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Forecasting stock market returns: The sum of the parts is more than the whole

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  • Ferreira, Miguel A.
  • Santa-Clara, Pedro
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    Abstract

    We propose forecasting separately the three components of stock market returns--the dividend-price ratio, earnings growth, and price-earnings ratio growth--the sum-of-the-parts (SOP) method. Our method exploits the different time series persistence of the components and obtains out-of-sample R-squares (compared with the historical mean) of more than 1.3% with monthly data and 13.4% with yearly data. This compares with typically negative R-squares obtained in a similar experiment with predictive regressions. The performance of the SOP method comes mainly from the dividend-price ratio and earnings growth components, and the robustness of the method is due to its low estimation error. An investor who timed the market using our method would have had a Sharpe ratio gain of 0.3.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Financial Economics.

    Volume (Year): 100 (2011)
    Issue (Month): 3 (June)
    Pages: 514-537

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    Handle: RePEc:eee:jfinec:v:100:y:2011:i:3:p:514-537

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    Web page: http://www.elsevier.com/locate/inca/505576

    Related research

    Keywords: Predictability Stock returns Equity premium Predictive regressions Trading strategies;

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    Cited by:
    1. Marcelo C. Medeiros & Eduardo F. Mendes, 2012. "Estimating High-Dimensional Time Series Models," CREATES Research Papers 2012-37, School of Economics and Management, University of Aarhus.
    2. Stephan Kessler & Bernd Scherer, 2013. "Momentum and macroeconomic state variables," Financial Markets and Portfolio Management, Springer, vol. 27(4), pages 335-363, December.
    3. Rangan Gupta & Shawkat Hammoudeh & Mampho P. Modise & Duc Khuong Nguyen, 2013. "Can Economic Uncertainty, Financial Stress and Consumer Sentiments Predict U.S. Equity Premium?," Working Papers 201351, University of Pretoria, Department of Economics.
    4. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2011. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Working Papers CoFie-02-2011, Sim Kee Boon Institute for Financial Economics.
    5. Zhu, Xiaoneng & Zhu, Jie, 2013. "Predicting stock returns: A regime-switching combination approach and economic links," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4120-4133.
    6. Lioui, Abraham & Poncet, Patrice, 2013. "Optimal benchmarking for active portfolio managers," European Journal of Operational Research, Elsevier, vol. 226(2), pages 268-276.
    7. repec:ipg:wpaper:20 is not listed on IDEAS
    8. Dangl, Thomas & Halling, Michael, 2012. "Predictive regressions with time-varying coefficients," Journal of Financial Economics, Elsevier, vol. 106(1), pages 157-181.
    9. Joscha Beckmann & Rainer Schüssler, 2014. "Forecasting Equity Premia using Bayesian Dynamic Model Averaging," CQE Working Papers 2914, Center for Quantitative Economics (CQE), University of Muenster.
    10. Minnick, Kristina & Rosenthal, Leonard, 2014. "Stealth compensation: Do CEOs increase their pay by influencing dividend policy?," Journal of Corporate Finance, Elsevier, vol. 25(C), pages 435-454.
    11. : Arie E. Gozluklu, 2012. "Inflation, Stock Market and Long-Term Investors: Real Effects of Changing Demographics," Working Papers wpn12-06, Warwick Business School, Finance Group.
    12. Wolff, Dominik & Bessler, Wolfgang & Opfer, Heiko, 2012. "Multi-Asset Portfolio Optimization and Out-of-Sample Performance: An Evaluation of Black-Litterman, Mean Variance and Naïve Diversification Approaches," Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62020, Verein für Socialpolitik / German Economic Association.
    13. Vilkovz, Grigory & Xiaox, Yan, 2013. "Option-implied information and predictability of extreme returns," SAFE Working Paper Series 5, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.

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