ESTIMATION OF THE SECTORS OF THE INVESTMENTS MADE ON VENTURE CAPITAL COMPANIES WITH ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LOGISTIC REGRESSION ANALYSIS
Abstract views: 181 / PDF downloads: 356
DOI:
https://doi.org/10.15637/jlecon.7.022Keywords:
Artificial Neural Network, Multiple Logistic Regression, entrepreneur, classificationAbstract
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
Downloads
References
ARIKAN KARGI, V. S. (2015) Yapay Sinir Ağ Modelleri ve Bir Tekstil Firmasında Uygulama. Bursa: Ekin Yayınevi.
AYTAÇ, Ö. and İLHAN, S. (2007) ‘Girişimcilik Ve Girişimci Kültür: Sosyolojik Bir Perspektif’, Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (18).
BURNS, R. B. and BURNS, R. A. (2008) Business research methods and statistics using SPSS. Los Angeles ; London: SAGE.
CHENG, B. and TITTERINGTON, D. M. (1994) ‘Neural Networks: A Review from a Statistical Perspective’, Statistical Science, 9(1), pp. 2–30. doi: 10.1214/ss/1177010638.
GOODFELLOW, I., BENGIO, Y. and COURVILLE, A. (2016) Deep learning. Cambridge, Massachusetts London, England: The MIT Press (Adaptive computation and machine learning).
Harvard Business Review (2019) Girişimcinin Elkitabı. 1st edn. Translated by L. Göktem.
HORNIK, K., STINCHCOMBE, M. and WHITE, H. (1989) ‘Multilayer feedforward networks are universal approximators’, Neural Networks, 2(5), pp. 359–366. doi: 10.1016/0893-6080(89)90020-8.
HOSMER, D. W., LEMESHOW, S. and STURDIVANT, R. X. (2013) Applied Logistic Regression. Third edition. Hoboken, New Jersey: Wiley (Wiley series in probability and statistics, 398).
ÖZTEMEL, E. (2012) Yapay Sinir Ağları. İstanbul: Papatya Yayıncılık Eğitim.
VELO, R., LÓPEZ, P. and MASEDA, F. (2014) ‘Wind speed estimation using multilayer perceptron’, Energy Conversion and Management, 81, pp. 1–9. doi: 10.1016/j.enconman.2014.02.017.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Jolistence Publications
This work is licensed under a Creative Commons Attribution 4.0 International License.
When the article is accepted for publication in the Journal of Life Economics, authors transfer all copyright in the article to the Holistence Publications.The authors reserve all proprietary right other than copyright, such as patent rights.
Everyone who is listed as an author in this article should have made a substantial, direct, intellectual contribution to the work and should take public responsibility for it.
This paper contains works that have not previously published or not under consideration for publication in other journals.