Features mined from registration statements for the listing prediction on the STAR market

To study whether the features of registration statements for the Science and Technology Innovation Board (STAR Market) can predict the outcome of listing reviews, we develop IPOhelper based on statistical (financial, technological innovation indicators) and semantic cues (textual indicators) in registration statements. We adopted a variety of advanced machine learning models, including LR, SVM, KNN, NB, RF, GBRT, XGBoost, and AdaBoost within our developed IPOhelper predictive system. It is a novel predictive system for initial public offering (IPO) prediction and AdaBoost performs exceptionally well in predicting IPO outcomes. Compared with statistical cues, the predictive abilities of semantic features are particularly prominent. From the official website of the Shanghai Stock Exchange, we collected 692 registration statements of companies that have applied for listing and have achieved results from the STAR Market from June 2019 to 2023. Among the 692 registration statements, 533 registration statements were for successful listed companies and 159 registration statements were for unsuccessful listed companies. Then, based on the collected registration statements, we used the Python crawler method to extract the relevant data of financial, science and technology innovation, and textual disclosure features. Finally, we obtained 18 indicators, including 6 financial indicators, 5 scientific and technological innovation indicators, and 7 semantic indicators from each of the 692 registration statements.