Modelling corporate bank accounts

John Fry, Vlad-Marius Griguta, Luciano Gerber, Helen Slater-Petty and Keeley Crockett

Economics Letters, 2021, vol. 205, issue C

Abstract: We discuss the modelling of corporate bank accounts using a proprietary dataset. We thus offer a principled treatment of a genuine industrial problem. The corporate bank accounts in our study constitute spare, irregularly-spaced time series that may take both positive and negative values. We thus builds on previous models where the underlying is real-valued. We describe an intra-monthly effect identified by practitioners whereby account uncertainty is typically lowest at the beginning and end of each month and highest in the middle. However, our theory also allows for the opposite effect to occur. In-sample applications demonstrate the statistical significance of the hypothesised monthly effect. Out-of-sample forecasting applications offer a 9% improvement compared to a standard SARIMA approach.

Keywords: Corporate bank accounts; Fin Tech; Forecasting applications; Machine learning (search for similar items in EconPapers)
JEL-codes: C22 E3 G1 G3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:205:y:2021:i:c:s0165176521002019

DOI: 10.1016/j.econlet.2021.109924

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