Prospectus Language and IPO Performance (2022)

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.

## introduction underpricing, the high average return over a stock's initial public offering (ipo) price on the first-day of trading, is a subject of great financial research @xcite . while the price of the offering is believed to be the best efforts of the underwriter, the individual or firm who assigns the final price, the average first-day return tends to be between 10-14% @xcite . the difference between the first-day closing price and the ipo price, magnified by the number of shares sold, is referred to as 'money left on the table' since it would seem that the firm whose stock is being sold at a premium was undervalued by the underwriter @xcite . a great wealth of literature has attempted explanations using industry, year, momentum, and an assortment of variables and incentive theories @xcite @xcite @xcite @xcite , to name a few. the efficient market hypothesis (emh) @xcite theorizes that current stock prices incorporate all present market information, including the previous price for which a stock traded. ipos do not have this luxury; market information that would be contained in its price must be discovered through other means, such as within the documents that must be filed with the sec in order to conduct the ipo: the prospectus filings. incorporating information from text sources, such as news articles and financial documents, has become exceedingly popular in stock pricing and market valuation, @xcite @xcite @xcite @xcite . the sentiment construction described below is a strong middle-ground between percentage-ofwords in a user defined list techniques common in the finance literature, @xcite @xcite , and the effective techniques of word embedding @xcite @xcite . in place of counting the number of times sentiment specific words occur in a document, the results utilize the cosine similarity between the embeddings of all words foot_0 and all words in each of the sentiment word lists of @xcite . four pretrained embeddings are compared with standard percentage-of-words. therefore, the contribution of this work is threefold. firstly, the analysis of initial prospectus sentiment in withdrawal prediction. the second con-tribution of this work is the expansion of existing sentiment scoring techniques to utilize a stronger, more modern tool: word embeddings. the third contribution of this work is the incorporation of all prospectus amendment document sentiments published before issuing, contributing to the growing literature studying the information revealed during the ipo filing process and how it relates to ipo valuation. ## data collection following the method used by @xcite and their published r code, ipo data is first collected from thomson financial securities data new issues database (sdc) for firms who issue or withdraw between 2004 and 2020. using the given sec file number, the correct cik numbers are identified and all prospectus related forms, the initial prospectus (s-1), prospectus amendments (s-1/a, 424a, all 424b foot_2 variants), are collected using @xcite . only those ciks who filed an s-1 between 2004 and 2020 and issued or withdrew in that time are considered. firms that have a non-missing withdrawn date field from sdc are considered to have withdrawn. while the withdrawn firms seldom publish after the s-1, the initial prospectus and final prospectus are fractions of the final picture for those that issue. the information revealed in the amendments must be taken into consideration when forecasting the final offer price and first-day return as it was likely disclosed strategically, @xcite ; this is especially true considering that the final prospectus is often published after the issue date, @xcite . a total of 2201 unique cik firms are found with sufficient, non-missing control variables following the method of @xcite with a total of 10,683 forms to evaluate, after the removal of common stop words (a, an, the, etc.) and all non-english characters foot_4 .of these, a remaining 1908 cik firms have qualifying forms to be processed and analyzed in this model. index and first-day returns are collected from the center for research in security prices (crsp) database accessed through the wharton research data services (wrds). the collection of additional firm identifiers was attempted, but the best results were obtained by uniquely identifying all permnos foot_5 on their first day of record, taking their cusip6 foot_6 matches with the firms and taking the sample with the least missing data following @xcite . @xcite continue to publish a ranking on underwriters. while this ranking is standard in the underpricing literature, @xcite @xcite , it only supplies a ranking in @xcite ; therefore an underwriter ranked 8 in 2000 is still considered to be rank 8 in 2003, but if their rank is missing in 2004, it will also be missing in 2005. the most recent ranking is from 2015. the up-to-date 7-sentiment loughran mcdonald word lists are downloaded from their website. ## methodology for these results, instead of using a percentage of words to represent each sentiment, the cosine similarity between every word in a document and every word in each loughran mcdonald sentiment list is calculated using the publicly available glove @xcite ) embeddings trained on wikipedia, sehrawat's glove embeddings @xcite trained on 10-k filings, bert @xcite embeddings trained on bookcorpus and wikipedia, and the finbert model from @xcite . algorithm 1 sentiment score matrix: t 1: inputs: embedding matrix: m loughran mcdonald word list: lm vocabulary: v 2: output: sentiment score matrix: t 3: for all @xmath0, and zero else 7 . see algorithm 1 for the construction of the word sentiment score matrix t . the formulas are the same for the standard glove, sehrawat, bert, and finbert embeddings 8 . for an entire document, the similarity vectors of all words are vector-summed into the document's total vector, which is normalized by dividing by the 2-norm to be the document's score vector; see algorithm 2. this process is very similar to the one used by @xcite . all documents are scored for each sentiment category by each embedding model; the percentage of words metric is also calculated for comparison. additionally, for every document after the initial prospectus of each firm, the difference between its sentiment score over the previous document is recorded; these sentiment differences are then summarized by an expanding average leaving the final prospectus with an average difference in the changing published sentiment. this metric will capture large spikes in new sentiments that had not yet been revealed; a similar metric that takes the average in absolute differences between the documents was tested, but the results were insignificant. the following predictions are made: use the initial prospectus sentiment, present market average return, and underwriter ranking to predict if a firm will withdraw, for those firms that issue, use the sentiment of the final prospectus to predict the first-day return, for those firms that issue,use the sentiment of the final prospectus and the average sentiment update difference to predict the first-day return. supplementary materials for reproducibility are available upon request; however, the complete data set will take over a week of machine time due to the inclusion of four embedding models and the number of forms to process. moreover, wrds and sdc are proprietary, restricting the ability to publicize the entire data set. as noted in @xcite , the years being evaluated often have measurable effects on the final results and thus a large volume of data is preferable. ## contributions and continuing future research this work contributes to the ever growing literature on nlp in finance by first evaluating prospectus sentiment on the likelihood of withdrawal, second by expanding the sentiment evaluation to the use of word embedding methods, which does significantly better at disentangling uncertainty and constraining language from that of strong and weak modality, and thirdly by incorporating a measurement for the change in sentiment throughout the filing process, rather than just at the beginning and end. the bert embedding method has additional strength beyond the embeddings themselves, which would imply that training a model on this data directly would likely improve the significance of the bert factors by better capturing the context of prospectus filings. while the inclusion of a probability of withdrawal factor was statistically insignificant, its insignificance raises more questions. the method presented is able to disentangle the effects of uncertain and litigious language throughout the filing process, but more work needs to be done to better evaluate the factors behind the ipo price and first-day returns. moreover, the ability of the embedding-based methods to first replicate and second out-perform that of the basic percentage-ofwords method is a necessary bridge to advance the existing financial literature to more modern techniques.