Skip to main content

Using Machine Learning to Demystify Startups’ Funding, Post-Money Valuation, and Success

  • 494 Accesses

Part of the Springer Series in Supply Chain Management book series (SSSCM,volume 11)

Abstract

This chapter develops a novel approach to predict post-money valuation of startups across various regions and sectors, as well as their probabilities of success. Using startup funding data and descriptions from Crunchbase over a 10-year period, we develop two models linking information such as description, region, and venture capital funding to successful outcomes such as the achievement of an acquisition or IPO. The first model utilizes latent Dirichlet allocation, a generative statistical model in natural language processing, to organize the startups in the dataset into clusters representing various sectors in the typical economy. A distributed gradient boosting regressor (XGBoost) with hyperparameters optimized through Bayesian optimization is subsequently deployed to make use of the resultant feature set to predict post-money valuation. Our model consistently achieves an accuracy of over 95% on hold-out test sets, even with some continuous features removed. The second model is a feed-forward neural network constructed using TensorFlow, with the final layer providing predicted probabilities of success. We find that post-money valuations across regions are typically log-normally distributed, and startups in regions such as San Francisco Bay Area typically witness higher valuations across most sectors. We also find that startups operating in specific geographical regions and sectors of economy (e.g., regions and sectors with higher number of investors) typically have higher predicted probabilities of success. Our approach offers an empirical perspective to startups, policymakers, and venture funds to benchmark and predict valuation and success, clearing some opacity in the modern startup economy.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-75729-8_10
  • Chapter length: 26 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   119.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-75729-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   159.99
Price excludes VAT (USA)
Fig. 10.1
Fig. 10.2
Fig. 10.3
Fig. 10.4
Fig. 10.5
Fig. 10.6
Fig. 10.7
Fig. 10.8
Fig. 10.9
Fig. 10.10
Fig. 10.11
Fig. 10.12
Fig. 10.13
Fig. 10.14
Fig. 10.15
Fig. 10.16

References

Download references

Acknowledgements

The authors would like to thank Crunchbase for providing academic access to the data for this paper. The authors attest that there is no conflict of interest in this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Soroush Saghafian .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 Editors

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Ang, Y.Q., Chia, A., Saghafian, S. (2022). Using Machine Learning to Demystify Startups’ Funding, Post-Money Valuation, and Success. In: Babich, V., Birge, J.R., Hilary, G. (eds) Innovative Technology at the Interface of Finance and Operations. Springer Series in Supply Chain Management, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-75729-8_10

Download citation