Predictability of Bull and Bear Markets: A New Look at Forecasting Stock Market Regimes (and Returns) in the US
73 Pages Posted: 16 Apr 2020 Last revised: 22 Nov 2021
Date Written: November 19, 2021
The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines dimensionality reduction, regime-switching models, and forecast combination to predict the S&P 500. First, we aggregate the weekly information of 146 popular macroeconomic and financial variables using different principal component analysis techniques. Second, we estimate Markov-switching models with time-varying transition probabilities using the principal components as predictors. Third, we pool the models in forecast clusters to hedge against model risk and to evaluate the usefulness of different specifications. Our weekly forecasts respond to regime changes in a timely manner to participate in recoveries or to prevent losses. This is also reflected in an improvement of risk-adjusted performance measures as compared to several benchmarks. However, when considering stock market returns, our forecasts do not outperform common benchmarks. Nevertheless, they do add statistical and, in particular, economic value during recessions or in declining markets.
Keywords: Forecast Combination, Markov-Switching Models, Shrinkage Methods, Stock Market Regimes, Time-Varying Transition Probabilities
JEL Classification: C53, G11, G17
Suggested Citation: Suggested Citation