Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can uncover subtle, contextual and non-linear relationships,...

Stock returns predictability has been a long-standing topic in the literature on financial economics. Developments in prediction technology have facilitated the wide use of machine learning techniques, which motivates our study of whether stock returns predictability can be improved using...

We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for time-varying expected returns and volatility are implemented with two Random Forest models. One model is...

This paper employs the post — Least Absolute Shrinkage and Selection Operator (post — LASSO) to make rolling 1-month--ahead currency excess return forecasts using all other currencies' lagged forward discounts as candidate predictors. The trading strategy of buying (selling) quintile...