A1024
Title: Forecasting returns and optimizing global portfolios with machine learning: The Korean and U.S. stock markets
Authors: Dohyun Chun - Kangwon National University (Korea, South) [presenting]
Abstract: The purpose is to evaluate the performance of international asset allocation strategies based on predictions of foreign exchange rates and stock market returns. Various machine learning models and a wide range of economic and financial variables are utilized to predict the KRW-USD exchange rate and U.S. and Korean stock market returns. The findings suggest that machine learning models outperform benchmark models in predicting both the exchange rate and stock market returns. Furthermore, a machine learning-driven global portfolio that accounts for exchange rate fluctuations demonstrates enhanced performance. Empirical evidence substantiating the use of machine learning techniques is presented to forecast foreign exchange rates and construct a compelling global portfolio.
