Optimization Techniques in Corporate Cash Management: A Hybrid Deterministic & Stochastic Framework

Abstract

Liquidity management by corporations is a blend of both financial theory and operational research. Excess cash hinders returns on assets, whereas cash shortages incur extra expenses due to emergency financing and reputation loss. This study develops an integrated optimization approach incorporating the Baumol-Allais-Tobin BAT inventory theory, the Miller-Orr stochastic control-band model, and a multi-objective linear programing LP problem supplemented with the particle swarm optimization PSO meta-heuristic. Through simulations using realistic cash flows based on actual Pakistani stock exchange companies' characteristics, we find that the new methodology achieves a remarkable reduction of annual cash flow management expenses by 34%, liquidity scores rise from 62 to 89 points based on a scale of 100, and optimal values become apparent within only 40 iterations. Furthermore, through sensitivity analysis, we demonstrate the validity of the methodology under varying interest rate regimes (3%-12%) and transaction fees. There remain, however, several gaps in research that have not been sufficiently explored, including integrating ESG-related liquidity constraints, developing real-time machine learning-based cash flow forecasts, and addressing regulatory challenges associated with emerging markets.