Machine Learning-Based Prediction of Dividend-Increase Behavior from Corporate Financial Indicators and Its Application to Portfolio Management

This study examines whether corporate information supplements management dividend forecasts in predicting dividend increases and whether the predictions improve stock selection in Japan. Using financial and market indicators, management forecasts, and dividend histories available at each prediction date, we develop machine learning models for Tokyo Stock Exchange firms with March fiscal year-ends, excluding banks. A rolling training, validation, and retraining procedure produces out-of-sample predictions for 2013–2023, with portfolio evaluation from June 2013 to May 2024. We compare forecast-based benchmarks and multivariate models. Then, we combine predicted probabilities with forecast dividend yields. Multivariate eXtreme Gradient Boosting (XGBoost) achieves a mean annual area under the receiver operating characteristic curve of 0.86, compared with 0.77 for XGBoost using only the forecast dividend yield change. The combined long–short strategy achieves an annualized Sharpe ratio of 1.26 and a positive alpha under the Fama–French three-factor model (t = 4.12, p < 0.001). Its arithmetic cumulative spread return remains positive under the examined transaction cost scenarios. These findings suggest that dividend increase predictions can complement forecast dividend yields in stock selection, whereas practical implementation requires consideration of borrowing costs and other trading constraints not separately assessed here.