Modeling the Financial Distress of Microenterprise Start- Ups Using Support Vector Machines

A Case Study

Bibliographic Data

ID11634408
AuthorsAntonio Blanco-Oliver (0000-0002-7550-5930), Antonio Blanco‐oliver (0000-0001-5540-692X, Universidad de Granada), Rafael Pino‐Mejías (0000-0002-2743-877X, Universidad de Granada, corresponding author), Rafael Pino-Mejías, Juan Lara‐rubio (0000-0001-7703-5920, Universidad de Granada)
Year2014
Volume24
Issue1Spe
Pages153-168
Publication date2014-02-01
Open AccessYes
TypeARTICLE
VenueInnovar (JOURNAL)
Journal identifiersISSN: 0121-5051 • E-ISSN: 2248-6968
PublisherUniversidad Nacional de Colombia (PUBLISHER • CO)
DOI10.15446/innovar.v24n1spe.47615
OpenAlexW2150621251
LanguageEN
References cited34

Despite the leading role that micro-entrepreneurship plays in economic development, and the high failure rate of microenterprise start-ups in their early years, very few studies have designed financial distress models to detect the financial problems of micro-entrepreneurs. Moreover, due to a lack of research, nothing is known about whether non-financial information and non-parametric statistical techniques improve the predictive capacity of these models. Therefore, this paper provides an innovative financial distress model specifically designed for microenterprise startups via support vector machines (SVMs) that employs financial, non-financial, and macroeconomic variables. Based on a sample of almost 5,500 micro-entrepreneurs from a Peruvian Microfinance Institution (MFI), our findings show that the introduction of non-financial information related to the zone in which the entrepreneurs live and situate their business, the duration of the MFI-entrepre-neur relationship, the number of loans granted by the MFI in the last year, the loan destination, and the opinion of experts on the probability that microenterprise start-ups may experience financial problems, significantly increases the accuracy performance of our financial distress model. Furthermore, the results reveal that the models that use SVMs outperform those which employ traditional logistic regression (LR) analysis

Actuarial science · Business · Distress · Economic growth · Economics · Financial distress · Financial institution · Financial system · Loan · Logistic regression · Machine learning · Microfinance · Sample (material · Support vector machine · Computer Science · Finance · Financial Distress and Bankruptcy Prediction · FinTech, Crowdfunding, Digital Finance · Psychology · Working Capital and Financial Performance

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