k-bosko/IPO_bubble
scrape, clean and model IPO data with supervised ML
This project helps individual investors and financial analysts evaluate the potential success of an Initial Public Offering (IPO). It takes publicly available data about recent IPOs and company stock performance, then processes it to predict if an IPO's price will increase on its first day of trading. The output is a prediction model that can help inform investment decisions around new stock offerings.
No commits in the last 6 months.
Use this if you are an individual investor or financial analyst interested in understanding and predicting IPO first-day trading performance to inform your investment strategy.
Not ideal if you need real-time, high-frequency trading signals or highly granular, complex financial derivatives analysis.
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10
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4
Language
Jupyter Notebook
License
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Category
Last pushed
Aug 20, 2020
Commits (30d)
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curl "https://pt-edge.onrender.com/api/v1/quality/perception/k-bosko/IPO_bubble"
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