6533b827fe1ef96bd12859ad
RESEARCH PRODUCT
Fitting random cash management models to data
Francisco Salas-molinasubject
Overdraft021103 operations researchGeneral Computer ScienceComputer science0211 other engineering and technologies02 engineering and technologyManagement Science and Operations ResearchComputer Science::Computers and SocietyProfit (economics)Modeling and Simulation0202 electrical engineering electronic engineering information engineeringEconometricsProbability distribution020201 artificial intelligence & image processingCash flowCash managementdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-06-01 | Computers & Operations Research |