This content originally appeared on HackerNoon and was authored by ExitStrategy
:::info 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
3 Dataset Overview, Preprocessing, and Features
3.1 Successful Companies Dataset and 3.2 Unsuccessful Companies Dataset
4 Model Training, Evaluation, and Portfolio Simulation and 4.1 Backtest
5 Other approaches
5.2 Founders ranking model and 5.3 Unicorn recommendation model
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:
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Through an autoencoder model with several modalities, we created vector representations for each investor
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According to experts’ estimates, we select a group of top investors, and further create the centroid of this group in the vector space
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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).
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:::info This paper is available on arxiv under CC 4.0 license.
:::
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This content originally appeared on HackerNoon and was authored by ExitStrategy
ExitStrategy | Sciencx (2024-08-08T15:00:26+00:00) The Role of Autoencoders and Centroid Analysis in Predicting Startup Outcomes. Retrieved from https://www.scien.cx/2024/08/08/the-role-of-autoencoders-and-centroid-analysis-in-predicting-startup-outcomes/
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