Dafei Yin, Jing Li, Gaosheng Wu
arXiv 15 Dec 2021 · Machine Learning · 2 citations (OpenAlex)
arXiv:2112.07985 · PDF · DOI · OpenAlex · Extracted main text
Predicting the success of startup companies is of great importance for both startup companies and investors. It is difficult due to the lack of available data and appropriate general methods. With data platforms like Crunchbase aggregating the information of startup companies, it is possible to predict with machine learning algorithms. Existing research suffers from the data sparsity problem as most early-stage startup companies do not have much data available to the public. We try to leverage the recent algorithms to solve this problem. We investigate several machine learning algorithms with a large dataset from Crunchbase. The results suggest that LightGBM and XGBoost perform best and achieve 53.03% and 52.96% F1 scores. We interpret the predictions from the perspective of feature contribution. We construct portfolios based on the models and achieve high success rates. These findings have substantial implications on how machine learning methods can help startup companies and investors.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chen, T., Guestrin, C (2016) Xgboost: A scalable tree boosting system | 1.000 | 5 | 3 | 100% |
| 2 | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Li… (2017) Lightgbm: A highly efficient gradient boosting decision tree | 1.000 | 5 | 3 | 100% |
| 3 | Frosst, N., Hinton, G (2017) Distilling a neural network into a soft decision tree | 0.843 | 3 | 3 | 100% |
| 4 | Arroyo, J., Corea, F., Jimenez-Diaz, G., Recio-Garcia, J.A (2019) Assessment of machine learning performance for decision support in venture capital investments | 0.737 | 3 | 2 | 100% |
| 5 | Kaiser, U., Kuhn, J.M (2020) The value of publicly available, textual and non-textual information for startup performance prediction | 0.737 | 3 | 2 | 100% |
| 6 | Yankov, B., Ruskov, P., Haralampiev, K (2014) Models and tools for technology start-up companies success analysis | 0.737 | 3 | 2 | 100% |
| 7 | Holmes, P., Hunt, A., Stone, I (2010) An analysis of new firm survival using a hazard function | 0.644 | 2 | 2 | 100% |
| 8 | McKenzie, D., Sansone, D (2017) Man vs. machine in predicting successful entrepreneurs: evidence from a business plan competition in Nigeria | 0.644 | 2 | 2 | 100% |
| 9 | Nahata, R (2008) Venture capital reputation and investment performance | 0.511 | 2 | 1 | 100% |
| 10 | Lundberg, S.M., Lee, S.I (2017) A unified approach to interpreting model predictions | 0.405 | 1 | 1 | 100% |
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