A Comprehensive Analysis of Startup Predictive Models

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

6 Conclusion

Traditionally, venture capital investment decisions have largely been guided by the investors’ intuition, experience, and market understanding. While these elements remain significant, there’s a growing recognition that these traditional approaches can be greatly enhanced by integrating data-driven insights into the investment decision-making process.

Our paper comprehensively examines a predictive model for startups based on an extensive dataset from CrunchBase. A meticulous review and analysis of the available data were conducted, followed by the preparing of a dataset for model training. Special attention was given to the selection of features which include information about founders, investors, and funding rounds.

The article also underlines a thoughtfully designed backtest algorithm, enabling a fair evaluation of the model’s behavior (and the simulation of a VC fund based on it) from a historical perspective. Rigorous efforts were made to avoid data leakage, ensuring training at any given point only utilized data that would have been known at that time. Several configurations were explored regarding the funding rounds at which the fund could invest in a company and the timing of exits. The primary evaluative metrics were derived from a backtest table (Table 2), which chronicles instances of company entries, exits, and the corresponding success statuses. Utilizing additional data on company valuations, we calculated the Capital Growth, illustrating the fund’s impressive economic impact over time. To sum up, this work primarily focused on the variety of input features, the integrity of the backtest, and the realistic simulation of the portfolio from a historical perspective. Additionally, we proffer a series of propositions aimed at enhancing the existing model, primarily revolving around the access to supplementary data repositories.

Within the highly competitive and dynamic investment environment, the assimilation of data-driven decision-making practices transitions from being an option to becoming a necessity. As such, venture capitalists that effectively harness the potential of AI and machine learning will likely secure a significant competitive advantage, positioning themselves for success in the new era of venture capitalism.