Predicting New Venture Gestation Outcomes With Machine Learning Methods

Abstract

This study explores the use of machine learning methods to forecast the likelihood of firm birth and firm abandonment during the first five years of a new business gestation. The predictability of traditional logistic regression is compared with several machine learning methods, including logistic regression, k-nearest neighbors, random forest, extreme gradient boosting, support vector machines, and artificial neural networks. While extreme gradient boosting shows the best overall model performance, neural networks provide good results by correctly classifying entrepreneurs who have not abandoned their business venture in the early stage of the gestation process. In addition, this study provides valuable insights in relation to the start-up activities leading to firm emergence. Entrepreneurs who perform a greater number of activities and who can orchestrate them at the right rate, concentration, and time are more likely to successfully launch a new business venture.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 23 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.