Machine learning approaches to stock return prediction: evidence from the JPX Tokyo stock exchange challenge

Predicting stock returns remains a central challenge in quantitative finance due to the noisy, non-stationary, and highly cross-sectional nature of financial markets. This paper investigates the use of machine learning methods for equity return prediction using data from the JPX Tokyo Stock Exchange Prediction Challenge. The dataset comprises daily price, volume, trading, and financial statement information for approximately 2,000 Japanese equities over the period 2017–2021, with performance evaluated through a ranking-based portfolio construction framework. We develop a modelling pipeline that emphasises data integrity, parsimonious feature engineering, and economically meaningful evaluation. Following minimal preprocessing focused on alignment and deduplication, features are constructed from adjusted prices, lagged returns, rolling window statistics, trading volume measures, and daily cross-sectional ranks. A set of linear regression models is employed as interpretable baselines, against which a gradient boosting decision tree model implemented via LightGBM is benchmarked. Model performance is assessed using both pointwise prediction diagnostics and a portfolio-based spread metric consistent with the competition objective, which measures the risk-adjusted return of a daily long–short strategy formed from predicted rankings. Results indicate that linear models struggle to capture the non-linear and interaction-driven structure of equity returns, while gradient boosting provides materially stronger performance in terms of portfolio level outcomes. Overall, the findings highlight the importance of cross-sectional evaluation and demonstrate that relatively simple, carefully constructed features combined with flexible tree-based models can deliver meaningful improvements in stock ranking tasks.