Open Access Research Article

An Explainable Robust Optimization Framework for Corporate Cash and Working Capital Allocation

Authors

Mahdi Gholipour * Department of Financial Mathematics, Semnan University, Semnan, Iran.

https://doi.org/10.22105/masi.v3i4.113

Downloads

Abstract

Corporate financial management requires a recurring allocation of scarce internal funds among liquidity reserves, working capital, debt reduction, and strategic investment. These uses are usually evaluated in separate analytical routines, even though their value is jointly determined by financing frictions, cash-flow uncertainty, and downside exposure. This study develops an integrated Financial Resilience Index (FRI) and a scenario-based allocation model that combines expected financial benefit with Conditional Value-at-Risk (CVaR) of target shortfall. The framework also accommodates Explainable Artificial Intelligence (XAI) when machine-learning forecasts are used to estimate scenario payoffs: driver-level explanations can be generated with SHapley Additive exPlanations (SHAP) before the optimization stage, while the allocation model itself remains transparent. Because no observational dataset is assumed, the paper presents a fully disclosed synthetic decision experiment rather than empirical claims. In the illustration, a growth-first policy yields the highest expected benefit but produces severe downside losses, whereas a balanced policy reduces CVaR materially and a resilience-first policy lowers downside shortfall CVaR by approximately 97% relative to the growth-first solution while sacrificing about 20% of expected benefit. The framework therefore identifies an interpretable resilience frontier instead of a single mechanically “optimal” cash policy. The principal contribution is to connect working-capital efficiency, corporate liquidity, debt capacity, and strategic investment within one auditable financial-management architecture, allowing chief financial officers to choose an allocation consistent with explicit risk appetite and liquidity floors. The framework is intended as a decision-support model that can subsequently be calibrated and tested with firm-level accounting, treasury, and cash-flow data.

Keywords

Financial resilience Working capital management Corporate liquidity Robust optimization Conditional value-at-risk Explainable artificial intelligence

References (37)

[1] Modigliani, F., & Miller, M. H. (1958). The cost of capital, corporation finance and the theory of investment. The American economic review, 48(3), 261–297. https://www.aeaweb.org/aer/top20/48.3.261-297.pdf

[2] Myers, S. C., & Majluf, N. S. (1984). Corporate financing and investment decisions when firms have information that investors do not have. Journal of financial economics, 13(2), 187–221. https://doi.org/10.1016/0304-405X(84)90023-0

[3] Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers. The American economic review, 76(2), 323–329. https://www.jstor.org/stable/1818789

[4] Fazzari, S. M., Hubbard, R. G., & Petersen, B. C. (1988). Financing constraints and corporate investment. Brookings papers on economic activity, 1988(1), 141–206. https://doi.org/10.2307/2534426

[5] Whited, T. M. (1992). Debt, liquidity constraints, and corporate investment: Evidence from panel data. The journal of finance, 47(4), 1425–1460. https://doi.org/10.1111/j.1540-6261.1992.tb04664.x

[6] Almeida, H., Campello, M., & Weisbach, M. S. (2004). The cash flow sensitivity of cash. The journal of finance, 59(4), 1777–1804. https://doi.org/10.1111/j.1540-6261.2004.00679.x

[7] Campello, M., Graham, J. R., & Harvey, C. R. (2010). The real effects of financial constraints: Evidence from a financial crisis. Journal of financial economics, 97(3), 470–487. https://doi.org/10.1016/j.jfineco.2010.02.009

[8] Opler, T., Pinkowitz, L., Stulz, R., & Williamson, R. (1999). The determinants and implications of corporate cash holdings. Journal of financial economics, 52(1), 3–46. https://doi.org/10.1016/S0304-405X(99)00003-3

[9] Bates, T. W., Kahle, K. M., & Stulz, R. M. (2009). Why do U.S. firms hold so much more cash than they used to? The journal of finance, 64(5), 1985–2021. https://doi.org/10.1111/j.1540-6261.2009.01492.x

[10] Gamba, A., & Triantis, A. (2008). The value of financial flexibility. The journal of finance, 63(5), 2263–2296. https://doi.org/10.1111/j.1540-6261.2008.01397.x

[11] Harford, J., Klasa, S., & Maxwell, W. F. (2014). Refinancing risk and cash holdings. The journal of finance, 69(3), 975–1012. https://doi.org/10.1111/jofi.12133

[12] Denis, D. J., & Sibilkov, V. (2010). Financial constraints, investment, and the value of cash holdings. The review of financial studies, 23(1), 247–269. https://doi.org/10.1093/rfs/hhp031

[13] Deloof, M. (2003). Does working capital management affect profitability of Belgian firms? Journal of business finance & accounting, 30(3–4), 573–588. https://doi.org/10.1111/1468-5957.00008

[14] García-Teruel, P. J., & Martínez-Solano, P. (2007). Effects of working capital management on SME profitability. International journal of managerial finance, 3(2), 164–177. https://doi.org/10.1108/17439130710738718

[15] Baños-Caballero, S., García-Teruel, P. J., & Martínez-Solano, P. (2010). Working capital management in SMEs. Accounting & finance, 50(3), 511–527. https://doi.org/10.1111/j.1467-629X.2009.00331.x

[16] Baños-Caballero, S., García-Teruel, P. J., & Martínez-Solano, P. (2012). How does working capital management affect the profitability of Spanish SMEs? Small business economics, 39(2), 517–529. https://doi.org/10.1007/s11187-011-9317-8

[17] Baños-Caballero, S., García-Teruel, P. J., & Martínez-Solano, P. (2014). Working capital management, corporate performance, and financial constraints. Journal of business research, 67(3), 332–338. https://doi.org/10.1016/j.jbusres.2013.01.016

[18] Aktas, N., Croci, E., & Petmezas, D. (2015). Is working capital management value-enhancing? Evidence from firm performance and investments. Journal of corporate finance, 30, 98–113. https://doi.org/10.1016/j.jcorpfin.2014.12.008

[19] Kieschnick, R., Laplante, M., & Moussawi, R. (2013). Working capital management and shareholders’ wealth. Review of finance, 17(5), 1827–1852. https://doi.org/10.1093/rof/rfs043

[20] Enqvist, J., Graham, M., & Nikkinen, J. (2014). The impact of working capital management on firm profitability in different business cycles: Evidence from Finland. Research in international business and finance, 32, 36–49. https://doi.org/10.1016/j.ribaf.2014.03.005

[21] Baños-Caballero, S., García-Teruel, P. J., & Martínez-Solano, P. (2020). Net operating working capital and firm value: A cross-country analysis. BRQ business research quarterly, 23(3), 234–251. https://doi.org/10.1177/2340944420941464

[22] Zeidan, R., & Shapir, O. M. (2017). Cash conversion cycle and value-enhancing operations: Theory and evidence for a free lunch. Journal of corporate finance, 45, 203–219. https://doi.org/10.1016/j.jcorpfin.2017.04.014

[23] Petersen, M. A., & Rajan, R. G. (1997). Trade credit: Theories and evidence. The review of financial studies, 10(3), 661–691. https://doi.org/10.1093/rfs/10.3.661

[24] Fisman, R., & Love, I. (2003). Trade credit, financial intermediary development, and industry growth. The journal of finance, 58(1), 353–374. https://doi.org/10.1111/1540-6261.00527

[25] Molina, C. A., & Preve, L. A. (2012). An empirical analysis of the effect of financial distress on trade credit. Financial management, 41(1), 187–205. https://doi.org/10.1111/j.1755-053X.2012.01182.x

[26] Faulkender, M., & Wang, R. (2006). Corporate financial policy and the value of cash. The journal of finance, 61(4), 1957–1990. https://doi.org/10.1111/j.1540-6261.2006.00894.x

[27] Riddick, L. A., & Whited, T. M. (2009). The corporate propensity to save. The journal of finance, 64(4), 1729–1766. https://doi.org/10.1111/j.1540-6261.2009.01478.x

[28] Acharya, V. V., Almeida, H., & Campello, M. (2007). Is cash negative debt? A hedging perspective on corporate financial policies. Journal of financial intermediation, 16(4), 515–554. https://doi.org/10.1016/j.jfi.2007.04.001

[29] Brown, J. R., & Petersen, B. C. (2011). Cash holdings and R&D smoothing. Journal of corporate finance, 17(3), 694–709. https://doi.org/10.1016/j.jcorpfin.2010.01.003

[30] Markowitz, H. (1952). Portfolio selection. The journal of finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x

[31] Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. The journal of risk, 2(3), 21–41. https://doi.org/10.21314/JOR.2000.038

[32] Bertsimas, D., Brown, D. B., & Caramanis, C. (2011). Theory and applications of robust optimization. SIAM review, 53(3), 464–501. https://doi.org/10.1137/080734510

[33] Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The review of financial studies, 33(5), 2223–2273. https://doi.org/10.1093/rfs/hhaa009

[34] Heaton, J. B., Polson, N. G., & Witte, J. H. (2017). Deep learning for finance: Deep portfolios. Applied stochastic models in business and industry, 33(1), 3–12. https://doi.org/10.1002/asmb.2209

[35] Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European journal of operational research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054

[36] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135–1144). ACM. https://doi.org/10.1145/2939672.2939778

[37] Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st international conference on neural information processing systems (pp. 4768 – 47). Curran Associates Inc. https://doi.org/10.5555/3295222.3295230

Article Metrics

Published 2026-12-20
Issue Vol. 3 No. 4 (2026): Management Analytics and Social Insights (MASI)
Section Articles
Document Type Research Article
Views 233
Downloads 218

License

Copyright (c) 2026 Management Analytics and Social Insights

Creative Commons License

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

View License Details

Authors retain copyright and grant Management Analytics and Social Insights (MASI) the right of first publication. Articles are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, including for commercial purposes, provided that appropriate credit is given to the author(s) and the source, a link to the license is provided, and any changes are indicated. Authors may deposit and share the published version of their article in institutional, subject, or other repositories and on personal or institutional websites.

How to Cite

Gholipour, M. . (2026). An Explainable Robust Optimization Framework for Corporate Cash and Working Capital Allocation. Management Analytics and Social Insights, 3(4), 272-284. https://doi.org/10.22105/masi.v3i4.113

Download Citation

Similar Articles

Sepideh Nafei, Decision-Calibrated Human–AI Collaboration: A Governance Framework for Generative and Agentic AI in Management , Management Analytics and Social Insights: Vol. 3 No. 4 (2026): Management Analytics and Social Insights (MASI)
Rajabali Ghasempour, Mahdi Rahmani, Yeganehsadat Mansourian, Saba Mirzaeian Aghamahalli, Reviewing the Application of Data Analysis in Managing Venture Capital , Management Analytics and Social Insights: Vol. 1 No. 1 (2024): Management Analytics and Social Insights
Hsien-Ming Chen, Jiun-Shiung Lin, Transforming Big Data Analytics into Supply Chain Resilience through Dynamic Capabilities and Management Practice , Management Analytics and Social Insights: Vol. 3 No. 3 (2026): Management Analytics and Social Insights (MASI)
Abbas Shekari, Rajabali Ghasem Pour, From Predictive Analytics to Agentic Decision Intelligence: A Governance-Aware Framework for Human–AI Management Systems , Management Analytics and Social Insights: Vol. 3 No. 4 (2026): Management Analytics and Social Insights (MASI)
Nikta Hosseini Sefidabi, Risk-Gated Capital Allocation for Radiological Equipment Replacement Under Safety and Budget Uncertainty , Management Analytics and Social Insights: Vol. 3 No. 4 (2026): Management Analytics and Social Insights (MASI)
Naomi Maiguwa Gankino, Ayomide Koleosho, Oluwaseun Yetunde Adetutu Obidipe, Compliance as a Change Agent: Strategic Leadership in Regulatory Transformation , Management Analytics and Social Insights: Vol. 3 No. 3 (2026): Management Analytics and Social Insights (MASI)
Hadi Zavieh, Managing the Generative Artificial Intelligence Capability Trap , Management Analytics and Social Insights: Articles in Press
Mojdeh Nikpour, Mahdi Rahmani, Explaining Uneven AI Policy Absorption Among Italian Small and Medium-Sized Enterprises , Management Analytics and Social Insights: Articles in Press
Abhishek Yadav, Prioritizing Sustainable City Factors: A Generative AI-Driven Fermatean Fuzzy Prioritization Framework , Management Analytics and Social Insights: Vol. 2 No. 2 (2025): Management Analytics and Social Insights
Mohammadtaghi Azimi, Seyedeh Elnaz Azimi, Calibrated Human–Generative AI Decision Intelligence: A Governance-Aware Framework for Managerial Analytics , Management Analytics and Social Insights: Vol. 3 No. 4 (2026): Management Analytics and Social Insights (MASI)

1-10 of 69 Next

You may also start an advanced similarity search for this article.