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Textual Representation of Business Plans and Firm Success

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  • Maria S. Mavillonio

Abstract

In this paper, we leverage recent advancements in large language models to extract information from business plans on various equity crowdfunding platforms and predict the success of firm campaigns. Our approach spans a broad and comprehensive spectrum of model complexities, ranging from standard textual analysis to more intricate textual representations - e.g. Transformers-, thereby offering a clear view of the challenges in understanding of the underlying data. To this end, we build a novel dataset comprising more than 640 equity crowdfunding campaigns from major Italian platforms. Through rigorous analysis, our results indicate a compelling correlation between the use of intricate textual representations and the enhanced predictive capacity for identifying successful campaigns.

Suggested Citation

  • Maria S. Mavillonio, 2024. "Textual Representation of Business Plans and Firm Success," Discussion Papers 2024/308, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    • Handle: RePEc:pie:dsedps:2024/308
    Note: ISSN 2039-1854
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    File URL: https://www.ec.unipi.it/documents/Ricerca/papers/2024-308.pdf
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    References listed on IDEAS

    as
    1. McKenzie, David & Sansone, Dario, 2019. "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria," Journal of Development Economics, Elsevier, vol. 141(C).
    2. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    3. Lingfei Deng & Qiang Ye & DaPeng Xu & Wenjun Sun & Guangxin Jiang, 2022. "A literature review and integrated framework for the determinants of crowdfunding success," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-70, December.
    4. Mi (Jamie) Zhou & Baozhou Lu & Weiguo (Patrick) Fan & G. Alan Wang, 2018. "Project description and crowdfunding success: an exploratory study," Information Systems Frontiers, Springer, vol. 20(2), pages 259-274, April.
    5. Tim Loughran & Bill McDonald, 2020. "Textual Analysis in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 12(1), pages 357-375, December.
    6. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    7. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    8. Jermain C. Kaminski & Christian Hopp, 2020. "Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals," Small Business Economics, Springer, vol. 55(3), pages 627-649, October.
    9. Signori, Andrea & Vismara, Silvio, 2018. "Does success bring success? The post-offering lives of equity-crowdfunded firms," Journal of Corporate Finance, Elsevier, vol. 50(C), pages 575-591.
    10. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Caterina Giannetti & Maria Saveria Mavillonio, 2024. "Crowdfunding Success: Human Insights vs Algorithmic Textual Extraction," Discussion Papers 2024/315, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.

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    More about this item

    Keywords

    Crowdfunding; Text Representation; Natural Language Processing; Transformers;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship

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