research-article

A machine learning approach to detect early signs of startup success

Authors:

Abhinav Nadh Thirupathi,

Mohammad M. GhassemiAuthors Info & Claims

ICAIF '21: Proceedings of the Second ACM International Conference on AI in Finance

Article No.: 9, Pages 1 - 8

https://doi.org/10.1145/3490354.3494374

Published: 04 May 2022 Publication History

Abstract

In this study, we investigate a heterogeneous set of startup ventures (different ages, products, teams, levels of maturity, etc.) to identify the time-independent factors associated with their future success. More specifically, we investigated 3,160 unique companies, all of which were recipients of Small Business Innovation Research (SBIR) or Small Business Technology Transfer (STTR) awards. For each company, we collected any publicly available information: the SBIR/STTR award (amount, agency, principal investigator, etc.), and Crunchbase business profile. The collected data were used to train a XGBoost model that predicts whether a company had an initial public offering (IPO), and/or merged with, and/or was acquired by another entity (M&A). The performance of the model assessed using leave one-out-cross validation (LOOCV) was strong: 84% accuracy and 0.91 AUC. We found that employees with entrepreneurial experience, arts, and/or STEM educational backgrounds, among other characteristics played a significant role in predicting the success of small businesses. Our results indicate that machine learning models may be used to assess the viability of small ventures.

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Cited By

Razaghzadeh Bidgoli MRaeesi Vanani IGoodarzi M(2024)Predicting the success of startups using a machine learning approachJournal of Innovation and Entrepreneurship10.1186/s13731-024-00436-x13:1Online publication date: 28-Oct-2024
https://doi.org/10.1186/s13731-024-00436-x
Gautam LWattanapongsakorn N(2024)Machine Learning Models to Investigate Startup Success in Venture Capital Using Crunchbase Dataset2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE)10.1109/JCSSE61278.2024.10613650(475-481)Online publication date: 19-Jun-2024
https://doi.org/10.1109/JCSSE61278.2024.10613650
Setty RElovici YSchwartz D(2024)Cost‐sensitive machine learning to support startup investment decisionsInternational Journal of Intelligent Systems in Accounting and Finance Management10.1002/isaf.154831:1Online publication date: 13-Feb-2024
https://dl.acm.org/doi/10.1002/isaf.1548
Show More Cited By

Index Terms

A machine learning approach to detect early signs of startup success
1. Applied computing
2. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods

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cover image ACM Conferences

ICAIF '21: Proceedings of the Second ACM International Conference on AI in Finance

November 2021

450 pages

ISBN:9781450391481

DOI:10.1145/3490354

General Chairs:
Anisoara Calinescu
University of Oxford, UK
,
Lukasz Szpruch
The Alan Turing Institute and University of Edinburgh, UK

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 04 May 2022

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ICAIF'21: 2nd ACM International Conference on AI in Finance

November 3 - 5, 2021

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Cited By

Razaghzadeh Bidgoli MRaeesi Vanani IGoodarzi M(2024)Predicting the success of startups using a machine learning approachJournal of Innovation and Entrepreneurship10.1186/s13731-024-00436-x13:1Online publication date: 28-Oct-2024
https://doi.org/10.1186/s13731-024-00436-x
Gautam LWattanapongsakorn N(2024)Machine Learning Models to Investigate Startup Success in Venture Capital Using Crunchbase Dataset2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE)10.1109/JCSSE61278.2024.10613650(475-481)Online publication date: 19-Jun-2024
https://doi.org/10.1109/JCSSE61278.2024.10613650
Setty RElovici YSchwartz D(2024)Cost‐sensitive machine learning to support startup investment decisionsInternational Journal of Intelligent Systems in Accounting and Finance Management10.1002/isaf.154831:1Online publication date: 13-Feb-2024
https://dl.acm.org/doi/10.1002/isaf.1548
Sullivan RLongo L(2023)Explaining Deep Q-Learning Experience Replay with SHapley Additive exPlanationsMachine Learning and Knowledge Extraction10.3390/make50400725:4(1433-1455)Online publication date: 9-Oct-2023
https://doi.org/10.3390/make5040072
Zhang YChen YZhang K(2023)Investment Value Evaluation of Listed Companies Based on Machine Learning2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394121(507-513)Online publication date: 1-Oct-2023
https://doi.org/10.1109/SMC53992.2023.10394121
Parikh AJain SMahalle PShinde G(2022)Offer and Deal-Quality Prediction using Machine learning and Fuzzy approachProceedings of the 4th International Conference on Information Management & Machine Intelligence10.1145/3590837.3590891(1-10)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3590837.3590891

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