Purpose: The Group Method of Data Handling (GMDH) neural network has demonstrated good performance in data mining, prediction, and optimization. Scholars have used it to forecast stock and real estate investment trust (REIT) returns in some countries and region, but not in the United States (US)...

Predicting long-term equity market returns is of great importance for investors to strategically allocate their assets. We apply machine learning methods to forecast 10-year-ahead U.S. stock returns and compare the results to traditional Shiller regression-based forecasts more commonly used in...

We investigate the use of machine learning (ML) and other robust-estimation techniques in event studies conducted on single securities for the purpose of securities litigation. Single-firm event studies are widely used in civil litigation, with billions of dollars in settlements hinging on the...

This study employs a variety of machine learning models and a wide range of economic and financial variables to enhance the forecasting accuracy of the Korean won-U.S. dollar (KRW/USD) exchange rate and the U.S. and Korean stock market returns. We construct international asset allocation...