The Inefficient Market

1. The Inefficient Market What Pays Off and Why Part 1: What Pays Off Abridged Prentice Hall 1999 Visit our web-site at HaugenSystems.com

2. Background • The evolution of academic finance

3. 1930’s 40’s 50’s 60’s 70’s 80’s 90’s beyond The Evolution of Academic Finance The Old Finance The Old Finance Theme:Analysis of Financial Statements and the Nature of Financial Claims Paradigms: Security Analysis Uses and Rights of Financial Claims (Graham & Dodd)(Dewing) Foundation: Accounting and Law

4. Old Finance • Best investment strategy = • Stock-picking / value-investing approach, such as Warren Buffett uses

5. 1930’s 40’s 50’s 60’s 70’s 80’s 90’s beyond The Evolution of Academic Finance The Old Finance Bob goes to college Modern Finance Modern Finance Theme: Valuation Based on Rational Economic Behavior Paradigms: Optimization Irrelevance CAPM EMH (Markowitz)(Modigliani & Miller) (Sharpe, Lintner & Mossen) (Fama) Foundation: Financial Economics

6. Modern Finance • Optimal investment strategy = • Invest in index funds, try to match market as closely as possible at as low a cost as possible

7. 1930’s 40’s 50’s 60’s 70’s 80’s 90’s beyond The Evolution of Academic Finance The Old Finance Bob goes to college The New Finance Modern Finance The New Finance Theme: Inefficient Markets Paradigms: Inductive ad hoc Factor Models Behavioral Models Expected Return Risk (Haugen) (Chen, Roll & Ross) (Kahneman & Tversky) Foundation: Statistics, Econometrics, and Psychology

8. New Finance • Market is inefficient, but hard to beat nonetheless • Optimal investment approach = • Use Markowitz optimization to create optimal portfolios • APT Risk-factor model to model risk • Ad hoc inductive expected return factor model to model expected returns • Quantitative hedge fund, such as • Enhanced index fund • Long / short fund

9. Hedge Fund Risk/Return Profile Ten Years Ending 2/03

10. Rest of Book • Part I: Describes one approach to developing a quantitative hedge fund • Focus of this class • Part II: Discusses why that approach works • Chapters 9 – 12 won’t be covered in class, but can read for own pleasure

11. Part I:What Pays Off

12. Probability Distribution For Returns to a Portfolio Probability Variance of Return Possible Rates of Returns Expected Return

13. Risk Factor Models • The variance of stock returns can be split into two components: • Variance = systematic risk + diversifiable risk • Systematic risk is modeled using an APT-type risk-factor model • Measures extent to which stocks’ returns [jointly] move up and down over time • Estimated using time-series data • Diversifiable risk is reduced through optimal diversification

14. Expected Return Factor Models • Expected return factor models measure / predict the extent to which the stocks’ returns are different from each other within a given period of time.

15. Expected Return Factor Models • The factors in an expected return model represent the character of the companies. • They might include the history of their stock prices, its size, financial condition, cheapness or dearness of prices in the market, etc. • Unlike CAPM and APT, not only risk factors such as market beta or APT betas are included • Factor payoffs are estimated by relating individual stock returns to individual stock characteristics over the cross-section of a stock population (here the largest 3000 U.S. stocks).

16. Five Factor Families • Risk • Market and APT betas, TIE, debt ratio, etc., values and trends thereof • Liquidity • Market cap., price, trading volume, etc. • Price level • E/P, B/P, Sales/P, CF/P, Div/P • Profitability • Profit margin, ROE, ROA, earnings surprise, etc. • Price history (technical factors) • Excess return over past 1, 2, 3, 6, 12, 24, & 60 months

17. The Most Important Factors • The monthly slopes (payoffs) are averages over the period 1979 through mid 1986. • “T” statistics on the averages are computed, and the stocks are ranked by the absolute values of the “Ts”.

18. 1979/01 through 1986/07 through 1993/12 1986/06 Factor Mean Confidence Mean Confidence One-month excess return -0.97% 99% -0.72% 99% Twelve-month excess 0.52% 99% 0.52% 99% return Trading volume/market -0.35% 99% -0.20% 98% cap Two-month excess return -0.20% 99% -0.11% 99% Earnings to price 0.27% 99% 0.26% 99% Return on equity 0.24% 99% 0.13% 97% Book to price 0.35% 99% 0.39% 99% Trading volume trend -0.10% 99% -0.09% 99% Six-month excess return 0.24% 99% 0.19% 99% Cash flow to price 0.13% 99% 0.26% 99% Most Important Factors

19. The Most Important Factors • Among the factors that are significant (i.e., that can be used to distinguish between which companies will have higher returns and which will have lower returns) are: • A number of liquidity factors • Various fundamental factors, indicating value with growth • Technical factors, indicating short-term reversals and intermediate term momentum • Suggest that technical factors provide marginal value when used in conjunction with fundamental analysis • Notably, no CAPM or APT risk factors are included!

20. Projecting Expected Return • The components of expected return are obtained by multiplying the projected payoff to each factor (here the average of the past 12) by the stock’s current exposure to the factor. • Exposures are measured in standard deviations from the cross-sectional mean. • The individual components are then summed to obtain the aggregate expected return for the next period (here a month).

21. Factor Exposure Payoff Component Book\Price 1.5 S.D. x 20 B.P. = 30 B.P. Short-Term Reversal 1.0 S.D. x -10 B.P. = -10 B.P. . . . . . . . . . . . . . . . . . . . . . . . . Trading Volume -2 S.D. x -20 B.P. = 40 B.P. Total Excess Return 80 B.P. Estimating Expected Stock Returns

22. The Model’s Out-of-sample Predictive Power • The 3000 stocks are ranked by expected return and formed into deciles (decile 10 highest). • The performance of the deciles is observed in the next month. • The expected returns are re-estimated, and the deciles are re-ranked. • The process continues through 1993.

23. Logarithm of Cumulative Decile Performance

24. Realized Return 30% 20% 10% 0% -10% -20% -30% -40% 0 1 2 3 4 5 6 7 8 9 10 Decile Realized Return for 1984 by Decile (Y/X = 5.5%) Y X

25. Extension of Study to Other PeriodsNardin Baker • The same family of factors is used on a similar stock population. • Years before and after initial study period are examined to determine slopes and spreads between decile 1 and 10.

26. 100% 90% difference 80% slope 70% 60% 50% 40% 30% 20% 10% 0% 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1998 Years Slope and Spread

27. Decile Risk Characteristics • The characteristics reflect the character of the deciles over the period 1979-1993.

28. Fama-FrenchThree- Factor Model • Monthly decile returns are regressed on monthly differences in the returns to the following: • S&P 500 and T bills • The 30% of stocks that are smallest and largest • The 30% of stocks with highest book-to-price and the lowest.

29. Market Beta 1.25 1.2 1.15 1.1 1.05 Decile 1 1 2 3 4 5 6 7 8 9 10 0.95 Sensitivities (Betas) to Market Returns

30. Size Beta 0.5 0.4 0.3 0.2 0.1 0 Decile 1 2 3 4 5 6 7 8 9 10 Sensitivities (Betas) to Relative Performance of Small and Large Stocks

31. Value/Growth Beta 0.3 0.2 0.1 10 8 9 Decile 0 1 2 3 4 5 6 7 -0.1 -0.2 Sensitivities (Betas) to Relative Performance of Value and Growth Stocks

32. Fundamental Characteristics Averaged over all stocks in each decile and over all months (1979-83).

33. Risk

34. Interest Coverage Market Beta Debt to Equity Stock Volatility 50% 8 Coverage 6.63 41.42% 7 40% 33.22% 6 Volatility 5 30% 4 20% 3 1.76 Beta 1.21 2 1.00 10% 1.03 Debt to Equity 0.85 1 0% 0 1 2 3 4 5 6 8 9 10 7 Decile Decile Risk Characteristics

35. Liquidity

36. Stock Price Size Trading Volume $70 $1,100 $60.89 Trading Volume $60 $1,000 $1011 $50 $900 $42.42 Size $40 $800 $30.21 $30 $700 Price $14.93 $20 $600 $470 $10 $500 $0 $400 1 2 3 4 5 6 7 8 9 10 Decile Size and Liquidity Characteristics

37. Price History

38. Excess Return 30% 30.01% 12 months 20% 16.60% 6 months 10% 8.83% 3 months 2 months 0.09% 1.21% 0% -0.14% 1 month -1.80% -6.89% -10% -12.14% -15.74% -20% 1 2 3 4 5 6 7 8 9 10 Decile Technical History

39. Profitability

40. Profit Margin Return on Assets Return on Equity Asset Turnover Earnings Growth Asset Turnover 20% 120% 115% Return on Equity 15.39% 110% 7.86% Profit Margin 10% 6.50% Return on Assets 100% 0.95% Earnings Growth 0% 90% -10% 80% 1 2 3 4 5 6 7 8 9 10 Decile Current Profitability

41. Trends in Profitability

42. 5 Year Trailing Growth Asset Turnover 0.0% -0.13% Profit Margin -0.5% Return on Assets -0.95% Return on Equity -1.11% -1.0% -1.18% -1.5% 1 2 3 4 5 6 7 8 9 10 Decile Profitability Trends (Growth In)

43. Cheapness in Stock Price

44. Cash Flow-to-Price Earnings-to-Price Sales-to-Price Book-to-Price Dividend-to-Price 20% 214% 200% Sales-to-Price 207% 17% Cash Flow-to-Price Earnings-to-Price 150% 10% 10% 6% 3.69% Dividend-to-Price 2.19% 100% 81% 80% Book-to-Price 0% -1.55% 50% -10% 0% 1 2 3 4 5 6 7 8 9 10 Decile Price Level

45. Simulation of Investment Performance • Efficient portfolios are constructed quarterly, assuming 2% round-trip transactions costs within the Russell 1000 population. • Turnover controlled to 20% to 40% per annum. • Maximum stock weight is 5%. • No more that 3X S&P 500 cap weight in any stock. • Industry weight to within 3% of S&P 500. • Turnover controlled to within 20% to 40%.

46. Optimized Portfolios in the Russell 1000 Population 1979-1993 H 20% I 18% G 1000 Index 16% Annualized total return 14% 12% L 10% 12% 13% 14% 15% 16% 17% 18% Annualized volatility of return

47. Possible Sources of Bias • Survival bias: • Excluding firms that go inactive during test period. • Look-ahead bias: • Using data that was unavailable when you trade. • Bid-asked bounce: • If this month’s close is a bid, there is 1 chance in 4 that next and last month’s close will be at an asked, showing reversals. • Data snooping: • Using the results of prior studies as a guide and then testing with their data. • Data mining: • Spinning the computer.

48. Using the Ad Hoc Expected Return Factor Model Internationally • The most important factors across the 5 largest stock markets (1985-93). • Simulating investment performance: • Within countries, constraints are those stated previously. • Positions in countries are in accord with relative total market capitalization.

49. United Kingdom United States Germany France Japan Mean Confidence Mean Confidence Mean Confidence Mean Confidence Mean Confidence Level Level Level Level Level (Different (Different (Different (Different (Different From Zero) From Zero) From Zero) From Zero) One-month stock return From Zero) -0.32% 99% -0.26% 99% -0.33% 99% -0.22% 99% -0.39% 99% Book to price 0.14% 99% 0.16% 99% 0.18% 99% 0.12% 99% 0.12% 99% Twelve-month stock return 0.23% 99% 0.08% 99% 0.12% 99% 0.21% 99% 0.04% 86% Cash flow to price 0.18% 99% 0.08% 99% 0.15% 99% 0.09% 99% 0.05% 91% Earnings to price 0.16% 99% 0.04% 83% 0.13% 99% 0.08% 99% 0.05% 94% Sales to price 0.08% 99% 0.10% 99% 0.05% 99% 0.05% 91% 0.13% 99% Three-month stock return -0.01% 38% -0.14% 99% -0.08% 99% -0.08% 99% -0.26% 99% Debt to equity -0.06% 96% -0.06% 96% -0.09% 99% -0.10% 99% -0.01% 31% Variance of total return Residual variance -0.06% 94% -0.04% 83% -0.12% 99% -0.01% 38% -0.11% 99% Five-year stock return -0.08% 99% -0.04% 80% -0.09% 99% -0.03% 77% 0.00% 8% Return on equity -0.01% 31% -0.02% 51% -0.06% 94% -0.06% 96% -0.07% 98% 0.11% 99% 0.01% 31% 0.10% 99% 0.04% 80% 0.05% 92% Mean Payoffs and Confidence Probabilities for the Twelve Most Important Factors of the World (1985-93)

50. Optimization in France, Germany, U. K., Japan and across the five largest countries. 1985-1994 H l five largest countries (including U.S.) l France I H U. K. H l l l Franceindex I I l index of five largest countries u l G U. K. index l G n l G Japan l H l H I Germany l l I Germanyindex t l G l Japanindex l G 19.0% 17.0% 15.0% 13.0% 11.0% 9.0% 7.0% 5.0% Annualized total return 10% 12% 14% 16% 18% 20% 22% 24% Annualized volatility of return