tart- Ups Using s Vector machines: a c ase study

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 de- signed 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 start- ups 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. Further -

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