arXiv
Open Access
2024
Fitting random cash management models to data
Francisco Salas-Molina
Abstrak
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.
Topik & Kata Kunci
Penulis (1)
F
Francisco Salas-Molina
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2024
- Bahasa
- en
- Sumber Database
- arXiv
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- Open Access ✓