Bayesian Factorization Machines for Risk Management and Robust Decision Making

Pablo Angulo (), Víctor Gallego (), David Gómez-Ullate () and Pablo Suárez-García ()
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Pablo Angulo: UPM, ETSIN
Víctor Gallego: ICMAT
David Gómez-Ullate: ICMAT
Pablo Suárez-García: UCM, Depto. Física Teórica, Facultad de Física

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 51-55 from Springer

Abstract: Abstract When considering different allocations of the marketing budget of a firm, some predictions, that correspond to scenarios similar to others observed in the past, can be made with more confidence than others, that correspond to more innovative strategies. Selecting a few relevant features of the predicted probability distribution leads to a multi-objective optimization problem, and the Pareto front contains the most interesting media plans. Using expected return and standard deviation we get the familiar two moment decision model, but other problem specific additional objectives can be incorporated. Factorization Machines, initially introduced for recommendation systems, but later used also for regression, are a good choice for incorporating interaction terms into the model, since they can effectively exploit the sparse nature of typical datasets found in econometrics.

Keywords: Data-driven decision making; Decision-support systems; Bayesian regression; Factorization machines; Multi-objective optimization; Risk metrics (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-3-319-89824-7_9

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