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A data-driven model of a firm's operations with application to cash flow forecasting
Kashish Arora, Vishal Gaur
Cornell University, United States of America
A firm’s cash flow from operations is a function of the contemporaneous and lagged values of its operational variables---sales, operating cost, inventory, payables, etc. Estimating this function is important for forecasting and managing cash flows. However, cash flow forecasting is a challenging problem. In this paper, we propose a generalizable and data-driven model of a firm's operations to disentangle this endogeneity and estimate causal impacts among variables.
How big should your data really be? Data-driven newsvendor and the transient of learning
Omar Mouchtaki, Omar Besbes
Columbia University, United States of America
We study the data-driven newsvendor problem in which the decision-maker must trade-off underage and overage costs and only observes historical demand. Our metric of interest is the worst-case relative expected regret, compared to an oracle knowing the demand distribution. We provide an exact analysis of Sample Average Approximation across all data sizes. We also derive a minimax optimal algorithm and its performance. Our work reveals that tens of samples are sufficient to perform efficiently.
