ESTIMATION OF THE SECTORS OF THE INVESTMENTS MADE ON VENTURE CAPITAL COMPANIES WITH ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LOGISTIC REGRESSION ANALYSIS
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Keywords

Artificial Neural Network
Multiple Logistic Regression
entrepreneur
classification Artificial Neural Network
Multiple Logistic Regression
entrepreneur
classification

How to Cite

ERİSLİK, K., & DENİZ BAŞAR, Özlem. (2020). ESTIMATION OF THE SECTORS OF THE INVESTMENTS MADE ON VENTURE CAPITAL COMPANIES WITH ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LOGISTIC REGRESSION ANALYSIS . JOURNAL OF LIFE ECONOMICS, 7(4), 297-308. https://doi.org/10.15637/jlecon.7.022

Abstract

Venture capital companies undergo three different phases as core, growth and maturity phases as of their establishment. There are different stages in these phases in terms of providing the finance. The stage of providing finance for the first introduction of the product to the market in the core phase is called Serial A, the stage of providing the increasing finance need during the continuation of the growth is called Serial B and the stage of providing the finance needed in the growth and maturity phases is called Serial C and it continues as Serial D. In this study, it has been aimed to estimate the sectors of the venture capital companies by benefiting from the phases and amounts of the investments made by the investors to the venture capital companies. In the study, 5 sectors with the highest investment from investors have been selected and the investment data of 709 venture capital companies taking place in this sector have been benefited. Artificial Neural Networks and Multiple Logistic Regression Analysis have been used in the estimation of the sectors covering the companies with the data attained from the investment series. When the attained results have been examined, it has been determined that the results attained with Artificial Neural Networks are more successful than the results attained with Multiple Logistic Regression analysis.

Jel Codes: C38, C39, C45, G24, L26

https://doi.org/10.15637/jlecon.7.022
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