HIGHLIGHTS
- What: This study investigates the impacts of international diversification and exchange rate variations on Korean investors. The authors provide empirical evidence that machine_learning models outperform traditional forecasting models in predictive accuracy even when using only well-known financial and economic variables. By forecasting key financial components such as the exchange rate and stock market returns, the authors provide nuanced global investment strategies that bridge traditional finance practices with sophisticated data-driven approaches for academics and practitioners. The study showed that factors often overlooked in traditional models play a significant role in model predictions.
- Who: Dohyun . . .
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