Stock Investment & Portfolio Management
Week 1 - 14 課程講義

# Week1_Equity_Analysis_Portfolio_Management.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 1 Graduate – Applied & Empirical Track Instructor: Shean -Bii Chi u --- ## Slide 2 Week 1 Overview: Investment as a Research Process Applied & Empirical course design (no heavy math) Investment = Question → Data → Analysis → Portfolio → Evaluation Reading: Bodie et al. Ch.1–2; CFA PM Vol.1 2 --- ## Slide 3 Graduate-Level Expectations Interpret academic evidence Translate data into investment conclusions Produce professional-style reports 3 --- ## Slide 4 Return vs Risk Average return: how much Volatility: uncertainty Drawdown: pain Graduate focus: trade-offs 4 --- ## Slide 5 Diversification Logic Portfolio risk depends on correlation Combining assets can reduce total risk Foundation of asset allocation 5 --- ## Slide 6 Active vs Passive Passive: market + factors Active: seek alpha Preview: does alpha survive fees? 6 --- ## Slide 7 Week 1 Takeaways Investment is structured research Risk matters as much as return Funds/ETFs are measurable objects 7 --- ## Slide 8 After-Class Assignment (Week 1) Analyze one ETF + one mutual fund Compute return, volatility, drawdown Submit 1-page interpretation 8 --- ## Slide 9 Reading Highlight – Bodie & CFA: Investment Process Key idea: portfolio management is a continuous loop 1. Set objectives & constraints 2. Form capital market expectations 3. Build portfolio 4. Monitor & rebalance Graduate emphasis: process discipline > single-period returns 9 --- ## Slide 10 Reading Highlight – Risk Measures (Bodie) Volatility: symmetric uncertainty Drawdown: asymmetric pain investors actually feel Correlation drives diversification benefit Conclusion: risk must be evaluated at portfolio level 10 --- ## Slide 11 Week 1 Deepening – Investment Objectives & Constraints (CFA) Return objective: required vs desired return Risk tolerance: ability vs willingness Constraints: liquidity, horizon, taxes, regulation Graduate focus: portfolio design starts with constraints, not assets 11 --- ## Slide 12 Bodie/CFA: Capital Market Expectations Inputs: expected returns, volatilities, correlations These are estimates, not truths Errors in expectations dominate portfolio outcomes Implication: robustness matters more than precision 12 --- ## Slide 13 Data Reality in Investment Practice Prices are observable Expected returns are not Risk estimates change over time Graduate takeaway: uncertainty is structural, not accidental 13 --- ## Slide 14 From Asset Choice to Portfolio Design Not: Which stock is best? But: How do assets interact together? Portfolio is the true decision variable Reading link: Bodie portfolio perspective 14 --- ## Slide 15 Example: Three Assets (Conceptual) Asset A: high return, high risk Asset B: medium return, medium risk Asset C: low return, low risk Key insight: optimal mix depends on correlations and objectives 15 --- ## Slide 16 Why Investors Still Focus on Single Assets Psychological salience of individual winners Media attention on stocks, not portfolios Performance reporting often asset-by-asset Graduate role: reframe discussion to portfolios 16 --- ## Slide 17 Active vs Passive – Economic Perspective Before costs: active is zero-sum After costs: active is negative-sum Passive captures market + factor premia cheaply Preview of Fama -French (2010) 17 --- ## Slide 18 Institutional vs Retail Thinking (Preview) Institutions start from asset allocation Retail investors start from stock ideas This course trains institutional-style thinking 18 --- ## Slide 19 Week 1 Integrated Summary Investment is a constrained optimization problem (conceptually) Expectations are noisy Diversification is structural Portfolio beats asset selection Sets foundation for factor analysis in Week 2 19 --- ## Slide 20 Extended Discussion (Week 1) 1. Should young investors care about drawdowns? 2. If expectations are unreliable, what anchors decisions? 3. How would you explain diversification to a non-finance client? 20 ---

# Week2_Equity_Analysis_Portfolio_Management.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 2 Graduate – Applied & Empirical Track Instructor: Shean -Bii Chiu --- ## Slide 2 Week 2 Overview: Factor Models Why returns differ across assets CAPM → Fama–French → Carhart Reading: Fama & French (2015); Carhart (1997) 2 --- ## Slide 3 What Is a Factor? Systematic characteristic shared by many stocks Economic risk or behavioral premium Applies to stocks and funds 3 --- ## Slide 4 From Stocks to Funds Funds inherit factor exposures Return = Alpha + Factor Returns Goal: separate skill from style 4 --- ## Slide 5 Illustrative Factor Regression (Simulated Data) Estimated Alpha ≈ 0.002 Market Beta ≈ 0.985 SMB(Size) Beta ≈ 0.281 HML(Value) Beta ≈ 0.352 Momentum Beta ≈ 0.294 5 --- ## Slide 6 Interpretation Market beta: equity exposure Other betas: style tilts Alpha: unexplained performance Focus on economic meaning 6 --- ## Slide 7 Link to Readings Fama & French: factor logic Carhart: momentum Fama & French (2010): alpha is rare 7 --- ## Slide 8 Week 2 Takeaways Factors explain most returns Alpha is unstable Performance must be decomposed 8 --- ## Slide 9 After-Class Assignment (Week 2) Run provided regression template Report alpha + betas Write interpretation (1–2 pages) 9 --- ## Slide 10 Fama–French (2015): Five-Factor Model – Core Conclusions Market, Size, Value, Profitability, Investment explain most stock returns Value factor weakens after 2000, profitability becomes more important Most cross-sectional return variation is systematic, not stock-specific Implication: diversification + factor exposure dominate outcomes 10 --- ## Slide 11 Fama –French (2015): Why This Matters for Funds Fund performance largely reflects factor tilts Apparent skill often equals exposure to known factors Graduate takeaway: always ask 'which factors?' before 'how much alpha?' 11 --- ## Slide 12 Carhart (1997): Momentum & Fund Persistence Short-term performance persistence mainly driven by momentum After adjusting for momentum, long-term alpha largely disappears Winners tend to revert once momentum fades Implication: chasing top funds is statistically dangerous 12 --- ## Slide 13 Fama & French (2010): Luck vs Skill Distribution of fund alpha centered near zero Very few managers show statistically reliable skill Extreme winners mostly explained by luck Conclusion: identifying skill ex ante is extremely difficult 13 --- ## Slide 14 Luck vs Skill – Economic Interpretation Investors observe noisy outcomes, not true ability Selection based on past returns mostly selects lucky managers This explains widespread underperformance after fees 14 --- ## Slide 15 Synthesis (Week 1–2): Putting Readings Together Bodie/CFA: investment is a disciplined process Fama–French: returns driven by systematic factors Carhart: momentum explains short-term persistence Fama–French (2010): alpha is rare Combined message: structure beats selection 15 --- ## Slide 16 Discussion Slide (for Time Extension) 1. If alpha is rare, why does active management persist? 2. Should investors pay for factor exposure? 3. How would you explain these papers to a retail client? 16 --- ## Slide 17 Bridge to Week 3 (Holdings-Based Analysis) Weeks 1–2: returns → factors Next: factors → holdings We move from regression to portfolio composition Goal: understand how managers actually implement styles 17 ---

# Week3_Equity_Analysis_Portfolio_Management.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 3 – Holdings-Based Style Analysis Graduate – Applied & Empirical Track Instructor: Shean -Bii Chi u --- ## Slide 2 Week 3 Overview Move from return-based analysis to holdings-based analysis Understand how portfolio composition explains performance Key Reading: Grinblatt & Titman (1989); Bodie et al. Ch.24 2 --- ## Slide 3 Why Returns Alone Are Not Enough Returns tell outcomes, not decisions Two funds with similar returns may have very different portfolios Holdings reveal manager intent Graduate focus: behavior behind performance 3 --- ## Slide 4 Return-Based vs Holdings-Based Return-based: infer style from time-series regressions Holdings-based: directly observe portfolio composition Holdings-based answers: what does the manager actually own? 4 --- ## Slide 5 Grinblatt & Titman (1989): Core Idea Use quarterly holdings to evaluate fund performance Measure whether managers systematically hold future winners Performance comes from stock selection, not timing This pioneered holdings-based fund analysis 5 --- ## Slide 6 What Is Holdings-Based Style? Classify portfolio by characteristics: • Size (large vs small) • Value vs growth • Industry exposures Style is revealed through weights, not returns 6 --- ## Slide 7 Economic Interpretation Holdings show investment beliefs Portfolio weights represent conviction Changes in holdings show active decisions Graduate takeaway: portfolios are economic statements 7 --- ## Slide 8 Example: Two Equity Funds Fund A: concentrated, value-tilted Fund B: diversified, growth-oriented Same return ≠ same strategy Holdings analysis distinguishes them 8 --- ## Slide 9 From Holdings to Attribution Step 1: classify holdings Step 2: compute style weights Step 3: compare to benchmark Result: allocation effect vs selection effect 9 --- ## Slide 10 Link to Bodie Ch.24 – Performance Evaluation Performance = Allocation + Selection + Interaction Holdings data allow decomposition Graduate emphasis: explain sources, not just levels 10 --- ## Slide 11 Why This Matters for Investors Detect unintended factor bets Identify closet indexing Understand concentration risk Improve due diligence 11 --- ## Slide 12 Closet Indexing Concept Funds claiming active management But holdings closely resemble benchmark High fees with passive exposure Holdings-based analysis exposes this 12 --- ## Slide 13 Active Share (Preview of Week 6) Measures how different a fund is from benchmark High Active Share ≠ guaranteed alpha But low Active Share + high fees = warning sign 13 --- ## Slide 14 Graduate-Level Perspective Returns answer: how much Holdings answer: how Serious evaluation requires both 14 --- ## Slide 15 Reading Connection Grinblatt & Titman (1989): stock selection via holdings Bodie Ch.24: attribution framework Cremers & Petajisto (later): Active Share builds on this 15 --- ## Slide 16 Conceptual Workflow (No In-Class Software) After class students: 1. Obtain fund holdings 2. Classify by size/value/industry 3. Compare with benchmark 4. Write interpretation (not procedure) 16 --- ## Slide 17 Discussion Questions 1. Would you trust returns or holdings more? Why? 2. Should investors pay for concentrated portfolios? 3. How would you explain closet indexing to a client? 17 --- ## Slide 18 Week 3 Integrated Summary Holdings reveal manager intent Style comes from weights, not narratives Performance must be decomposed Sets foundation for Active Share (Week 6) 18 --- ## Slide 19 Bridge to Week 4 – Behavioral Finance So far: rational portfolio construction Next: systematic behavioral biases We move from portfolio mechanics to investor psychology 19 ---

# Week4_Equity_Analysis_Portfolio_Management.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 4 – Behavioral Finance & Market Anomalies Graduate – Applied & Empirical Track Instructor: Shean -Bii Chiu --- ## Slide 2 Week 4 Overview Shift from rational portfolio mechanics to investor behavior Understand why prices deviate from fundamentals Key Readings: Barberis & Thaler (2003); Baker & Wurgler (2006) 2 --- ## Slide 3 Standard Finance vs Behavioral Finance Standard view: investors are rational Behavioral view: investors are systematically biased Prices reflect both information and psychology Graduate focus: when does psychology dominate? 3 --- ## Slide 4 Why Behavioral Finance Matters Explains bubbles and crashes Explains return predictability Explains persistent anomalies Provides intuition behind factor premia 4 --- ## Slide 5 Barberis & Thaler (2003): Big Picture Markets are not fully efficient Investors make predictable mistakes Limits to arbitrage prevent easy correction Anomalies persist longer than theory predicts 5 --- ## Slide 6 Key Behavioral Biases Overconfidence Representativeness Loss aversion Mental accounting These biases systematically affect prices 6 --- ## Slide 7 Overconfidence Investors overestimate their information Leads to excessive trading Associated with lower average returns Seen strongly in retail trading 7 --- ## Slide 8 Loss Aversion & Disposition Effect Losses hurt more than gains feel good Investors hold losers too long Sell winners too early Creates return patterns in markets 8 --- ## Slide 9 Limits to Arbitrage Mispricing does not guarantee profit Risks, funding constraints, career concerns Professional investors cannot always correct errors Explains persistence of anomalies 9 --- ## Slide 10 From Biases to Anomalies Behavioral biases → predictable trading patterns Trading patterns → return anomalies Graduate insight: anomalies are symptoms, not causes 10 --- ## Slide 11 Baker & Wurgler (2006): Investor Sentiment Sentiment fluctuates over time Hard-to-arbitrage stocks are most affected High sentiment → overpricing of speculative stocks Low sentiment → undervaluation 11 --- ## Slide 12 Which Stocks Are Sentiment Sensitive? Small stocks Young firms Unprofitable companies High volatility stocks Prices move more with mood than fundamentals 12 --- ## Slide 13 Economic Interpretation of Sentiment Not all investors are rational Noise traders move prices Arbitrage is costly Creates time-varying mispricing 13 --- ## Slide 14 Connecting to Factors Value premium partly reflects correction of over-optimism Momentum reflects slow information diffusion Behavioral stories complement risk-based stories 14 --- ## Slide 15 Implications for Active Management Opportunities exist, but are risky Timing sentiment is difficult Crowded trades unwind violently Skill requires discipline, not prediction 15 --- ## Slide 16 Professional Perspective Do not fight every anomaly Focus on robust patterns Diversify behavioral bets Avoid narrative-driven trades 16 --- ## Slide 17 After-Class Conceptual Assignment Choose one anomaly or behavioral bias Explain economic mechanism Discuss investment implications (1–2 pages) 17 --- ## Slide 18 Discussion Questions 1. Are anomalies disappearing? 2. Can behavioral edges be scaled? 3. How should institutions exploit sentiment? 18 --- ## Slide 19 Week 4 Integrated Summary Markets reflect psychology Biases create anomalies Limits to arbitrage sustain mispricing Behavioral logic complements factor models 19 --- ## Slide 20 Bridge to Week 5 – Alpha & Fund Skill If markets are imperfect… Can managers systematically exploit them? Next: Luck vs Skill in mutual funds 20 ---

# Week5_Equity_Analysis_Portfolio_Management.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 5 – Mutual Fund Alpha: Luck vs Skill Graduate – Applied & Empirical Track Instructor: Shean -Bii Chiu --- ## Slide 2 Week 5 Overview Central question: can fund managers generate persistent alpha? Shift from behavioral anomalies to professional performance Key Readings: Fama (1972); Fama & French (2010) 2 --- ## Slide 3 What Is Alpha? Return unexplained by market and factor exposures Intended to capture manager skill Observed alpha = true skill + noise Graduate focus: separating signal from randomness 3 --- ## Slide 4 Fama (1972): Components of Investment Performance Security selection Market timing Diversification Costs Performance must be decomposed, not viewed as a single number 4 --- ## Slide 5 From Individual Skill to Fund Performance Managers make many small bets Outcomes are noisy Short samples exaggerate extremes Observed winners may simply be lucky 5 --- ## Slide 6 Fama & French (2010): Core Empirical Design Estimate alpha for thousands of mutual funds Adjust for common risk factors Study distribution of alpha Ask: how many managers truly beat zero? 6 --- ## Slide 7 Key Result: Alpha Distribution Average alpha after fees ≈ negative Most funds cluster near zero Few extreme winners Extreme losers more common than extreme winners 7 --- ## Slide 8 Luck vs Skill Interpretation Extreme outcomes largely explained by sampling variation True skill is rare and difficult to detect Past winners mostly regress toward average 8 --- ## Slide 9 Why Identifying Skill Is Hard Short track records Changing strategies Crowded trades Capacity constraints Graduate takeaway: ex-ante selection is extremely challenging 9 --- ## Slide 10 Economic Perspective Before costs: active is zero-sum After costs: active is negative-sum Passive captures factor premia cheaply Alpha must overcome fees + trading costs 10 --- ## Slide 11 Implications for Investors Do not extrapolate short-term outperformance Diversify across managers if using active Control fees aggressively Treat alpha as scarce resource 11 --- ## Slide 12 Connecting to Previous Weeks Week 2: factor exposures explain much of returns Week 3: holdings reveal strategy Week 4: behavioral effects create opportunities Week 5: but converting opportunities into alpha is difficult 12 --- ## Slide 13 Professional Due Diligence Framework Understand factor tilts Analyze holdings Evaluate process, not just performance Assess organizational stability 13 --- ## Slide 14 After-Class Conceptual Assignment Pick one active fund Decompose return into factors + alpha Discuss whether performance reflects skill (1–2 pages) 14 --- ## Slide 15 Discussion Questions 1. Why does active management persist if alpha is rare? 2. Should institutions allocate to active managers? 3. How would you explain luck vs skill to clients? 15 --- ## Slide 16 Week 5 Integrated Summary Alpha is rare and unstable Most performance comes from factors Fees matter enormously Process evaluation beats performance chasing 16 --- ## Slide 17 Bridge to Week 6 – Active Share & Concentration If alpha is scarce… Does being more different help? Next: Active Share and portfolio concentration 17 ---

# Week6_Equity_Analysis_Portfolio_Management.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 6 – Active Share & Portfolio Concentration Graduate – Applied & Empirical Track Instructor: Shean -Bii Chiu --- ## Slide 2 Week 6 Overview Question: does being more different help generate alpha? Introduce Active Share and concentration concepts Key Reading: Cremers & Petajisto (2009); Grinblatt & Titman (1992) 2 --- ## Slide 3 From Alpha to Portfolio Structure Week 5: alpha is rare Now ask: what portfolio characteristics are associated with skill? Shift from returns to portfolio design 3 --- ## Slide 4 What Is Active Share? Measures how different a fund is from its benchmark Ranges from 0% (index-like) to 100% (completely different) Captures degree of active management 4 --- ## Slide 5 Economic Intuition Low Active Share: closet indexing High Active Share: concentrated bets Alpha requires taking positions away from benchmark 5 --- ## Slide 6 Cremers & Petajisto (2009): Core Findings Funds with high Active Share outperform on average Closet indexers underperform after fees Active Share + tracking error jointly matter Not all 'active' funds are truly active 6 --- ## Slide 7 Active Share vs Tracking Error Active Share: holdings-based difference Tracking error: return-based volatility Two dimensions describe active management style 7 --- ## Slide 8 Portfolio Concentration Number of holdings Weight of top positions Sector concentration Represents conviction level 8 --- ## Slide 9 Grinblatt & Titman (1992): Persistence Revisited Managers with strong past performance show some persistence More evident in concentrated portfolios Selection ability linked to focused strategies 9 --- ## Slide 10 Why Concentration Can Help High-conviction ideas matter Dilution reduces impact of skill But concentration increases idiosyncratic risk 10 --- ## Slide 11 Trade-Off: Concentration vs Risk More concentration → higher tracking error Higher potential alpha → higher volatility Graduate focus: risk-adjusted outcomes 11 --- ## Slide 12 Closet Indexing Problem Funds charge active fees But mimic benchmark holdings Deliver market returns minus fees Major concern for institutional allocators 12 --- ## Slide 13 Professional Evaluation Framework Check Active Share Analyze concentration Understand investment process Relate structure to expected alpha 13 --- ## Slide 14 Connecting Weeks 3–6 Week 3: holdings reveal style Week 4: behavioral forces create opportunities Week 5: alpha is scarce Week 6: structure determines whether skill can show up 14 --- ## Slide 15 After-Class Conceptual Assignment Select one active fund Estimate Active Share (using provided template) Assess concentration Write interpretation (1–2 pages) 15 --- ## Slide 16 Discussion Questions 1. Should institutions avoid low Active Share funds? 2. How much concentration is optimal? 3. Can high Active Share be faked? 16 --- ## Slide 17 Week 6 Integrated Summary True active management requires differentiation Concentration enables skill but raises risk Closet indexing destroys value Structure is prerequisite for alpha 17 --- ## Slide 18 Bridge to Week 7 – Report Preparation Weeks 1–6 built analytical toolkit Next: prepare midterm empirical report Apply factors, holdings, and Active Share together 18 ---

# Week7_Report_Preparation.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 7 – Report Preparation Graduate – Applied & Empirical Track Instructor: Shean -Bii Chiu (Self-study & Consultation Week) --- ## Slide 2 Purpose of Week 7 No formal lecture this week Focus on preparing midterm empirical report Integrate tools from Weeks 1–6 Instructor available for consultation 2 --- ## Slide 3 Midterm Report Objective Evaluate one mutual fund or ETF empirically Decompose performance into factors + structure Translate results into investment narrative Demonstrate graduate-level interpretation 3 --- ## Slide 4 Required Components 1. Fund description & benchmark 2. Return & risk profile 3. Factor exposure (alpha + betas) 4. Holdings/style or Active Share 5. Economic interpretation 4 --- ## Slide 5 How Weeks 1–6 Fit Together Week 1: risk, diversification, objectives Week 2: factor decomposition Week 3: holdings-based style Week 4: behavioral context Week 5: alpha skepticism Week 6: Active Share & concentration 5 --- ## Slide 6 Suggested Report Structure Introduction (fund & motivation) Data & methodology (brief) Empirical results Economic interpretation Conclusion & investor implications 6 --- ## Slide 7 Empirical Section – What Matters Do NOT emphasize procedures Do emphasize results Explain what drives performance Discuss robustness and limitations 7 --- ## Slide 8 Economic Interpretation Examples Is performance mainly factor-driven? Is alpha economically meaningful? Does portfolio structure support claimed strategy? Are results consistent with behavioral stories? 8 --- ## Slide 9 Common Pitfalls Reporting numbers without explanation Over-interpreting short samples Ignoring fees Confusing factor exposure with skill 9 --- ## Slide 10 Evaluation Criteria (Indicative) Clarity of framework Correct use of concepts Depth of interpretation Professional presentation 10 --- ## Slide 11 Expected Deliverables Written report (approx. 5–8 pages) Key tables/figures Concise investment narrative Prepared slides for Week 8 11 --- ## Slide 12 Consultation Guidance Prepare concrete questions Bring preliminary results Focus on interpretation, not software Use office hours efficiently 12 --- ## Slide 13 Bridge to Week 8 – Midterm Presentations Next week: empirical presentations Focus on insights, not mechanics Peer learning through comparison 13 ---

# Week8_Midterm_Presentations.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 8 – Midterm Presentations Graduate – Applied & Empirical Track Instructor: Shean -Bii Chiu --- ## Slide 2 Purpose of Week 8 Present empirical fund / ETF analysis Practice professional investment communication Learn from peer comparisons Focus on insights, not mechanics 2 --- ## Slide 3 Presentation Objective Explain what the fund does Explain what drives performance Assess whether results reflect skill or exposures Translate findings into investor implications 3 --- ## Slide 4 Suggested Presentation Structure 1. Fund overview & benchmark 2. Return & risk profile 3. Factor results (alpha + betas) 4. Holdings / Active Share insights 5. Economic interpretation 6. Conclusion 4 --- ## Slide 5 What Examiners Care About Clear analytical framework Correct use of concepts from Weeks 1–6 Depth of interpretation Professional logic 5 --- ## Slide 6 Empirical Results – Best Practice Highlight key numbers only Use charts/tables selectively Explain what changed and why Avoid dumping raw output 6 --- ## Slide 7 Economic Interpretation Examples Is performance mainly factor-driven? Is alpha economically meaningful? Does portfolio structure match stated strategy? Are results consistent with behavioral stories? 7 --- ## Slide 8 Common Weak Presentations Too many tables, too little explanation Confusing factor exposure with skill Ignoring fees and risk No clear conclusion 8 --- ## Slide 9 Strong Presentation Characteristics Logical story line Focused slides Explicit investment implications Confidence about limitations 9 --- ## Slide 10 Indicative Evaluation Rubric Framework clarity (25%) Empirical correctness (25%) Economic interpretation (30%) Presentation quality (20%) 10 --- ## Slide 11 Peer Learning Observe how others frame similar problems Compare different fund styles Learn alternative interpretations 11 --- ## Slide 12 Time Management Approx. 10–12 minutes per group Reserve time for questions Practice concise delivery 12 --- ## Slide 13 Instructor Feedback Focus Logic consistency Use of course concepts Depth vs breadth Professional reasoning 13 --- ## Slide 14 After Week 8 Incorporate feedback into final project Begin thinking about multi-asset portfolios Transition from fund analysis to portfolio construction 14 --- ## Slide 15 Bridge to Week 9 – Asset Allocation So far: evaluating individual funds Next: combining assets into portfolios Move from selection to allocation 15 ---

# Week9_Equity_Analysis_Portfolio_Management.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 9 – Global Asset Allocation & ETF Integration Graduate – Applied & Empirical Track Instructor: Shean -Bii Chiu --- ## Slide 2 Week 9 Overview Transition from fund selection to portfolio construction Introduce global asset allocation framework Understand ETFs as implementation layer Key Readings: CFA PM Vol.6; Swensen (2009); Ben-David et al. (2018) 2 --- ## Slide 3 Why Asset Allocation Dominates Outcomes Security selection explains only part of performance Long-term returns largely driven by asset mix Graduate focus: portfolio-level thinking 3 --- ## Slide 4 Core Asset Classes Equities (growth engine) Bonds (stability and income) Cash (liquidity) Alternatives (diversification) 4 --- ## Slide 5 Global Diversification Logic Different economies move differently Currency exposure matters Correlation across regions < 1 Global portfolios reduce concentration risk 5 --- ## Slide 6 CFA Framework: Asset Allocation Process Define objectives & constraints Form capital market expectations Construct strategic allocation Implement tactically Monitor and rebalance 6 --- ## Slide 7 Strategic vs Tactical Allocation Strategic: long-term policy weights Tactical: short-term deviations Graduate insight: most investors should emphasize strategic layer 7 --- ## Slide 8 Swensen (2009): Institutional Allocation Philosophy Equity-oriented portfolios Diversification across asset classes Illiquidity premia (for institutions) Discipline over market timing 8 --- ## Slide 9 From Funds to Portfolios Weeks 1–8: evaluate individual funds Week 9 onward: combine assets Portfolio is the true investment decision 9 --- ## Slide 10 Role of ETFs in Allocation Low-cost market exposure Efficient access to regions/sectors Building blocks for portfolios Implementation of factor and asset views 10 --- ## Slide 11 ETF Structure (Quick Review) Open-ended vehicle Creation/redemption mechanism Keeps prices close to NAV Provides intraday liquidity 11 --- ## Slide 12 Ben-David et al. (2018): ETF Market Impact ETF flows affect underlying volatility Increased co-movement across stocks Liquidity externalities Graduate takeaway: ETFs change market structure 12 --- ## Slide 13 Economic Interpretation ETFs improve access but may amplify shocks Convenience can increase correlation Asset allocation now interacts with market microstructure 13 --- ## Slide 14 Simple Multi-Asset Example (Conceptual) Equities: expected return Bonds: risk reduction ETF vehicles implement both Key: balance growth vs stability 14 --- ## Slide 15 Portfolio Construction Philosophy Diversify across assets Control risk at portfolio level Use ETFs for efficient exposure Avoid over-concentration 15 --- ## Slide 16 After-Class Conceptual Assignment Design a 3–4 asset portfolio Explain role of each asset Discuss risk-return tradeoff (1–2 pages) 16 --- ## Slide 17 Discussion Questions 1. How global should portfolios be? 2. Do ETFs make investing too easy? 3. Should institutions time asset allocation? 17 --- ## Slide 18 Week 9 Integrated Summary Asset allocation drives outcomes ETFs implement allocation efficiently Global diversification reduces concentration Structure matters more than selection 18 --- ## Slide 19 Bridge to Week 10 – ESG Investing Next: incorporating sustainability into portfolios From risk-return to risk-return-impact 19 ---

# Week10_ESG_Investing_Portfolio_Implications.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 10 – ESG Investing & Portfolio Implications Graduate – Applied & Empirical Track Instructor: Shean -Bii Chiu --- ## Slide 2 Week 10 Overview Introduce ESG as a portfolio design dimension Discuss performance debate and implementation challenges Key Readings: CFA PM Vol.5; JPM ESG Special Issue 2 --- ## Slide 3 What Is ESG? Environmental: climate, resources, emissions Social: labor, supply chain, community Governance: board, incentives, transparency Graduate focus: ESG as risk + preference constraint 3 --- ## Slide 4 Why ESG Matters to Investors Regulatory pressure Client preferences Reputation risk Potential impact on cash flows and cost of capital 4 --- ## Slide 5 Three ESG Integration Approaches Exclusion / screening Best-in-class tilting Full integration into valuation Each has different portfolio consequences 5 --- ## Slide 6 ESG and Expected Returns Theory ambiguous: constraints may reduce opportunity set Practice: depends on implementation Graduate insight: ESG is not free lunch 6 --- ## Slide 7 CFA Perspective: ESG in Portfolio Management Define ESG objectives Incorporate into investment policy Align with fiduciary duty Measure ESG outcomes 7 --- ## Slide 8 JPM ESG Special Issue – Core Messages ESG performance varies widely by region and sector Climate risks increasingly priced Transition risk differs from physical risk Data quality remains uneven 8 --- ## Slide 9 ESG as Risk Management Carbon exposure → regulatory risk Governance failures → tail risk Supply chain issues → earnings volatility ESG often reframed as downside protection 9 --- ## Slide 10 Active vs Passive ESG Passive ESG: rules-based screening Active ESG: engagement + security selection Graduate focus: engagement may matter more than exclusion 10 --- ## Slide 11 Implementation via ETFs ESG equity ETFs Low-carbon ETFs Thematic sustainability ETFs ETFs operationalize ESG tilts 11 --- ## Slide 12 Common ESG Pitfalls Greenwashing Inconsistent ratings across providers Over-concentration in certain sectors Ignoring tracking error 12 --- ## Slide 13 Portfolio-Level ESG Thinking ESG changes sector weights May increase factor tilts Must monitor diversification Graduate takeaway: ESG affects portfolio structure 13 --- ## Slide 14 Economic Interpretation ESG reflects preferences + risk beliefs May lower cost of capital for leaders But imposes constraints on portfolios Trade-offs are unavoidable 14 --- ## Slide 15 After-Class Conceptual Assignment Design ESG-tilted portfolio Explain ESG objective Discuss impact on diversification and returns (1–2 pages) 15 --- ## Slide 16 Discussion Questions 1. Should ESG sacrifice returns? 2. Can ESG alpha exist? 3. How should institutions measure impact? 16 --- ## Slide 17 Week 10 Integrated Summary ESG is a portfolio constraint and risk lens Implementation matters more than labels Must evaluate ESG at portfolio level No universal ESG solution 17 --- ## Slide 18 Bridge to Week 11 – Institutional Asset Allocation Next: how large institutions integrate ESG and asset allocation From individual portfolios to institutional frameworks 18 ---

# Week11_Institutional_Asset_Allocation.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 11 – Institutional Asset Allocation & Investment Philosophy Graduate – Applied & Empirical Track Instructor: Shean -Bii Chiu --- ## Slide 2 Week 11 Overview Shift from individual portfolios to institutional frameworks Understand how large investors allocate capital Key Readings: CFA PM Vol.6; Swensen (2009); Prado (2019) 2 --- ## Slide 3 Who Are Institutional Investors? Pension funds Insurance companies Endowments & foundations Sovereign wealth funds Graduate focus: scale changes everything 3 --- ## Slide 4 Institutional Objectives & Constraints Long investment horizons Liability matching Regulatory constraints Liquidity needs Governance structure 4 --- ## Slide 5 Asset Allocation as Policy Decision Strategic allocation dominates outcomes Reflects risk tolerance + liabilities Rarely changed Graduate insight: policy beats tactics 5 --- ## Slide 6 Swensen Framework (Endowment Model) Equity-oriented portfolios Broad diversification Exposure to illiquidity premia Strong governance + discipline 6 --- ## Slide 7 Why Institutions Emphasize Diversification Large portfolios cannot exit quickly Avoid single-source risk Stability of funding obligations Portfolio resilience over cycles 7 --- ## Slide 8 Prado (2019): Robust Portfolio Construction Avoid overfitting Diversify across models and signals Emphasize out-of-sample performance Graduate takeaway: robustness over optimization 8 --- ## Slide 9 Liabilities Matter Pensions: future payments Insurers: claim obligations Endowments: spending rules Assets must be matched to liabilities 9 --- ## Slide 10 Governance & Decision Process Investment committees External managers Risk oversight Clear accountability 10 --- ## Slide 11 Active vs Passive at Institutional Scale Passive for core exposures Active for specialized opportunities Cost control is critical Blend depends on governance quality 11 --- ## Slide 12 Role of Alternatives Private equity Real assets Hedge funds Aim: diversification + return enhancement 12 --- ## Slide 13 Common Institutional Mistakes Chasing recent performance Over-complex portfolios Weak governance Underestimating liquidity risk 13 --- ## Slide 14 From Individual to Institutional Thinking Focus on total portfolio Explicit risk budgeting Long-term discipline Process over prediction 14 --- ## Slide 15 After-Class Conceptual Assignment Design institutional-style portfolio Specify objectives & constraints Justify asset allocation (1–2 pages) 15 --- ## Slide 16 Discussion Questions 1. Should institutions pursue illiquidity premia? 2. How much active management is appropriate? 3. What governance structures work best? 16 --- ## Slide 17 Week 11 Integrated Summary Institutions invest with liabilities in mind Asset allocation is strategic policy Diversification and governance are central Robustness matters more than precision 17 --- ## Slide 18 Bridge to Week 12 – Risk Forecasting & Scenario Analysis Next: quantifying risk From allocation philosophy to risk implementation 18 ---

# Week12_Risk_Forecasting_Scenario_Analysis.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 12 – Risk Forecasting & Scenario Analysis Graduate – Applied & Empirical Track Instructor: Shean -Bii Chiu --- ## Slide 2 Week 12 Overview Shift from allocation philosophy to risk implementation Introduce risk forecasting and scenario thinking Key Readings: CFA PM Vol.7; Idzorek (2005) – intuition only 2 --- ## Slide 3 Why Risk Forecasting Matters Expected returns are uncertain Risk is observable and manageable Portfolio construction depends on forward-looking risk Graduate focus: manage what you can estimate 3 --- ## Slide 4 Types of Portfolio Risk Market risk Interest rate risk Credit risk Liquidity risk Concentration risk 4 --- ## Slide 5 Backward-Looking vs Forward-Looking Risk Historical volatility: easy but backward-looking Forecast volatility: model-based Scenarios: narrative-driven Graduate takeaway: combine all three 5 --- ## Slide 6 Volatility as Core Risk Metric Standard deviation of returns Changes over time Clustering in crises Input for portfolio decisions 6 --- ## Slide 7 Correlation Is Unstable Diversification works most in calm periods Correlations rise in stress Portfolio risk spikes when protection is needed most 7 --- ## Slide 8 CFA Perspective: Risk Estimation Use multiple horizons Stress test assumptions Avoid single-model dependence Emphasize robustness 8 --- ## Slide 9 Scenario Analysis – Concept Ask: what if markets move together? What if rates rise sharply? What if equities fall 30%? Scenarios translate narratives into portfolio impact 9 --- ## Slide 10 Common Portfolio Scenarios Equity bear market Inflation shock Recession Liquidity freeze 10 --- ## Slide 11 Idzorek (2005): Intuition of Black– Litterman Start with market equilibrium Incorporate investor views Blend views with confidence Graduate insight: disciplined way to express opinions 11 --- ## Slide 12 Views vs Confidence Strong views with low confidence Weak views with high confidence Model adjusts portfolio weights accordingly Avoid extreme allocations 12 --- ## Slide 13 Risk Constraints in Practice Maximum volatility Tracking error limits Asset class bounds These shape final portfolios 13 --- ## Slide 14 From Risk Forecast to Portfolio Design Estimate volatilities and correlations Apply constraints Test scenarios Iterate until acceptable 14 --- ## Slide 15 Why Optimization Often Fails Inputs are noisy Extreme weights Overfitting Graduate takeaway: constrain and simplify 15 --- ## Slide 16 Professional Risk Management Philosophy Diversify risk sources Avoid single-point estimates Prepare for bad outcomes Design resilient portfolios 16 --- ## Slide 17 After-Class Conceptual Assignment Take existing portfolio Define two stress scenarios Explain portfolio behavior Suggest adjustments (1–2 pages) 17 --- ## Slide 18 Discussion Questions 1. Should portfolios be built for average or crisis? 2. How much should investors trust models? 3. What risks worry you most today? 18 --- ## Slide 19 Week 12 Integrated Summary Risk forecasting guides allocation Scenarios reveal hidden vulnerabilities Constraints prevent overconfidence Robust portfolios beat optimized ones 19 --- ## Slide 20 Bridge to Week 13 – Constrained Portfolio Construction Next: building portfolios under real-world constraints From risk estimation to implementation 20 ---

# Week13_Constrained_Portfolio_Construction.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 13 – Constrained Portfolio Construction Graduate – Applied & Empirical Track Instructor: Shean -Bii Chiu --- ## Slide 2 Week 13 Overview Move from risk forecasting to actual portfolio building Focus on real-world constraints Key Readings: Bodie et al. Ch.25; Prado (2019) 2 --- ## Slide 3 Why Constraints Matter Pure optimization produces extreme weights Real portfolios face legal, liquidity, and policy limits Constraints stabilize outcomes Graduate focus: feasibility over theoretical optimality 3 --- ## Slide 4 Common Portfolio Constraints Asset class bounds (e.g., equities ≤ 60%) Single position limits Tracking error limits Liquidity requirements 4 --- ## Slide 5 From Theory to Practice Mean–variance gives intuition Constraints make portfolios investable Implementation layer dominates results 5 --- ## Slide 6 Bodie Ch.25: Portfolio Construction Logic Start with strategic allocation Apply risk limits Add active tilts Re-evaluate total portfolio risk 6 --- ## Slide 7 Risk Budgeting Concept Allocate risk, not just capital Each asset contributes to total volatility Graduate insight: control where risk comes from 7 --- ## Slide 8 Active Risk vs Total Risk Total risk: volatility of portfolio Active risk: deviation from benchmark Institutions manage both simultaneously 8 --- ## Slide 9 Why Optimization Often Breaks Noisy inputs Highly correlated assets Unstable covariances Leads to corner solutions 9 --- ## Slide 10 Prado (2019): Robust Construction Principles Diversify across signals Avoid overfitting Favor simplicity Test out-of-sample 10 --- ## Slide 11 Practical Construction Workflow 1. Set strategic weights 2. Apply constraints 3. Add factor or active tilts 4. Check risk contribution 5. Stress test 11 --- ## Slide 12 Example: Multi-Asset Portfolio (Conceptual) Equities: growth Bonds: stability Alternatives: diversification Cash: liquidity Constraints determine final mix 12 --- ## Slide 13 Rebalancing Logic Markets move weights away from targets Rebalancing restores risk profile Graduate focus: discipline over timing 13 --- ## Slide 14 Transaction Costs & Turnover Frequent trading erodes returns Constraints reduce unnecessary turnover Implementation costs matter 14 --- ## Slide 15 Professional Portfolio Design Philosophy Start simple Add complexity only when justified Control downside Design for persistence 15 --- ## Slide 16 After-Class Conceptual Assignment Construct constrained portfolio Specify constraints Explain risk contributions Discuss trade-offs (1–2 pages) 16 --- ## Slide 17 Discussion Questions 1. Should constraints be strict or flexible? 2. How much active risk is appropriate? 3. What constraints matter most in practice? 17 --- ## Slide 18 Week 13 Integrated Summary Constraints turn theory into reality Risk budgeting guides allocations Robust portfolios beat optimized ones Implementation dominates outcomes 18 --- ## Slide 19 Bridge to Week 14 – Performance Attribution Next: evaluating portfolio outcomes From construction to performance explanation 19 ---

# Week14_Performance_Attribution_Risk_Sources.pptx ## Slide 1 Equity Analysis & Portfolio Management Week 14 – Performance Attribution & Risk Sources Graduate – Applied & Empirical Track Instructor: Shean -Bii Chiu --- ## Slide 2 Week 14 Overview Final technical module: explaining portfolio outcomes Move from construction to evaluation Key Readings: Bodie et al. Ch.24; Hou, Xue & Zhang (2020) 2 --- ## Slide 3 Why Performance Attribution Matters Returns alone do not explain decisions Investors need to know what drove outcomes Attribution links process to results Graduate focus: accountability 3 --- ## Slide 4 Basic Attribution Framework (Bodie) Allocation effect: asset mix Selection effect: security choice Interaction effect: combined impact Together explain active return 4 --- ## Slide 5 Return vs Risk Attribution Return attribution: sources of performance Risk attribution: sources of volatility Professional evaluation requires both 5 --- ## Slide 6 Allocation Effect Did asset class weights add value? Global vs domestic equity Equity vs bonds Graduate insight: policy decisions dominate 6 --- ## Slide 7 Selection Effect Did chosen funds or securities outperform peers? Links back to Weeks 3–6 True skill should appear here 7 --- ## Slide 8 Interaction Effect Timing of allocation and selection Usually smaller but informative Reveals coordination of decisions 8 --- ## Slide 9 Risk Attribution Concept Which assets contribute most to volatility? Not always those with largest weights Correlations matter 9 --- ## Slide 10 From Attribution to Learning Identify persistent strengths Detect unintended bets Improve future allocations 10 --- ## Slide 11 Hou, Xue & Zhang (2020): Replicating Anomalies Many published anomalies fail replication Data mining inflates findings Out-of-sample performance is weaker Graduate takeaway: skepticism is essential 11 --- ## Slide 12 Implications for Attribution Do not over-interpret short-term effects Separate noise from structure Focus on economically meaningful drivers 12 --- ## Slide 13 Professional Attribution Workflow 1. Define benchmark 2. Decompose returns 3. Attribute risk 4. Interpret economically 5. Feed back into process 13 --- ## Slide 14 Common Attribution Mistakes Attributing noise Ignoring benchmark choice Over-focusing on selection Neglecting risk 14 --- ## Slide 15 Integrating the Whole Course Factors (Week 2) Holdings (Week 3) Behavior (Week 4) Alpha (Week 5) Structure (Week 6) Allocation (Week 9–13) Attribution closes the loop 15 --- ## Slide 16 After-Class Conceptual Assignment Take your portfolio Perform attribution (conceptually) Explain main drivers Suggest improvements (1–2 pages) 16 --- ## Slide 17 Discussion Questions 1. Should attribution focus on returns or risk? 2. How long a period is meaningful? 3. What would you report to clients? 17 --- ## Slide 18 Week 14 Integrated Summary Attribution links decisions to outcomes Allocation usually dominates Selection is hard to sustain Risk must be evaluated alongside return 18 --- ## Slide 19 Bridge to Week 15–16 Next: Final project presentations Then: Final exam & course synthesis Focus on integrated investment reasoning 19 ---