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Solving the Data Sparsity Problem in Predicting the Success of the Startups with Machine Learning Methods

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  • Dafei Yin
  • Jing Li
  • Gaosheng Wu

Abstract

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.

Suggested Citation

  • Dafei Yin & Jing Li & Gaosheng Wu, 2021. "Solving the Data Sparsity Problem in Predicting the Success of the Startups with Machine Learning Methods," Papers 2112.07985, arXiv.org.
    • Handle: RePEc:arx:papers:2112.07985
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    References listed on IDEAS

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    Cited by:

    1. Lele Cao & Vilhelm von Ehrenheim & Sebastian Krakowski & Xiaoxue Li & Alexandra Lutz, 2022. "Using Deep Learning to Find the Next Unicorn: A Practical Synthesis," Papers 2210.14195, arXiv.org, revised Jun 2024.
    2. Lele Cao & Gustaf Halvardsson & Andrew McCornack & Vilhelm von Ehrenheim & Pawel Herman, 2023. "Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems," Papers 2309.16888, arXiv.org, revised Jun 2024.

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