Tree-based models for evaluating and screening VC investments in TEI-and OTC- listed companies: Taiwan experience

This study sought to create a model for screening and evaluating firms for private equity and venture capital investments and to validate the hypothesis that the profiles of the entrepreneur, investor firm and strategic investment decisions significantly influence the predictor variables for screening and evaluating venture capital investments from among the TEI and OTC-listed companies. Venture capital firms spend a significant amount of time and effort evaluating and screening investment opportunities. The CHAID algorithm was used to generate the tree-based model based on survey data. The results of the study as presented in the gains charts and CHAID risk statistics validate the hypothesis that profiles of the entrepreneur and the enterprise significantly influence the predictor variables for screening and evaluating venture capital investments. From 37 variables evaluated by respondent-firms, CHAID analysis generated five predictors with definitive statistical significance, these are: o Position of Entrepreneur = 96% o Age at the founding of the firm = 96% o Source of funds = 96% o Capitalization = 96% and o Percentage share in the initial investment = 96%. The tree-based model for screening and evaluating prospect firms in Taiwan that was statistically generated from a sample of TEI and OTC-listed firms is generalizable to the total population with a high degree of predictive accuracy. The model can be used to select the best prospects for private equity from among the TEI and OTC-listed companies at their seed stage