Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

- City Momentum Index by JLL 2069 views
- Price momentum evaporates by johnjordan576 123 views
- Thông tin cơ bản chỉ số VN30 - TaiTran by Tai Tran 2876 views
- Numeric investors by Rohan Khandelwal 2830 views
- Stock Analysis: Priceline by Kara Stessl 1506 views
- RMIT Vietnam - Risk Management - Op... by Tai Tran 1923 views

5,971 views

Published on

No Downloads

Total views

5,971

On SlideShare

0

From Embeds

0

Number of Embeds

834

Shares

0

Downloads

0

Comments

0

Likes

10

No embeds

No notes for slide

- 1. Stock Return ForecastTheory and Empirical Evidence<br />19/5/2010<br />Tai Tran<br />Phuong Ho<br />Khuong Nguyen<br />ZinyauHeng<br />
- 2. The need of forecast<br />Practitioners: portfolio managers, institutional investors, retail investors, financial advisors, traders, analysts<br />For trading strategies<br />
- 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. Discussion of Beta <br />
- 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. The estimation for the dynamics of betas (Ghysels & Jacquier)<br />
- 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. 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. 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. 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. 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. Multi-Factor Models<br />
- 13. Anomalies<br />
- 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. 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. 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. 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. Fama French three-factor framework 5/6<br />Book-to-market equity and slopes on HML proxy for relative distress<br />
- 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. Four-Factor Model<br />
- 21. Definition of Momentum<br />
- 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. The four-factor model<br />
- 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. 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. 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. Application of the Price momentum<br />1. Technical analyst<br />2. Fundamental analyst<br />
- 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. 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. Asset Growth<br />
- 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. 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. Data & Methodology<br />Asset growth deciles<br />
- 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. Finding II<br /><ul><li>Return premium of low growth stocks over high growth stocks is remarkably persistent over time.
- 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. 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. 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. Asset Growth - Key Findings<br /><ul><li>Strong negative relationship between the firm’s asset growth and stock returns
- 40. Larger explanatory power than other previously factors (i.e, size, prior returns, book-to market ratios, momentum…)
- 41. Asset growth effect is more important for small capitalization stocks</li></li></ul><li>Empirical Research<br />
- 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.
- 44.
- 45.
- 46.
- 47.
- 48.
- 49. Statistics<br />Momentum is very significant to forecast power<br />Beta is less significant in forecast power compared to actual return<br />
- 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. Historical, Contemporary and Future Research<br />
- 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.
- 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 <http://faculty.fuqua.duke.edu/~charvey/Teaching/BA453_2001/PDF/A1PDF.htm> <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 />

No public clipboards found for this slide

Be the first to comment