Comparing the Performance of Four Regression Models in Predicting Stock Returns

Zimu Tang ()
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Zimu Tang: Capital University of Economics and Business, Department of International Business and Trade

A chapter in Proceedings of the 2024 2nd International Conference on Finance, Trade and Business Management (FTBM 2024), 2024, pp 20-27 from Springer

Abstract: Abstract In recent years, stock market investment has seen rapid growth, yet many investors may lack sufficient relevant knowledge. This article aims to help investors achieve higher returns by comparing the predictive results of several models. Using four regression models in ML algorithms, namely LightGBM, decision tree, XGBoost, and CatBoost, to predict the returns of 1500 Japanese stocks. By analyzing the RMSE and MAE, the errors are evaluated to assess the accuracy of the models. LightGBM and XGBoost are gradient boosting-based models offering high training speed and accuracy, suitable for large datasets. Decision trees are easy to interpret but prone to overfitting. CatBoost handles categorical variables seamlessly. Comparing RMSE and MAE, all models perform similarly, with XGBoost showing superior performance. This research contributes to stock market prediction by analyzing model strengths and weaknesses, offering insights for future research.

Keywords: Stock Returns Prediction; LGBM; Decision Tree; XGBoost; CatBoost (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-546-1_4

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DOI: 10.2991/978-94-6463-546-1_4

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