Predicting cash holdings using supervised machine learning algorithms

Şirin Özlem () and Omer Tan ()
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Şirin Özlem: MEF University

Financial Innovation, 2022, vol. 8, issue 1, 1-19

Abstract: Abstract This study predicts the cash holdings policy of Turkish firms, given the 20 selected features with machine learning algorithm methods. 211 listed firms in the Borsa Istanbul are analyzed over the period between 2006 and 2019. Multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), decision trees (DT), extreme gradient boosting algorithm (XGBoost) and multi-layer neural networks (MLNN) are used for prediction. Results reveal that MLR, KNN, and SVR provide high root mean square error (RMSE) and low R2 values. Meanwhile, more complex algorithms, such as DT and especially XGBoost, derive higher accuracy with a 0.73 R2 value. Therefore, using advanced machine learning algorithms, we may predict cash holdings considerably.

Keywords: XGBoost; MLNN; Cash holdings; Turkey; Machine learning (search for similar items in EconPapers)
JEL-codes: C38 C53 G30 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1186/s40854-022-00351-8

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