Recent research suggests that machine learning models dominate traditional linear models in predicting cross-sectional stock returns. We confirm this finding when predicting one-month forward-looking returns based on a set of common stock characteristics, including predictors such as short-term...

Prediction of stock prices has become an important area of research in the field of financial analytics and has garnered a lot of attention among academicians. Drawing on the literature on application of econometric tools and also machine learning techniques, this paper presents a framework for...

We apply four machine learning methods to cross-sectional return prediction for hedge fund selection. We equip the forecast model with a set of idiosyncratic features, which are derived from historical returns of a hedge fund and capture a variety of fund-specific information. Evaluating the...

We use machine learning tools to analyze industry return predictability based on theinformation in lagged industry returns from across the entire economy. Controlling forpost-selection inference and multiple testing, we nd significant in-sample evidence ofindustry return predictability. Lagged...

Carry trade refers to a risky arbitrage in interest rate differentials between two currencies. Persistent excess carry trade returns pose a challenge to foreign exchange market efficiency. Using a data set of ten currencies between 1990 and 2017, we find: (i) a machine learning model, long...

The Prediction of a dynamic, volatile and unpredictable stock market has been a challenging issue for the researchers over the past few years. This paper discusses stock market related technical indicators, computing mathematical models , most preferred algorithms used in data science industries...