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

7 Further Research

In terms of further work, a promising direction is the usage of different sources of text data about companies, founders, and investors. This could involve leveraging social media platforms such as Twitter and LinkedIn, as well as parsing the websites of the companies themselves.

Additionally, it may be worth adjusting the foundation date filter to include companies founded in 1995, rather than the current start date of 2000-01-01. However, this could potentially result in an influx of companies from the "dotcom bubble" period.

The current strict filters used to determine successful companies (IPO/ACQ/UNICORN) could also be loosened to potentially capture more companies in the "gray area" between success and failure.

Finally, it may be worth conducting experiments to determine the optimal threshold value for adding companies to the portfolio, taking into account the size of the portfolio.

These additional tasks can provide valuable insights and enhance the effectiveness of the AI investor backtest model. Analyzing the presentation materials, video interviews, and source code of software companies can provide a better understanding of the company’s strategy, goals, and potential. Developing information collection systems to automate this process can save time and improve accuracy.

Evaluating the influence of macroeconomic elements and technological trajectories on startups may facilitate the identification of potential risks and opportunities. It can also aid in the development of exit strategies. Additionally, analyzing competing studies can provide insights into the market and competition, which can inform investment decisions.

References

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A Appendix A