Designing an Optimal Model for Predicting Stock Liquidity Using the Random Forest

Articles in Press

Document Type : Research Paper

Authors

1 Department of Accounting, Ke.C., Islamic Azad University, Kerman, Iran.

2 Department of Accounting, Shahid Bahonar university of Kerman, Kerman, Iran.

10.22103/jak.2025.24973.4156

Abstract

Objective: Stock markets are inherently volatile and risky due to their dynamic nature, financial complexities, and the limited understanding of price determination among many investors. This study aims to design an optimal predictive model for stock liquidity to provide practical support for investors with insufficient knowledge of these complex dynamics
 
Method: A hybrid approach combining data analysis, machine learning, and predictive modeling was employed. Data related to stock liquidity were collected and preprocessed from various scientific sources. The Random Forest algorithm was then applied to develop an optimal model for predicting stock liquidity.
 
Results: The findings demonstrate that the Random Forest algorithm can provide a relatively accurate and efficient model for predicting stock liquidity. Beta, rate of change, and volatility in stock returns (Rmc) were identified as the most influential factors affecting liquidity. The model achieved higher predictive accuracy compared to previous approaches, as evidenced by its low mean squared error (MSE).
 
Conclusion: The low MSE confirms the suitability of the Random Forest algorithm for predicting stock liquidity. The results of this study can assist investors and financial analysts in making more informed and precise decisions.

Keywords

Main Subjects

References

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Articles in Press, Accepted Manuscript
Available Online from 30 September 2025

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  • Receive Date: 13 May 2025
  • Revise Date: 20 September 2025
  • Accept Date: 21 September 2025

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