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.