 Predicting stock splits using ensemble machine learning and SMOTE oversamplingAng Li, Mark Liu and Simon Sheather Pacific-Basin Finance Journal, 2023, vol. 78, issue C Abstract: This study predicts stock splits using two ensemble machine learning techniques: gradient boosting machines (GBMs) and random forests (RFs). The goal is to form implementable portfolios based on positive predictions to generate abnormal returns. Since splits are rare events, we use SMOTE oversampling to synthesize new observations of splits in the sample to improve predictions. When predicting stock splits in the next quarter, GBM and RF achieve area under the receiver operating characteristic curve (AUC) scores of around 0.86 and 0.87, respectively. GBM and RF predictions generate monthly five-factor alphas (Fama and French, 2015) of 0.26% and 0.95% among stocks in the smallest size quintile. Three important features for predicting stock splits in both ensemble ML methods are current price levels, the ratio of current price to the price at last split, and stock returns in the past twelve months. When predicting stock splits in the next year, GBMs generate monthly five-factor alphas of 0.38% among small stocks. Keywords: Stock splits; Ensemble machine learning; Gradient boosting machines; Random forests; SHAP feature importance; Hyperparameter tuning; SMOTE oversampling (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:78:y:2023:i:c:s0927538x23000148 DOI: 10.1016/j.pacfin.2023.101948 Access Statistics for this article Pacific-Basin Finance Journal is currently edited by K. Chan and S. Ghon Rhee More articles in Pacific-Basin Finance Journal from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.