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Predicting cash holdings using supervised machine learning algorithms

Author

Listed:
  • Şirin Özlem

    (MEF University)

  • Omer Faruk Tan

    (Marmara University)

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.

Suggested Citation

  • Şirin Özlem & Omer Faruk Tan, 2022. "Predicting cash holdings using supervised machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-19, December.
    • Handle: RePEc:spr:fininn:v:8:y:2022:i:1:d:10.1186_s40854-022-00351-8
    DOI: 10.1186/s40854-022-00351-8
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    More about this item

    Keywords

    XGBoost; MLNN; Cash holdings; Turkey; Machine learning;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General

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