Experimenting with Multi-modal Information to Predict Success of Indian IPOs

TL;DR

This paper presents a multi-modal machine learning approach that combines textual, numerical, and categorical data to predict the success of Indian IPOs, considering factors like prospectus content, macroeconomic conditions, and market trends.

Contribution

It introduces two new datasets and explores the integration of diverse data modalities for IPO success prediction, advancing data-driven investment decision tools.

Findings

Multi-modal data improves IPO success prediction accuracy.

Textual and numerical features significantly influence IPO outcome predictions.

The approach outperforms existing single-modality models.

Abstract

With consistent growth in Indian Economy, Initial Public Offerings (IPOs) have become a popular avenue for investment. With the modern technology simplifying investments, more investors are interested in making data driven decisions while subscribing for IPOs. In this paper, we describe a machine learning and natural language processing based approach for estimating if an IPO will be successful. We have extensively studied the impact of various facts mentioned in IPO filing prospectus, macroeconomic factors, market conditions, Grey Market Price, etc. on the success of an IPO. We created two new datasets relating to the IPOs of Indian companies. Finally, we investigated how information from multiple modalities (texts, images, numbers, and categorical features) can be used for estimating the direction and underpricing with respect to opening, high and closing prices of stocks on the IPO listing day.