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Forecasting Stock Returns under Economic Constraints

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  • Pettenuzzo, Davide
  • Timmermann, Allan G
  • Valkanov, Rossen

Abstract

We propose a new approach to imposing economic constraints on time-series forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two types of constraints: Non-negative equity premia and bounds on the conditional Sharpe ratio, the latter of which incorporates timevarying volatility in the predictive regression framework. Empirically, we find that economic constraints systematically reduce uncertainty about model parameters, reduce the risk of selecting a poor forecasting model, and improve both statistical and economic measures of out-of-sample forecast performance. The Sharpe ratio constraint, in particular, results in considerable economic gains.

Suggested Citation

  • Pettenuzzo, Davide & Timmermann, Allan G & Valkanov, Rossen, 2013. "Forecasting Stock Returns under Economic Constraints," CEPR Discussion Papers 9377, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:9377
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    Cited by:

    1. Li, Jiahan & Tsiakas, Ilias, 2017. "Equity premium prediction: The role of economic and statistical constraints," Journal of Financial Markets, Elsevier, vol. 36(C), pages 56-75.
    2. Manuel Lukas & Eric Hillebrand, 2014. "Bagging Weak Predictors," CREATES Research Papers 2014-01, Department of Economics and Business Economics, Aarhus University.
    3. Davide Pettenuzzo & Francesco Ravazzolo, 2016. "Optimal Portfolio Choice Under Decision‐Based Model Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
    4. Faria, Gonçalo & Verona, Fabio, 2018. "Forecasting stock market returns by summing the frequency-decomposed parts," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 228-242.
    5. repec:eee:finana:v:52:y:2017:i:c:p:49-61 is not listed on IDEAS
    6. Davide Pettenuzzo & Antonio Gargano & Allan Timmermann, 2014. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," Working Papers 75, Brandeis University, Department of Economics and International Businesss School.
    7. Wang, Yudong & Liu, Li & Diao, Xundi & Wu, Chongfeng, 2015. "Forecasting the real prices of crude oil under economic and statistical constraints," Energy Economics, Elsevier, vol. 51(C), pages 599-608.
    8. Davide Pettenuzzo & Zhiyuan Pan & Yudong Wang, 2017. "Forecasting Stock Returns: A Predictor-Constrained Approach," Working Papers 116R, Brandeis University, Department of Economics and International Businesss School.
    9. Kruttli, Mathias S., 2016. "From Which Consumption-Based Asset Pricing Models Can Investors Profit? Evidence from Model-Based Priors," Finance and Economics Discussion Series 2016-027, Board of Governors of the Federal Reserve System (U.S.), revised 26 Sep 2016.
    10. repec:eee:finlet:v:22:y:2017:i:c:p:35-41 is not listed on IDEAS
    11. Afees A. Salisu & Raymond Swaray & Tirimisyu F. Oloko, 2017. "A multi-factor predictive model for oil-US stock nexus with persistence, endogeneity and conditional heteroscedasticity effects," Working Papers 024, Centre for Econometric and Allied Research, University of Ibadan.
    12. Gonçalo Faria & Fabio Verona, 2016. "Forecasting the equity risk premium with frequency-decomposed predictors," Working Papers de Economia (Economics Working Papers) 06, Católica Porto Business School, Universidade Católica Portuguesa.
    13. repec:eee:empfin:v:43:y:2017:i:c:p:159-184 is not listed on IDEAS
    14. Daniele Bianchi & Kenichiro McAlinn, 2018. "Large-Scale Dynamic Predictive Regressions," Papers 1803.06738, arXiv.org.
    15. Cotter, John & Eyiah-Donkor, Emmanuel & Potì, Valerio, 2017. "Predictability and diversification benefits of investing in commodity and currency futures," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 52-66.
    16. Jiawen Xu & Pierre Perron, 2015. "Forecasting in the presence of in and out of sample breaks," Boston University - Department of Economics - Working Papers Series wp2015-012, Boston University - Department of Economics.
    17. repec:eee:ecofin:v:44:y:2018:i:c:p:92-108 is not listed on IDEAS
    18. Rapach, David E. & Ringgenberg, Matthew C. & Zhou, Guofu, 2016. "Short interest and aggregate stock returns," Journal of Financial Economics, Elsevier, vol. 121(1), pages 46-65.
    19. 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.

    More about this item

    Keywords

    Bayesian analysis; Economic constraints; Sharpe Ratio; Stock return predictability;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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