Predicting Startup Valuation Using Deep Learning: A Data-Driven Analysis

DOI

10.2991/978-94-6463-612-3_22How to use a DOI?

Keywords

Startup Valuation; Deep Learning; Artificial Neural Network (ANN); Entrepreneurial Finance; Predictive Analytics

Abstract

Assessing the value of startups has distinct issues owing to their early development, limited operating experience, and elevated risk profile. Conventional valuation techniques, such the Berkus Method, First Chicago Method, Venture Capital Method, and Scorecard Method, provide diverse strategies for evaluating startup value but often encounter constraints owing to limited financial data and the dynamic characteristics of the market. This article examines the complexities of startup valuation, including the financing phases from seed capital to venture capital and private equity, and their influence on value. It underscores the challenge of using traditional financial measures for companies that may not possess significant sales or profitability. The emergence of deep learning models presents a viable alternative to conventional valuation techniques. Advanced methods, such Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), enable these models to analyse extensive data sets for more precise predictions of startup success. Deep learning methodologies may mitigate the data constraints of traditional approaches by revealing concealed patterns and insights from a wider array of non-financial metrics. This research analyses the impact of deep learning on startup valuation, highlighting how these models may improve predictive accuracy and provide a more thorough evaluation of a business’s potential. It also examines the interaction between quantitative data and qualitative elements, such as management quality and product-market alignment, in determining startup value. Although deep learning models signify a considerable progression in startup valuation, they serve to enhance rather than supplant existing methodologies. The amalgamation of novel analytical methods with traditional valuation models may provide a more refined comprehension of a startup’s value, so assisting investors in making more enlightened choices in a swiftly changing entrepreneurial environment.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shubham Rajpal
AU  - Amit Manglani
AU  - Shreya Kuchwaha
AU  - Sanjay Kumar Verma
PY  - 2024
DA  - 2024/12/31
TI  - Predicting Startup Valuation Using Deep Learning: A Data-Driven Analysis
BT  - Proceedings of the 5th International Conference on the Role of Innovation, Entrepreneurship and Management for Sustainable Development (ICRIEMSD-2024)
PB  - Atlantis Press
SP  - 333
EP  - 348
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-612-3_22
DO  - 10.2991/978-94-6463-612-3_22
ID  - Rajpal2024
ER  -