Fitting random cash management models to data

被引：4

作者：

Salas-Molina, Francisco ^{[1
]}

机构：

[1] Univ Valencia, Ave Tarongers S-N, Valencia 46022, Spain

来源：

COMPUTERS & OPERATIONS RESEARCH | 2019年 / 106卷

关键词：

Machine learning; Stochastic programming; Data-driven models; Ensembles; Control bounds; DEMAND; MONEY;

D O I：

10.1016/j.cor.2018.04.007

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：298 / 306

页数：9

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