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Data-Driven Analysis of Venture Investments. Best-Practices of Venture Funds

Student: Shmygalyova Evgeniya

Supervisor: Sergey V. Kurochkin

Faculty: Faculty of Economic Sciences

Educational Programme: Financial Engineering (Master)

Final Grade: 9

Year of Graduation: 2021

Within the paper the best-practices analysis of Venture funds, using data-driven algorithms to choose startups to look into, is completed . The transformation concept, containing 4 stages, is proposed, based on the open-source publications of 83 funds. In order to start implementing data-driven decisions the fund must create an effective deal flow, gather data on the projects, build and educate algorithms and set up constant market screening of the startups data. Quite obviously, most of the funds prefer not to share any detailed results of the models they use, so the custom classification model, based on open via API data and machine learning algorithms is demonstrated in the paper. The R2 score of several classification algorithms on test data is higher than 0.7. The quality of rather simple models supports the fact that the barrier of the fund decision making transformation from knowledge-driven to data-driven is low. The paper is valuable for all VC market players, that are interested in how hundreds of startups are prescreened and preevaluated by top players, such as Hatcher+, 500 Startups and others.

Full text (added May 10, 2021)

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