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 one-step 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, these add statistical and, in particular, economic value during recessions or in declining markets.
Matthias Neuenkirch is a professor of economics at Universität Trier. His research is focused on applied econometrics, internatitonal macroeconomics and finance, monetary policy, and political economy.The presentation will be held on-site and may be followed online via live-stream. To receive further information and event details, please sign up here.