Analysing forward-looking statements in initial public offering prospectuses: a text analytics approach

Abstract

Forward-looking statements (FLSs) have informational value in applications such as predicting stock prices. Management Discussion & Analysis (MD&A) sections in initial public offering (IPO) prospectuses contain FLSs that provide prospective information about the company's future growth and performance. This study focuses on evaluating the relationship between features extracted from FLSs and IPO valuation. To that end, we propose an analytical pipeline for identifying FLSs using machine learning techniques. The FLS classifier is built on the best performing deep learning architecture that outperforms extant methods reported in related studies. In order to demonstrate the value of identified FLSs, we conduct predictive analysis of pre-IPO price revisions and post-IPO first-day returns. We engineer a variety of linguistics features from FLSs including topics, sentiments, readability, semantic similarity, and general text features. The study finds that FLS features are more predictive for pre-IPO as compared to post-IPO valuation prediction. The analytical pipeline contributes to the text classification knowledge base while the findings from the predictive analysis shed light on understanding the underpricing phenomenon occurring in the IPO process.

Digital Industry Financial Risk Early Warning System Based on Improved K-Means Clustering Algorithm

“…For text information content, natural language processing (NLP) combined with machine learning technology can be used to complete information extraction [ 11 ]. For example, financial entities can be identified in real time from the text data of news, public opinion and forum information, the correlation of financial events can be found, and the related factors depicting economic uncertainty can be extracted [ 12 ]. From the data of annual reports, initial public offerings (IPO) prospectuses and forward-looking statements of listed companies, information such as corporate income, business development scale, and strategic tendency of corporate development can be mined [ 13 ].…”

Text analysis in financial disclosures

“…Numerous other researchers analyzed MDA to explain future stock performance (Tao, Deokar, and Deshmukh (2018a)), future returns, volatility, and firm profitability (Amel-Zadeh and Faasse ( 2016)), bankruptcy (Yang, Dolar, and Mo (2018)), going-concern (Mayew, Sethuraman, and Venkatachalam (2015),Enev (2017)), litigation risk (Bourveau, Lou, and Wang (2018)), and incremental information over earnings surprises, accruals and operating cash flows (OCF) (Feldman et al (2008), Feldman et al (2010)).…”

“…They combined word sentiment polarities and their count as a weighted feature vector for SVM and deep learning approaches. Tao, Deokar, and Deshmukh (2018b) constructed numerous linguistics features from Forward-Looking Statements (FLSs) like topics, sentiments, readability, semantic similarity to analyze pre-IPO predictability vs. post-IPO valuation.…”

“…Encouraged by the trend, some researchers have tried to use deep learning in text analysis of finance. Tao, Deokar, and Deshmukh (2018a) used deep learning methods to predict pre-IPO price revisions and post-IPO first-day returns using forward-looking statements (FLSs).…”

Bankruptcy prediction using disclosure text features

“…Numerous researchers tried to explain various firm attributes using disclosure narratives. Some analyzed MDA to explain future stock performance (Tao, Deokar, and Deshmukh (2018)), future returns, volatility, and firm profitability (Amel-Zadeh and Faasse (2016)), bankruptcy (Yang, Dolar, and Mo (2018)), going-concern (Mayew, Sethuraman, and Venkatachalam (2015), Enev (2017)), litigation risk (Bourveau, Lou, and Wang (2018)), and incremental information over earnings surprises, accruals and operating cash flows (OCF) (Feldman et al (2008), Feldman et al (2010)).…”