Experimenting with Multi-modal Information to Predict Success of Indian IPOs
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909437479878656 |
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| author | Ghosh, Sohom Maji, Arnab Vardhan, N Harsha Naskar, Sudip Kumar |
| author_facet | Ghosh, Sohom Maji, Arnab Vardhan, N Harsha Naskar, Sudip Kumar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16174 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Experimenting with Multi-modal Information to Predict Success of Indian IPOs Ghosh, Sohom Maji, Arnab Vardhan, N Harsha Naskar, Sudip Kumar Computation and Language General Economics Economics 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. |
| title | Experimenting with Multi-modal Information to Predict Success of Indian IPOs |
| topic | Computation and Language General Economics Economics |
| url | https://arxiv.org/abs/2412.16174 |