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Sum-of-the-Parts Revised: Economic Regimes and Flexible Probabilities

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  • Felix Haase

Abstract

Building on the success of Ferreira and Santa-Clara (2011) in separately forecasting the return components of the stock market, this paper examines the links between economic regimes and these components to predict the aggregate U.S. stock market. We propose a three-step methodology that we call the flexible regime approach. First, we estimate the regime dynamics of ten macro-financial variables using Markov-switching regressions. Second, we treat the regime filtering results from the Hamilton filter as views and test the predicted regime classification, the predicted regime probabilities, and the conditional and mixture densities as view generators. We use entropy pooling to re-weight the historical distribution and to derive posterior probabilities. Finally, we link these probabilities to the realized outcomes of earnings growth and changes in the price-earnings multiple to form the sum-of-the-parts forecast. Our results demonstrate significant predictability from a statistical and economic perspective. We emphasize the role of default spreads and interest rates in predicting earnings growth and stock market volatility and inflation in predicting multiple growth. Finally, our results suggest that the predictability of both return components varies over time and is affected by the business cycles. While earnings growth is more predictable during periods of expansion, forecasting multiple growth is more advantageous during recessions.

Suggested Citation

  • Felix Haase, 2024. "Sum-of-the-Parts Revised: Economic Regimes and Flexible Probabilities," Research Papers in Economics 2024-10, University of Trier, Department of Economics.
  • Handle: RePEc:trr:wpaper:202410
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
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    6. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    7. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    8. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
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    More about this item

    Keywords

    Economic Restrictions; Entropy Pooling; Flexible Probabilities; Markov-switching Models; Return Predictability; Stock Market Regimes; Sum-of-the Parts;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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