Fitting random cash management models to data

ArXiv ID: 2401.08548 “View on arXiv”

Authors: Unknown

Abstract

Organizations use cash management models to control balances to both avoid overdrafts and obtain a profit from short-term investments. Most management models are based on control bounds which are derived from the assumption of a particular cash flow probability distribution. In this paper, we relax this strong assumption to fit cash management models to data by means of stochastic and linear programming. We also introduce ensembles of random cash management models which are built by randomly selecting a subsequence of the original cash flow data set. We illustrate our approach by means of a real case study showing that a small random sample of data is enough to fit sufficiently good bound-based models.

Keywords: Cash Management, Stochastic Programming, Control Bounds, Ensemble Methods, Liquidity Management, Corporate Cash Management

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical formulations, including stochastic programming and mixed-integer linear programming, leading to high math complexity. It demonstrates empirical rigor with a real case study using actual cash flow data and discusses implementation details like computational burden, though it lacks explicit code or extensive backtest results.

  flowchart TD
    A["Research Goal:<br>Fit cash management models<br>without strict distributional assumptions"] --> B["Key Methodology<br>Stochastic & Linear Programming"]
    B --> C["Data Input<br>Real-world cash flow sequences"]
    C --> D["Computational Process<br>Ensemble of random cash models<br>using subsampled data"]
    D --> E["Outcome 1:<br>Models fit well on small random samples"]
    D --> F["Outcome 2:<br>Effective bound-based control<br>achieved via data fitting"]