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        <title>Prospectus Language and IPO Performance</title>
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            <title>Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)</title>
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        <name type="personal">
            <namePart type="given">Chung-Chi</namePart>
            <namePart type="family">Chen</namePart>
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            <namePart type="given">Hsin-Hsi</namePart>
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    <abstract>Pricing a firm’s Initial Public Offering (IPO) has historically been very difficult, with high average returns on the first-day of trading. Furthermore, IPO withdrawal, the event in which companies who file to go public ultimately rescind the application before the offering, is an equally challenging prediction problem. This research utilizes word embedding techniques to evaluate existing theories concerning firm sentiment on first-day trading performance and the probability of withdrawal, which has not yet been explored empirically. The results suggest that firms attempting to go public experience a decreased probability of withdrawal with the increased presence of positive, litigious, and uncertain language in their initial prospectus, while the increased presence of strong modular language leads to an increased probability of withdrawal. The results also suggest that frequent or large adjustments in the strong modular language of subsequent filings leads to smaller first-day returns.</abstract>
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    <identifier type="doi">10.18653/v1/2022.finnlp-1.21</identifier>
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%0 Conference Proceedings
%T Prospectus Language and IPO Performance
%A Sharpe, Jared
%A Decker, Keith
%Y Chen, Chung-Chi
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%S Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F sharpe-decker-2022-prospectus
%X Pricing a firm’s Initial Public Offering (IPO) has historically been very difficult, with high average returns on the first-day of trading. Furthermore, IPO withdrawal, the event in which companies who file to go public ultimately rescind the application before the offering, is an equally challenging prediction problem. This research utilizes word embedding techniques to evaluate existing theories concerning firm sentiment on first-day trading performance and the probability of withdrawal, which has not yet been explored empirically. The results suggest that firms attempting to go public experience a decreased probability of withdrawal with the increased presence of positive, litigious, and uncertain language in their initial prospectus, while the increased presence of strong modular language leads to an increased probability of withdrawal. The results also suggest that frequent or large adjustments in the strong modular language of subsequent filings leads to smaller first-day returns.
%R 10.18653/v1/2022.finnlp-1.21
%U https://aclanthology.org/2022.finnlp-1.21
%U https://doi.org/10.18653/v1/2022.finnlp-1.21
%P 154-162

Markdown (Informal)

[Prospectus Language and IPO Performance](https://aclanthology.org/2022.finnlp-1.21) (Sharpe & Decker, FinNLP 2022)

• Prospectus Language and IPO Performance (Sharpe & Decker, FinNLP 2022)

ACL

• Jared Sharpe and Keith Decker. 2022. Prospectus Language and IPO Performance. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages 154–162, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.