Date of Award
Daniels College of Business
Forecasting international stock returns, Machine learning, Model averaging, Model uncertainty, Non-linearities, Parameter instability
Using a multi-level ensemble design, we forecast international stock market returns with a novel high-dimensional data set of aggregated cross sectional firm-level predictors. The method includes considerations of model uncertainty, parameter instability, model density and non-linearities with machine learning, shrinkage and model averaging. We provide evidence that it is important to systematically focus on all four sources of forecast failure, shed light on the sparsity/density debate in the stock return forecasting dialogue and contribute interesting findings on the efficacy dimensionality reduction with principal components analysis and partial least squares. The robustness of the approach is demonstrated through applications in four international markets, the long range Welch & Goyal stock returns data set, and US interest rate forecasting.
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Received from ProQuest
Mekelburg, Erik, "Problems with Machine Learning, High-Dimensional Data and Forecasting Stock Returns" (2023). Electronic Theses and Dissertations. 2218.
Available for download on Friday, August 01, 2025