IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v75y2021icp300-314.html
   My bibliography  Save this article

Let’s take a smooth break: Stock return predictability revisited

Author

Listed:
  • Luo, Shikong
  • Yan, Xinyan
  • Yang, Haoyi

Abstract

The hypothesis that dividend-price ratio predicts stock returns has a long tradition, but the empirical evidence has been mixed. Lettau and Van Nieuwerburgh (2008) find that by taking account of discrete structural changes in the steady state can improve the in-sample fit of a univariate predictive regression, whereas we argue such changes could be smooth rather than abrupt. Using a modification of flexible Fourier form to approximate the smooth shifts in the steady state of dividend-price ratio, our smooth break approach outperforms Lettau and Van Nieuwerburgh’s sharp break approach both in sample and out of sample. Monte Carlo experiments also support our empirical findings and suggest that our approach tends to be more appropriate if the underlying true structural change is indeed smooth. Overall, our novel approach provides a simple, yet effective, way to improve return predictability with economic motivation.

Suggested Citation

  • Luo, Shikong & Yan, Xinyan & Yang, Haoyi, 2021. "Let’s take a smooth break: Stock return predictability revisited," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 300-314.
  • Handle: RePEc:eee:reveco:v:75:y:2021:i:c:p:300-314
    DOI: 10.1016/j.iref.2021.04.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S105905602100085X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.iref.2021.04.020?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
    2. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    3. Kwang Hun Choi & Chang‐Jin Kim & Cheolbeom Park, 2017. "Regime Shifts in Price‐Dividend Ratios and Expected Stock Returns: A Present‐Value Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(2-3), pages 417-441, March.
    4. Ľluboš Pástor & Robert F. Stambaugh, 2001. "The Equity Premium and Structural Breaks," Journal of Finance, American Finance Association, vol. 56(4), pages 1207-1239, August.
    5. John Y. Campbell, Robert J. Shiller, 1988. "The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors," The Review of Financial Studies, Society for Financial Studies, vol. 1(3), pages 195-228.
    6. Christopoulos, Dimitris K. & León-Ledesma, Miguel A., 2010. "Smooth breaks and non-linear mean reversion: Post-Bretton Woods real exchange rates," Journal of International Money and Finance, Elsevier, vol. 29(6), pages 1076-1093, October.
    7. Martin Lettau & Sydney Ludvigson, 2001. "Consumption, Aggregate Wealth, and Expected Stock Returns," Journal of Finance, American Finance Association, vol. 56(3), pages 815-849, June.
    8. Lettau, Martin & Ludvigson, Sydney C., 2005. "Expected returns and expected dividend growth," Journal of Financial Economics, Elsevier, vol. 76(3), pages 583-626, June.
    9. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
    10. Hammerschmid, Regina & Lohre, Harald, 2018. "Regime shifts and stock return predictability," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 138-160.
    11. Nazlioglu, Saban & Gupta, Rangan & Bouri, Elie, 2020. "Movements in international bond markets: The role of oil prices," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 47-58.
    12. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    13. Baillie, Richard T. & Morana, Claudio, 2009. "Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach," Journal of Economic Dynamics and Control, Elsevier, vol. 33(8), pages 1577-1592, August.
    14. Engel, Charles, 1994. "Can the Markov switching model forecast exchange rates?," Journal of International Economics, Elsevier, vol. 36(1-2), pages 151-165, February.
    15. Favero, Carlo A. & Gozluklu, Arie E. & Tamoni, Andrea, 2011. "Demographic Trends, the Dividend-Price Ratio, and the Predictability of Long-Run Stock Market Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 46(5), pages 1493-1520, October.
    16. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    17. Walter Enders & Yu Liu & Ruxandra Prodan, 2009. "Forecasting Series Containing Offsetting Breaks: Old School And New School Methods Of Forecasting Transnational Terrorism," Defence and Peace Economics, Taylor & Francis Journals, vol. 20(6), pages 441-463.
    18. Gallant, A. Ronald, 1981. "On the bias in flexible functional forms and an essentially unbiased form : The fourier flexible form," Journal of Econometrics, Elsevier, vol. 15(2), pages 211-245, February.
    19. Dangl, Thomas & Halling, Michael, 2012. "Predictive regressions with time-varying coefficients," Journal of Financial Economics, Elsevier, vol. 106(1), pages 157-181.
    20. Lewellen, Jonathan, 2004. "Predicting returns with financial ratios," Journal of Financial Economics, Elsevier, vol. 74(2), pages 209-235, November.
    21. Pettenuzzo, Davide & Timmermann, Allan, 2011. "Predictability of stock returns and asset allocation under structural breaks," Journal of Econometrics, Elsevier, vol. 164(1), pages 60-78, September.
    22. Walter Enders & Junsoo Lee, 2012. "A Unit Root Test Using a Fourier Series to Approximate Smooth Breaks," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(4), pages 574-599, August.
    23. Walter Enders & Matthew T. Holt, 2012. "Sharp Breaks or Smooth Shifts? an Investigation of the Evolution of Primary Commodity Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(3), pages 659-673.
    24. Enders, Walter & Li, Jing, 2015. "Trend-cycle decomposition allowing for multiple smooth structural changes in the trend of US real GDP," Journal of Macroeconomics, Elsevier, vol. 44(C), pages 71-81.
    25. Paye, Bradley S. & Timmermann, Allan, 2006. "Instability of return prediction models," Journal of Empirical Finance, Elsevier, vol. 13(3), pages 274-315, June.
    26. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
    27. Ralf Becker & Walter Enders & Junsoo Lee, 2006. "A Stationarity Test in the Presence of an Unknown Number of Smooth Breaks," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(3), pages 381-409, May.
    28. Cochrane, John H, 1991. "Production-Based Asset Pricing and the Link between Stock Returns and Economic Fluctuations," Journal of Finance, American Finance Association, vol. 46(1), pages 209-237, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    2. Demetrescu, Matei & Georgiev, Iliyan & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2022. "Testing for episodic predictability in stock returns," Journal of Econometrics, Elsevier, vol. 227(1), pages 85-113.
    3. Daniel Mantilla-García & Vijay Vaidyanathan, 2017. "Predicting stock returns in the presence of uncertain structural changes and sample noise," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(3), pages 357-391, August.
    4. Baetje, Fabian & Menkhoff, Lukas, 2016. "Equity premium prediction: Are economic and technical indicators unstable?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1193-1207.
    5. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
    6. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers wp677, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    7. Smith, Simon C., 2021. "International stock return predictability," International Review of Financial Analysis, Elsevier, vol. 78(C).
    8. , & Stein, Tobias, 2021. "Equity premium predictability over the business cycle," CEPR Discussion Papers 16357, C.E.P.R. Discussion Papers.
    9. Simon C. Smith, 2020. "Equity premium prediction and structural breaks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(3), pages 412-429, July.
    10. Carlos Carvalho & Jared D. Fisher & Davide Pettenuzzo, 2018. "Optimal Asset Allocation with Multivariate Bayesian Dynamic Linear Models," Working Papers 123, Brandeis University, Department of Economics and International Business School.
    11. Charles, Amelie & Darne, Olivier & Kim, Jae, 2016. "Stock Return Predictability: Evaluation based on Prediction Intervals," MPRA Paper 70143, University Library of Munich, Germany.
    12. Chen, Nan-Kuang & Chen, Shiu-Sheng & Chou, Yu-Hsi, 2017. "Further evidence on bear market predictability: The role of the external finance premium," International Review of Economics & Finance, Elsevier, vol. 50(C), pages 106-121.
    13. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
    14. Lof, Matthijs & Nyberg, Henri, 2024. "Discount rates and cash flows: A local projection approach," Journal of Banking & Finance, Elsevier, vol. 162(C).
    15. Paye, Bradley S. & Timmermann, Allan, 2006. "Instability of return prediction models," Journal of Empirical Finance, Elsevier, vol. 13(3), pages 274-315, June.
    16. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
    17. Della Corte, Pasquale & Sarno, Lucio & Valente, Giorgio, 2010. "A century of equity premium predictability and the consumption-wealth ratio: An international perspective," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 313-331, June.
    18. David I. Harvey & Stephen J. Leybourne & Robert Sollis & A.M. Robert Taylor, 2021. "Real‐time detection of regimes of predictability in the US equity premium," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 45-70, January.
    19. Haase, Felix & Neuenkirch, Matthias, 2023. "Predictability of bull and bear markets: A new look at forecasting stock market regimes (and returns) in the US," International Journal of Forecasting, Elsevier, vol. 39(2), pages 587-605.
    20. Wang, Yudong & Pan, Zhiyuan & Liu, Li & Wu, Chongfeng, 2019. "Oil price increases and the predictability of equity premium," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 43-58.

    More about this item

    Keywords

    Stock return predictability; Structural break; Dividend-price;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reveco:v:75:y:2021:i:c:p:300-314. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620165 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.