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

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.