From Data to Decisions - Role of Machine Learning in Predicting Cash Holdings of Manufacturing Firms in Pakistan

Aug 6, 2026

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Effective cash management is critical for sustaining financial health and long-term viability. This study assesses how multiple machine learning algorithms predict cash holdings by identifying firm-specific determinants among Pakistan’s manufacturing companies, an emerging market context. Using secondary data, models include linear regression variants and ensemble and kernel-based approaches such as random forest, gradient boosting, support vector regression, and decision trees. Results show random forest and gradient boosting outperform others, while decision trees perform worst, supporting optimization for managers and guidance for policymakers.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages33

Reading Time1 h 23 min

AccessFree to download

Introduction

Corporate cash holdings and cash management importance

Pakistan’s manufacturing sector context and financial challenges

Motivation for predictive financial tools and machine learning

Journal of Finance and Accounting Research (JFAR) Volume 7 Issue 1, Spring 2025 ISSN(P) : 2617-2232, ISSN(E) : 2663-838X Homepage: [https://ojs.umt.edu.pk/index.php/jfar](https://ojs.umt.edu.pk/index.php/jfar) Article QR Title: From Data to Decisions: Role of Machine Learning in Predicting Cash Holdings of Manufacturing Firms in Pakistan Author (s): Atia Alam1, Nimra Ansar1, Syeda Fizza Abbas1, and Zia Ul Rehman2 Affiliation (s): 1Kinnaird College for Women, Lahore, Pakistan 2Government College University, Lahore, Pakistan DOI: [https://doi.org/10.32350/jfar.71.04](https://doi.org/10.32350/jfar.71.04) History: Received: February 03, 2024, Revised: April 25, 2025, Accepted: May 09, 2025, Published: June 25, 2025 Citation: Alam, A., Anser, N., Abbas, S. F., & Rehman, Z. U. (2025) . From data to decisions: Role of machine learning in predicting cash holdings of manufacturing firms in Pakistan. Journal of Finance and Accounting Research, 7(1), 78–110. [https://doi.org/10.32350/jfar.71.04](https://doi.org/10.32350/jfar.71.04) Copyright: © The Authors Licensing: This article is open access and is distributed under the terms of Creative Commons Attribution 4.0 International License Conflict of Author(s) declared no conflict of interest Interest: A publication of Department of Banking and Finance, Dr. Hasan Murad School of Management (HSM) University of Management and Technology, Lahore, Pakistan From Data to Decisions: Role of Machine Learning in Predicting Cash Holdings of Manufacturing Firms in Pakistan Atia Alam1∗, Nimra Ansar1, Syeda Fizza Abbas1, and Zia Ul Rehman2 1Kinnaird College for Women, Lahore, Pakistan 2Government College University, Lahore, Pakistan Abstract Effective cash management is essential to maintain a firm's financial health and sustainability. Hence, this study evaluates the prediction performance of various machine learning (ML) algorithms in identifying firm-specific determinants of cash holdings among the manufacturing firms of an emerging market, namely Pakistan. Using secondary data, the analysis employs ML techniques such as multiple linear regression, LASSO regression, ridge regression, elastic net regression, as well as random forest, gradient boosting, support vector regression, and decision tree models. The findings reveal that random forest and gradient boosting models outperformed others in predicting cash holdings, while the decision tree model exhibited the poorest performance. These insights are valuable for managers and decision-makers in optimizing cash retention, capital allocation, and investment planning. Additionally, policymakers can leverage these findings to develop policies that enhance financial resilience and foster growth in the manufacturing sector of Pakistan. Keyword: cash holdings, machine learning algorithms, manufacturing firms JEL Codes: C450, C53, D22 Introduction Corporate cash holdings have become an increasingly important topic in financial literature, particularly since the early 2000s when companies began to accumulate significant cash reserves to mitigate financial uncertainties. Effective cash management practices are crucial for a firm’s stability and liquidity, enabling it to finance its operations, avail investment opportunities, and avoid expensive external financing. Cash is a critical asset, allowing firms to navigate economic fluctuations and maintain operational continuity, even in uncertain conditions (Juliana & Budionno, ∗Corresponding Author: [atia.a1am@kinnaird.edu.pk](atia.a1am@kinnaird.edu.pk) 2024) . In 2022, US firms accumulated more than $0.7 trillion in cash reserves, highlighting the growing importance of strategic cash management in the current economic landscape (Ahn et al., 2024) Moreover, adequate cash holdings can provide liquidity to manage economic shifts and capitalize on growth prospects, which is vital for sustainable corporate growth. In Pakistan, the manufacturing sector is a co

FAQs

What is the main purpose of the study?

The study evaluates how different machine learning algorithms predict cash holdings by identifying firm-specific determinants for Pakistan’s manufacturing firms.

Which machine learning models performed best and worst?

Random forest and gradient boosting outperformed other models, while the decision tree model showed the poorest predictive performance.

How can the findings help stakeholders?

Managers and decision-makers can use the insights to improve cash retention, capital allocation, and investment planning, while policymakers can design policies that strengthen financial resilience in the manufacturing sector.

Aug 6, 2026

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