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Predicting cash holdings using supervised machine learning algorithms

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info:eu-repo/semantics/openAccess

Date

2022

Author

Özlem, Şirin
Tan, Ömer Faruk

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Citation

Ozlem, S., & Tan, O. F. (May 2022). Predicting cash holdings using supervised machine learning algorithms. Financial Innovation, 8(1), pp.1-19. https://doi.org/10.1186/s40854-022-00351-8

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.

Volume

8

Issue

1

URI

https://doi.org/10.1186/s40854-022-00351-8
https://hdl.handle.net/20.500.11779/1780

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  • Araştırma Çıktıları, PubMed Koleksiyonu [72]
  • Araştırma Çıktıları, Scopus İndeksli Yayınlar Koleksiyonu [455]
  • Araştırma Çıktıları, WOS İndeksli Yayınlar Koleksiyonu [482]
  • MF, EM, Makale Koleksiyonu [34]



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