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

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  • Sohom Ghosh
  • Arnab Maji
  • N Harsha Vardhan
  • Sudip Kumar Naskar

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.

Suggested Citation

  • Sohom Ghosh & Arnab Maji & N Harsha Vardhan & Sudip Kumar Naskar, 2024. "Experimenting with Multi-modal Information to Predict Success of Indian IPOs," Papers 2412.16174, arXiv.org.
    • Handle: RePEc:arx:papers:2412.16174
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    File URL: http://arxiv.org/pdf/2412.16174
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