dmamakas2000/ipo
This GitHub repository implements a novel approach for detecting Initial Public Offering (IPO) underpricing using pre-trained Transformers. The models, extended to handle large S-1 filings, leverage both textual information and financial indicators, outperforming traditional machine learning methods.
This project helps financial analysts and investment professionals predict whether an Initial Public Offering (IPO) will be underpriced or overpriced. It takes extensive S-1 SEC filing documents and key financial indicators as input, then uses advanced deep learning models to output a classification of the IPO's pricing. Investment professionals, traders, and institutional investors can use this to inform their IPO investment strategies.
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Use this if you need to analyze large volumes of IPO S-1 filings and financial data to predict IPO underpricing or overpricing with higher accuracy than traditional methods.
Not ideal if you are looking for a general-purpose tool for a wide range of corporate event predictions beyond IPO pricing.
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7
Forks
3
Language
Python
License
MIT
Category
Last pushed
Dec 02, 2024
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