Textual Representation of Business Plans and Firm Success

Maria S. Mavillonio

Discussion Papers from Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy

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.

Keywords: Crowdfunding; Text Representation; Natural Language Processing; Transformers (search for similar items in EconPapers)
JEL-codes: C45 C53 G23 L26 (search for similar items in EconPapers)
Date: 2024-05-01
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp, nep-ent, nep-pay and nep-sbm
Note: ISSN 2039-1854
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:pie:dsedps:2024/308

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