Predicting Restaurant Facility Closures

36 Pages Posted: 18 Jul 2019

Date Written: January 23, 2018

Abstract

This paper predicts the likelihood that a restaurant will close within the next one to two years using a Yelp restaurant dataset and a high dimensional gradient boosting model called LightGBM (hereafter GBM). This model, trained on more than 20,000 individual restaurants, has an accuracy of 97% and a ROC (AUC) score of 75%. An AUC score above 70% is ordinarily classified as a “fair model” in performance. Using the prediction model, we can also quantify the most predictive variables and higher order variable interaction, both of which produce further compelling insights into non-linear relationships. A model that predicts facility closures has implications for both equity and debt providers. In this paper, I argue that capital providers should make use of publicly available datasets to aid their capital allocation decision making process.

Keywords: Machine Learning, Applied, FirmAI, Restaurant, Bankruptcy, Failure, Closures

JEL Classification: C38, C45, C52, C53, C54

Suggested Citation

Snow, Derek, Predicting Restaurant Facility Closures (January 23, 2018). Available at SSRN: https://ssrn.com/abstract=3420490 or http://dx.doi.org/10.2139/ssrn.3420490

Derek Snow (Contact Author)

FirmAI, UoA, NYU FRE ( email )

NYC, Cambridge, Auckland

HOME PAGE: http://www.firmai.org

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