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Financial Markets and Portfolio Management

, Volume 31, Issue 3, pp 357–391 | Cite as

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

  • Daniel Mantilla-GarcíaEmail author
  • Vijay Vaidyanathan
Article
  • 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

  1. Ang, A., Bekaert, G.: International asset allocation with regime shifts. Rev. Financ. Stud. 15(4), 1137 (2002)CrossRefGoogle Scholar
  2. Ashley, R.: Beyond optimal forecasting. Working Paper, Virginia Polytechnic Institute and State University (2006)Google Scholar
  3. Bai, J., Perron, P.: Estimating and testing linear models with multiple structural changes. Econometrica. 66(1), 47–78 (1998)CrossRefGoogle Scholar
  4. Bai, J., Perron, P.: Computation and analysis of multiple structural change models. J. Appl. Econ. 18, 1–22 (2003)CrossRefGoogle Scholar
  5. Barry, D., Hartigan, J.: A Bayesian analysis for change point problems. J. Am. Stat. Assoc. 88, 309–319 (1993)Google Scholar
  6. Binsbergen, V., Jules, H., Koijen, R.S.: Predictive regressions: a present-value approach. J. Financ. 65(4), 1439–1471 (2010)CrossRefGoogle Scholar
  7. 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
  8. Campbell, J.Y.: Stock returns and the term structure. J. Financ. Econ. 18(2), 373–399 (1987)CrossRefGoogle Scholar
  9. 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
  10. 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
  11. Cochrane, J.: Explaining the variance of price-dividend ratios. Rev. Financ. Stud. 5(2), 243–280 (1992)CrossRefGoogle Scholar
  12. Cochrane, J.: The dog that did not bark: a defense of return predictability. Rev. Financ. Stud. 21(4), 1533 (2008)CrossRefGoogle Scholar
  13. Connor, G.: Sensible return forecasting for portfolio management. Financ. Anal. J. 53(5), 44–51 (1997)CrossRefGoogle Scholar
  14. Dangl, T., Halling, M.: Predictive regressions with time-varying coefficients. J. Financ. Econ. 106, 157–181 (2012)CrossRefGoogle Scholar
  15. 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
  16. 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
  17. Erdman, C., Emerson, J.: A fast Bayesian change point analysis for the segmentation of microarray data. Bioinformatics 24(19), 2143 (2008)CrossRefGoogle Scholar
  18. Fama, E.F., French, K.R.: Dividend yields and expected stock returns. J. Financ. Econ. 22(1), 3–25 (1988)CrossRefGoogle Scholar
  19. 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
  20. 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
  21. Ferson, W.E., Harvey, C.R.: The variation of economic risk premiums. J. Polit. Econ. 99, 385–415 (1991)CrossRefGoogle Scholar
  22. Ferson, W.E., Sarkissian, S., Simin, T.: Spurious regressions in financial economics? J. Financ. 58(4), 1393–1414 (2003)CrossRefGoogle Scholar
  23. Goyal, A., Welch, I.: Predicting the equity premium with dividend ratios. Manag. Sci. 49(5), 639–654 (2003)CrossRefGoogle Scholar
  24. Goyal, A., Welch, I.: A comprehensive look at the empirical performance of equity premium prediction. Rev. Financ. Stud. 21(4), 1455 (2008)CrossRefGoogle Scholar
  25. 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
  26. Henkel, S.J., Martin, J.S., Nardari, F.: Time-varying short-horizon predictability. J. Financ. Econ. 99(3), 560–580 (2011)CrossRefGoogle Scholar
  27. 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
  28. Keim, D.B., Stambaugh, R.F.: Predicting returns in the stock and bond markets. J. Financ. Econ. 17(2), 357–390 (1986)CrossRefGoogle Scholar
  29. Lacerda, F., Santa-Clara, P.: Forecasting dividend growth to better predict returns. Working Paper, Universidade Nova de Lisboa (2010)Google Scholar
  30. Lettau, M., Ludvigson, S.: Expected returns and expected dividend growth. J. Financ. Econ. 76, 583–626 (2005)CrossRefGoogle Scholar
  31. Lettau, M., van Nieuwerburgh, S.: Reconciling the return predictability evidence. Rev. Financ. Stud. 21(4), 1607 (2008)CrossRefGoogle Scholar
  32. Lewellen, J.: The time series relations among expected return, risk, and book-to-market. J. Financ. Econ. 54, 5–53 (1999)CrossRefGoogle Scholar
  33. McCracken, M.: Asymptotics for out of sample tests of Granger causality. J. Econ. 140(2), 719–752 (2007)CrossRefGoogle Scholar
  34. 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
  35. 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
  36. Nelson, C.C., Kim, M.J.: Predictable stock returns: the role of small sample bias. J. Financ. 43, 641–661 (1993)CrossRefGoogle Scholar
  37. Pastor, L., Stambaugh, R.: The equity premium and structural breaks. J. Financ. 56(4), 1207–1239 (2001)CrossRefGoogle Scholar
  38. Pastor, L., Stambaugh, R.: Predictive systems: living with imperfect predictors. J. Financ. 64(4), 1583–1628 (2009)CrossRefGoogle Scholar
  39. Paye, B.S., Timmermann, A.: Instability of return prediction models. J. Empir. Financ. 13(3), 274–315 (2006)CrossRefGoogle Scholar
  40. Pesaran, M.H., Timmermann, A.: Predictability of stock returns: robustness and economic significance. J. Financ. 50(4), 1201–1228 (1995)CrossRefGoogle Scholar
  41. 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
  42. Pettenuzzo, D., Timmermann, A., Valkanov, R.: Forecasting stock returns under economic constraints. Available at SSRN (2012)Google Scholar
  43. Rapach, D., Zhou, G.: Forecasting stock returns. Handb. Econ. Forecast. 2, 328–383 (2012)Google Scholar
  44. 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
  45. Spiegel, M.: Forecasting the equity premium: where we stand today. Rev. Financ. Stud. 21(4), 1453–1454 (2008)CrossRefGoogle Scholar
  46. Stambaugh, R.F.: Bias in regressions with lagged stochastic regressors. Working Paper, University of Chicago (1986)Google Scholar
  47. Stambaugh, R.F.: Predictive regressions. J. Financ. Econ. 54, 375–421 (1999)CrossRefGoogle Scholar
  48. Timmermann, A.: Elusive return predictability. Int. J. Forecast. 24(1), 1–18 (2008)CrossRefGoogle Scholar

Copyright information

© Swiss Society for Financial Market Research 2017

Authors and Affiliations

  1. 1.Optimal Asset ManagementLos AltosUSA
  2. 2.Edhec-Risk InstituteNiceFrance

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