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Stock Return Forecast - Theory and Empirical Evidence


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Stock Return Forecast - Theory and Empirical Evidence

  1. 1. Stock Return ForecastTheory and Empirical Evidence<br />19/5/2010<br />Tai Tran<br />Phuong Ho<br />Khuong Nguyen<br />ZinyauHeng<br />
  2. 2. The need of forecast<br />Practitioners: portfolio managers, institutional investors, retail investors, financial advisors, traders, analysts<br />For trading strategies<br />
  3. 3. Agenda<br />CAPM with Beta<br />Fama French three-factor model<br />Four-factor model with Momentum<br />Five-factor model with Asset growth<br />
  4. 4. Discussion of Beta <br />
  5. 5. The estimation for the dynamics of betas (Ghysels & Jacquier)<br />Empirical limitation of beta<br />Conditional betas depend on firm characteristics & state variables driving the opportunity set<br />Levered equity betas rise with financial leverage<br />
  6. 6. The estimation for the dynamics of betas (Ghysels & Jacquier)<br />
  7. 7. The estimation for the dynamics of betas (Ghysels & Jacquier)<br />Estimate  dynamics<br />Design an instrumental variables estimator of α, γ,  and the dynamics of the true unobserved <br />
  8. 8. Quarterly betas have strong autocorrelation on the order of 0.95; standard method much lower ~ 0.6<br />Variables don’t explain much of time series variation of portfolio quarterly betas.<br />Cannot use overlapping long-window filters to estimate the dynamics of β, but could predict future s effectively<br />Daily returns produce uniformly better beta filters than monthly<br />The estimation for the dynamics of betas (Ghysels & Jacquier) - Finding<br />
  9. 9. Estimation of expected return (Jan Bartholdy, Paula Peare)<br />Instruments for estimating beta: the return on a market index and the return on the stock, over the estimation period<br />Simple OLS regression<br />Finding: <br />5 years of monthly data and an equal-weighted index provide the best estimate. <br />Performance of the model is very poor<br />Explains on average 3% of difference in returns<br />
  10. 10. Cross-sectional tests of the CAPM (Grauer, Janmaat)<br />Alleviate problem of reduced beta spread in cross-sectional tests of CAPM<br />Repackage the data with zero-weight portfolios<br />When CAPM is true<br />Simulation shows average values of the intercept and slope converge to their true values more rapidly<br />R2 and power of the tests increase<br />When the CAPM is false<br />Slope and intercept of the regression change<br />
  11. 11. Conclusion<br />Used widely by academics and practitioners<br />Simple model<br />May forecast effectively for the expected return<br />Limitation of beta<br />Just measure systematic risk<br />Require a large sample of stock ->significant expense<br />
  12. 12. Multi-Factor Models<br />
  13. 13. Anomalies<br />
  14. 14. Fama French three-factor framework 1/6<br />Chan and Chen (1991)<br />Huberman and Kandel (1987)<br />cov(returns,distress)<br />Covariation in returns on small stock<br />The need for multi-factor model to improve the CAPM model<br />
  15. 15. Fama French three-factor framework 2/6<br />Solution: three-factor model<br />Fama and French (1996)<br />Anomalies largely disappear in the three-factor model<br />Capture much of the cross-sectional variation in average stock returns<br />
  16. 16. Fama French three-factor framework 3/6<br />Market premium: excess return on a broad market portfolio<br />Size premium (SMB - small minus big): difference between the return on a portfolio of small stocks and the return on a portfolio of large stocks<br />Value premium (HML - high minus low): difference between the return on a portfolio of high book-to-market stocks and the return on a portfolio of low book-to-market stocks<br />
  17. 17. Fama French three-factor framework 4/6<br />The Fama French model is the time series multivariate regression<br />E(RM) - Rf, E(SMB), and E(HML) are expected premiums, and the factor sensitivities or loadings<br />i, si, hi areslopes in the time-series regression<br />i is the error term of the formula<br />
  18. 18. Fama French three-factor framework 5/6<br />Book-to-market equity and slopes on HML proxy for relative distress<br />
  19. 19. Fama French three-factor framework (Bartholdy, Peare) 6/6<br />Estimate for beta for each factor, using simple OLS regression, 5 years of monthly data<br />Findings:<br />Fama French model is at best able to explain, on average, 5% of differences in returns on individual stocks, independent of the index used<br />Small gain in explanatory power of Fama French probably does not justify extra work for including two additional factors (size premium & value premium)<br />
  20. 20. Four-Factor Model<br />
  21. 21. Definition of Momentum<br />
  22. 22. Price Momentum formula<br />M: Momentum<br />Pt: Closing price in the current period<br />Pt-n: Closing price N periods ago<br />Drawback: the formula is not normalized<br />
  23. 23. The four-factor model<br />
  24. 24. Trading strategy<br />Take advantage of human behavior e.g. "herding" mentality, overreaction to news<br />Employing price momentum means taking additional risk<br />Higher return should be rewarded<br />Jegadeesh N. and Titman S., 1993<br />
  25. 25. Fric, P., 'Use of Momentum in trading across Industry Sectors'<br />Model 1: 4-quartile model. Best performers<br />1m lookback & 1m holding period<br />12m lookback & 6m holding<br />Model 2: 20 fractile model (long 5% top & short 5% bottom)<br />Select these two portfolios for observation<br />
  26. 26. Model 3: Sustainable Return Model - quintile (5 fractiles)<br />Finding: momentum is not sustainable<br />The two selected portfolios still prevail<br />Fric, P., 'Use of Momentum in trading across Industry Sectors'<br />
  27. 27. Application of the Price momentum<br />1. Technical analyst<br />2. Fundamental analyst<br />
  28. 28. Technical analyst<br />Buy past winners<br />Sell past losers<br />Technical analysts prefer<br />Past price performance<br />Historical market information<br />Two main usage: rate of change & moving average<br />(Reeves 2008)<br />
  29. 29. Fundamental Analysis<br />Contrarian investing strategy<br />Take the opposite approach<br />For example, a fundamental analyst might conclude: A stock that has been rising may now be overvalued, while a stock that has been falling may be undervalued.<br />Use Relative Strength (Reeves 2008)<br />
  30. 30. Asset Growth<br />
  31. 31. Asset Growth<br />Cooper, M. J., Gulen, H. & Schill, M. J. 2009. The Asset Growth Effect in Stock Returns<br />Lipson, M. L., Mortal, S. & Schill, M. J. 2008. What Explains the Asset Growth Effect in Stock Returns?<br />
  32. 32. Data & Methodology<br />Broad sample of US stocks over past 40 years (from 1968 to 2007)<br />Stock returns: NYSE, Amex and NASDAQ<br />Total assets data: CRSP and Compusat<br />Sort stocks in year t+1 based on the asset growth rate in year t defined as: <br />
  33. 33. Data & Methodology<br />Asset growth deciles<br />
  34. 34. Finding I<br />Strong negative relationship between the asset growth rate and the portfolio returns<br />Annual mean returns over 39 years<br />
  35. 35. Finding II<br /><ul><li>Return premium of low growth stocks over high growth stocks is remarkably persistent over time.
  36. 36. Firm asset growth rates are a strong predictor of future returns </li></li></ul><li>Finding III<br />Asset growth effect is more important for small capitalization stocks<br />T-stat for relationship between small size firm and asset growth rate is higher.<br />Regression results according to firm size<br />
  37. 37. Finding IV<br />Larger explanatory power with respect to other previously documented factors (i.e, size, prior returns, book-to market ratios, momentum…)<br />
  38. 38. Explanations<br />Risk-based explanation<br />More investments  more costs, exposure to more risks  less returns<br />Arbitrage-based explanation<br />Study suggests that asset growth effect does not arise from changes in risk but rather from mispricing.<br />
  39. 39. Asset Growth - Key Findings<br /><ul><li>Strong negative relationship between the firm’s asset growth and stock returns
  40. 40. Larger explanatory power than other previously factors (i.e, size, prior returns, book-to market ratios, momentum…)
  41. 41. Asset growth effect is more important for small capitalization stocks</li></li></ul><li>Empirical Research<br />
  42. 42. The Research<br />Purpose: extend Fama French, Momentum and Asset Growth research<br />Methodology: daily data of Coca-Cola (KO) in 2005<br />
  43. 43.
  44. 44.
  45. 45.
  46. 46.
  47. 47.
  48. 48.
  49. 49. Statistics<br />Momentum is very significant to forecast power<br />Beta is less significant in forecast power compared to actual return<br />
  50. 50. Key Findings<br />CAPM alone is somewhat limited<br />Three-factor model greatly improve forecasting power<br />Four-factor model and five-factor model: trade-off between forecasting power (R2 increases by 0.57%) and efforts<br />Limitation: selection bias (KO is a mature company with less volatility and little asset growth), beta rolling period<br />
  51. 51. Historical, Contemporary and Future Research<br />
  52. 52. Application of Forecast in practice<br />Forecast<br />Multivariate model<br />Market information, market movement, algorithm trading<br />Avoid pitfall of over-reliance on forecast<br />
  53. 53.
  54. 54. References<br />Bartholdy J & Peare P 2004, ‘Estimation of expected return: CAPM vs. Fama and French’, International Review of Financial Analysis, vol. 14, pp. 407-427, accessed 11 May 2010 from ScienceDirect.<br />Chan, K. C., and Chen, N., 1991, 'Structural and return characteristics of small and large firms', Journal of Finance 46, 1467-1484, 1991<br />Cooper, M. J., Gulen, H. & Schill, M. J. 2009, 'The Asset Growth Effect in Stock Returns' <br />Fama E. F., and French, K. R., ‘Multifactor Explanations of Asset Pricing Anomalies’, Journal of Finance, vol. LI, no.1, 1996<br />Fric, P., 'Use of Momentum in trading across Industry Sectors', accessed 15 May 2010, from <> <br />Grauer R R & Janmaat J A 2010, ‘Cross-sectional tests of the CAPM and Fama-French three-factor model’, Journal of banking & finance, vol.34, pp. 457-470, accessed 15 May 2010 from ScienceDirect.<br />Huberman G., Kandel S., 1987, ‘Mean-variance spanning’, Journal of Finance 42, 873-888, 1987<br />Jegadeesh, N., and Titman, S., 1993, ‘Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency’<br />Lipson, M. L., Mortal, S. & Schill, M. J. 2008, 'What Explains the Asset Growth Effect in Stock Returns?' <br />