Forecasting returns with machine learningand optimizing global portfolios:evidencefrom the Korean and U.S.stock markets  

作　　者：Dohyun Chun Jongho Kang Jihun Kim 

机构地区：[1]College of BusinessAdministration,KangwonNational University,Chuncheon,Korea [2]College of BusinessAdministration,ChonnamNational University,Gwangju,Korea [3]College of Humanitiesand Social SciencesConvergence,Yonsei University,Wonju,Korea

出　　处：《Financial Innovation》2024年第1期64-93,共30页金融创新(英文)

基　　金：supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2022S1A5A8055710).

摘　　要：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 portfolios based on these forecasts and evaluate their performance.Our analysis finds that the Elastic Net and LASSO regression models outperform traditional benchmark models in predicting exchange rate and stock market returns,as evidenced by their superior out-of-sample R-squared values.We also identify the key factors crucial for improving the accuracy of forecasting the KRW/USD exchange rate and stock market returns.Furthermore,a machine learning-driven global portfolio that accounts for exchange rate fluctuations demonstrated superior performance.Global portfolios constructed using LASSO(Sharpe ratio=3.45)and Elastic Net(Sharpe ratio=3.48)exhibit a notable performance advantage over traditional benchmark portfolios.This suggests that machine learning models outperform traditional global portfolio construction methods.

关 键 词：International asset allocation Foreign exchange rate Stock marketprediction Portfolio diversifcation Machine learning 

分 类 号：F22[经济管理—国民经济]