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Prospectus Content as Predictor of IPO Outcome: A topic model approach

Emidi, Christian LU and Galan, Sebastian LU (2022) DABN01 20221
Department of Statistics
Department of Economics
Abstract
It is beneficial for both investors and companies to avoid the detrimental consequences of overpricing during an initial public offering (IPO). Prospectuses are an important source of information for potential investors. Through Latent Dirichlet Allocation (LDA) we extract topics from the summary section of prospectuses S-1 for companies holding an IPO in the U.S. in 2019-2020. We represent the uniqueness of the companies through the topic proportions each document is composed of and use them, together with the initial offering price, to predict the outcome of the IPO. For the best performing model, we obtain an AUC of 0.80. In line with signalling theory, we argue that prospectuses may indeed send signals able to influence potential... (More)
It is beneficial for both investors and companies to avoid the detrimental consequences of overpricing during an initial public offering (IPO). Prospectuses are an important source of information for potential investors. Through Latent Dirichlet Allocation (LDA) we extract topics from the summary section of prospectuses S-1 for companies holding an IPO in the U.S. in 2019-2020. We represent the uniqueness of the companies through the topic proportions each document is composed of and use them, together with the initial offering price, to predict the outcome of the IPO. For the best performing model, we obtain an AUC of 0.80. In line with signalling theory, we argue that prospectuses may indeed send signals able to influence potential investors. (Less)
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author
Emidi, Christian LU and Galan, Sebastian LU
supervisor
organization
course
DABN01 20221
year
type
H1 - Master's Degree (One Year)
subject
keywords
initial public offering, overpricing, signalling theory, topic model
language
English
id
9083567
date added to LUP
2022-06-08 12:50:49
date last changed
2022-10-10 15:59:18
@misc{9083567,
  abstract     = {{It is beneficial for both investors and companies to avoid the detrimental consequences of overpricing during an initial public offering (IPO). Prospectuses are an important source of information for potential investors. Through Latent Dirichlet Allocation (LDA) we extract topics from the summary section of prospectuses S-1 for companies holding an IPO in the U.S. in 2019-2020. We represent the uniqueness of the companies through the topic proportions each document is composed of and use them, together with the initial offering price, to predict the outcome of the IPO. For the best performing model, we obtain an AUC of 0.80. In line with signalling theory, we argue that prospectuses may indeed send signals able to influence potential investors.}},
  author       = {{Emidi, Christian and Galan, Sebastian}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Prospectus Content as Predictor of IPO Outcome: A topic model approach}},
  year         = {{2022}},
}