Menu Button / Key
From within any function, press the green <Menu> key or click Menu Button to see related functions and navigate back up the hierarchy.
💡
How menus work: Menus start broad (market overview) and let you drill down to specific functions. For example: <Curncy><GO> → Currency Markets menu → Price Discovery category → WCR (World Currency Rates).
🖥️ Screen Elements & Bloomberg Panels pp. 11–14
▼
Screen Interaction Elements (every function)
Element
What it is
How to use it
Menu Bar
Red bar at the top of each function
Contains the function title, drop-down lists, page count. Your control panel.
Amber Fields
Yellow/amber highlighted areas on screen
Editable inputs - click or tab to change dates, tickers, parameters.
Clickable Navigation
Areas that highlight on mouseover
Click to drill down, expand sections, or navigate to related data.
Number <GO>s
Numbers next to list items
Type the number + press <GO> to jump directly without mouse. Fastest navigation.
Bloomberg Panels - Working with 4 Windows
The terminal delivers up to four independent desktop panels. Each works simultaneously - you can have equity analysis on one, a bond screen on another, news on a third, and a chart on a fourth.
Toolbar
Menu button + recent securities/mnemonics. Export, Help, Display icons on the right.
Command Line
Top of every panel. Search functions, load securities, enter direct commands.
Function Area
The main body of the screen - where the function content and data appear.
Information Panel
Bottom strip - highlights new or enhanced Bloomberg functionality.
💡
Panel tip: Press the blue <PANEL> key to switch between your four windows. The panel number (1–4) appears in the top-left of the frame. Clicking the X "Close" icon logs you out entirely - don't confuse it with minimise.
The Bloomberg keyboard uses colour-coded keys that map directly to the system's logic. Once you know the colour scheme, you can navigate intuitively without memorising every shortcut.
ESC CANCEL
Red - ESC / Cancel
Exit the current function and return to the home page with Bloomberg contact information. Also used to cancel an action.
Enter GO
Green - GO (Enter)
Execute a typed command. Every mnemonic and command must be followed by <GO> to run. Equivalent to Enter on a standard keyboard.
MENU
Green - Menu
Navigate from any function back to a menu of related functions, then back through the menu hierarchy to Home. Also navigates to the previous screen. (On new keyboards, this function moves to <End/Back>.)
End Back
Green - End / Back
Navigate back to the previous screen. Same functionality as the old <Menu> key on the traditional Bloomberg keyboard.
F1 HELP
Green - Help
Press once: opens the function's Help Page (online user guide with definitions, calculations, how-to instructions). Press twice: opens a live chat with the Bloomberg Help Desk - available 24/7.
Switch between the 2 or 4 Bloomberg windows on your desktop. Configure 2 vs 4 panels via PDFU7 <GO>.
Sessions 7–8›7.2 Function Reference
⚡ Keyboard Tricks & Tips Keyboard.pdf p.2
▼
Command
What it does
<GO>
Equivalent to Enter. Must follow every mnemonic to execute the command.
MENU
Takes you back to the previous screen. Essential for navigation.
PAGE FWD / PAGE BACK
Scroll forward or back within a multi-page function. To jump to page 15 from page 2: type 13 then press Page Fwd.
COMMAND
Shows a recap of the previously used function in the top-left of the screen.
LAST <GO>
Review the last 8 functions used. Useful for quickly returning to recent work.
PRINT
Print the current page. Press # then PRINT to print multiple pages (e.g. press 5 then PRINT to print 5 pages).
<HELP> once
Open the function's Help Page - online user guide with definitions and how-to instructions.
<HELP> twice
Open a live chat window with the 24-hour Bloomberg Help Desk.
HDSK <GO>
View all your historical Help Desk enquiries.
EASY <GO>
A complete list of Bloomberg tips and shortcuts. Bookmark this for the Trading Room.
Alt + K
Display a full visual map of the Bloomberg keyboard on screen. Press K again to dismiss.
Part 3 - Course Function Reference
The course's Curated List - Sessions 7–8
📌
This is The course's own selection of Bloomberg functions for this course - the mnemonics you will actually use in the Trading Room sessions. Functions marked with (*) are security-specific and require a security to be loaded first. Start with the General & Navigation group to orient yourself, then drill into the asset class relevant to each session.
🧭 General Navigation & Setup
▼
Mnemonic
Function Name
When to use it
BU
Bloomberg University
Training resources, tutorials, and learning materials. Start here if you're new.
WEI
World Equity Indices
At-a-glance view of all major global equity indices with performance.
HCP
Historical Commodity Prices
Chart and compare commodity prices historically.
HP
Historical Prices *
Historical price data for a loaded security - prices, volume, returns.
COMP
Comparative Returns
Compare the performance of multiple securities on a single chart.
DES
Security Description *
Full description of any loaded security - key terms, issuer, ratings, structure.
GP
Graph Price *
Price chart for a loaded security. Starting point for any technical analysis.
GIP
Graph Intraday Price *
Intraday (tick-level) price chart for a loaded security.
CN
Company News *
All news items related to a loaded company or security.
CIXB
Custom Index Builder
Build a custom index from a basket of securities to track performance.
G
Custom Chart
Create custom charts from templates. Combine multiple data series.
GLCO
Global Commodity Overview
Overview of all major commodity markets - energy, metals, agriculture.
XLTP
Excel Template Library
Pre-built Excel templates for Bloomberg data. Export directly to spreadsheet.
🌍 Macro - Economic Data Session 6 · Session 14
▼
Mnemonic
Function Name
When to use it
ECFC
Economic Forecasts
Consensus economic forecasts (GDP, inflation, rates) across countries. Key for macro views.
STAT
Country Statistics
Historical economic statistics for any country - GDP, CPI, employment, trade balance.
CN
Company / Country News
Search news by company or country. Use with a country or company loaded.
💼 Portfolios - The Trading Room Core Sessions 7–8 practical
▼
These three functions are the core of the Trading Room practical sessions - where you build, manage, and analyse real portfolios in Bloomberg.
Mnemonic
Function Name
When to use it
PRTU
Portfolio Manager
Create and manage portfolios. Add securities, set weights, input transactions. Start here to build your course portfolio.
PORT
Portfolio Risk & Analytics
Full portfolio analytics - risk decomposition, factor exposures, performance attribution, benchmark comparison, VaR. The most powerful portfolio tool on the terminal.
HFA
Hedge Fund Analytics
Analyse portfolios with hedge fund characteristics - absolute return focus, drawdown analysis, strategy attribution.
📝
Trading Room workflow: PRTU → build and maintain your portfolio → PORT → analyse risk, return, and attribution → present findings. Know these three commands cold before you enter the Trading Room.
Part 4 - Full Bloomberg Function Reference
Bloomberg Getting Started Card
📋
The broader Bloomberg function universe - all market sectors and workflows. Use this to explore beyond the course-specific functions above. Functions marked (*) are security-specific.
🌐 World Monitors & Economy
▼
World Monitors
WEI
World Equity Indices
WEIF
World Equity Index Futures
WB
World Government Bonds
WBF
World Bond Futures
WIR
World Interest Rate Futures
WCDS
World CDS Pricing
WCRS
World Currency Ranker
BTMM
US Treasury & Money Markets
The Economy
ECO
Economic Calendars
ECFC
Economic Forecasts
ECOF
Economic Indicators
ECST
World Economic Statistics
CENB
Central Banks
N ECO
TOP Economic News
📈 Equity, Credit & Fund Markets
▼
Equity Markets
EQS
Equity Screening
EVTS
Events & Earnings Calendar
MA
Mergers & Acquisitions
FA*
Financial Analysis
EEO*
Earnings Projections
RV*
Peer Group Relative Value
N STK
Top Stock News
Credit & Funds
FICM
Fixed Income Credit Monitor
NIM
New Issue Monitor
RATC
Credit Rating Revisions
YAS*
Yield & Spread Analysis
FSRC
Fund Search
HFND
Hedge Fund Home Page
FREP*
Fund Performance Reports
💱 FX, Commodities & Portfolio Management
▼
FX Markets
XDSH
FX Dashboard
FRD
Forward Rate Calculator
FXC
Currency Rates Matrix
FXFC
FX Composite Forecasts
FXDV
FX Derivatives Menu
SWPM
Interest Rate Swap Manager
SOVR
Sovereign CDS Monitor
Commodities & Portfolios
GLCO
Global Commodity Overview
FDM
Commodity Fundamental Data
OIL
Oil Markets
PRTU
Portfolio Manager
PORT
Portfolio Risk & Analytics
PREP
Portfolio Reports
DAPI
Excel Data & Calculations
Sessions 7–8 - Trading Room Cheat Sheet
Print This
Essential Commands for the Bloomberg Trading Room
Navigation
mnemonic <GO> → run function
LAST <GO> → last 8 functions
EASY <GO> → tips & shortcuts
<HELP> once → Help Page
<HELP> twice → Help Desk chat
Alt+K → keyboard map
Definition, HF vs mutual funds, performance, industry size.
2
9.3
Allocation & Illiquidity
Should you allocate? Illiquidity premium mechanics.
3
Session 9›9.1 AIS Overview
Accordions:
Part 1 - Introduction to Alternative Investment Strategies
Session 9 Slides pp. 3–14
🌐 What Are Alternative Investments? S9 pp. 3–5
▼
There is no universal definition of Alternative Investment Strategies (AIS). The category is intentionally heterogeneous - what these investments share is a single common objective: to generate returns that are not correlated with traditional markets.
Traditional Investments
Strategies that invest directly in public stocks, bonds, and cash in order to make a return.
Investment strategies that aim to exploit inefficiencies in public markets or invest in private, less-liquid assets.
Potential illiquidity
Available in both private and public markets
Active shareholders - sometimes sole owners
Performance less sensitive to global market moves
Instruments: Real Estate, Infrastructure, PE, Hedge Funds, Commodities
The Alternative Asset Class Universe (Source: RREEF Alternatives)
Real Estate
Equity - Public (REITs)
Equity - Private
Debt - Public (CMBS, CDOs)
Debt - Private
Market: $7.8T
Private Equity
Leveraged Buyout
Venture Capital
Growth Capital
Angel Investing
Mezzanine Capital
Market: $1.38T
Hedge Funds
Relative Value
Event Driven
Long/Short Equity
Global Macro
Market: $1.44T
Infrastructure
Economic: Transport, Utilities
Social: Education, Health, Housing
Market: $7.6T (econ. only)
💡
Key takeaway from slides p.3: The only classification rule that truly holds is this - alternatives seek returns uncorrelated with traditional equity and bond markets. Every other characteristic (liquidity, regulation, minimum investment) is secondary and varies by sub-category.
📈 Why Did AIS Grow So Dramatically? S9 pp. 7–8
▼
Demand-Side Reasons
High correlation between traditional markets - diversification benefit of stocks + bonds eroded
Low interest rate environment since the dot-com bubble - bonds ceased to provide adequate return
Academic research validating uncorrelated return sources
Desire to show sophistication - institutional mandates began requiring alternatives exposure
Supply-Side Reasons
Launch of vehicles linked to AIS - Funds of Funds, listed alternatives, structured products
High fees - attractive economics drew talent and capital into the sector
Increased global equity market integration - multinational corporations, coordinated monetary policy, free capital flows - all raised correlations between traditional markets, making alternatives comparatively more attractive
Global Alternative AUM Growth (Source: J.P. Morgan, HFR, Preqin)
$1.3T
2000
$3.9T
2005
$6.7T
2010
$9.7T
2015
$13.9T
2019
$17.3T
2021
$19.0T
2023
$19.6T
2024
Global alternative AUM reached approximately $19.6 trillion by end of 2024, covering private equity, real estate, infrastructure, hedge funds, natural resources and private credit.
📐 Alternatives & the Efficient Frontier S9 pp. 9–11
▼
Adding uncorrelated alternatives to a traditional portfolio shifts the efficient frontier up and to the left - higher expected return for the same risk, or lower risk for the same expected return. This is the core portfolio construction argument for AIS.
63% Fixed Income + 20% Large Cap + 13% Global Equity Risk: 6.7%
2021
37% Large Cap + 23% Global + 17% PE + 13% Real Estate + 3% FI Risk: 17.3%
📝
Exam Point: To earn the same 7% return, investors in 2021 needed to accept almost 16× more risk than in 1991, and required significant alternatives exposure (PE, real estate). This is the core argument for including AIS in portfolios.
The Yale Endowment Model - David Swensen's Revolution
Yale's endowment, under David Swensen, pioneered the shift away from traditional stocks and bonds toward alternatives. By Fiscal 2019, the allocation was:
26%
Leveraged Buyouts
18%
Foreign Equity
15.5%
Venture Capital
15%
Absolute Return (HF)
9.5%
Absolute Return
6.5%
Natural Resources
6.5%
Domestic Equity
3%
Bonds + Real Estate
💡
Result: Over Swensen's tenure, Yale's endowment grew from ~$1B to over $30B, dramatically outperforming traditional 60/40 portfolios. The model proved that a high alternatives allocation - especially to illiquid strategies - could deliver superior long-run results for investors with long time horizons who can tolerate illiquidity.
🧠 Comprehension Check
The primary reason alternative investments grew dramatically post-2010 was:
(a) Regulators mandated pension funds to hold alternatives
(b) Near-zero bond yields pushed investors to seek returns elsewhere
(c) Alternative investments are guaranteed to outperform
(d) Stock markets were closed to new investors
(b) is correct. Prolonged low/zero interest rates meant bonds couldn’t deliver target returns. Investors turned to PE, hedge funds, and real assets for the illiquidity premium and diversification benefits.
Session 9›9.2 Hedge Funds
🎯 Why Manager Selection Matters More in Alternatives S9 p. 13
▼
In traditional asset classes (equities, fixed income), the spread between top-quartile and bottom-quartile managers is relatively narrow. In alternatives, this dispersion is enormous - picking the wrong manager in private equity can mean losing money while the top quartile earns 14%+.
Asset Class
Quartile Spread (Top vs Bottom)
Implication
Equities
~2.9%
Index investing often wins after fees
Fixed Income
~2.9%
Manager selection less critical
Private Equity
~14.3%
Access to top-quartile GPs is essential
Hedge Funds
~14.2%
Strategy and manager alpha are decisive
Real Estate
~12.0%
Local expertise and deal flow critical
Infrastructure
~11.8%
Regulatory expertise separates managers
Private Debt
~5.4%
Credit underwriting skill matters greatly
Source: BlackRock Investment Institute / Morningstar / Pitchbook. Data through 31 Dec 2022.
📝
Exam Point: This dispersion table is the key argument for why alternatives require due diligence and access - not just exposure. In public equities, you can buy an index fund and roughly match the market. In private equity, being in a bottom-quartile fund can destroy capital. Access to top-tier GPs is itself a competitive advantage.
Part 2 - Hedge Funds
Session 9 Slides pp. 15–28
🏛️ What Is a Hedge Fund? Definition & Origin S9 pp. 16–18
▼
Origin - Alfred Winslow Jones (1949)
The first hedge fund was created by Alfred Winslow Jones in 1949. His innovation: combine long positions in undervalued stocks with short positions in overvalued ones, using leverage to amplify returns while hedging market exposure. The goal was to generate alpha - returns driven by manager skill rather than market beta.
Forthergill & Coke Definition
"All forms of investment funds, companies and private partnerships that use derivatives for directional investing and/or are allowed to go short and/or use significant leverage through borrowing."
Note: the name "hedge fund" is increasingly a misnomer - many hedge funds today take highly directional, unhedged positions.
Pool capital from multiple sophisticated investors
② Absolute Return Oriented
No benchmark. Target positive return in all market conditions
③ Few Restrictions
Can short sell, use OTC derivatives, illiquid assets, any security type
④ Low Regulation
Lighter touch than mutual funds; AIFMD in Europe provides some oversight
⑤ High Minimum Investment
Typically $500K–$5M+ minimum; restricted to sophisticated / institutional investors
⑥ Less Transparency
Holdings not public; NAV reported periodically; strategy details are proprietary
💡
10-Point HF Summary (Slide p.20): Heterogeneity · Lack of transparency · Offshore structure · Leverage · Long and short positions · Absolute return / no benchmark · Exploit inefficiencies · Mixed fees (flat + performance) · Low correlation with traditional assets / alpha-seeking · Behaviour during crises (often disappoints - correlations converge).
⚖️ Hedge Funds vs Mutual Funds - Full Comparison S9 pp. 21–24
▼
Dimension
Hedge Funds
Mutual Funds
Investment Objective
Absolute, positive returns in all market conditions
Outperform a benchmark index
Primary Return Driver
Manager skill - the alpha coefficient
Overall market performance (beta)
Investment Constraints
"Unconstrained" - long, short, leveraged, any instrument
Typically follow a benchmark; limited to regulated instruments
Positions
Long and short positions
Usually long only
Leverage
Can use significant leverage
Normally do not use leverage
Risk Measure
Standard deviation of NAV; max drawdown
Tracking error vs benchmark
Performance Measured Against
Risk per unit (Sharpe); absolute NAV
Benchmark index; risk vs index
Manager Compensation
"2 and 20" - 2% management fee + 20% performance fee (above hurdle). Manager often co-invests.
Fixed % of AUM (typically 0.5–1.5%); manager is an employee
Regulation
Little regulation, few restrictions
Strict regulation; UCITS in Europe; SEC in US
Transparency
Results known only to investors; strategy proprietary
Results public; full portfolio disclosure
Investor Access
Closed to the public; institutional & high-net-worth only
Open to general public; retail investors
Minimum Investment
Generally very high ($500K–$5M+)
Generally low (€/$ hundreds or thousands)
Liquidity
Low - lock-up periods, redemption gates, quarterly/annual liquidity windows
High - daily liquidity (UCITS)
Investment Approach
Opportunistic, flexible, dynamic
Constrained by benchmark and mandate
📝
Exam Favourite: The "2 and 20" fee structure. 2% annual management fee on AUM + 20% performance fee on profits above the hurdle rate (typically LIBOR/SOFR or 8%). High water mark provisions mean the manager only earns performance fees on new profits - cannot earn fees on recovering prior losses. This creates strong alignment but also incentive to take risk after drawdowns.
📊 Hedge Fund Performance - Has the Promise Been Delivered? S9 p. 25
▼
The course's Bloomberg chart (slide p.25) compares the HEDGNAV Index vs the S&P 500 from Dec 1993 to Feb 2026. The result is uncomfortable for hedge fund advocates.
HEDGNAV (Hedge Fund Main Index)
+880.8%
Total return since Dec 1993 Annual equivalent: 7.35%
S&P 500 Index
+2,590.2%
Total return since Dec 1993 Annual equivalent: 10.77%
💡
What this tells us (slide p.25): Hedge funds as a group delivered a 7.35% annualised return vs 10.77% for the S&P 500 over 32 years. The aggregate case for hedge funds on a return basis alone is weak vs public equities. However, this misses the key argument: hedge funds are not competing against equities - they are competing against bonds as a low-volatility, low-correlation diversifier. The comparison should be risk-adjusted and portfolio-context specific.
📝
Exam angle: If a course shows you a hedge fund performance chart, the question is rarely "did it beat the S&P?" - it's "did it improve the portfolio's Sharpe ratio?" A hedge fund returning 6% with 4% volatility and zero correlation to equities adds more value to a diversified portfolio than simply comparing returns to the S&P implies.
Session 9›9.3 Allocation & Illiquidity
🌍 Hedge Fund Industry - Size & Geography S9 pp. 26–28
▼
Hedge funds remain the second largest alternative asset class by AUM after private equity. The industry is concentrated geographically - North America dominates overwhelmingly.
$2,783bn
North America (US: $2,718bn)
$673bn
Europe (UK: $463bn)
$121bn
Asia-Pacific (HK: $61bn)
World's Biggest Hedge Funds (2022, by AUM)
#
Fund
AUM
HQ
1
Bridgewater Associates
$126.4B
USA
2
Man Group
$73.5B
UK
3
Renaissance Technologies
$57.0B
USA
4
Millennium Management
$55.0B
USA
5
Citadel
$53.0B
USA
6
D.E. Shaw Group
$47.9B
USA
7
Two Sigma
$41.0B
USA
Source: Pensions & Investments. Assets as of June 30, 2022. ~15,000 hedge funds globally with combined $4.5T AUM at time of ranking; most based in North America.
Part 3 - AIS, Asset Allocation & the Illiquidity Premium
Session 9 Slides pp. 29–33
⚖️ Should You Allocate to Alternatives? S9 p. 30
▼
✅ Reasons to Allocate to AIS
Income: Real Estate provides stable rental income streams
Real Return: Real Estate and PE? - inflation protection via asset values
High Returns: Private Equity has historically outperformed public equity
High Sharpe Ratio: Hedge funds (in theory) offer equity-like returns with bond-like volatility
Flight-to-quality protection: Commodities (gold) in stress environments
Diversification: All alternatives, if genuinely uncorrelated, improve portfolio efficiency
❌ Reasons Not to Allocate to AIS
Not investor-friendly: Complex structures, limited redemption, opaque reporting
Low regulation: Less protection vs mutual funds; potential for fraud
Necessary knowhow: Requires specialised due diligence capacity most investors lack
Illiquidity: Capital locked up for years; cannot exit in market stress
📝
Exam trap: Do not confuse "low regulation" as a reason to invest - it is a risk. The low regulatory burden makes HFs flexible, but it also means less investor protection. The table in the slides frames it correctly: low regulation is a characteristic of HFs, not a selling point.
🔒 The Illiquidity Premium S9 pp. 31–33
▼
One of the most important concepts in alternative investing: investors who accept illiquidity should be compensated with higher returns. This premium is the theoretical foundation for long-term allocations to PE, real estate, infrastructure, and private credit.
── THE ILLIQUIDITY PREMIUM ──
Expected Return (Illiquid) = Risk-Free Rate + Equity/Credit Risk Premium + Illiquidity Premium── WHO CAPTURES IT ──
Private Equity → Pure illiquidity play (5–10 year lock-up). Highest premium.
Real Estate → Illiquidity + income. Lock-up via asset nature.
Some Hedge Funds → Strategies with lock-ups or gates capture a partial illiquidity premium.
Infrastructure → Very long duration, illiquid - high premium for patient capital.
Private Equity
Pure illiquidity premium game. 5–10 year lock-up. Investors must not need capital returned.
Real Estate
Plays illiquidity premium. Also provides income (rental yield) and inflation hedge.
Some Hedge Funds
Strategies with lock-up periods or redemption gates partly capture the premium.
Listed PE / Listed RE
⚠️ Listing removes illiquidity → premium disappears. Correlation with public equity rises sharply.
💡
The Listed PE Paradox (Slide p.33 - Bloomberg SPLPEQTY vs SPX): The course shows that the S&P Listed Private Equity Index (SPLPEQTY) returned +321.5% total vs the S&P 500 at +914.4% over the same period - and crucially, the chart shows the two indexes moving in near-lockstep, especially from 2017 onward. Once PE is listed, it trades like equity. The diversification benefit - which justified the allocation in the first place - largely disappears. The illiquidity premium requires accepting genuine illiquidity.
📝
Exam Point - The Listed Alternative Trap: Students often think "I can get PE exposure through listed PE funds and still have liquidity." The slide shows why this is flawed: listed PE is highly correlated with public markets. The illiquidity premium only exists when capital is truly locked up. Liquid alternatives approximate the strategy but not the return premium.
Session 9 - Key Concepts & Metrics
Exam Ready
💰 HF Fee Structure - "2 and 20"
Management Fee
2% × AUM per year
Charged regardless of performance
Performance Fee
20% × Profit above hurdle
High water mark prevents double-charging on recovered losses
📈 PE Return Metrics
IRRDiscount rate that makes NPV of all cash flows = 0. Primary return metric in PE.
MOICTotal Value ÷ Cost Invested. 3.0× = €3 back per €1 in.
DPIDistributions ÷ Paid-in Capital. Measures actual cash returned to LPs.
Investors who accept illiquidity should earn a premium. PE captures the most (5–10yr lock-up). Listed alternatives lose the premium as soon as they become liquid.
⚖️ HF vs Mutual Fund - Risk Measures
Hedge Fund
Risk = Standard Deviation of NAV
Sharpe = (Rp − Rf) / σp
Mutual Fund
Risk = Tracking Error vs benchmark
IR = Active Return / Tracking Error
Sessions 10–11 · Slides pp. 1–28
Asset Allocation
SAA, TAA, performance attribution, macro regime frameworks, monetary policy, tactical drivers, and portfolio construction - the capstone of the investment process.
4 Sub-sectionsSAA/TAAMacro RegimesTactical DriversPortfolio Construction
Return estimation, building blocks, risk parity, 60/40 evolution.
4
Sessions 10–11›10.1 SAA & TAA Framework
Accordions:
Part 1 - Introduction to Asset Allocation
Slides pp. 3–9
🎲 The Callan Periodic Table - Why Diversification Wins S10–11 p. 3
▼
The Callan Periodic Table ranks 9 asset classes by annual return for each of the past 20 years. Each column is one year. Each colour is one asset class. Read it top-to-bottom: the highest-returning asset class sits at the top.
What the Table Teaches1. No asset class wins every year. The "best" asset rotates almost randomly. 2. Last year's winner is often next year's loser. Emerging Markets +78.5% in 2009 → −18.4% in 2011. 3. Spread between best and worst is wide. In 2008, REITs −37% vs Treasuries +14%. A 50pp gap. 4. Therefore: diversify. Holding multiple asset classes smooths the ride. Concentrating bets requires forecasting skill - most managers don't have it.
📝
Exam intuition: The Callan Table is the empirical case for asset allocation. If you could predict next year's winner, you'd concentrate. Since you can't reliably, you diversify across asset classes that respond differently to growth and inflation shocks.
9 Asset Classes TrackedLarge Cap (S&P 500) · Small Cap (Russell 2000) · Developed ex-US Equity (MSCI EAFE) · Emerging Markets · US Fixed (Bloomberg US Agg) · High Yield · Real Estate (REITs) · Cash · Global ex-US Fixed
Notable Years2008 GFC: Bonds +5%, US Large Cap −37%, REITs −37% 2009 Recovery: EM +78%, Small Cap +27%, Cash 0% 2022 Rate shock: US Fixed −13% (worst since 1976), Cash positive, all equity negative
🔺 The 3-Layer Hierarchy: SAA → TAA → Security Picking S10–11 p. 4
▼
Every asset allocation decision sits in one of three layers. Each layer has a different time horizon, a different decision-maker, and contributes differently to portfolio returns.
The 3-Layer Hierarchy
SAA
Strategic · 3–5 years
Set by
Client
via IPS
TAA
Tactical · 1–12 months
Set by
Market
macro view
Picking
Continuous
Set by
Manager
fundamentals
~90%
of return variability comes from SAA Brinson, Hood & Beebower (1986)
SAA - Strategic Asset AllocationDecision-maker: Client (driven by IPS - Investment Policy Statement) Horizon: 3–5 years, sometimes longer Question answered: "What is my long-run target mix of equity / bonds / cash given my risk tolerance and goals?" Reviewed: Annually or less
TAA - Tactical Asset AllocationDecision-maker: Market (driven by macro / valuation / cycle views) Horizon: 1–12 months Question answered: "Should I tilt away from my SAA right now?" Bounded by ranges set in the IPS (typically ±5–10pp from SAA target)
Security PickingDecision-maker: Portfolio Manager (driven by fundamentals / models) Question answered: "Which specific stocks/bonds inside each asset class?" Generates: Alpha vs benchmark - but small share of total return variability
📝
Brinson, Hood & Beebower (1986): Asset allocation explains roughly 90% of portfolio return variability over time. Stock-picking explains far less. The implication: get SAA right first; obsess over stock selection second.
🧮 Performance Attribution - Calculator S10–11 p. 5
▼
The course's slide-5 example: a portfolio with the SAA below, free to tilt within the tactical range. Question: if you outperform your benchmark, how much came from SAA, TAA, or stock picking?
Asset Class
SAA Target
Tactical Range
U.S. Equity
35%
25% – 45%
Non-U.S. Equity
25%
15% – 35%
Bonds
35%
25% – 45%
Cash
5%
0% – 15%
Performance Attribution - set your actual weights & returns
Asset Class
Actual W (%)
Asset Ret (%)
Bench Ret (%)
U.S. Equity (SAA 35%)
Non-U.S. Equity (SAA 25%)
Bonds (SAA 35%)
Cash (SAA 5%)
Weights sum: 100% ✓
SAA Return
6.75%
TAA Effect
+0.15%
Security Picking
+1.00%
Total Return7.90%
Excess vs SAA: +1.15%
📝
The decomposition rule: Total Return = SAA Benchmark + TAA Effect + Security Picking Effect. TAA effect = how much your over/underweights helped, scored against the asset-class benchmark. SP effect = how much your security selection beat the asset-class benchmark, weighted by your actual exposure.
A portfolio manager deviated from SAA weights: 70% equity (vs 60% benchmark) when equities returned 15% and bonds returned 3%. The attribution of this overweight is:
(a) +1.2% from TAA (timing effect)
(b) +0.6% from security selection
(c) No impact - only SAA matters
(d) Cannot be determined
(a) is correct. TAA contribution = Δw × (Rasset − Rbenchmark) = (+10%) × (15% − 3%) = +1.2%. The overweight in equities added 120bp because equities outperformed bonds.
Sessions 10–11›10.2 Cycles & Rotation
📊 The "7% Return" Portfolio: 1991 → 2006 → 2021 S10–11 p. 6
▼
Same projected return target. Three decades. The mix needed to hit 7% has shifted dramatically - because risk-free rates collapsed and investors had to push further out the risk curve.
Year
Portfolio Mix
Expected Return
Risk (Vol)
1991
Cash 98% · US Fixed 2%
7.0%
1.1%
2006
US Fixed 63% · Large Cap 20% · Global ex-US 13% · SMID 4%
7.0%
6.7%
2021
Large Cap 37% · Global ex-US 23% · Private Equity 17% · Real Estate 13% · SMID 7% · US Fixed 3%
7.0%
17.3%
1991 - Cash was kingRisk-free rate ~7%. Investors earned target return holding nearly 100% short-term cash. No equity needed. No risk needed.
2006 - Bonds did most of the workRisk-free rate ~5%. A third in equity got the rest of the way. Volatility 6×.
2021 - Push out the risk curveZero rates forced 97% in return-seeking assets - public equity, private equity, real estate. Volatility 16× the 1991 portfolio.
The take-awaySame 7% target. Same money. Vastly more risk to get there. This is the structural pressure on every pension fund and endowment today.
⚠️
Why this matters: Pension liabilities still demand 6–8% returns. With sovereign yields suppressed for a decade, sponsors had to choose: accept higher risk, accept lower returns, or accept higher contributions. Most chose higher risk - which is now being tested.
🌀 The Equity Style Cycle - 4 Phases S10–11 p. 7
▼
Merrill Lynch back-tested 4 complete cycles (1992–2004) against the ML Composite Macro Indicator. The result: equity styles rotate predictably with the economic cycle. Each phase favours different style tilts.
Phase 1 - Rising & Accelerating (Recovery)Growth is positive and strengthening. Risk-on: overweight Small Cap, High Risk, Rising Momentum vs Large Caps. Neutral Value vs Growth.
Phase 2 - Rising & Decelerating (Late Expansion)Growth is positive but slowing. Quality up: overweight Growth vs Value, Rising Momentum, High Quality, Low Risk, Large Caps vs Small Caps.
Phase 3 - Falling & Decelerating (Slowdown)Growth is negative and worsening. Defensive: overweight Value vs Growth, High Quality, Low Risk. Neutral Large vs Small Caps.
Phase 4 - Falling & Accelerating (Trough)Growth is negative but bottoming. Re-risk early: overweight Value, Momentum, Low Quality, High Risk, Small Caps vs Large Caps. Anticipate the turn.
📝
Mental model: The cycle drives styles. Quality wins as growth slows. Value wins coming out of the trough. Momentum wins where the trend is strongest (Phases 1 & 2 on the way up; Phase 4 on the way out).
🕐 Asset Rotation & The Multi-Asset Investment Clock S10–11 pp. 8–9
▼
The same cycle logic that drives styles drives cross-asset rotation. Bonds lead at the trough. Stocks lead the recovery. Commodities peak with growth. Cash wins in stagflation. The Citi Multi-Asset Investment Clock formalises this around a 12-hour face.
Asset Class Leadership Across the Cycle
Phase
Growth
Inflation
Leading Asset
Central Bank
Reflation
↓ Falling
↓ Falling
Bonds
Cutting rates aggressively
Recovery
↑ Rising
↓ Still falling
Stocks
Rates near zero, holding
Overheat
↑ Rising
↑ Rising
Commodities
Beginning to hike
Stagflation
↓ Falling
↑ Still rising
Cash
Hiking aggressively
📝
Why this works: Bonds rally first because central banks cut rates as the economy weakens. Stocks rally next because earnings recover. Commodities lag because they need actual demand. Cash wins last when CBs hike to break inflation.
Citi Multi-Asset Clock - Quick Reference9 o'clock: Sweet Spot - strong buy on equities, value stocks, early cycle sectors, high-yield bonds, small caps 12 o'clock: Top of the boom - overweight equities still, but rotate to large caps, late-cycle sectors, inflation-linked bonds 3 o'clock: Slowdown - sell equities, defensive sectors, long-term gov bonds, growth stocks, high quality assets 6 o'clock: Depth of recession - bottom in equities, cycle is about to turn - corporate bonds, small caps, early cycle sectors
Part 2 - Macro Regimes & Monetary Policy
Slides pp. 10–14
🌡️ The Macro Regime Quadrant - Growth × Inflation S10–11 p. 11
▼
ISSG's regime framework collapses the entire macro picture onto two axes: direction of growth and direction of inflation. Every economic environment maps to one of five regimes - each with a distinct asset-class winner.
PERFECTION - GoldilocksGrowth ↑ Inflation ↓ - best regime for risk assets. Winners: US Equity, Int'l Equity, REITs, Hedge Funds. Loser: Cash.
WARMING - ReflationGrowth ↑ Inflation ↑ - pro-cyclical with inflation hedges. Winners: Private Equity, EM Equity, Oil, Commodities (GSCI), High Yield. Loser: Long bonds.
COOLING - DisinflationGrowth ↓ Inflation ↓ - bond-friendly. Winners: US Treasuries, Corporate Bonds, Gold. Equity flat to slightly negative.
TOO HOT - StagflationGrowth ↓ Inflation ↑ - worst for traditional 60/40. Winners: Oil, Gold, TIPS. Losers: most equities, nominal bonds.
TOO COLD - Deep RecessionSevere Growth ↓ · Severe Inflation ↓ - flight to quality. Winners: US Treasuries, TIPS, Cash. Losers: nearly everything else.
📝
Exam frame: Don't memorise winners and losers - internalise the logic. Equities like growth. Bonds like falling inflation. Commodities like inflation. Cash wins when nothing else does. Map any environment onto these axes and the answer falls out.
Sessions 10–11›10.3 Tactical Macro Drivers
🏦 Monetary Policy - Central Bank vs Market S10–11 pp. 12–13
▼
The yield curve is split: the short end is set by central banks (policy rate); the long end is set by markets (growth + inflation expectations + term premium). Both ends matter, but for different reasons.
Short End - CB PolicyDrives: Currency, FX carry trades, money market yields, bank funding costs, repo rates. Tool: Policy rate + forward guidance.
Long End - Market PricingDrives: Mortgage rates, corporate borrowing costs, equity valuations (discount rate), real economy investment decisions. Built from: real growth + inflation expectations + term premium.
── 10Y NOMINAL YIELD DECOMPOSITION ──
10Y Nominal Yield = Real Growth Expectations
+ Inflation Expectations
+ Term PremiumCBs influence inflation expectations via credibility,but cannot directly control the term premium.
📝
Why both ends matter for AA: Equity valuation (P/E) is anchored to the long end via the discount rate. Equity cyclicals respond to the short end via funding cost and the curve slope. Steepening curve → cyclicals win. Flattening/inverting → defensives win.
💰 QE and Equity Markets - The Liquidity Trade S10–11 p. 14
▼
Post-2008, the four major central banks (Fed, ECB, BoJ, BoE) expanded their combined balance sheets from ~$3T to over $20T. The S&P 500 tracked this liquidity expansion almost step for step. Quantitative easing did not just lower rates - it pushed financial assets up by suppressing risk-free yields and forcing reach-for-yield.
Mechanism - How QE Lifts Equities1. CB buys long-dated bonds → bond yields fall 2. Lower discount rate → equities re-rate higher (P/E ↑) 3. Lower bond yields → asset managers rotate into equities & credit (TINA effect) 4. Cheaper corporate funding → buybacks, M&A, higher EPS
Implication for AAQE is structural support for equities. QT (quantitative tightening) is the opposite. Watch CB balance sheet trajectory as a leading indicator of equity multiple expansion or compression.
⚠️
2022 demonstrated the reverse: when the Fed shifted from QE to QT and hiked aggressively, the S&P fell ~25% from peak and the Bloomberg US Agg Bond Index posted its worst year since 1976 (−13%). The 60/40 portfolio lost on both legs simultaneously - a direct consequence of unwinding the liquidity trade.
Part 3 - Tactical Macro Drivers
Slides pp. 15–17
⚙️ The Master Equation: P = EPS × P/E S10–11 p. 16
▼
Every equity-market call collapses to one identity: Price = EPS × P/E multiple. Asset allocation is the practice of forming a view on each side of this equation.
Tactical question: When you read a strategist's "S&P target", ask: are they betting on EPS, P/E, or both? EPS calls are about the cycle. P/E calls are about rates and risk appetite. The two often move in opposite directions - earnings rise as rates rise, compressing the multiple.
📈 Economic Indicators - ISM & PMI S10–11 p. 16
▼
For tactical asset allocation, two indicators dominate: ISM (US) and PMI (global). Both are diffusion indices built from monthly purchasing-manager surveys. Both are released early. Both lead the official GDP print by 3–6 months.
ISM - Institute for Supply ManagementWhat: US manufacturing & services surveys Released: First business day of each month Read: · >50 = expansion · <50 = contraction · <42.5 sustained = recession threshold
PMI - Purchasing Managers' IndexWhat: Same methodology, applied globally (S&P Global, formerly Markit) Coverage: Eurozone, China, UK, Japan, EMs Use: Cross-country comparison; identify which region is decelerating fastest
📝
The 50 line is a regime switch. Crossing below 50 historically coincides with the start of equity drawdowns. Crossing above 50 from below has been one of the most reliable buy signals for cyclical equities and high-yield credit.
Other indicators worth trackingInitial jobless claims (weekly) - fastest leading indicator · Conference Board LEI - composite leading index · Yield curve (10Y−2Y) - inversion historically precedes recession by 12–18 months · Housing starts & permits - interest-rate-sensitive demand
🧠 Comprehension Check
The ISM Manufacturing Index falls to 48. As a tactical asset allocator, you would:
(a) Overweight cyclical equities - manufacturing is about to boom
(b) Overweight long-duration government bonds - ISM < 50 signals contraction
(c) Buy commodities - input demand will surge
(d) ISM is irrelevant for asset allocation
(b) is correct. ISM < 50 signals manufacturing contraction. This is a leading indicator of economic slowdown. Tactically, you would reduce equity exposure (especially cyclicals) and increase allocation to safe-haven assets like government bonds.
Sessions 10–11›10.4 Portfolio Construction
⏱️ Business Cycle vs Stock Market Cycle - The Lead/Lag S10–11 p. 17
▼
The stock market is not the economy. It is a leading indicator of the economy - typically by 3 to 6 months. Stocks peak before GDP peaks. Stocks trough before GDP troughs. This phase shift is critical for tactical allocation.
GDP (solid) leads by ~3–6 months. Stocks (dashed) move first.
📝
The tactical implication: By the time GDP confirms a recession, the bear market is mostly over. By the time GDP confirms recovery, the bull market has already done much of its work. Wait for the official data and you are always late. This is why ISM and PMI matter - they lead the official prints.
Severity classificationRecession - two consecutive quarters of negative GDP growth (rule of thumb; NBER uses a broader committee judgement) Depression - a severe, long-lasting recession. Last US example: 1929–1939.
Part 4 - Sector & Style Allocation
Slides pp. 18–20
🔄 Sector Rotation Across the Cycle (Schroders) S10–11 p. 19
▼
Schroders maps 7 sector groups onto 4 cycle phases. Same logic as the styles cycle - different sectors lead at different points. The grouping below is what the course materials highlighted for the exam.
7 Sector Groups (memorise the order)1. Growth · 2. Growth Defensives · 3. Value Defensives · 4. Financials · 5. Consumer Cyclicals · 6. Commodity Cyclicals · 7. Industrial Cyclicals
Phase
Overweight
Underweight
Slowdown Growth ↓ Inflation ↑
Growth · Growth Defensives · Value Defensives · Financials
Pattern to internalise: Defensives win when growth is falling (Slowdown, Recession). Cyclicals win when growth is rising (Recovery, Expansion). Commodity cyclicals specifically win when inflation is rising too (Expansion). Financials are bridge sectors - they like the early recovery and curve steepening.
🧬 Smart Beta - Factor Exposures Across the Cycle S10–11 p. 20
▼
Beyond sectors, factor investing tilts portfolios toward systematic style premia: Quality, Value, Momentum, Size. Each factor has a distinct macro signature. BlackRock's framework maps factor sensitivities to growth and inflation.
Cycle Phase
Leading Factors
Lagging Factors
Early Cycle / Recovery
Size (small cap) · Value · Momentum
Quality (rich, defensives unloved)
Mid Cycle / Slowdown
Quality · Momentum (mid-cycle leaders)
Size (small underperforms as growth slows)
Late Cycle / Pickup
Quality · Momentum · Value (rotation)
Size (small still lagging)
Contraction
Quality · Value (defensive value)
Size · Momentum
Real GDP Growth RiskOutperform when growth is strong: Equal-Weight · Momentum · Risk-Weighted · Value · Small Cap
Outperform when growth is weak: High Dividend Yield · Quality · Min Volatility
Inflation RiskOutperform when inflation rises: Equal-Weight · Momentum · High Div Yield · Quality · Risk-Weighted · Small Cap
Outperform when inflation falls: Min Volatility
📝
Why factors matter for AA: Diversification is not just across asset classes - it's across return drivers. Two equity portfolios can hold the same names but tilt to different factors and behave differently across regimes. Smart Beta gives a third axis alongside SAA and TAA: factor allocation.
Part 5 - Historical Returns
Slides pp. 21–24
🥤 Decomposition: Price = EPS × Multiple - The Coca-Cola Lesson S10–11 p. 23
▼
The course materials use Coca-Cola (1982–2014) to make a structural point: over multi-decade periods, price tracks EPS. The multiple oscillates around its mean. The terminal price is dominated by the earnings trajectory, not the re-rating.
What the chart showsThe blue line (price) and red line (EPS × stable multiplier) overlap closely from 1982 to 2014. Multi-decade equity returns are an EPS story. The multiple is mean-reverting; earnings compound.
Implication for AA: If you hold equities for 30 years, your return is essentially the dividend + the EPS growth rate. If you hold for 1 year, the multiple change dominates. Time horizon determines whether earnings or valuation matters more.
📚 The Dimson-Marsh-Staunton Long Run - 125 Years of Returns S10–11 p. 24
▼
The DMS Database (UBS Global Investment Returns Yearbook, 2025 edition) covers 1900–2024 across 35 countries. It is the canonical empirical reference for long-run asset-class behaviour. The US numbers below are real (inflation-adjusted) annual returns.
Asset Class
USA Nominal (1900–2024)
USA Real (1900–2024)
Cumulative Real ($1 → ?)
Equities
9.7%
6.6%
$2,911
Bonds (long Treasury)
4.6%
1.6%
$7.30
Bills (T-Bills)
3.4%
0.5%
$1.80
Inflation
2.9%
-
-
The Equity Risk PremiumEquities − Bonds = ~5pp/yr in real terms Over 125 years, this is the empirical risk premium investors earned for holding equity over long government bonds. $1 invested in equities in 1900 grew to $2,911 in real terms by 2024. The same $1 in bonds grew only to $7.30.
The Cash DragT-Bills barely beat inflation (0.5% real) Holding cash for 125 years multiplied real wealth by 1.8×. Holding equities multiplied it by ~1,600×. The cost of avoiding equity-market volatility is enormous over long horizons.
📝
Memorise these numbers: US real equity ~6.6%, real bonds ~1.6%, real bills ~0.5%, inflation ~2.9%. They anchor every conversation about long-run expected returns. They are also the building blocks for "ERP estimation" questions.
💵 Nominal vs Real - Why Inflation Changes the Picture S10–11 pp. 22, 24
▼
A 7% nominal return with 5% inflation is a 2% real return. Real returns are what matter for purchasing power. Asset allocation must always distinguish the two.
When the distinction matters1. Long horizons - small inflation gaps compound massively 2. Comparing across decades - 1970s 7% nominal ≠ 2010s 7% nominal 3. Pension liabilities - most are inflation-linked 4. Sovereign bond markets - TIPS vs nominal Treasuries
Quick mental conversions1980 - 14% nominal, 13% inflation → ~1% real 2000 - 6% nominal, 3% inflation → ~3% real 2021 - 25% nominal, 7% inflation → ~17% real 2022 - −18% nominal, 7% inflation → ~−24% real
Part 6 - Asset Class Behaviour
Slides pp. 25–29
🎭 Three Macro Environments - How Asset Classes Actually Behave S10–11 p. 26
▼
The course materials reduce the entire macro landscape to three stress environments that matter for asset allocation. If your portfolio survives all three, you are diversified.
① InflationWhat breaks: nominal bonds, cash, fixed-rate debtors What works: commodities, TIPS, real assets, equities (selectively), gold Historical case: Weimar Germany 1919–23, US 1970s, post-COVID 2021–22
② Fly to QualityWhat breaks: credit, EM, high-beta equity, illiquid assets What works: US Treasuries, gold, USD, JPY (historically), Bunds Historical case: 1998 LTCM, 2008 GFC, 2020 COVID shock
③ Low Growth + Low InflationWhat breaks: cyclicals, banks, value (often) What works: long-duration bonds, growth equities, quality, defensives Historical case: Japan 1990s–2010s, Eurozone 2014–19, secular stagnation thesis
📝
Stress-test framing: A truly diversified portfolio holds something that wins in each of the three environments. The 60/40 fails environment ① (inflation hits both stocks and bonds). True diversification needs commodity exposure or real assets.
🇩🇪 Case Study - Weimar Germany 1919–1923 S10–11 pp. 27, 29
▼
The most extreme inflation episode of the modern era. The German mark collapsed against gold by a factor of ~500 million. Equities massively outperformed bonds in nominal terms - and held value in real terms - while cash and fixed-income holders were wiped out.
Date
Marks per oz of gold
Mark inflation factor
Jan 1919
170
1×
Jan 1920
1,340
~8×
Jan 1922
3,976
~23×
Jan 1923
372,477
~2,200×
Sept 1923
269,439,000
~1.6 million×
Nov 30, 1923
87,000,000,000,000
~500 billion×
German equity indexThe Berlin stock index rose from 1 (Feb 1920) to 134.45 billion (Oct 1923) in nominal terms - riding inflation upward, preserving some real purchasing power. Equity claims on real assets are inflation hedges; cash claims are not.
The lessonIn hyperinflation, only real-asset claims survive: equities, real estate, gold, commodities. Anyone holding nominal-currency assets (bonds, cash, savings, fixed pensions) was destroyed.
🇧🇷 Case Study - Brazil IBOV in BRL vs USD S10–11 p. 28
▼
From Jan 1992 to Apr 1997 (272 weeks):
Index
Currency
Cumulative Return
Annualised
IBOV
BRL
+1,186,506%
504%/yr
IBOV
USD
+349%
+33%/yr
S&P 500
USD
+81%
+15%/yr
Two stories in one chartBRL story: 1.2 million% return - almost entirely currency debasement (Brazil was emerging from hyperinflation, the cruzeiro was replaced). USD story: 349% return - strips out the FX effect. Still massive, but driven by real economic and corporate gains.
Why this matters for AACurrency exposure is itself an asset allocation decision. A globally-allocated investor must decide: hedge FX or take the exposure? Unhedged EM equity is partly an EM currency bet. Hedged is closer to a pure equity bet.
📝
Bigger point: Reported nominal returns from countries with high inflation are noise. Always convert to a stable reference currency (USD or EUR) - or to real local terms - before comparing.
Part 7 - Estimating Future Returns
Slides pp. 30–34
🧱 The Building-Block Approach S10–11 p. 31
▼
To estimate future returns, start with the risk-free rate and add a premium for each layer of risk. This is the framework BlackRock, GMO, AQR and most asset allocators use to set Capital Market Assumptions (CMAs).
── BUILDING BLOCK FOR EQUITY ──
E(Requity) = Risk-Free Rate + Equity Risk Premium
ERP ≈ Real GDP growth + Inflation + Earnings growth premium − Multiple change
────────────────────────────────────────
For bonds: E(Rbond) ≈ Yield-to-Maturity (approximately)
For cash: E(Rcash) ≈ Short rate
Why the Risk-Free Rate Anchors EverythingThe risk-free rate is the floor. Every other asset must pay a premium above it to justify the additional risk. When risk-free rates fall, expected returns on every asset class fall too - because investors bid up prices in search of yield.
Why GDP Growth Drives Equity ReturnsCorporate earnings can't grow faster than the economy forever. Real GDP growth is the long-run ceiling for real earnings growth. Add inflation and a small re-rating component, and you have nominal equity returns.
📝
Exam intuition: If asked "what's a reasonable long-run equity return for the US?", build it up: ~2% real GDP growth + ~2% inflation + ~2% dividend yield + ~0.5% multiple drift ≈ 6.5–7% nominal. This matches the 125-year DMS data.
📈 BlackRock 10-Year Expected Returns (Aug 2025) S10–11 p. 31
▼
BlackRock Investment Institute publishes 10-year forward Capital Market Assumptions quarterly. Below is a simplified snapshot of the central expected returns by asset class (Aug 2025 vintage, USD, geometric).
Asset Class
Expected Return
Bucket
US Private Equity (Buyout)
12.5%
Private markets
Direct Lending
9.5%
Private markets
Hedge Funds (Global)
7.0%
Alternatives
US Real Estate
7.5%
Real assets
Emerging Markets Equity
8.0%
Equity
Europe Equity
7.5%
Equity
Global 60/40 Portfolio
6.5%
Mixed
US Equity (Large Cap)
6.0%
Equity
High Yield Credit
6.5%
Credit
USD EM Debt
5.5%
Credit
TIPS
4.5%
Bonds
US Treasuries (Aggregate)
4.0%
Bonds
Long Treasuries
4.5%
Bonds
⚠️
Caveat: Expected returns are forecasts, not guarantees. Actual realised returns can deviate materially over 10-year horizons. BlackRock publishes uncertainty bands alongside central estimates - for equities, the interquartile range typically spans ±3–4pp.
Why Private Markets Sit at the TopPrivate equity expected returns of 12.5% reflect the illiquidity premium (~3–4pp), the leverage premium (typical LBO uses 5–6× leverage), and operational alpha. Higher returns are compensation for: long lock-ups, no daily liquidity, GP risk, and concentration. Public-market beta + leverage explains most of it.
🌍 Long-Run GDP Growth - The Maddison Hockey Stick S10–11 p. 32
▼
The Maddison Project Database (Bolt & van Zanden, 2020) tracks GDP per capita for major economies back to year 1 AD. For almost 1,800 years, GDP per capita was essentially flat - humans lived at subsistence. Then the Industrial Revolution: an explosion of growth that compounds to today.
2016 GDP per capita (international-$, 2011 prices)United States: ~$54,000 Austria, UK, France, South Korea: $40–45,000 Argentina: ~$18,000 Indonesia: ~$11,000
Why This Matters for AALong-run equity returns are bounded by real GDP growth. Slowing demographics + lower productivity = lower expected returns. Developed-market trend growth has shifted from ~3% (1950–2000) to ~1.5–2% (2000–today).
📝
The compounding lesson: A 1pp difference in real growth rate, sustained for 50 years, doubles the gap in real living standards. This is why analysts spend so much time on productivity, demographics, and capital deepening - they determine the long-run return ceiling.
⚖️ Earnings Yield Gap - Equities vs Bonds Simulator S10–11 pp. 33–34
▼
The Earnings Yield Gap (EYG) is the single most-watched tactical equity-vs-bonds indicator. It compares the earnings yield of the S&P 500 (the inverse of P/E) with the 10-year Treasury yield. A wide gap = equities cheap relative to bonds. A negative gap = equities expensive (the "1999 bubble" signal).
── EARNINGS YIELD GAP ──
EY = 1 / P/E(earnings yield, expressed as a %)
EYG = EY − 10Y Yield────────────────────────────────────────
Long-term average: ~230 bp (1985–2020, Goldman Sachs)
Earnings Yield Gap - set current values
22.0×
4.2%
Earnings Yield
4.55%
10Y Yield
4.20%
Yield Gap
+35 bp
230bp avg
−200bp · expensive0bp+230bp · avg+500bp · cheap
⚠️ NEAR FAIR VALUE - gap below long-term average. Equities slightly expensive vs bonds.
Caveats1. EYG ignores inflation expectations (real yields would be cleaner) 2. Earnings yield uses trailing or forward EPS - both can be wrong 3. Works best as a relative signal across time, not a precise level
📝
Tactical use: EYG > long-run average → overweight equities. EYG < long-run average → consider trimming. The signal works best at extremes (above 500bp or below 0bp), less reliable in the middle.
Part 8 - Asset Allocation & Funds
Slides pp. 35–39
💼 Why Build a Portfolio of Funds? S10–11 p. 36
▼
Asset allocators rarely buy individual stocks. They build portfolios of funds - each fund delivering a specific exposure (region, style, factor, asset class). This is why portfolio construction starts with selecting building blocks, not securities.
✅ Advantages1. Diversification - instant exposure to dozens or hundreds of names 2. Expertise - specialist PMs do the bottom-up work 3. Transparency - daily NAV, regulated reporting 4. Flexibility - switch managers/styles without trading single stocks
❌ Disadvantages1. Fees - 0.5–2.0% management fee, sometimes a performance fee 2. Benchmark underperformance - most active funds underperform their index after fees over long periods (SPIVA studies)
📝
The fund-of-funds layer adds another fee. If a fund of funds charges 0.5% on top of its underlying funds (each charging 1%), the all-in cost is ~1.5%. This is why open-architecture private banking often uses funds directly rather than fund-of-funds, and why ETF cores are growing fastest.
👥 Who Uses Third-Party Funds? S10–11 p. 37
▼
Buyer
Why they buy funds
Typical AUM
Funds of Funds
Manager selection is their entire product
$50M – $50B+
Private Banking (open architecture)
Best-in-class selection across multiple AMs
$1M – $1B+ per client
Pension Plans
Specialist mandates (EM debt, EM equity, hedge funds)
$100M – $1T+
Family Offices
Single-family or multi-family wealth management
$100M – $10B+
Financial Institutions
Insurance reserves, treasury portfolios
$1B – $1T+
Companies
Corporate treasury, employee benefit plans
$10M – $10B+
Open Architecture vs Closed ArchitectureClosed architecture: the private bank only sells its in-house funds (think: traditional Spanish bank distributing its own SICAVs). Conflict of interest, but high distribution margin. Open architecture: the bank sells third-party funds alongside (or instead of) its own. Better client outcomes, lower margins for the distributor.
🏦 Industry Scale - $128 Trillion AuM (2024) S10–11 pp. 38–39
▼
The global asset management industry grew from $36T (2005) to $128T (2024) - a 12% jump in 2024 alone, driven by strong markets and net inflows of ~$3.7T. The industry is highly concentrated: the top 10 firms control nearly half of all global AuM.
Top 10 Asset Managers (Mar 2022 snapshot, $ trillion AuM)1. BlackRock - $9.6T 2. Vanguard - $8.1T 3. Fidelity - $4.2T 4. UBS - $4.2T 5. State Street GA - $4.0T 6. Morgan Stanley IM - $3.3T 7. JPMorgan AM - $2.9T 8. Crédit Agricole / Amundi - $2.8T 9. Allianz / PIMCO - $2.7T 10. Capital Group - $2.7T
Industry structure: Passive giants (BlackRock, Vanguard, State Street) dominate by scale. They are the "Big Three" and collectively own meaningful stakes in nearly every large public company. Active managers (Fidelity, Capital Group, Allianz/PIMCO) compete on alpha, not scale.
Part 9 - Diversification & Risk
Slides pp. 40–42
📉 The Diversification Curve - How Many Stocks Is "Enough"? S10–11 p. 41
▼
Portfolio risk falls steeply as you add stocks - but the marginal benefit shrinks fast. The classic result: ~12 stocks gets you most of the way; ~50 is nearly fully diversified; beyond 50, additional names barely move the needle.
Portfolio Risk vs Number of Stocks
── PORTFOLIO RISK DECOMPOSITION ──
σ²portfolio = Systematic Risk + Idiosyncratic Risk
(β² · σ²market) (σ²ε / N for equal-weighted)
────────────────────────────────────────
As N grows: idiosyncratic risk → 0
systematic risk stays
📝
Implication for AA: Holding 200 stocks instead of 50 buys almost no risk reduction - but adds operational complexity and tracking error. True diversification requires diversifying across asset classes, factors, and regions, not just adding more stocks.
⚠️ The Diversification Paradox - Correlation Spikes in Crises S10–11 p. 42
▼
The cruel irony of diversification: correlations rise exactly when you need them to fall. In normal times, equity markets across countries might correlate at 0.4–0.6. In a crisis (2008, 2020) - correlations spike to 0.85–0.95. Everything sells off together.
Why Correlations Spike1. Liquidity demand - investors sell what they can, not what they want 2. Margin calls - forced selling cascades across assets 3. Risk-off flows - capital exits all risk simultaneously 4. Global investor base - same flows hit all markets in parallel
Examples1998 LTCM: EM debt + Russian debt + relative-value all sold off together 2008 GFC: Global equities, credit, EM all fell ~40% simultaneously 2020 COVID: Even gold sold off in March as investors needed cash 2022: Stocks and bonds fell together as inflation forced rate hikes
⚠️
Practical lesson: Don't rely on equity-only diversification to protect against tail risk. Add true uncorrelated assets - long-dated Treasuries, gold, market-neutral strategies, trend-following - even when they look unattractive in normal times. They earn their keep during the 5% of days that matter most.
Globalization & Long-Run Correlation DriftCross-country equity correlations have risen over the past 30 years - from ~0.4 (1980s) to ~0.7 (2020s) - driven by trade integration, global investor base, and centralised monetary policy responses. The diversification benefit of "international" investing has eroded, especially among developed markets.
Part 10 - Portfolio Weights
Slides pp. 43–46
⚖️ The 6 Weighting Approaches S10–11 p. 44
▼
Once you've chosen your asset-class building blocks, you must decide how to weight them. Six approaches dominate institutional practice:
Approach
Logic
Used by
1. Optimal Portfolio (Markowitz)
Maximise Sharpe ratio given expected returns + covariance matrix
Theory; rarely in pure form
2. Equal Weighted
1/N - simplest possible diversification
Small portfolios, robustness focus
3. Core-Satellite
Large passive "core" + small active "satellites"
Most modern wealth managers
4. Risk Parity
Equal risk contribution, not equal capital
Bridgewater All Weather, AQR, others
5. Under/Overweight
SAA target ± tactical tilt vs benchmark
Active institutional mandates
6. 60/40
60% equity + 40% bonds - the canonical "balanced" portfolio
Pensions, retail, target-date funds
📝
Why Markowitz "optimal" portfolios are rarely used in pure form: the optimizer is exquisitely sensitive to expected return inputs. Small changes in inputs produce large changes in weights ("error maximization"). In practice, allocators use it as a diagnostic, not a prescription.
⚖️ Risk Parity - Capital Weights vs Risk Weights S10–11 p. 45
▼
The core insight of risk parity: equal-capital allocation gives unequal risk allocation. A 60/40 portfolio looks balanced by capital, but ~90% of its risk comes from equities (because equities are 5× more volatile than bonds). Risk parity rebalances so each asset class contributes equally to portfolio risk.
Capital Weights
Heavy bond tilt - bonds are low-vol, so you need more capital to match equity risk
Risk Weights
Each asset contributes roughly 1/3 to total portfolio volatility
📝
How risk parity gets to "balanced": it uses leverage. Bonds are low-vol → risk parity holds 60–70% bonds at unlevered → applies 2–3× leverage on the bond sleeve to bring its risk up to equity levels. This is also the strategy's main vulnerability: 2022 was catastrophic for risk parity as both stocks AND levered bonds sold off.
Bridgewater "All Weather"The original risk parity fund (launched 1996). Targets equal risk contribution from 4 macro environments: rising growth, falling growth, rising inflation, falling inflation. Holds equities, nominal bonds, TIPS, gold, commodities - each sized to contribute equally to total risk in its "favourable" environment.
🛰️ Core-Satellite - The Workhorse Approach S10–11 p. 46
▼
The dominant approach in modern wealth management. The "core" is a large, low-cost passive allocation (ETFs, index funds) - typically 60–80% of the portfolio. The "satellites" are smaller, targeted active or thematic positions - country/sector/factor/commodity ETFs, alternatives, or single names.
Why Core-Satellite Won1. Low cost. Most of the portfolio sits in 5–10 bp ETFs. 2. Beta exposure is solved. Index funds capture the market return cheaply. 3. Active risk is concentrated. The satellites carry the active bets - easier to monitor and size. 4. Scalable. Works equally well for a $1M family office and a $100B pension.
Typical ImplementationCore (70–80%): Global equity (MSCI ACWI), Global aggregate bonds (Bloomberg Global Agg) Country satellites: US large cap, Japan, India, China Sector satellites: Tech, Energy, Healthcare Factor satellites: Value, Quality, Momentum Commodity satellites: Gold, broad commodities
📝
Sizing rule of thumb: No single satellite exceeds 5% of the portfolio. The core stays at 70%+ for cost efficiency. Active fees are paid only where you have a genuine view.
Part 11 - Summary: The Economic Clock
Slides pp. 47–48
🕰️ Spellman Investment Clock - The Master Summary S10–11 p. 48
▼
Everything we've covered - macro regimes, sector rotation, style cycle, asset behaviour - collapses to Spellman's Investment Clock. Four quadrants defined by growth × inflation, each with its winning asset classes, sectors, styles, and sizes.
📝
How to use the clock on the exam: Given a macro scenario (e.g., "rising growth, falling inflation"), identify the quadrant (Recovery), then list the winning asset class (Equity), sector style (Cyclical), size (Small), and value/growth tilt (Growth/Value). The clock is essentially a fast lookup table.
The "stuck clock" insightSpellman observed that economies don't move smoothly around the four quadrants. They get stuck, typically in Recovery (6–9), and then oscillate between Recovery and Soft Landing (12–3). This is the normal expansion path. Stagflation episodes (3–6) and overheating (9–12) are rarer. Most asset-allocation decisions are between Recovery and Soft Landing positioning.
🎯 Key Indicators to Monitor S10–11 p. 48
▼
What to watch each week to identify which quadrant the economy is in - and where it's heading next.
Indicator
What It Tells You
Frequency
ISM Manufacturing PMI
US manufacturing direction. >50 expansion. Leading by 3–6 mo.
Monthly
Conference Board LEI
Composite of 10 leading indicators. Best single recession predictor.
Monthly
Initial Jobless Claims
Fastest signal. Rising claims = labour market weakening.
Weekly
Core CPI / Core PCE
Underlying inflation. Drives Fed policy.
Monthly
10Y − 2Y Yield Curve
Inversion historically precedes recession by 12–18 mo.
Daily
Fed Funds Rate / Forward Path
Direction of monetary policy.
FOMC meetings + speeches
Earnings Revisions Breadth
% of stocks with positive EPS revisions vs negative. Cycle indicator.
You've completed Sessions 10–11. You now have: the SAA/TAA/SP hierarchy, the macro regime framework, sector + style rotation rules, historical return benchmarks, the EYG tactical signal, fund-portfolio construction, and the Spellman summary clock. This is the analytical core of asset management.
Session 12 · Slides pp. 1–28
Derivatives & ETFs
Exchange-Traded Funds, futures contracts, options mechanics, Black-Scholes pricing, and how derivatives are used in portfolio management.
Calls, puts, delta, Black-Scholes, intrinsic vs time value, futures vs options.
3
Session 12›12.1 ETF Fundamentals
📦 Definition of ETFs S12 pp. 2–3
▼
An ETF is an open-ended investment product that allows investors to buy and sell shares representing fractional ownership of a portfolio of securities. ETFs trade on exchanges like stocks throughout the day, unlike mutual funds which price once daily at NAV.
✅
ETF Advantages • Liquidity - trades intraday • Low minimum investment • Low fees (as low as 0.03%) • Diversification in one trade • Trades like a stock (limit orders, short selling) • NAV ≈ Price (arbitrage mechanism)
⚠
ETF Disadvantages • Brokerage fees on each trade • Bid-ask spread cost • Dividend treatment (may not reinvest automatically) • Tracking error vs index • Counterparty risk (synthetic ETFs)
🔁 ETF Arbitrage & Creation/Redemption S12 p. 4 · NYSE Paper
▼
How ETF Arbitrage WorksIf ETF price > NAV: Authorized Participants (APs) buy the underlying basket of stocks, deliver them to the ETF sponsor, receive new ETF shares (creation units of ~50,000 shares), and sell them on the exchange. This drives the ETF price down toward NAV.
If ETF price < NAV: APs buy ETF shares on the exchange, redeem them with the sponsor for the underlying stocks, and sell the stocks. This drives the ETF price up toward NAV.
📝
Exam key: The creation/redemption mechanism by Authorized Participants is what keeps ETF prices close to NAV. This is an in-kind process (no cash changes hands at the fund level), making ETFs more tax-efficient than mutual funds.
🔄 Replication Methods S12 p. 5
▼
Method
How It Works
Advantage
Risk
Physical (Full)
Holds all securities in the index
Exact tracking, transparent
Low
Physical (Sampled)
Holds a representative subset
Lower cost for large indices
Tracking error
Synthetic (Swap)
Uses a swap contract with a counterparty to replicate index return
Can access hard-to-replicate indices
Counterparty risk
📈 Leveraged, Inverse ETFs & Market Structure S12 pp. 6–7 · NYSE Paper
▼
Leveraged & InverseLeveraged (2x, 3x): Target a daily multiple of index return. Over longer periods, compounding causes significant deviation from expected multiples.
Inverse (−1x, −2x): Profit when the index falls. Same daily-reset compounding issue.
⚠ Not suitable for buy-and-hold. Designed for short-term traders.
Market ConcentrationThe ETF market is highly concentrated: Top 3 sponsors (BlackRock/iShares, Vanguard, State Street/SPDR) control ~70% of global ETF AUM.
Fee compression is intense - some broad-market ETFs charge 0.03% (3 basis points).
🧠 Comprehension Check
A 2x leveraged ETF tracks an index that rises 5% on Day 1 and falls 5% on Day 2. The index is down −0.25% over the two days. The ETF return is approximately:
(a) −0.50% (exactly 2× the index loss)
(b) −1.00% (compounding makes it worse)
(c) 0% (the gains and losses cancel out)
(d) +0.50%
(b) is correct. Day 1: ETF rises 10% (value = 110). Day 2: ETF falls 10% (value = 110 × 0.90 = 99). Total return = −1.0%, which is 4× the index loss, not 2×. This is the daily-reset compounding trap that makes leveraged ETFs unsuitable for long holding periods.
Session 12›12.2 Futures
📋 Definition & Mechanics of Futures S12 pp. 14–16 · Harvard Note
▼
A futures contract is a standardized, exchange-traded agreement to buy or sell a specific amount of an underlying asset at a specified price on a specified future date. Both parties are obligated to perform.
Feature
Futures
Forwards
Trading venue
Exchange (standardized)
OTC (customized)
Counterparty risk
Low (clearing house)
High (bilateral)
Settlement
Daily mark-to-market (margin)
At maturity
Liquidity
High
Variable
💰 Futures Pricing & Arbitrage S12 pp. 17–18
▼
- COST-OF-CARRY PRICING -F = S × (1 + r − d)TF = futures price, S = spot price, r = risk-free rate, d = dividend yield, T = timeIf F > theoretical price → sell futures, buy spot (cash-and-carry arbitrage)If F < theoretical price → buy futures, sell spot (reverse cash-and-carry)
📝
Key concepts:Contango = futures price > spot (normal for non-dividend assets). Backwardation = futures price < spot (common in commodities with convenience yield).
- INTRINSIC VALUE -Call: max(S − K, 0)Put: max(K − S, 0)S = spot price, K = strike priceIn-the-money: intrinsic value > 0 | Out-of-the-money: intrinsic value = 0
Δ Delta & Option Pricing S12 pp. 21–22
▼
Delta (Δ)Delta measures how much the option price changes for a $1 change in the underlying asset price.
Call delta: ranges from 0 to +1. At-the-money ≈ 0.5 Put delta: ranges from −1 to 0. At-the-money ≈ −0.5 Deep in-the-money: delta approaches ±1 (behaves like the stock) Deep out-of-the-money: delta approaches 0 (unlikely to be exercised)
Black-Scholes Pricing InputsThe Black-Scholes model prices European options using 5 inputs:
1. Price of underlying asset (S) 2. Time to maturity (T) 3. Risk-free interest rate (r) 4. Dividend yield (d) 5. Volatility of underlying (σ)
Key use cases in portfolio management: Hedging equity exposure with index puts, generating income by writing covered calls, gaining temporary exposure via index futures (cheaper than buying stocks), and currency hedging for international portfolios.
🧠 Comprehension Check
A portfolio manager wants to protect against a 10% equity market decline but keep upside potential. The best instrument is:
(a) Sell equity index futures
(b) Buy equity index put options
(c) Buy equity index call options
(d) Write covered calls
(b) is correct. Buying put options provides downside protection (the put profits as the market falls) while preserving upside participation. Selling futures would hedge the downside but also eliminate the upside. The cost of this insurance is the put premium.
Session 13 · Slides pp. 1–38
Analyzing Return & Risk
Performance ratios (Sharpe, Treynor, Jensen, Information, Sortino, Calmar, Sterling), downside risk metrics, VaR, behavioural biases, and database pitfalls.
Absolute vs Relative ReturnAbsolute return: Total portfolio return regardless of benchmark. “Did I make money?”
Relative return: Performance vs a benchmark index. “Did I beat the index?”
Hedge funds target absolute return. Long-only funds are measured on relative return.
Why Ratios MatterRaw return alone is meaningless without context. A 15% return with 30% volatility is worse than a 10% return with 8% volatility. Ratios adjust return for the risk taken to generate it.
📈 The 7 Key Ratios S13 pp. 32–35 · Harvard Note
▼
Ratio
Formula
Risk Measure Used
Best For
Sharpe
(Rp − Rf) / σp
Total risk (σ)
Standalone portfolio evaluation
Treynor
(Rp − Rf) / βp
Systematic risk (β)
Portfolio as part of larger allocation
Jensen’s α
Rp − [Rf + β(Rm − Rf)]
CAPM benchmark
Did manager beat CAPM prediction?
Information
(Rp − Rbenchmark) / TE
Tracking error
Consistency of alpha generation
Sortino
(Rp − Rf) / σdownside
Downside deviation only
When investors care only about losses
Calmar
Annual Return / Max Drawdown
Worst peak-to-trough loss
Drawdown-sensitive investors
Sterling
Annual Return / Avg Drawdown
Average of worst drawdowns
Smoother version of Calmar
📝
Exam tip: Know the denominator differences. Sharpe uses total risk (σ), Treynor uses systematic risk (β), Information uses tracking error, Sortino uses downside deviation. The numerator is always excess return.
🧠 Comprehension Check
Fund A has Sharpe = 0.9 and Treynor = 0.12. Fund B has Sharpe = 0.7 and Treynor = 0.15. Which is better for an investor allocating their entire wealth to one fund?
(a) Fund A - higher Sharpe (which uses total risk)
(b) Fund B - higher Treynor
(c) They are equal
(d) Cannot determine without alpha
(a) Fund A. For an investor putting all wealth into one fund, total risk matters - use Sharpe. Treynor is appropriate when the fund is part of a diversified portfolio (only systematic risk matters then, since specific risk is diversified away).
Session 13›13.2 Risk Metrics
📉 Downside Risk & Drawdown S13 pp. 10–15
▼
Downside RiskStandard deviation treats up and down moves equally. But investors care more about losses. Downside deviation measures volatility using only negative returns below a threshold (typically 0 or the risk-free rate).
Used in the Sortino ratio denominator.
DrawdownMaximum drawdown = largest peak-to-trough decline before a new high is reached.
Example: Fund peaks at $120, falls to $90. Max DD = (120−90)/120 = −25%.
Used in Calmar and Sterling ratios. Measures “worst pain” an investor would have experienced.
📈 Value at Risk (VaR) S13 pp. 16–20
▼
- VALUE AT RISK -VaR(95%, 1 day) = Portfolio Value × σ × 1.65At 95% confidence: there is only a 5% chance losses exceed this amountAt 99% confidence: multiply by 2.33 instead of 1.65
⚠
VaR limitations: VaR tells you the loss threshold but NOT how bad losses can be beyond that threshold (tail risk). It assumes normal distribution (returns are not). It gives false comfort in calm markets and breaks down in crises. Expected Shortfall (CVaR) addresses some of these issues.
📊 Quartile Ranking & Performance Attribution S13 pp. 21–25
▼
Quartile RankingFund managers are ranked within their peer group: 1st quartile = top 25% performers. 4th quartile = bottom 25%.
Consistency matters: a manager who is 1st quartile for 1 year may be 4th quartile the next. Look for managers who consistently appear in 1st or 2nd quartile over multiple periods (high Information Ratio).
Session 13›13.3 Manager Selection & Biases
🧠 Behavioural Finance & Manager Selection S13 pp. 26–30
▼
Bias
Description
Impact on Investing
Overconfidence
Managers overestimate their skill
Excessive trading, concentrated bets
Loss aversion
Losses hurt 2x more than equivalent gains feel good
Holding losers too long, selling winners too early
Anchoring
Fixating on irrelevant reference points
Holding because of purchase price, not fundamentals
Herding
Following the crowd
Bubbles and panic selling
Confirmation bias
Seeking info that confirms existing beliefs
Ignoring negative signals about holdings
📈 Database Biases S13 p. 8
▼
Bias
What Happens
Effect
Survivorship bias
Failed funds are removed from databases
Overstates average returns
Backfill bias
New funds add historical (good) track records retroactively
Overstates returns
Selection bias
Only successful managers report voluntarily
Overstates returns
Information bias
Data quality varies across providers
Inconsistent comparisons
📝
Exam favourite: Survivorship bias is the most commonly tested. It inflates reported hedge fund returns by 2–4% p.a. because dead funds (the failures) are removed from the dataset.
⚠ Volatility ≠ Risk & Currency Risk S13 pp. 36–38
▼
Volatility ≠ RiskStandard deviation measures variability, not true risk. A stock that steadily rises 1% per day has low volatility but isn’t “risky.” True risk includes: permanent capital loss, liquidity risk, concentration risk, leverage, and tail events that standard deviation underestimates.
Currency RiskFor international portfolios, returns have two components: asset return + currency return. A US investor in European equities faces EUR/USD risk. This can be hedged (at a cost) or left unhedged (adds volatility but can diversify).
👉 Covered in depth in Session 6 (Forex).
🧠 Comprehension Check
A hedge fund database reports average annual returns of 12%. After adjusting for survivorship bias, the true industry average is likely closer to:
(a) 14% (survivorship bias understates returns)
(b) 12% (no adjustment needed)
(c) 8–10% (survivorship bias overstates returns by 2–4%)
(d) 15%+ (successful funds are under-reported)
(c) 8–10%. Survivorship bias inflates reported returns by 2–4% p.a. because failed/closed funds are removed from the database. The surviving funds look better than the true average of all funds that ever existed.
Session 14 · Course Wrap-Up
Managing Portfolios in the Current Environment
Financial markets outlook, structural trends, and course conclusions - connecting all 13 sessions into a unified investment framework.
OutlookTrendsCourse Summary
🌎 Financial Markets & Economy Today S14 · Outlook
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The course uses Session 14 to contextualise all prior content against the current macro backdrop. The specific data points update each term, but the analytical framework is constant:
Framework Checklist for Current Environment1. Where are we in the business cycle? (S10–11: Investment Clock) 2. What is the monetary policy stance? (S10–11: Central bank vs market rates) 3. What does the yield curve signal? (S6: Shape & inversion) 4. What are earnings expectations? (S4–5: P = EPS × P/E) 5. Where are valuations relative to history? (S4–5: Multiples) 6. Which macro regime are we in? (S10–11: Growth × Inflation quadrant)
📈 Trends in Financial Markets S14 · S1 pp. 15–43
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Structural Trends (from S1) • AUM will continue growing globally • Split between ETFs and alternatives deepening • ETFs growing fast, compressing fees further • AI integration accelerating across the industry • Regulatory burden increasing (MiFID II, ESG disclosure)
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Key Concerns • Higher rate environment compresses valuations • Market volatility → AUM declines → revenue pressure • AIS depends on performance and interest rates • Geopolitical risk (trade wars, conflicts) adding uncertainty • Concentration risk in tech-heavy indices
📚 Course Summary & Conclusions S14 · Summary
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The Complete FrameworkS1: How the AM industry works and makes money S2–3: Portfolio theory - diversification, CAPM, alpha & beta S4–5: Equity analysis - DCF, multiples, investment styles S6: Fixed income & Forex - duration, yield curve, FX drivers S7–8: Bloomberg - the practitioner’s toolkit S9: Alternatives - hedge funds, illiquidity premium S10–11: Asset allocation - SAA/TAA, macro regimes, portfolio construction S12: Derivatives & ETFs - futures, options, ETF mechanics S13: Return & risk analysis - ratios, VaR, behavioural finance
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Exam reminder: 25 MCQ, 40 minutes, closed book, only 1 correct answer, questions randomised, can backtrack, no penalty for wrong answers. Course note: “Will be easy.”
Session 15
Final Exam Preparation
Consolidated formula sheet, mock questions, exam traps.
⏳
Coming After All Sessions
Will consolidate all sessions into one cheat sheet + mock exam.
Exam Prep - the course
Exam Cram: Master Formula Sheet
Every formula, key number, and decision rule you need for the Asset Management exam. Organised by topic across all sessions. Use this as your final-pass review.
25 MCQ40 MinutesClosed BookNo Penalty
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Asset Management - Exam Cram Format: 25 MCQ, 40 minutes, closed book, only 1 correct answer, questions randomised, can backtrack, no penalty for wrong answers. “Will be easy.”
S1 - The Asset Management Industry
Key Facts
🏢 Industry Structure & Key Numbers
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NAV = Net AUM / Number of Shares
Management Fee Revenue = AUM × Fee %Typical: 0.5–2% for traditional, “2 and 20” for alternatives
Top 500 AUM (2023): $128T - Top 20 hold 45.5% Top 3: BlackRock ($10.5T), Vanguard ($8.6T), Fidelity ($4.6T) Fee compression: Passive avg ~3bps, Active avg ~50–70bps 4Ps: Philosophy, Process, People, Performance Open vs Closed Architecture: Open = multi-manager distribution Open-end fund: creates/redeems shares at NAV daily Closed-end fund: fixed shares, trades on exchange (premium/discount to NAV)
Price ↔ Yield: Inverse relationship - rates up → prices down Zero-coupon: MacD = maturity exactly (most rate-sensitive) Higher coupon → lower duration (cash flows pulled earlier) Convexity: Prices rise MORE when yields fall, fall LESS when yields rise Yield curve inversion (10Y−2Y): Recession signal, 12–18 month lead Investment grade: ≥ BBB−/Baa3 FX drivers: Interest rate differentials, inflation, trade flows, central bank policy
S9 - Alternative Investments
Hedge Funds & PE
🌍 Alternatives Framework
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“2 and 20”: 2% mgmt fee + 20% performance fee above hurdle High-water mark: Performance fee only on new highs Illiquidity premium: 1–4% p.a. extra return for lock-up risk J-curve (PE): Negative returns years 1–3, positive years 4–6, exits years 7–10 Yale Model (Swensen): 60%+ in alternatives, 13.7% p.a. over 20yr Survivorship bias: Inflates reported returns by 2–4% p.a. Fund of Funds: Extra fee layer (1% + 5–10%) on top of underlying Long/Short equity: Long winners, short losers - reduces market risk Global Macro: Directional bets on rates, currencies, commodities Event-Driven: Merger arb, distressed debt, activist investing
S10–11 - Asset Allocation
SAA & TAA
📊 Allocation Framework & Cycles
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Earnings Yield Gap = E/P (equities) − Bond YieldLong-term avg ~230bp • Positive = equities cheap vs bonds • Negative = equities expensive
SAA: Long-term strategic weights (3–5yr) based on risk tolerance & objectives TAA: Short-term tilts around SAA to exploit market conditions Security Picking: Individual asset selection within asset classes Investment Clock: Recovery → Expansion → Slowdown → Recession
• Recovery: equities + cyclicals • Expansion: commodities + inflation hedges
• Slowdown: bonds + defensives • Recession: cash + govts Style rotation: Value leads in recovery, Growth leads in expansion Risk Parity (All Weather): Equal risk contribution across 4 macro environments 60/40 failed in 2022: Inflation forced rate hikes → both stocks AND bonds fell Correlation in crises → 1: Liquidity crises force sales of ALL assets
S12 - Derivatives & ETFs
Options & Futures
📉 Options, Futures & ETFs
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Futures Price: F = S × (1 + r − d)TS = spot, r = financing cost, d = income yield, T = time
Option Value = Intrinsic Value + Time ValueCall intrinsic = max(S − K, 0) • Put intrinsic = max(K − S, 0)
Delta = ΔOption / ΔUnderlyingCall δ: 0 to +1 • Put δ: −1 to 0 • ATM ≈ ±0.5
Futures: OBLIGATION to buy/sell • Options: RIGHT to buy (call) or sell (put) ETF replication: Physical (holds assets) vs Synthetic (swaps) Inverse ETF: −1× daily return (volatility drag over time) Leveraged ETF: 2× or 3× daily (not for buy-and-hold) ITM Call: S > K • ITM Put: S < K Protective put: Buy puts to hedge portfolio downside Covered call: Own stock + sell calls for income (caps upside)
S13 - Return & Risk Analysis
Performance Ratios
📏 The Seven Ratios & VaR
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Sharpe = (Rp − Rf) / σpExcess return per unit of TOTAL risk
Treynor = (Rp − Rf) / βpExcess return per unit of SYSTEMATIC risk
Format: 25 MCQ, 40 min, 1 correct, randomised, can backtrack, no penalty Time budget: ~96 seconds per question (1.5 min avg) Strategy: First pass - answer all easy/medium, flag hard ones. Second pass - tackle flagged. Never leave blank (no penalty). Common traps:
• “Always” and “never” in answer options - usually wrong
• Confusing Sharpe (σ) with Treynor (β)
• Bond prices move INVERSELY to yields
• Futures = obligation, Options = right
• Survivorship bias OVERSTATES returns (not understates)
• Positive EYG = equities CHEAP (not expensive)
• Correlation → 1 in crises (diversification fails when you need it most)
• Value outperforms over cycles but UNDERPERFORMS in momentum rallies
Session 1 · Practice
Session 1 - Practice & Quiz
Test yourself on every key concept from this chapter. Click an answer to check immediately. Review explanations to reinforce your understanding.
Session 1 Quiz
Chapter Questions
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MCQ and True/False questions covering Session 1. Click an answer to check it immediately.
Sessions 2–3 · Practice
Sessions 2–3 - Practice & Quiz
Test yourself on every key concept from this chapter. Click an answer to check immediately. Review explanations to reinforce your understanding.
Sessions 2–3 Quiz
Chapter Questions
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MCQ and True/False questions covering Sessions 2–3. Click an answer to check it immediately.
Sessions 4–5 · Practice
Sessions 4–5 - Practice & Quiz
Test yourself on every key concept from this chapter. Click an answer to check immediately. Review explanations to reinforce your understanding.
Sessions 4–5 Quiz
Chapter Questions
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MCQ and True/False questions covering Sessions 4–5. Click an answer to check it immediately.
Session 6 · Practice
Session 6 - Practice & Quiz
Test yourself on every key concept from this chapter. Click an answer to check immediately. Review explanations to reinforce your understanding.
Session 6 Quiz
Chapter Questions
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MCQ and True/False questions covering Session 6. Click an answer to check it immediately.
Sessions 7–8 · Practice
Sessions 7–8 - Practice & Quiz
Test yourself on every key concept from this chapter. Click an answer to check immediately. Review explanations to reinforce your understanding.
Sessions 7–8 Quiz
Chapter Questions
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MCQ and True/False questions covering Sessions 7–8. Click an answer to check it immediately.
Session 9 · Practice
Session 9 - Practice & Quiz
Test yourself on every key concept from this chapter. Click an answer to check immediately. Review explanations to reinforce your understanding.
Session 9 Quiz
Chapter Questions
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MCQ and True/False questions covering Session 9. Click an answer to check it immediately.
Sessions 10–11 · Practice
Sessions 10–11 - Practice & Quiz
Test yourself on every key concept from this chapter. Click an answer to check immediately. Review explanations to reinforce your understanding.
Sessions 10–11 Quiz
Chapter Questions
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MCQ and True/False questions covering Sessions 10–11. Click an answer to check it immediately.
Session 12 · Practice
Session 12 - Practice & Quiz
Test yourself on every key concept from this chapter. Click an answer to check immediately. Review explanations to reinforce your understanding.
Session 12 Quiz
Chapter Questions
🎯
MCQ and True/False questions covering Session 12. Click an answer to check it immediately.
Session 13 · Practice
Session 13 - Practice & Quiz
Test yourself on every key concept from this chapter. Click an answer to check immediately. Review explanations to reinforce your understanding.
Session 13 Quiz
Chapter Questions
🎯
MCQ and True/False questions covering Session 13. Click an answer to check it immediately.
Exam Preparation
Mock Exams
Three timed exams matching the real format: 25 multiple choice questions, 40 minutes, no penalty for wrong answers. Mock 3 is deliberately harder.
25 Questions Each40 MinutesAuto-Graded
Mock 1
Standard Difficulty
Balanced coverage of S1–S13. Conceptual questions matching the course's style.
1
Mock 2
Standard Difficulty
Second full exam. Different question set, same balanced coverage.
2
Mock 3
Higher Difficulty
Trickier distractors, multi-concept questions, and calculation-based items.
3
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Exam format reminder: 25 MCQ, 40 minutes, only 1 correct answer, questions randomised, can backtrack, closed book, wrong answers do NOT penalise. Course note: “Will be easy.”
Mock Exams›Mock 1 - Standard
⏱ 40:00
Mock Exams›Mock 2 - Standard
⏱ 40:00
Mock Exams›Mock 3 - Higher Difficulty
⏱ 40:00
📖 Reference
Course Glossary
Key terms for Asset Management & Global Markets. Searchable and organised by session. Use the search box to filter instantly.
💡 Curiosity Articles
Beyond the Slides
Bite-sized deep dives, market anecdotes, and "did you know?" insights connecting course theory to real-world asset management. Coming as the course progresses.
📰
Coming Soon
Curiosity articles will be added throughout the course - check back after each session for real-world case studies, market anecdotes, and thought-provoking reads that go beyond the slides.
📊 Real-World Research · 2026
Equity Research Reports
Live analyst notes from UBS, Morningstar, and Jefferies - used in the course to illustrate how professional equity analysis works in practice. Read alongside Sessions 4–5 (Equity Management).
Tesla · Adidas · RenaultUBSMorningstarJefferies
How to Read a Research Report
Framework
🗺️ Anatomy of a Sell-Side Research Note
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Element
What It Tells You
Where to Look
Rating
Buy / Hold / Sell - analyst's overall view
Top of first page, bold
Price Target (PT)
12-month expected price - based on DCF or multiples
Alongside rating
Upside/Downside %
(PT − Current Price) / Current Price
Next to PT
Estimate Changes
How much the analyst revised Revenue/EPS vs. prior note
Summary table, first page
Investment Thesis
2–4 bullet points summarising the bull case
Long View / Thesis section
Risk/Reward Scenarios
Base / Bull / Bear cases with PTs and conditions
Scenario section
Financial Summary
Revenue, EBIT, EPS actuals + forecasts 2–4 years out
Financial exhibits
Valuation Multiples
P/E, EV/EBITDA, P/BV vs. consensus and peers
Valuation table
Adidas AG (ADS.GR)
Morningstar · Mar 2026 · 4-Star ⭐⭐⭐⭐
👟 Morningstar - Adidas: Undervalued, Recovery On Track Mar 4 2026
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Key Stats (as of Mar 2026)Rating: ⭐⭐⭐⭐ (Undervalued) Last Price: €141.80 Fair Value Estimate (Morningstar): €185.00 Price / FVE: 0.77 → trading at 23% discount Market Cap: €25.96B Economic Moat: Narrow Uncertainty: Medium
Bull thesis: Under CEO Bjørn Gulden, Adidas' recovery from the Yeezy scandal is ahead of schedule. The 8.3% EBIT margin in 2025 puts the medium-term target of ≥10% well within reach. A new €1B buyback programme and 40% dividend hike signal management confidence. Extension of Gulden's contract through 2030 = continuity.
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Bear risks: US tariffs and FX headwinds suppress 2026 operating profit guidance (€2.3B vs. Morningstar's €2.5B estimate). Inventories up 17% at year-end (FIFA World Cup build). Competition from Nike, Puma, and Chinese brands. Morningstar considers these transitory, not structural.
Metric
2023A
2024A
2025A
2026E
2027E
Revenue (€M)
21,427
23,683
24,811
27,109
29,118
Operating Margin %
1.3%
5.6%
8.3%
9.2%
10.1%
EPS (diluted, €)
−0.42
4.28
7.55
10.71
12.80
EV/EBITDA
27.1x
18.4x
8.8x
7.5x
6.6x
ROE %
−1.5%
13.0%
19.7%
26.8%
28.6%
AM Course Connection - Sessions 4–5
This is a textbook contrarian recovery play. The Yeezy write-down caused a one-off loss in 2023 (EPS −€0.42). The market punished the stock severely. Analysts who looked through the noise to normalised earnings captured the subsequent 77% EPS growth in 2025. Notice how EV/EBITDA compressed from 27x → 8.8x as earnings recovered - this is exactly the multiple-normalisation dynamic the course materials discuss in valuation timing.
Tesla Inc. (TSLA.O)
UBS · Mar 2026 · SELL ❌
⚡ UBS - Tesla: Deliveries Are Now a Sideshow Mar 19 2026
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Key Stats (UBS, Mar 2026)Rating: SELL (exception to core band) Current Price: $399.27 12-Month Price Target: $352.00 Implied Downside: −12% Market Cap: $1.41 Trillion EV/EBITDA 2026E: 86.6x (very expensive)
Financial Snapshot (USD M)Revenue 2025A: $94,827M (−2.9% YoY) EBIT Margin 2025A: 7.6% (down from 11.1% in 2023) EPS 2026E (UBS): $1.93 (+16% YoY) Net Cash: $35.7B (very strong balance sheet) Capex 2026E: $20B - heavy reinvestment cycle
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UBS's key insight: "Do deliveries even matter? For the stock price? Probably not." The stock is now driven by narrative and future optionality (Robotaxi, Optimus robot, AI ventures), not current auto fundamentals. This is a classic example of how sentiment and growth narrative can divorce a stock from near-term earnings.
Metric
2023A
2024A
2025A
2026E
2027E
Revenue ($M)
96,773
97,690
94,827
98,587
110,791
EBIT Margin %
11.1%
9.3%
7.6%
7.8%
9.1%
EPS (UBS, $)
3.12
2.29
1.66
1.93
2.34
EV/EBITDA
39.8x
44.2x
79.0x
86.6x
68.8x
Net Cash ($B)
23.9
28.4
35.7
27.3
25.8
AM Course Connection - Sessions 4–5
Tesla is the ultimate "growth vs. value" debate case study. With EV/EBITDA at 86.6x, the stock is priced for perfection across Robotaxi, Optimus, and AI ventures - none of which contribute material revenue yet. The auto business (which pays the bills) is declining in margin. This illustrates the course's warning: "Very, very high growth expectations = already priced in." The SELL rating with a $352 PT vs. $399 price reflects the risk/reward asymmetry when sentiment-driven narratives dominate.
Renault SA (RNO.FP)
Jefferies · Mar 2026 · HOLD
🚗 Jefferies - Renault: A Road Less Travelled Mar 24 2026
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Key Stats (Jefferies, Mar 2026)Rating: HOLD Current Price: €27.64 Price Target: €31.00 (from €34.00) Implied Upside: +12% Market Cap: €8.2B 52-Week Range: €27.15 – €50.68
Structural edge - Horse powertrain JV: Renault's 2019 decision to externalise its ICE powertrain operations into a 45%-owned JV with Geely (called "Horse") gives it scale well beyond its size, access to broader technical solutions (including EREV), and shared restructuring risk as ICE volumes decline globally. Jefferies sees this as the company's main long-term competitive advantage.
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Why a HOLD not a BUY despite +12% upside: Jefferies trimmed EBIT estimates by 12% on slower 2026 volume ramp and higher material/energy costs. The 2025 EPS of −€39.98 is due to a one-off impairment - normalised 2026E EPS is €5.73. Investors need time to test Renault's reliance on dividends from financial services (RCI, ~€500M/yr) and Horse to support FCF.
Metric
2023A
2024A
2025A
2026E
2027E
Revenue (€M)
52,376
56,232
57,922
57,221
58,465
Recurring EBIT Margin
4.7%
4.6%
6.3%
4.7%
4.6%
EPS (reported)
€7.99
€2.72
−€39.98
€5.73E
€5.80E
FCF (€M)
3,196
3,025
1,482
955E
1,055E
AM Course Connection - Sessions 4–5 & 6
Renault is a master class in normalised earnings vs. reported earnings. The 2025 reported EPS of −€39.98 is distorted by a massive write-down (Nissan alliance restructuring). The analyst's job is to strip this out and focus on recurring EBIT (€3.6B in 2025). This is exactly what the course materials mean when he says: "We are only interested in recurring earnings." Also note the HOLD despite positive upside - illustrating that analyst ratings incorporate risk/reward beyond just the price gap.
Side-by-Side Comparison
All Three Stocks
📋 Three Stocks - One Table
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Attribute
Adidas (ADS)
Tesla (TSLA)
Renault (RNO)
Analyst / House
Morningstar (Swartz)
UBS (Spak)
Jefferies (Houchois)
Rating
4-Star / Undervalued
SELL
HOLD
Current Price
€141.80
$399.27
€27.64
Price Target
€185.00 (+30%)
$352.00 (−12%)
€31.00 (+12%)
EV/EBITDA (2025)
8.8x
79.0x
~3.5x (est.)
Key Thesis
Recovery ahead of schedule; Gulden continuity; buyback