# Predicting stock returns in the presence of uncertain structural changes and sample noise

- 186 Downloads

## Abstract

The predictive power of the dividend-price ratio has been the subject of intense scrutiny. Most studies on return predictability assume that predictor variables follow stationary processes with constant long-run means. Following recent evidence on the role of structural breaks in the dividend-price ratio mean, we propose an estimation method that explicitly incorporates uncertainty about the location and magnitude of structural breaks in the predictor that extracts the regime mean component of the dividend-price ratio. Adjusting for structural changes in the ratio’s mean and estimation error significantly improves predictive power of the dividend-price ratio as well as other standard predictors in sample and out of sample.

## Keywords

Bayesian methods Dividend-price ratio Return predictability Statistical shrinkage## JEL Classification

C58 G17 C11## Notes

### Acknowledgements

We thank an anonymous referee whose comments greatly improved the paper, Ravi Bansal, Ren’e Garcia, Abraham Lioui, Lionel Martellini, Gideon Ozik, and Raman Uppal for their valuable feedback and comments, Khanh-Linh Loth for her excellent research assistance, conference participants at the 3rd International Conference on Computational and Financial Econometrics (CFE’09), Limassol, Cyprus, and seminar participants at EDHEC-France for useful discussions and comments. A former version of this article is part of the authors’ Ph.D. Thesis at Edhec Business School.

## References

- Ang, A., Bekaert, G.: International asset allocation with regime shifts. Rev. Financ. Stud.
**15**(4), 1137 (2002)CrossRefGoogle Scholar - Ashley, R.: Beyond optimal forecasting. Working Paper, Virginia Polytechnic Institute and State University (2006)Google Scholar
- Bai, J., Perron, P.: Estimating and testing linear models with multiple structural changes. Econometrica.
**66**(1), 47–78 (1998)CrossRefGoogle Scholar - Bai, J., Perron, P.: Computation and analysis of multiple structural change models. J. Appl. Econ.
**18**, 1–22 (2003)CrossRefGoogle Scholar - Barry, D., Hartigan, J.: A Bayesian analysis for change point problems. J. Am. Stat. Assoc.
**88**, 309–319 (1993)Google Scholar - Binsbergen, V., Jules, H., Koijen, R.S.: Predictive regressions: a present-value approach. J. Financ.
**65**(4), 1439–1471 (2010)CrossRefGoogle Scholar - Bossaerts, P., Hillion, P.: Implementing statistical criteria to select return forecasting models: what do we learn? Rev. Financ. Stud.
**12**(2), 405–428 (1999)CrossRefGoogle Scholar - Campbell, J.Y.: Stock returns and the term structure. J. Financ. Econ.
**18**(2), 373–399 (1987)CrossRefGoogle Scholar - Campbell, J., Shiller, R.: The dividend-price ratio and expectations of future dividends and discount factors. Rev. Financ. Stud.
**1**(3), 195–228 (1988)CrossRefGoogle Scholar - Campbell, J.Y., Thompson, S.B.: Predicting excess stock returns out of sample: can anything beat the historical average? Rev. Financ. Stud.
**21**(4), 1509–1531 (2008)CrossRefGoogle Scholar - Cochrane, J.: Explaining the variance of price-dividend ratios. Rev. Financ. Stud.
**5**(2), 243–280 (1992)CrossRefGoogle Scholar - Cochrane, J.: The dog that did not bark: a defense of return predictability. Rev. Financ. Stud.
**21**(4), 1533 (2008)CrossRefGoogle Scholar - Connor, G.: Sensible return forecasting for portfolio management. Financ. Anal. J.
**53**(5), 44–51 (1997)CrossRefGoogle Scholar - Dangl, T., Halling, M.: Predictive regressions with time-varying coefficients. J. Financ. Econ.
**106**, 157–181 (2012)CrossRefGoogle Scholar - DiDonato, A., Morris Jr., A.: Algorithm 708: significant digit computation of the incomplete beta function ratios. ACM Trans. Math. Softw. (TOMS)
**18**(3), 360–373 (1992)CrossRefGoogle Scholar - Erdman, C., Emerson, J.: BCP: an R package for performing a Bayesian analysis of change point problems. J. Stat. Softw.
**23**, 1–13 (2007)CrossRefGoogle Scholar - Erdman, C., Emerson, J.: A fast Bayesian change point analysis for the segmentation of microarray data. Bioinformatics
**24**(19), 2143 (2008)CrossRefGoogle Scholar - Fama, E.F., French, K.R.: Dividend yields and expected stock returns. J. Financ. Econ.
**22**(1), 3–25 (1988)CrossRefGoogle Scholar - Fama, E.F., French, K.R.: Business conditions and expected returns on stocks and bonds. J. Financ. Econ.
**25**(1), 23–49 (1989)CrossRefGoogle Scholar - Ferreira, M.A., Santa-Clara, P.: Forecasting stock market returns: the sum of the parts is more than the whole. J. Financ. Econ.
**100**(3), 514–537 (2011)CrossRefGoogle Scholar - Ferson, W.E., Harvey, C.R.: The variation of economic risk premiums. J. Polit. Econ.
**99**, 385–415 (1991)CrossRefGoogle Scholar - Ferson, W.E., Sarkissian, S., Simin, T.: Spurious regressions in financial economics? J. Financ.
**58**(4), 1393–1414 (2003)CrossRefGoogle Scholar - Goyal, A., Welch, I.: Predicting the equity premium with dividend ratios. Manag. Sci.
**49**(5), 639–654 (2003)CrossRefGoogle Scholar - Goyal, A., Welch, I.: A comprehensive look at the empirical performance of equity premium prediction. Rev. Financ. Stud.
**21**(4), 1455 (2008)CrossRefGoogle Scholar - Hamilton, J.: A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: J. Econ. Soc.
**57**(2), 357–384 (1989)CrossRefGoogle Scholar - Henkel, S.J., Martin, J.S., Nardari, F.: Time-varying short-horizon predictability. J. Financ. Econ.
**99**(3), 560–580 (2011)CrossRefGoogle Scholar - Inoue, A., Kilian, L.: In-sample or out-of-sample tests of predictability: which one should we use? Econ. Rev.
**4**, 371 (2004)Google Scholar - Keim, D.B., Stambaugh, R.F.: Predicting returns in the stock and bond markets. J. Financ. Econ.
**17**(2), 357–390 (1986)CrossRefGoogle Scholar - Lacerda, F., Santa-Clara, P.: Forecasting dividend growth to better predict returns. Working Paper, Universidade Nova de Lisboa (2010)Google Scholar
- Lettau, M., Ludvigson, S.: Expected returns and expected dividend growth. J. Financ. Econ.
**76**, 583–626 (2005)CrossRefGoogle Scholar - Lettau, M., van Nieuwerburgh, S.: Reconciling the return predictability evidence. Rev. Financ. Stud.
**21**(4), 1607 (2008)CrossRefGoogle Scholar - Lewellen, J.: The time series relations among expected return, risk, and book-to-market. J. Financ. Econ.
**54**, 5–53 (1999)CrossRefGoogle Scholar - McCracken, M.: Asymptotics for out of sample tests of Granger causality. J. Econ.
**140**(2), 719–752 (2007)CrossRefGoogle Scholar - McMillan, D.: Revisiting dividend yield dynamics and returns predictability: evidence from a time-varying ESTR model. Q. Rev. Econ. Financ.
**49**(3), 870–883 (2009)CrossRefGoogle Scholar - Mincer, J.A., Zarnowitz, V.: The evaluation of economic forecasts. In: Mincer, J.A. (ed.) Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pp. 3–46. NBER, New York (1969)Google Scholar
- Nelson, C.C., Kim, M.J.: Predictable stock returns: the role of small sample bias. J. Financ.
**43**, 641–661 (1993)CrossRefGoogle Scholar - Pastor, L., Stambaugh, R.: The equity premium and structural breaks. J. Financ.
**56**(4), 1207–1239 (2001)CrossRefGoogle Scholar - Pastor, L., Stambaugh, R.: Predictive systems: living with imperfect predictors. J. Financ.
**64**(4), 1583–1628 (2009)CrossRefGoogle Scholar - Paye, B.S., Timmermann, A.: Instability of return prediction models. J. Empir. Financ.
**13**(3), 274–315 (2006)CrossRefGoogle Scholar - Pesaran, M.H., Timmermann, A.: Predictability of stock returns: robustness and economic significance. J. Financ.
**50**(4), 1201–1228 (1995)CrossRefGoogle Scholar - Pettenuzzo, D., Timmermann, A.: Predictability of stock returns and asset allocation under structural breaks. Department of Economics, University of California, San Diego (2005)Google Scholar
- Pettenuzzo, D., Timmermann, A., Valkanov, R.: Forecasting stock returns under economic constraints. Available at SSRN (2012)Google Scholar
- Rapach, D., Zhou, G.: Forecasting stock returns. Handb. Econ. Forecast.
**2**, 328–383 (2012)Google Scholar - Rapach, D., Strauss, J., Zhou, G.: Out-of-sample equity premium prediction: combination forecasts and links to the real economy. Rev. Financ. Stud.
**23**, 821–862 (2009)CrossRefGoogle Scholar - Spiegel, M.: Forecasting the equity premium: where we stand today. Rev. Financ. Stud.
**21**(4), 1453–1454 (2008)CrossRefGoogle Scholar - Stambaugh, R.F.: Bias in regressions with lagged stochastic regressors. Working Paper, University of Chicago (1986)Google Scholar
- Stambaugh, R.F.: Predictive regressions. J. Financ. Econ.
**54**, 375–421 (1999)CrossRefGoogle Scholar - Timmermann, A.: Elusive return predictability. Int. J. Forecast.
**24**(1), 1–18 (2008)CrossRefGoogle Scholar