Document Type

Article

Publication Date

11-6-2024

Abstract

This study examines the relationship between corporate cash holdings and firm performance within the IT industry, which is characterized by intense competition and rapid technological advancements. We propose an integrated framework that combines principal component analysis (PCA), machine learning (ML) algorithms, and Shapley additive explanation (SHAP) values to estimate and interpret model outcomes. Based on 21,051 corporate financial statement data items from 2004 and 2023, the empirical evidence supports an inverted U-shaped relationship between cash holdings and profitability, suggesting that holding either too little or too much cash is suboptimal. Among the tested models, the random forest demonstrates the highest explanatory power (R2) and the lowest prediction errors (RMSE), outperforming the traditional ordinary least squares (OLS) regression by explaining 47% more variance. Our findings provide practical implications for researchers and stakeholders interested in enhancing corporate risk management and performance.

This article was published Open Access through the CCU Libraries Open Access Publishing Fund. The article was first published in the Journal of Risk and Financial Management: https://doi.org/10.3390/jrfm18110625

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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