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International Investment 2005-2006

International Investment 2005-2006. Professor André Farber Solvay Business School Université Libre de Bruxelles. Notions of Market Efficiency. An Efficient market is one in which: Arbitrage is disallowed: rules out free lunches

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International Investment 2005-2006

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  1. International Investment 2005-2006 Professor André Farber Solvay Business School Université Libre de Bruxelles

  2. Notions of Market Efficiency • An Efficient market is one in which: • Arbitrage is disallowed: rules out free lunches • Purchase or sale of a security at the prevailing market price is never a positive NPV transaction. • Prices reveal information • Three forms of Market Efficiency • (a) Weak Form Efficiency • Prices reflect all information in the past record of stock prices • (b) Semi-strong Form Efficiency • Prices reflect all publicly available information • (c) Strong-form Efficiency • Price reflect all information PhD 01-1

  3. Realization Expectation Efficient markets: intuition Price Price change is unexpected Time PhD 01-1

  4. Weak Form Efficiency • Random-walk model: • Pt -Pt-1 = Pt-1 * (Expected return) + Random error • Expected value (Random error) = 0 • Random error of period t unrelated to random component of any past period • Implication: • Expected value (Pt) = Pt-1 * (1 + Expected return) • Technical analysis: useless • Empirical evidence: serial correlation • Correlation coefficient between current return and some past return • Serial correlation = Cor (Rt, Rt-s) PhD 01-1

  5. S&P500 Daily returns PhD 01-1

  6. Semi-strong Form Efficiency • Prices reflect all publicly available information • Empirical evidence: Event studies • Test whether the release of information influences returns and when this influence takes place. • Abnormal return AR : ARt = Rt - Rmt • Cumulative abnormal return: • CARt = ARt0 + ARt0+1 + ARt0+2 +... + ARt0+1 PhD 01-1

  7. Efficient Market Theory Announcement Date PhD 01-1

  8. Example: How stock splits affect value -29 0 30 Source: Fama, Fisher, Jensen & Roll

  9. Event Studies: Dividend Omissions Efficient market response to “bad news” S.H. Szewczyk, G.P. Tsetsekos, and Z. Santout “Do Dividend Omissions Signal Future Earnings or Past Earnings?” Journal of Investing (Spring 1997) PhD 01-1

  10. Strong-form Efficiency • How do professional portfolio managers perform? • Jensen 1969: Mutual funds do not generate abnormal returns • Rfund - Rf =  + (RM - Rf) • Insider trading • Insiders do seem to generate abnormal returns • (should cover their information acquisition activities) PhD 01-1

  11. What moves the market • Who knows? • Lot of noise: • 1985-1990: 120 days with | DJ| > 5% • 28 cases (1/4) identified with specific event (Siegel Stocks for the Long Run Irwin 1994 p 184) • Orange juice futures (Roll 1984) • 90% of the day-to-day variability cannot explained by fundamentals • Pity financial journalists! PhD 01-1

  12. Trading Is Hazardous to Your Wealth(Barber and Odean Journal of Finance April 2000) • Sample: trading activity of 78,000 households 1991-1997 • Main conclusions: 1. Average household underperforms benchmark by 1.1% annually 2. Trading reduces net annualized mean returns Infrequent traders: 18.5% Frequent traders: 11.4% 3. Households trade frequently (75% annual turnover) 4. Trading costs are high: for average round-trip trade 4% (Commissions 3%, bid-ask spread 1%) PhD 01-1

  13. US Equity Mutual Funds 1982-1991(Malkiel, Journal of Finance June 1995) • Average Annual Return • Capital appreciation funds 16.32% • Growth funds 15.81% • Small company growth funds 13.46% • Growth and income funds 15.97% • Equity income funds 15.66% • S&P 500 Index 17.52% • Average deviation from benchmark -3.20% (risk adjusted) PhD 01-1

  14.  : Excess Return • Excess return = Average return - Risk adjusted expected return Return Expected return Average return  Risk Risk PhD 01-1

  15. Jensen 1968 - Distribution of “t” values for “”115 mutual funds 1955-1964 Not significantly different from 0 PhD 01-1

  16. US Mutual FundsConsistency of Investment Result Successive Period Performance Initial Period Performance Top Half Bottom Half Goetzmann and Ibbotson (1976-1985) Top Half 62.0% 38.0% Bottom Half 36.6% 63.4% Malkiel, (1970s) Top Half 65.1% 34.9% Bottom Half 35.5% 64.5% Malkiel, (1980s) Top Half 51.7% 48.3% Bottom Half 47.5% 52.5% Source: Bodie, Kane, Marcus Investments 4th ed. McGraw Hill 1999 (p.118) PhD 01-1

  17. Decomposition of Mutual Fund Returns(Wermers Journal of Finance August 2000) • Sample: 1,758 funds 1976-1994 • Benchmark 14.8% +1% • Gross return 15.8% • Expense ratio 0.8% • Transaction costs 0.8% • Non stock holdings 0.4% • Net Return 13.8% Funds outperform benchmark Stock picking +0.75% No timing ability Deviation from benchmark +0.55% Not enough to cover costs PhD 01-1

  18. Performance evaluation

  19. Introduction • Complicated subject • Theoretically correct measures are difficult to construct • Different statistics or measures are appropriate for different types of investment decisions or portfolios • Many industry and academic measures are different • The nature of active management leads to measurement problems PhD 01-1

  20. Dollar- and Time-Weighted Returns Dollar-weighted returns • Internal rate of return considering the cash flow from or to investment • Returns are weighted by the amount invested in each stock Time-weighted returns • Not weighted by investment amount • Equal weighting PhD 01-1

  21. Text Example of Multiperiod Returns PeriodAction 0 Purchase 1 share at $50 1 Purchase 1 share at $53 Stock pays a dividend of $2 per share 2 Stock pays a dividend of $2 per share Stock is sold at $108 per share PhD 01-1

  22. Dollar-Weighted Return Period Cash Flow 0 -50 share purchase 1 +2 dividend -53 share purchase 2 +4 dividend + 108 shares sold Internal Rate of Return: PhD 01-1

  23. Time-Weighted Return Simple Average Return: (10% + 5.66%) / 2 = 7.83% PhD 01-1

  24. Averaging Returns Arithmetic Mean: Text Example Average: (.10 + .0566) / 2 = 7.81% Geometric Mean: Text Example Average: [ (1.1) (1.0566) ]1/2 - 1 = 7.83% PhD 01-1

  25. Comparison of Geometric and Arithmetic Means • Past Performance - generally the geometric mean is preferable to arithmetic • Predicting Future Returns- generally the arithmetic average is preferable to geometric • Geometric has downward bias PhD 01-1

  26. Abnormal Performance What is abnormal? Abnormal performance is measured: • Benchmark portfolio • Market adjusted • Market model / index model adjusted • Reward to risk measures such as the Sharpe Measure: E (rp-rf) / p PhD 01-1

  27. Factors That Lead to Abnormal Performance • Market timing • Superior selection • Sectors or industries • Individual companies PhD 01-1

  28. rp = Average return on the portfolio • rf = Average risk free rate = Standard deviation of portfolio return Risk Adjusted Performance: Sharpe 1) Sharpe Index P PhD 01-1

  29. M2 Measure • Developed by Modigliani and Modigliani • Equates the volatility of the managed portfolio with the market by creating a hypothetical portfolio made up of T-bills and the managed portfolio • If the risk is lower than the market, leverage is used and the hypothetical portfolio is compared to the market PhD 01-1

  30. M2 Measure: Example Managed Portfolio: return = 35% standard deviation = 42% Market Portfolio: return = 28% standard deviation = 30% T-bill return = 6% Hypothetical Portfolio: 30/42 = .714 in P (1-.714) or .286 in T-bills (.714) (.35) + (.286) (.06) = 26.7% Since this return is less than the market, the managed portfolio underperformed PhD 01-1

  31. rp = Average return on the portfolio • rf = Average risk free rate • ßp = Weighted average ß for portfolio p Risk Adjusted Performance: Treynor 2) Treynor Measure PhD 01-1

  32. Risk Adjusted Performance: Jensen 3) Jensen’s Measure p= Alpha for the portfolio rp= Average return on the portfolio ßp= Weighted average Beta rf = Average risk free rate rm = Avg. return on market index port. PhD 01-1

  33. Appraisal Ratio Appraisal Ratio = ap / s(ep) Appraisal Ratio divides the alpha of the portfolio by the nonsystematic risk Nonsystematic risk could, in theory, be eliminated by diversification PhD 01-1

  34. Which Measure is Appropriate? It depends on investment assumptions 1) If the portfolio represents the entire investment for an individual, Sharpe Index compared to the Sharpe Index for the market. 2) If many alternatives are possible, use the Jensen or the Treynor measure The Treynor measure is more complete because it adjusts for risk PhD 01-1

  35. Limitations • Assumptions underlying measures limit their usefulness • When the portfolio is being actively managed, basic stability requirements are not met • Practitioners often use benchmark portfolio comparisons to measure performance PhD 01-1

  36. Market Timing Adjusting portfolio for up and down movements in the market • Low Market Return - low ßeta • High Market Return - high ßeta PhD 01-1

  37. rp - rf * * * * * * * * * * * * * * * * * * * * rm - rf * * * Steadily Increasing the Beta Example of Market Timing PhD 01-1

  38. Performance Attribution • Decomposing overall performance into components • Components are related to specific elements of performance • Example components • Broad Allocation • Industry • Security Choice • Up and Down Markets PhD 01-1

  39. Process of Attributing Performance to Components Set up a ‘Benchmark’ or ‘Bogey’ portfolio • Use indexes for each component • Use target weight structure PhD 01-1

  40. Process of Attributing Performance to Components • Calculate the return on the ‘Bogey’ and on the managed portfolio • Explain the difference in return based on component weights or selection • Summarize the performance differences into appropriate categories PhD 01-1

  41. Formula for Attribution Where B is the bogey portfolio and p is the managed portfolio PhD 01-1

  42. Contributions for Performance Contribution for asset allocation (wpi - wBi) rBi + Contribution for security selection wpi (rpi - rBi) = Total Contribution from asset class wpirpi -wBirBi PhD 01-1

  43. Complications to Measuring Performance • Two major problems • Need many observations even when portfolio mean and variance are constant • Active management leads to shifts in parameters making measurement more difficult • To measure well • You need a lot of short intervals • For each period you need to specify the makeup of the portfolio PhD 01-1

  44. Theory of asset pricing under certainty PhD 01-1

  45. Passive Portfolio Management Professor André Farber Solvay Business School Université Libre de Bruxelles

  46. Academic Foundations of Passive Investment • Portfolio Theory (Markowitz 1952) • Benefits of diversification • Capital Asset Pricing Model (Sharpe, Lintner) • Relationship between expected return and risk • Market Efficiency (Fama 1970) • Stock prices reflect all available information. • Mutual Fund Performance (Jensen 1968) • Professionally managed portfolio seem unable to make consistent abnormal returns PhD 01-1

  47. Portfolio characteristics expected return risk (standard deviation) Risk determined by covariances Efficient frontier If riskless asset: one optimal portfolio Expected return Portfolio Theory Risk PhD 01-1

  48. Capital Asset Pricing Model • Equilibrium model, optimal portfolio = market portfolio • Risk of individual security = beta (systematic risk) • Risk - expected return relationship E(r) = Risk-free rate + Market risk premium x Beta Expected return E(rmarket) Beta 1 PhD 01-1

  49. Efficient Market Hypothesis (EMH) • Strong version: “Security prices fully reflect all available information” • Weaker version: “Prices reflect information to the point where the marginal benefit of acting on information (the profit to be made) do not exceed the marginal costs” (Fama 1991) PhD 01-1

  50. EMH (continued) • A theoretical result: • Bachelier (1900) Théorie de la spéculation • Samuelson (1965) Proof that properly anticipated prices fluctuate randomly. • A vast empirical litterature • “weak-form tests”: do past returns provide information? • “semistrong-form”: is public information reflected in stock prices? • “strong-form tests”: do stock prices reflect private information? PhD 01-1

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