Date of Award

2023

Document Type

Dissertation

Degree Name

Ph.D.

Organizational Unit

Daniels College of Business

First Advisor

Jack Strauss

Second Advisor

Ryan Elmore

Third Advisor

Shahram Amini

Keywords

Forecasting international stock returns, Machine learning, Model averaging, Model uncertainty, Non-linearities, Parameter instability

Abstract

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.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Erik Mekelburg

Provenance

Received from ProQuest

File Format

application/pdf

Language

en

File Size

107 pgs

Discipline

Finance, Statistics

Available for download on Friday, August 01, 2025



Share

COinS