Developing a Liquidity Optimization Model to Strengthen Transparency and Operational Financial Performance

References

[1] Abass, O.S., Balogun, O. and Didi, P.U., 2019. A predictive analytics framework for optimizing preventive healthcare sales and engagement outcomes. IRE Journals, 2(11), pp.497-503.

[2] Aduwo, M.O. and Nwachukwu, P.S., 2019. Dynamic Capital Structure Optimization in Volatile Markets: A Simulation-Based Approach to Balancing Debt and Equity Under Uncertainty. IRE Journals, 3(2), pp.783-792.

[3] Aduwo, M.O., Akonobi, A.B. and Okpokwu, C.O., 2019. A Predictive HR Analytics Model Integrating Computing and Data Science to Optimize Workforce Productivity Globally. IRE Journals, 3(2), pp.798-807.

[4] Aduwo, M.O., Akonobi, A.B. and Okpokwu, C.O., 2019. Strategic human resource leadership model for driving growth, transformation, and innovation in emerging market economies. IRE Journals, 2(10), pp.476-485.

[5] Agrell, P.J. and Bogetoft, P., 2017. Regulatory benchmarking: Models, analyses and applications. Data Envelopment Analysis Journal, 3(1-2), pp.49-91.

[6] Ahmadvand, M., Pretschner, A., Ball, K. and Eyring, D., 2018, June. Integrity protection against insiders in microservice-based infrastructures: From threats to a security framework. In Federation of International Conferences on Software Technologies: Applications and Foundations (pp. 573-588). Cham: Springer International Publishing.

[7] Akomea-Agyin, K. and Asante, M., 2019. Analysis of security vulnerabilities in wired equivalent privacy (WEP). International Research Journal of Engineering and Technology, 6(1), pp.529-536.

[8] Akonobi, A.B. and Okpokwu, C.O., 2019. Designing a Customer-Centric Performance Model for Digital Lending Systems in Emerging Markets. IRE Journals, 3(4), pp.395-402.

[9] Albertsson, A. and Wendeberg, R., 2016. Emerging Innovations in the Swedish Financial System. Investigating potential disruption.

[10] Albrecht, W. and Steinrücke, M., 2017. Continuous-time production, distribution and financial planning with periodic liquidity balancing. Journal of Scheduling, 20(3), pp.219-237.

[11] Anichukwueze, C. C., Osuji, V. C., & Oguntegbe, E. E. (2019). Global Marketing Law and Consumer Protection Challenges: A Strategic Framework for Multinational Compliance. IRE Journals, 3(6), 325–333. ISSN 2456-8880

[12] Arkhipova, D. and Bozzoli, C., 2017. Digital capabilities. In CIOs and the digital transformation: A new leadership role (pp. 121-146). Cham: Springer International Publishing.

[13] Asante, M. and Akomea-Agyin, K., 2019. Analysis of security vulnerabilities in wifi-protected access pre-shared key.

[14] Atere, D., Shobande, A. O., & Toluwase, I. H. (2019). Framework for Designing Effective Corporate Restructuring Strategies to Optimize Liquidity and Working Capital. IRE Journals, 2(10), 555–562. ISSN 2456-8880.

[15] Bank, D., 2018. Interim Report as of June 30, 2018. Frankfurt am Main.

[16] BAYEROJU, O.F., SANUSI, A.N., QUEEN, Z. and NWOKEDIEGWU, S., 2019. Bio-Based Materials for Construction: A Global Review of Sustainable Infrastructure Practices.

[17] Calvard, T.S., 2016. Big data, organizational learning, and sensemaking: Theorizing interpretive challenges under conditions of dynamic complexity. Management learning, 47(1), pp.65-82.

[18] Carvalho, D., 2018. How do financing constraints affect firms’ equity volatility?. The Journal of Finance, 73(3), pp.1139-1182.

[19] Chang, V.Y. and Yang, M.H., 2016. Do insurers partially adjust their liquidity toward target levels? Evidence from the US property-liability insurance industry. Journal of Financial Studies, 24(1), pp.103-139.

[20] Cho, W., Ke, J.Y.F., Chu, E. and Han, C., 2018. An exploratory study of firms’ bargaining power and cash liquidity performance during the financial crisis. Journal of Supply Chain and Operations Management, 16(1), p.132.

[21] Ciulla, T., Annadorai, B. and Joshi, G., 2016. The Convergence of Collateral and Liquidity. Liquidity Risk Management: A Practitioner's Perspective, pp.81-104.

[22] Clark, T., Kabaker, M. and Sachs, L., 2018. Bank Capital, Phase 2, The Banks: Reviving the System.

[23] Degiannakis, S. and Floros, C., 2016. Modelling and forecasting high frequency financial data. Springer.

[24] Dempster, M.A.H., Kanniainen, J., Keane, J. and Vynckier, E. eds., 2018. High-performance computing in finance: problems, methods, and solutions. CRC Press.

[25] Diaz, D.D., Loreno, S., Ettl, G.J. and Davies, B., 2018. Tradeoffs in timber, carbon, and cash flow under alternative management systems for Douglas-Fir in the Pacific Northwest. Forests, 9(8), p.447.

[26] Dimitras, A., 2016. The contribution of management commentary index (Ma. Co. I) in annual banking reports (ABR) and the chronicle of the great Greek crisis. Theoretical Economics Letters.

[27] Eaton, M. and Bertoncin, C., 2018. State of Offices of Data Analytics (ODA) in the UK. London, UK: Nesta. https://media. nesta. org. uk/documents/State_of_Offices_of_Data_Analytics_ODA _in_the_UK_WEB_v5. pdf.

[28] Edem, D.B., 2017. Liquidity management and performance of deposit money banks in Nigeria (1986–2011): An investigation. International Journal of Economics, Finance and Management Sciences, 5(3), pp.146-161.

[29] Evans-Uzosike, I.O. and Okatta, C.G., 2019. Strategic human resource management: trends, theories, and practical implications. Iconic Research and Engineering Journals, 3(4), pp.264-270.

[30] Farounbi, B. O., Okafor, C. M., & Oguntegbe, E. E. (2019). Conceptual Model for Innovative Debt Structuring to Enhance Mid-Market Corporate Growth Stability. IRE Journals, 2(12), 451–458. ISSN 2456-8880

[31] Farounbi, B. O., Okafor, C. M., & Oguntegbe, E. E. (2019). Empirical Review of Risk-Adjusted Return Metrics in Private Credit Investment Portfolios. IRE Journals, 3(4), 494–501. ISSN 2456-8880

[32] Farounbi, B. O., Okafor, C. M., & Oguntegbe, E. E. (2019). Framework for Leveraging Private Debt Financing to Accelerate SME Development and Expansion. IRE Journals, 2(10), 540–547. ISSN 2456-8880

[33] Fleckenstein, M., Fellows, L. and Ferrante, K., 2018. Modern data strategy (pp. 63-76). New York, USA: Springer International Publishing.

[34] Franco, V.G.C., 2016. Supply chain finance as a value added service offered by a lead logistics provider. Academic Dissertation for Master Degree, Eindhoven University of Technology.

[35] Franco, V.G.C., 2016. Supply chain finance as a value added service offered by a lead logistics provider. Academic Dissertation for Master Degree, Eindhoven University of Technology.

[36] Galariotis, E., Germain, C. and Zopounidis, C., 2018. A combined methodology for the concurrent evaluation of the business, financial and sports performance of football clubs: the case of France. Annals of Operations Research, 266(1), pp.589-612.

[37] García, J.A.L., Peña, A.B., Pérez, P.Y.P. and Pérez, R.B., 2017. Project control and computational intelligence: trends and challenges. International Journal of Computational Intelligence Systems, 10(1), pp.320-335.

[38] Gavetti, G. and Menon, A., 2016. Evolution cum agency: Toward a model of strategic foresight. Strategy Science, 1(3), pp.207-233.

[39] Gelsomino, L.U.C.A., Mangiaracina, R., Perego, A. and Tumino, A., 2016. Supply chain finance: modelling a dynamic discounting programme. Journal of Advanced Management Science, 4(4), pp.283-291.

[40] Glissa, G. and Meddeb, A., 2017, June. 6LoWPAN multi-layered security protocol based on IEEE 802.15. 4 security features. In 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC) (pp. 264-269). IEEE.

[41] Gough, D., Maidment, C. and Sharples, J., 2018. UK What Works Centres: aims, methods and contexts.

[42] Helmold, M. and Terry, B., 2016. Global sourcing and supply management excellence in China. Singapur: Springer Verlag.

[43] Jonasson, T. and Papaioannou, M.M.G., 2018. A primer on managing sovereign debt-portfolio risks. International Monetary Fund.

[44] Ketter, W., Peters, M., Collins, J. and Gupta, A., 2016. Competitive benchmarking. MIS quarterly, 40(4), pp.1057-1080.

[45] Knechel, W.R. and Salterio, S., 2016. Auditing: Assurance and risk. Routledge.

[46] Lang, F., Signore, S. and Gvetadze, S., 2016. The role of cooperative banks and smaller institutions for the financing of SMEs and small midcaps in Europe (No. 2016/36). EIF Working Paper.

[47] Leibfried, P. and Linden, K., 2018. Bridge the Gaps-How to improve reporting of Alternative Performance Measures. KPMG White Paper.

[48] Leuz, C. and Wysocki, P.D., 2016. The economics of disclosure and financial reporting regulation: Evidence and suggestions for future research. Journal of accounting research, 54(2), pp.525-622.

[49] Leuz, C. and Wysocki, P.D., 2016. The economics of disclosure and financial reporting regulation: Evidence and suggestions for future research. Journal of accounting research, 54(2), pp.525-622.

[50] Maier, J.F., Eckert, C.M. and Clarkson, P.J., 2017. Model granularity in engineering design–concepts and framework. Design Science, 3, p.e1.

[51] Makridakis, S. and Bakas, N., 2016. Forecasting and uncertainty: A survey. Risk and Decision Analysis, 6(1), pp.37-64.

[52] Mandala, V., 2016. Latency-Aware Cloud Pipelines: Redefining Real-Time Data Integration with Elastic Engineering Models. Global Research Development (GRD) ISSN: 2455-5703, 1(12).

[53] Mertzanis, C., 2018. Complexity, big data and financial stability. Quantitative Finance and Economics, 2(3), pp.637-660.

[54] Mills, D.C., Wang, K., Malone, B., Ravi, A., Marquardt, J., Badev, A.I., Brezinski, T., Fahy, L., Liao, K., Kargenian, V. and Ellithorpe, M., 2016. Distributed ledger technology in payments, clearing, and settlement.

[55] Milosevic, Z., Chen, W., Berry, A., Rabhi, F.A., Buyya, R., Calheiros, R.N. and Dastjerdi, A.V., 2016. Real-time analytics. Big Data: Principles and Paradigms, 2016, pp.39-61.

[56] Nicoletti, B., 2018. Agile Procurement Finance and Its KPI. In Procurement Finance: The Digital Revolution in Commercial Banking (pp. 93-106). Cham: Springer International Publishing.

[57] Ocheni, S., 2016. Treasury Single Account: A catalyst for public financial management in Nigeria. The Nation, 25(2016), p.22.

[58] Onyekwelu, U.L., Chukwuani, V.N. and Onyeka, V.N., 2018. Effect of liquidity on financial performance of deposit money banks in Nigeria. Journal of Economics and Sustainable Development, 9(4), pp.19-28.

[59] Pedron, Z., 2018. The skills revolution of the 21st century: It’s time to re-calibrate. On Research (Journal of EU Business School), 1, pp.20-28.

[60] Popescu, G.H., Nica, E., Ciurlău, F.C., Comănescu, M. and Bițoiu, T., 2017. Stabilizing valences of an optimum monetary zone in a resilient economy—approaches and limitations. Sustainability, 9(6), p.1051.

[61] Puaschunder, J.M., 2018. Value at looking back: Towards an empirical validation of the role of reflexivity in econo-historic backtesting: Economic market prediction corrections correlate with future market performance. Oxford Journal of Finance and Risk Perspectives, 8, pp.223-239.

[62] Rachmad, Y.E., 2016. Central Bank Digital Currency. Education Training Centre, Singapore.

[63] Rahman, K.M. and Bremer, M., 2016. EFFECTIVE CORPORATE GOVERNANCE AND FINANCIAL REPORTING IN JAPAN. Asian Academy of Management Journal of Accounting & Finance, 12.

[64] Raykov, E., 2017. The liquidity-profitability trade-off in Bulgaria in terms of the changed financial management functions during crisis. Management: journal of contemporary management issues, 22(1), pp.135-156.

[65] Sang, L.Q. and Quang, L., 2016. Value of financial flexibility, investment efficiency and adjustment speed of working capital.

[66] SANUSI, A.N., BAYEROJU, O.F., QUEEN, Z. and NWOKEDIEGWU, S., 2019. Circular Economy Integration in Construction: Conceptual Framework for Modular Housing Adoption.

[67] Schwarzbichler, M., Steiner, C. and Turnheim, D., 2018. Financial steering: valuation, KPI management and the interaction with IFRS. Springer.

[68] Shobande, A. O., Atere, D., & Toluwase, I. H. (2019). Conceptual Model for Evaluating Mid-Market M&A Transactions Using Risk-Adjusted Discounted Cash Flow Analysis. IRE Journals, 2(7), 241–247. ISSN 2456-8880.

[69] Tibon, M.V. and Reyes, N.D.M., 2016. Action Research in Finance Department Interdepartmental Collaboration in Resolving Account Payable Aging to Suppliers.

[70] Umoren, O., Didi, P.U., Balogun, O., Abass, O.S. and Akinrinoye, O.V., 2019. Linking macroeconomic analysis to consumer behavior modeling for strategic business planning in evolving market environments. IRE Journals, 3(3), pp.203-213.

[71] Venkat, S. and Baird, S., 2016. Liquidity risk management: a practitioner's perspective. John Wiley & Sons.

[72] Venkat, S., 2016. A New Era of Liquidity Risk Management. Liquidity Risk Management: A Practitioner's Perspective, pp.5-26.

[73] Walker, M.C., 2018. Capital Markets Technology-New Approaches to Infrastructure. Available at SSRN 4609697.

[74] Walker, M.C., 2018. Capital Markets Technology-New Approaches to Infrastructure. Available at SSRN 4609697.

[75] Wang, X., Hampson, K., Kenley, R., Sutrisna, M., Chong, H.Y., Wang, J., Shou, W. and Harry, R., 2017. National BIM guidelines and case studies for infrastructure (report no. 3.28).

@article{1712941,
      author = {Titilayo Elizabeth Oduleye, Jonathan Jemine Medon},
      title = {Developing a Liquidity Optimization Model to Strengthen Transparency and Operational Financial Performance},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {11},
      pages = {671-690},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712941.pdf},
      abstract = {Developing a liquidity optimization model enhances corporate transparency and operational financial performance by aligning short-term cash management with strategic working-capital objectives. This proposes a structured, data-driven framework that integrates cash-flow forecasting, treasury optimization, counterparty liquidity exposure assessment, and real-time reconciliation to optimize cash reserves while minimizing opportunity costs and funding risks. The model combines deterministic cash-flow projections with stochastic scenario analysis to capture temporal variability in receivables, payables, inventory movements, and capital expenditures. A constrained optimization engine formulated as a mixed-integer linear program allocates internal cash pools, short-term investments, and external credit facilities subject to liquidity coverage, covenant, and regulatory constraints. Risk-adjusted objective functions balance expected return on surplus cash against downside liquidity shortfall penalties, with conditional value-at-risk (CVaR) employed to manage tail exposures. The architecture embeds a rolling-horizon reoptimization cadence and integrates intraday telemetry from bank accounts and ERP systems to support dynamic liquidity transfers and automated sweep mechanisms. Transparency is advanced through standardized data schemas, immutable audit trails, and role-based dashboards that present reconciled cash positions, forecast confidence intervals, counterparty concentrations, and covenant headroom. The model further prescribes governance controls for limit setting, stress-testing, and scenario governance to ensure compliance and auditability. Empirical evaluation using synthetic and anonymized enterprise datasets demonstrates material reductions in idle cash balances, borrowing costs, and days-payable-outstanding variability, while improving cash conversion cycle visibility. Implementation considerations highlight data quality, legacy system integration, change management, and cybersecurity around treasury interfaces. Overall, the proposed liquidity optimization model offers a practical pathway for finance organizations to reconcile operational liquidity needs with strategic capital efficiency, enhancing transparency and strengthening financial resilience. Future research should focus on integrating multi-currency optimization, intragroup netting strategies, and machine-learning-based forecast improvement to increase model robustness and support scalable deployment across multinational enterprises with diverse treasury operating models and enhanced regulatory-compliance automation frameworks globally.},
      keywords = {Liquidity Optimization, Cash-Flow Forecasting, Treasury Management, Working Capital, Cvar, Mixed-Integer Programming, Real-Time Reconciliation, Centralized Treasury, Cash Visibility, Financial Resilience.},
      month = {May},
  }