The Role of Autoencoders and Centroid Analysis in Predicting Startup Outcomes

Authors: (1) Mark Potanin, a Corresponding (authorpotanin.m.st@gmail.com); (2) Andrey Chertok, (a.v.chertok@gmail.com); (3) Konstantin Zorin, (berzqwer@gmail.com); (4) Cyril Shtabtsovsky, (cyril@aloniq.com). Table of Links Abstract and 1. Introduction 2 Related works 3 Dataset Overview, Preprocessing, and Features 3.1 Successful Companies Dataset and 3.2 Unsuccessful Companies Dataset 3.3 Features 4 Model Training, Evaluation, and Portfolio Simulation and 4.1 Backtest 4.2 Backtest settings 4.3 Results 4.4 Capital Growth 5 Other approaches 5.1 Investors ranking model 5.2 Founders ranking model and 5.3 Unicorn recommendation model 6 Conclusion 7 Further Research, References and Appendix 5 Other approaches 5.1 Investors ranking model All investors could be scored in terms of frequency, amount, and field of investments. Also, an investor could be an indicator of a company’s potential failure or success. This scoring was carried out in three stages: Through an autoencoder model with several modalities, we created vector representations for each investor According to experts’ estimates, we select a group of top investors, and further create the centroid of this group in the vector space We rank investors according to distance from the centroid An elevated score corresponds to a proximate alignment with top investors. Results are presented in Table 4. If the lead investor of a company has a low score, it could be an indicator that such a company should be excluded from consideration. Example: Company 14W has a score of 0.9 and invests in IT companies, incl. unicorns (for example, European travel management startup TravelPerk). This paper is available on arxiv under CC 4.0 license. Authors: (1) Mark Potanin, a Corresponding (authorpotanin.m.st@gmail.com); (2) Andrey Chertok, (a.v.chertok@gmail.com); (3) Konstantin Zorin, (berzqwer@gmail.com); (4) Cyril Shtabtsovsky, (cyril@aloniq.com). Authors: Authors: (1) Mark Potanin, a Corresponding (authorpotanin.m.st@gmail.com); (2) Andrey Chertok, (a.v.chertok@gmail.com); (3) Konstantin Zorin, (berzqwer@gmail.com); (4) Cyril Shtabtsovsky, (cyril@aloniq.com). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related works 2 Related works 3 Dataset Overview, Preprocessing, and Features 3 Dataset Overview, Preprocessing, and Features 3.1 Successful Companies Dataset and 3.2 Unsuccessful Companies Dataset 3.1 Successful Companies Dataset and 3.2 Unsuccessful Companies Dataset 3.3 Features 3.3 Features 4 Model Training, Evaluation, and Portfolio Simulation and 4.1 Backtest 4 Model Training, Evaluation, and Portfolio Simulation and 4.1 Backtest 4.2 Backtest settings 4.2 Backtest settings 4.3 Results 4.3 Results 4.4 Capital Growth 4.4 Capital Growth 5 Other approaches 5.1 Investors ranking model 5.1 Investors ranking model 5.2 Founders ranking model and 5.3 Unicorn recommendation model 5.2 Founders ranking model and 5.3 Unicorn recommendation model 6 Conclusion 6 Conclusion 7 Further Research, References and Appendix 7 Further Research, References and Appendix 5 Other approaches 5.1 Investors ranking model All investors could be scored in terms of frequency, amount, and field of investments. Also, an investor could be an indicator of a company’s potential failure or success. This scoring was carried out in three stages: Through an autoencoder model with several modalities, we created vector representations for each investor According to experts’ estimates, we select a group of top investors, and further create the centroid of this group in the vector space We rank investors according to distance from the centroid Through an autoencoder model with several modalities, we created vector representations for each investor Through an autoencoder model with several modalities, we created vector representations for each investor According to experts’ estimates, we select a group of top investors, and further create the centroid of this group in the vector space According to experts’ estimates, we select a group of top investors, and further create the centroid of this group in the vector space We rank investors according to distance from the centroid We rank investors according to distance from the centroid An elevated score corresponds to a proximate alignment with top investors. Results are presented in Table 4. If the lead investor of a company has a low score, it could be an indicator that such a company should be excluded from consideration. Example : Company 14W has a score of 0.9 and invests in IT companies, incl. unicorns (for example, European travel management startup TravelPerk). Example This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv