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Lecture 2: Asset Classes and Financial Instruments Overview of Financial Instruments Definition of an Asset: ○ An asset is defined as a sequence of cash flows: ○ Valuation of an asset requires assessing the present value of these cash flows: Types of Assets:...

Lecture 2: Asset Classes and Financial Instruments Overview of Financial Instruments Definition of an Asset: ○ An asset is defined as a sequence of cash flows: ○ Valuation of an asset requires assessing the present value of these cash flows: Types of Assets: ○ Real Assets: Contribute to productive capacity and net income of the economy. Examples include land, buildings, equipment, and knowledge. ○ Financial Assets: Claims on real assets and do not directly contribute to productive capacity. Focus of the course is on financial assets. Types of Financial Assets 1. Financial Markets: ○ Segmented into money markets and capital markets. ○ Money Market Instruments: Short-term, marketable, liquid, low-risk debt securities. ○ Capital Markets: Include longer-term and riskier securities such as bonds, equities, and derivatives. 2. Debt (Fixed Income Securities): ○ Payments are fixed or determined by a formula. ○ Money Market Debt: Short-term, highly marketable, usually low credit risk. ○ Capital Market Debt: Long-term bonds, typically riskier. 3. Equity: ○ Common stock represents ownership in a corporation. ○ Payments to stockholders depend on the firm's success (residual claim). ○ Limited liability for shareholders. 4. Derivatives: ○ Instruments whose value derives from the price of other securities (e.g., stocks, bonds). ○ Used for various applications, including risk management. Money Market Securities Characteristics: ○ Short-term (maturity < 1 year), liquid, low risk. ○ Often used as collateral and have large denominations. Examples: ○ Treasury bills (T-bills), commercial paper. Bond Market Types of Bonds: ○ Government Debt: Treasury Notes (T-Notes) and Bonds (T-Bonds). T-Notes: Maturity < 10 years; T-Bonds: 10 to 30 years. Semi-annual coupon payments. ○ Corporate Bonds: Issued by companies, higher default risk than government securities. ○ International Bonds: Yankee bonds (dollar-denominated bonds sold in the U.S. by non-American firms). Samurai bonds (yen-denominated bonds sold in Japan by non-Japanese firms). Equity Market Types of Equity Securities: ○ Common Stock: Ownership shares in public companies, dividends depend on company performance. ○ Preferred Stock: Fixed dividend payments, priority over common stock, no voting rights. Stock Market Indexes Uses: ○ Track average returns, compare performance of managers, base for derivatives. Types of Indexes: ○ Price-weighted Average: (e.g., DJIA). ○ Equally-weighted Average: Invest equal amounts in each stock. ○ Value-weighted Average: (e.g., S&P 500), weighted by total capitalization. Derivatives Markets Types of Derivatives: ○ Options: Call options (right to buy) and put options (right to sell). Defined by exercise price and expiration date. ○ Forward Contracts: Agreement to buy/sell an asset at a future date for a specified price. Customized, no money changes hands until maturity. ○ Futures Contracts: Standardized contracts traded on exchanges, daily settlement (marked to market). Trading Securities Types of Markets: ○ Direct search, brokered markets, dealer markets, auction markets. Order Types: ○ Market orders, limit orders, stop orders. Trading Costs:  ○ Commissions, bid-ask spread, potential taxes. Buying on Margin Definition: ○ Borrowing part of the total purchase price using a loan from a broker. Key Concepts: ○ Initial margin (set by the Fed, currently 50%). ○ Maintenance margin (minimum equity required). ○ Margin call (notification to add funds if equity falls below maintenance margin). Short Sale Definition: ○ Selling a security not owned, expecting to buy it back at a lower price. Mechanics: ○ Borrow shares, sell them, and maintain collateral in a margin account. ○ Maximum risk is unlimited. Lecture 3: Risk-Return Relation and Portfolio Analysis 1. Risk-Return Relation Historical performance shows that stocks yield significantly higher returns than T-bills and T-bonds. Average annual returns (1927-2015): ○ Stocks: 11.77% ○ T-bills: 3.47% The difference (8.3%) is termed the Equity Risk Premium. The document questions whether the equity risk premium is excessively large. 2. Measuring Risk Discusses both statistical and economic measures of risk: ○ Variance: Average of squared deviations from the mean. ○ Standard Deviation (σ): Square root of variance, indicating volatility. Highlights the importance of higher moments of risk: ○ Skewness: Indicates the asymmetry of return distribution. Negative skewness: More frequent extreme bad returns. Positive skewness: More frequent extreme good returns. Sharpe Ratio: Measures excess return per unit of risk. ○ Formula: 3. Portfolio Basics  Definition: A portfolio is a collection of securities. Benefits of portfolio formation: ○ Diversification reduces unnecessary risks. ○ Portfolios can exploit market anomalies (e.g., value premium). Examples of portfolio construction: ○ Portfolio weights must sum to 1. ○ Example calculations provided for different stock allocations. 4. Conventions for Annualizing Returns Annual Percentage Rate (APR): Without compounding, calculated as 𝐴𝑃𝑅 = 𝐻𝑃𝑅 * 𝑛 Effective Annual Rate (EAR): With compounding, calculated as 𝑛 𝐸𝐴𝑅 = (1 + 𝐻𝑃𝑅) − 1 5. Portfolio Risk Expected return of a portfolio: ○ Formula: Volatility of a portfolio: ○ Formula: Lecture 4: Portfolio Theory I. Introduction to Modern Portfolio Theory Pioneers: Developed by Harry Markowitz; extended by William Sharpe to create the Capital Asset Pricing Model (CAPM). Recognition: Both awarded the Nobel Prize in Economics in 1990. Framework: Focuses on optimizing risk/reward characteristics of investment portfolios using a mean-variance approach. II. Key Concepts Mean-Variance Framework: ○ Investors prefer high expected returns but dislike high volatility (measured as standard deviation). ○ Mean, standard deviations, and correlations are treated as constant parameters. Objectives: ○ Maximize expected return for a given level of volatility. ○ Minimize volatility for a given level of expected return. III. Important Concepts Investment Opportunity Set: All possible portfolios available to an investor. Efficient Frontier: Portfolios that offer the best possible expected return for a given level of risk. Minimum Variance Portfolio: Portfolio with the lowest risk. Diversification: Reducing risk by adding more assets; the trade-off between the benefits of diversification and its costs. Capital Market Line (CML) and Security Market Line (SML): Tools for visualizing risk-return trade-offs. Tangency Portfolio: The optimal risky portfolio when combined with a risk-free asset; independent of individual risk aversion. Two Fund Theorem: All investors should hold a combination of the tangency portfolio and a risk-free asset, with the proportion depending on their risk aversion. IV. Two Risky Asset Portfolio Expected Return Formula: ○ Standard Deviation Formula: ○ V. Large Risky Asset Portfolio Expected Return:  ○ Standard Deviation: ○ VI. Risk of a Large Portfolio Average Covariance Risk: As the number of assets increases, the portfolio risk is largely determined by average covariance risk. VII. When There Is a Risk-Free Asset Investment Opportunity Set: Becomes a straight line when combining a risky asset with a risk-free asset. Efficient Frontier: Coincides with the Capital Market Line (CML). Tangency Portfolio: Represents the optimal risky portfolio; all investors will hold this along with a risk-free asset. VIII. Formal Analysis Expected Return of a Portfolio: ○ Variance of a Portfolio: ○ Efficient Portfolio Variance: ○ Minimum Variance Portfolio: ○ Variance: ○ Weights: IX. Implementing Portfolio Theory Input Requirements: Knowledge of expected returns (µ) and variance-covariance matrix (σ).  Estimation Approaches: ○ Markowitz Model: Uses historical data to estimate parameters. ○ Single Index Model: Imposes assumptions to reduce the number of parameters needing estimation. X. Example Application Minimum Variance Portfolio: Example using IBM, Boeing, American Express, and Walmart to illustrate the application of the Markowitz implementation. Lecture 5: The CAPM Capital Asset Pricing Model (CAPM): A framework for understanding the risk-return trade-off and asset pricing in finance. Key Concepts: ○ Risk-Return Trade-off: The relationship between the risk of an investment and its expected return. ○ Systematic vs. Idiosyncratic Risk: Systematic risk affects the entire market, while idiosyncratic risk is unique to a specific asset. The Link Between Portfolio Theory and CAPM Portfolio Theory: Analyzes how investors form portfolios based on asset risk and return characteristics. ○ Diversification: Helps improve the risk-return trade-off. ○ Tangency Portfolio: The optimal portfolio that combines the risk-free asset and risky assets. Two Fund Theorem: Investors can achieve any desired risk-return profile by combining the risk-free asset and the tangency portfolio. CAPM Assumptions Investor Assumptions: ○ Homogeneous expectations regarding mean and standard deviations of securities. ○ Investors are price takers in a perfectly competitive market. ○ Static model with a single-period investment horizon. ○ Mean-variance optimizers. Market Assumptions: ○ Availability of a risk-free rate for all investors. ○ No taxes or transaction costs. ○ All securities are publicly owned and traded. ○ All relevant information is publicly available. CAPM Predictions 1. Tangency Portfolio as Market Portfolio: The tangency portfolio is equivalent to the market portfolio (e.g., S&P 500). 2. Expected Return Formula:  ○ ○Where ( E[ri] ) is the expected return on asset ( i ), ( rf ) is the risk-free rate, ( βi ) is the asset's beta, and ( E[rm] ) is the expected market return. 3. Market Risk Premium: ○ ○ ( A ) represents the risk aversion of the average investor. Implications of CAPM Market Portfolio: All investors should hold the market portfolio, which is on the efficient frontier. Passive Investment Strategy: It is impossible to consistently beat the market; thus, a passive strategy is efficient. Performance Benchmark: The market return serves as a benchmark for evaluating professional money managers. Empirical Tests of CAPM Testing Methods: ○ Portfolio Sorts: Sorting stocks based on characteristics (e.g., beta) and analyzing average returns. ○ Time Series Regression: Estimating the relationship between excess returns of assets and market returns. ○ Cross-Sectional Regression: Analyzing average excess returns against estimated betas. ○ Fama-MacBeth Regression: A two-step regression process that improves standard errors and test statistics. Empirical Performance Mixed Results: Empirical tests have shown weak support for the CAPM. Anomalies: The CAPM fails to explain various market anomalies, such as momentum and profitability. Critiques: ○ Roll’s critique highlights that the true market portfolio is unobservable. ○ Measurement errors in beta can bias results. ○ The CAPM's simplistic assumptions may not hold in real-world scenarios. Lecture 6: Asset Pricing Author: Guanglian Hu, University of Sydney Context: Week 6 of a course on Asset Pricing. State Space Model Definition: A framework for modeling investments under uncertainty, introduced by Arrow (1964) and Debreu (1959).  States of the World: There are S states of nature, each labeled by s (s = 1, 2,..., S), representing different economic conditions. Probabilities: Each state s occurs with probability πs (0 < πs < 1), and the sum of probabilities across all states equals 1 (Σπs = 1). Arrow-Debreu Securities: These are contingent claims that pay off $1 if a specific state occurs and $0 otherwise. Each state has a corresponding security. State Prices: The price of each Arrow-Debreu security is denoted as ws, where 0 < ws < 1. Pricing Risky Assets Random Payoff: A risky asset with a random payoff Y can be represented as a vector of payoffs across states. Valuation: The current price of Y can be calculated using the state prices: ○ Risk-Free Bond: The price of a risk-free bond (B) that pays off $1 in all states can be derived from state prices: ○ Stochastic Discount Factor (SDF) Definition: The SDF (Ms) is defined as Expected Value: The value of an asset is equal to the expected value of discounted future cash flows: ○ Risk Neutral Probabilities Transformation: Risk-neutral probabilities (˜πs) can be derived from state prices and the price of the risk-free asset: ○  Valuation under Risk Neutrality: The price of a risky asset can also be expressed using risk-neutral probabilities. Inferring State Prices from Option Prices Options as Replicating Claims: State prices can be inferred from the prices of options with different strike prices. Example: The payoff of a European call option can be used to replicate the payoffs of Arrow-Debreu securities. Consumption-Based Asset Pricing Intertemporal Choice Problem: Investors maximize expected lifetime utility: ○ Euler Equation: The first-order condition for optimal consumption and portfolio choice: ○ Fundamental Asset Pricing Equation Derivation: The fundamental equation in asset pricing is: ○ Implications: The expected return of an asset is related to its covariance with the stochastic discount factor. Expected Return - Beta Representation CAPM-Type Expression: The fundamental equation can be rewritten to show the relationship between expected returns and risk: ○ Beta Definition: Imposing Log-Normality Log-Normal Distribution: Assumes that asset returns and the stochastic discount factor are log-normally distributed.  Expected Returns: The expected excess return of a risky asset over the risk-free rate is influenced by the covariance with the stochastic discount factor. Consumption-Based Asset Pricing with Power Utility Utility Function: A representative agent maximizes a power utility function: ○ Risk-Free Rate: The risk-free rate is influenced by the expected consumption growth and the coefficient of relative risk aversion. Equity Risk Premium Puzzle Definition: The equity premium puzzle refers to the observed high average returns on stocks compared to risk-free assets. Risk Aversion: Economists generally believe that the coefficient of relative risk aversion should be less than 10. Hansen-Jagannathan Bound Bound Definition: The standard deviation of the stochastic discount factor must be greater than the Sharpe ratio for any asset. Implications: This bound suggests that the stochastic discount factor is not volatile enough to explain observed asset returns. Extensions to Asset Pricing Models Second Generation Models: New models that depart from log-normality and power utility include: ○ Long-run risks model (Bansal and Yaron, 2004) ○ Habit model (Campbell and Cochrane, 1999) ○ Rare disaster model (Rietz, 1988; Barro, 2006; Wachter, 2013) Lecture 7: Anomalies and Multi-Factor I. Introduction to Anomalies Anomalies are empirical patterns in average asset returns that contradict CAPM predictions. Common anomalies include: ○ Size ○ Value ○ Momentum ○ Low Beta ○ Profitability ○ Investment II. Anomalies: Size Key Findings: ○ Small stocks tend to outperform large stocks. ○ The size effect has weakened since the 1980s but has seen a resurgence recently. ○ Controlling for firm quality reveals a robust size effect. Historical Context: ○ Banz (1981) identified the size anomaly, challenging CAPM. ○ The size effect is particularly pronounced in January, linked to tax-based selling. III. Anomalies: Value Key Findings: ○ Value stocks (high book-to-market ratio) generally yield higher future returns than growth stocks (low book-to-market ratio). ○ Value stocks are characterized by low prices relative to their fundamentals. IV. Anomalies: Momentum Key Findings: ○ Stocks that have performed well over the past 3-12 months tend to continue performing well. ○ Momentum is a consistent anomaly across various markets and asset classes. Empirical Evidence: ○ Jegadeesh and Titman (1993) established the momentum effect. V. Anomalies: Low Beta Key Findings: ○ Low beta stocks often provide higher CAPM alphas than high beta stocks. ○ The expected return-beta relationship is weak, suggesting a flat Security Market Line (SML). VI. Anomalies: Investment Key Findings: ○ Firms with lower capital expenditures or asset changes tend to earn higher average future returns. VII. Anomalies: Profitability Key Findings: ○ More profitable companies yield higher average returns. ○ Profitability is often measured as gross profits/assets. VIII. Arbitrage Pricing Theory (APT) Key Concepts: ○ APT is a multi-factor model that explains asset returns through various systematic factors (e.g., inflation, interest rates).  ○ APT makes fewer assumptions than CAPM and does not specify which factors to include. IX. Multi-Factor Models Key Concepts: ○ Multi-factor models extend CAPM by incorporating multiple sources of systematic risk. ○ Fama-French 3-Factor Model: Adds size (SMB) and value (HML) factors to CAPM. Model Equation: 𝐸[𝑟𝑖] = 𝑟𝑓 + β1,𝑖𝑀𝐾𝑇 + β2,𝑖𝑆𝑀𝐵 + β3,1𝐻𝑀𝐿 ○ Performance: The Fama-French model effectively explains size and value anomalies but struggles with newer anomalies like momentum. X. Other Key Factor Models Fama-French 4-Factor Model: Adds momentum to the 3-factor model. Fama-French 5-Factor Model: Incorporates investment and profitability factors. Fama-French 6-Factor Model: Further augments the 5-factor model with momentum. Importance of Factor Models Widely used by market participants for performance measurement and strategy construction. Serve as benchmarks for mutual funds and hedge funds. Cross-Section of Stock Returns Empirical studies focus on differences in average returns across stocks. Two main study categories: ○ Relationships between expected returns and stock characteristics. ○ Portfolios based on factor exposure and their performance relative to benchmarks. Factor Zoo The proliferation of factors in academic literature leads to a "factor zoo." Concerns include: ○ Poor out-of-sample performance. ○ Data mining and publication bias. ○ Need for economically motivated factors. Lecture 8: Investment Vehicles and Strategies Investment Vehicles Definition: Instruments or legal structures that allow investors to deploy their capital. Types: ○ Mutual Funds: Pooled investments in publicly listed companies. ○ Pension Funds: Pooled retirement money invested through various structures. ○ Hedge Funds: Use complex strategies (long-short equity, global macro) with leverage and derivatives.  ○ Private Equity: Investments in illiquid assets with long time horizons. ○ Real Estate Investment Trusts (REITs): Provide liquid exposure to real estate. ○ Exchange-Traded Funds (ETFs): Managed portfolios that trade like stocks, offering real-time liquidity. Investment Strategies Definition: Decision-making processes and techniques applied by investment vehicles. Key Non-Performance Metrics: ○ Costs: Management fees, performance fees, and expense ratios. ○ Turnover Ratio: Percentage of portfolio holdings replaced within a year; high turnover can lead to higher costs and taxes. ○ Liquidity: Ease of buying/selling assets without affecting prices; high liquidity is essential for frequent trading. ○ Concentration/Diversification: Portfolio's focus on few assets vs. many; concentrated strategies carry higher risk but potential upside. ○ Tracking Error: Deviation of portfolio returns from its benchmark; high tracking error indicates active strategies aiming to outperform. Active vs Passive Management Active Strategies: ○ High fees and turnover. ○ Lower liquidity. ○ Higher concentration and tracking error. ○ Aim to generate alpha (excess returns) through stock picking and market timing. Passive Strategies: ○ Low fees and turnover. ○ High liquidity and broad market exposure. ○ Focus on achieving benchmark returns rather than outperforming. Investment Styles Definition: Preferences that drive security selection. Key Styles: ○ Growth ○ Value ○ Momentum ○ Income ○ Size ○ Factor-based styles Performance Variability: Different styles perform better in varying market conditions (e.g., growth in expansions, value in recoveries). Asset Management Companies Role: Manage investment vehicles and strategies. Top Global Asset Managers (2024): ○ BlackRock, Vanguard, Fidelity, State Street, J.P. Morgan, etc. Top Australian Asset Managers (2024):  ○ Macquarie Asset Management, Australian Foundation Investment Co, Argo Investments, etc. Mutual Funds and ETFs Mutual Funds: ○ Issue/redeem shares at net asset value (NAV). ○ Risk of portfolio disruption during large redemptions. ETFs: ○ Trade throughout the day at market prices. ○ Use a creation/redemption process to manage underlying assets, minimizing disruption. Advanced Investment Strategies Smart Beta: Combines passive indexing with active factor selection (e.g., value, momentum). ESG Strategies: Incorporate sustainability factors into investment processes. Quantitative Strategies: Use data models and algorithms to identify market inefficiencies. Tactical Asset Allocation: Adjusts portfolio allocations based on market conditions. Key Non-Performance Metrics Fees and Expense Ratios: Costs associated with managing investments; higher fees can erode returns. Turnover Ratio: Indicates trading frequency; high turnover can lead to increased costs and tax implications. Liquidity: Essential for strategies involving frequent trading; high liquidity is preferred. Concentration/Diversification: Impacts risk and potential returns; concentrated portfolios can yield higher returns but carry more risk. Tracking Error: Measures deviation from benchmarks; important for assessing active vs. passive strategies. Lecture 9: Factor Investing Definition: Factor investing is a strategy where investors aim to capture excess returns by deliberately allocating to specific factors (e.g., value, momentum, size) that have historically outperformed the market. Types of Strategies: ○ Active Strategies: Involves stock picking based on identified factors. ○ Passive Strategies: Utilizes factor-tilted ETFs or mutual funds to gain exposure to these factors. Focus: The primary goal is to capture specific risk premiums through systematic exposure to chosen factors. Smart Beta Definition: Smart beta is a subset of factor investing that employs rules-based quantitative strategies with factor tilts.  Origin: The term was coined by Towers Watson in 2007, referring to their fundamental weighting strategy. Key Characteristics: ○ Uses alternative metrics (e.g., book value vs. market cap) to measure firm size. ○ Moves beyond traditional market-cap weighting to address issues like concentration risk and performance chasing. Evolution of Smart Beta Growth: Significant increase in Total Net Assets (TNA) for smart beta ETFs from 2013 to 2023. Factor Categories: Growth observed across various factors including size, value, quality, dividend, and momentum. Smart Beta Steps 1. Define Objective and Framework: Establish the goal of outperforming the market through specific financial metrics. 2. Select Factor Metrics: Choose metrics to define factors (e.g., Price-to-Earnings, Price-to-Book). 3. Define Investment Universe: Select the market segment (e.g., ASX200, S&P 500) for stock selection. 4. Create Factor Score: Rank stocks based on standardized metrics (Z-scores). 5. Portfolio Construction: Decide on stock selection and weighting schemes (e.g., fundamental, equal, or factor-score weighting). 6. Apply Constraints: Introduce risk management constraints (e.g., sector constraints, liquidity filters). 7. Rebalancing Strategy: Determine frequency and criteria for rebalancing the portfolio. 8. Performance Monitoring and Evaluation: Regularly assess portfolio performance against benchmarks. Single Factor ETF Multiple Metrics: Discusses the implications of using multiple metrics for the same factor (e.g., P/B, P/E). Z-Score Calculation: Each stock's z-score is calculated for each metric to create a composite score. Multi-Factor ETF Combining Factors: Investigates whether combining different factors (e.g., value and size) affects overall portfolio risk and return. Cyclical Nature: Factors are cyclical, and combining them can provide diversification benefits. Factor Overlaid Portfolio Definition: This strategy applies factor strategies on top of an existing portfolio to enhance risk-adjusted returns. Implementation: Overweight stocks that load highly on certain factors or add new allocations based on these factors.  Example: Using a quality factor overlay on an S&P 500 portfolio. Generalized Approach Fama-McBeth Regression: Used to identify factors with strong price of risk for constructing factor overlaid strategies. Weighting: Stocks are weighted based on their exposure to selected factors. Factor Tracking and Long-Short Investable Portfolio Long-Short Strategy: Involves going long on stocks with higher factor loadings and shorting those with lower loadings. Tracking Error: Discusses constructing portfolios that track a benchmark while maintaining a specified tracking error. Backtesting Importance: Essential for evaluating the performance of factor-based strategies. Risks: ○ Data Snooping Bias: Repeated testing can lead to false discoveries. ○ Overfitting: Tailoring strategies too closely to past data can result in poor future performance. Lecture 10: Performance Evaluation Section 1: Why Performance Evaluation? Purpose of Performance Evaluation: ○ Assesses the skill of fund managers. ○ Critical for investment decisions and benchmarking. ○ Important for market efficiency. Complexity: ○ Performance evaluation is complicated due to the interplay between risk and return. ○ High returns with high risk do not necessarily indicate skill. General Principle: ○ Aim for higher returns without excessive risk. Section 2: Conventional Performance Evaluation: Average Returns Types of Average Returns: ○ Arithmetic Average: Simple average calculated as ○ Geometric Average: Time-weighted average calculated as  ○ Dollar Weighted Average: Internal rate of return that equates present values of cash inflows and outflows. Section 3: Risk-Adjusted Performance Need for Risk Adjustment: ○ Average returns must be adjusted for risk for meaningful comparisons. Types of Risks: ○ Total risk (σ), systematic risk (β), idiosyncratic risk (σep). Risk-Adjusted Performance Measures: 𝑟𝑝−𝑟𝑓 ○ Sharpe Ratio: 𝑆 = σ𝑝 ○ M2 Measure: Compares managed portfolio returns to a passive index 𝑀2 = 𝑟𝑝 − (𝑟𝑓 + β𝑝(𝑟𝑚 − 𝑟𝑓)). 𝑟𝑝−𝑟𝑓 ○ Treynor Ratio: 𝑇𝑝 = β𝑝. ○ Jensen’s Alpha: Measures excess return over CAPM prediction α = 𝑟𝑝 − (𝑟𝑓 + β𝑝(𝑟𝑚 − 𝑟𝑓). α𝑝 ○ Information Ratio: 𝐼𝑅𝑝 = σ𝑒𝑝 → NB: denominator is tracking error. Section 4: Role of Alpha in Performance Measures Alpha's Importance: ○ All performance measures are positively related to alpha. ○ Higher alpha generally leads to higher performance measure values. Application: ○ Particularly relevant for hedge funds and mutual funds. ○ Hedge funds often do not create well-diversified portfolios. Section 5: Skill vs Luck Performance Variability: ○ Some funds outperform benchmarks due to skill, while others may do so by chance. ○ Outperformers attract more investor funds. Skill Measurement: ○ Skill defined as the ability to consistently outperform benchmarks. ○ Berk and van Binsbergen (2016) suggest skill is a product of fund alpha and size. ○ Net performance is affected by fund fees, which can obscure true skill. Lecture 11: Overall Performance of Alternative Assets Definition: Alternative investments refer to asset classes that are not traditional investments like stocks, bonds, or cash. They include hedge funds, private equity, real estate, commodities, infrastructure, and cryptocurrencies. Characteristics: ○ Illiquidity: These investments are not easily traded, requiring a longer-term commitment, which can introduce both risk and reward.  ○ Diversification: They often have low correlation with traditional markets, potentially reducing overall portfolio risk. ○ Higher Fees: Management and performance fees for alternative investments are typically higher than those for traditional investments. ○ Complexity: Many alternative investments require specialized knowledge and analysis to understand and manage effectively. ○ Sophisticated Investors: Primarily targeted at institutional investors and high-net-worth individuals due to their complexity and risk profile. Hedge Funds Definition: Investment pools that employ various strategies to earn active returns. Net Asset Value (NAV): Represents the value of an investor’s stake in the hedge fund portfolio. Strategies: ○ Directional: Speculate on the market direction or specific assets. ○ Nondirectional: Exploit temporary misalignments in relative pricing, often market neutral. ○ Pure Plays: Focus on specific mispricing across multiple securities. Performance: ○ Historically outperformed passive indexes, but recent trends show a decline in performance metrics like the Sharpe ratio and alpha. ○ Performance measures include Sharpe ratio, alpha, and information ratio, but biases such as backfill and survivorship can distort reported results. Fees: ○ Typical structure includes a management fee (1-2%) and an incentive fee (20% of profits beyond a benchmark). ○ High-Water Mark: Ensures managers only earn performance fees on new profits, not on recovered losses. ○ Lockup Period: Investors cannot redeem their capital for a specified time (6 months to 2 years). Private Equity Definition: Investments in privately owned companies or startups, often through leveraged buyouts (LBOs). Types: ○ Angels: Wealthy individuals providing early-stage funding. ○ Venture Capital: Managed funds investing in startups at various stages. ○ LBOs: Use of debt to acquire mature companies, aiming to improve operations and exit through IPO or acquisition. ○ Growth Capital: Minority equity investments in companies needing capital for expansion. ○ Mezzanine Capital: Subordinated debt or preferred stock investments supporting growth. Valuation Challenges: Valuing private companies is more complex than public companies, often requiring a capital multiplier to determine ownership stakes. Example: A venture capitalist investing $2 million with a target exit multiple of 10x in 8 years must calculate the required ownership share to justify the investment. Real Estate  Definition: Investments in physical properties or through Real Estate Investment Trusts (REITs). Types: ○ Direct Ownership: Investing directly in properties. ○ REITs: Companies that own and manage income-generating properties. ○ Private Real Estate Funds: Pooled investments that purchase and manage real estate assets. Characteristics: ○ Provides stable income through rental yields and potential for capital appreciation. ○ Direct investments are illiquid, while REITs offer higher liquidity. Lecture 12: Overview of Sustainable Investing and ESG Definition of Sustainable Finance: The process of incorporating Environmental, Social, and Governance (ESG) considerations into investment decisions, promoting long-term investments in sustainable economic activities. ○ Environmental Considerations: Climate change mitigation, biodiversity preservation, pollution prevention, and circular economy. ○ Social Considerations: Issues of inequality, inclusiveness, labor relations, investment in human capital, and human rights. ○ Governance: Management structures, employee relations, and executive remuneration are crucial for integrating ESG factors into decision-making. Evolution of Sustainable Investing Historical Timeline: ○ 2000s: Responsible Investment (RI) focused on ethics and final investors. ○ 2010s: Rise of ESG investing, gaining traction in asset management. ○ 2020s: Expansion of sustainable finance across all financial actors, including issuers and banks. ESG Strategies and Data Investment Strategies: ○ Negative Screening/Exclusion: Excluding sectors or companies based on ESG criteria (e.g., tobacco, fossil fuels). ○ Positive Screening/Best-in-Class: Investing in companies with superior ESG performance relative to peers. ○ ESG Integration: Systematic inclusion of ESG factors in financial analysis. ○ Thematic Investing: Focusing on specific sustainability themes (e.g., clean energy). ○ Corporate Engagement/Shareholder Activism: Influencing corporate behavior through direct engagement and voting. Importance of ESG Data: ○ Requires extensive data from various sources, including public reports, regulatory filings, and proprietary data. ○ Examples of data sources: Corporate annual reports, Carbon Disclosure Project (CDP) responses, and ESG rating agencies. ESG Regulations and Associations  Growth of ESG Associations: ○ The Principles for Responsible Investment (PRI) launched in 2006, with significant growth in signatories from 63 to over 1,400 by 2021. Regulatory Landscape: ○ Increasing number of regulations affecting issuers, investors, and financial regulators, indicating a shift towards more structured ESG frameworks. Performance of ESG Investing Dynamic Relationship: ○ The relationship between ESG factors and financial performance is complex and varies over time. ○ Academic studies indicate that while ESG investing can enhance performance, it is not universally applicable. Green Bonds and Greenium Green Bonds: Debt instruments used to finance projects with positive environmental impacts. Greenium: The premium associated with green bonds compared to traditional bonds, reflecting investor demand for sustainable investments. Ownership and Engagement Active Ownership: Investors engage with companies to influence management and operations without changing control. Types of Engagement: ○ Ongoing engagement to promote best practices. ○ Engagement for influence to express dissatisfaction and push for changes. ○ Pre-AGM engagement to discuss resolutions. Greenwashing Definition: Misleading claims by companies about their environmental practices to appear more sustainable than they are. Risks: Includes mis-selling and misinterpretation risks, highlighting the need for transparency and accountability in ESG claims.