IPO Watch: Debuts, GMPs, Trajectories

IPO Performance Dataset:

This dataset provides information about the performance of Initial Public Offerings (IPOs) for various companies. The columns in the dataset include:

1 : Company Information:

-IPO: Name of the company and its stock code.
-Listing Date: The date on which the company's stock was listed on the stock exchange.

2 : Financial Details:

-IPO Size (in Crore): The size or amount of money raised through the IPO (in crore rupees).
-Subscription: The subscription ratio, indicating how oversubscribed the IPO was.
-Grey Market Premium (GMP) (in Rupees): The premium at which the shares are traded in the unofficial
market before listing (in rupees).
-Estimated Price (in Rupees): The expected price at which it will list, based on GMP
-Estimated Percentage: The estimated percentage at which it will list, based on GMP

3 : IPO Pricing Information:

-IPO Price (in Rupees): The price at which the IPO shares were offered to the public.
-Estimated Price (in Rupees): The expected price at which it will list, based on GMP
-Estimated Percentage: The estimated percentage at which it will list, based on GMP

4 : Listing Information:

-Listing Price (in Rupees): The price at which the IPO shares were officially listed on the stock exchange.
-Listing Percentage: The percentage difference between the listing price and the IPO price.

5 : Long-Term (LT) Performance:

-LT Price (in Rupees): The closing price of the stock in the long term after listing.
-LT Percentage: The percentage difference between the long-term price and the listing price.

Please note that:

IPO Size is denoted in crore rupees.
Grey Market Premium (GMP) is denoted in rupees.
Listing Price and Estimated Price are denoted in rupees.

Applications :

IPO Price Prediction:

Objective: Develop a predictive model to estimate the listing price of an IPO based on features such as subscription, GMP, and IPO size.
Approach: Utilize regression analysis or machine learning algorithms to identify patterns and factors influencing the listing price.
Benefits: Investors can use the model to anticipate the likely listing price of an IPO, aiding in decision-making regarding participation.

Risk Analysis Using Correlation:

Objective: Explore the correlation between different variables (e.g., subscription, GMP, IPO size) and assess the risk associated with each IPO.
Approach: Conduct correlation analysis to identify relationships between variables and evaluate their impact on the success or failure of an IPO.
Benefits: Investors can make more informed decisions by considering the level of risk associated with each IPO, enhancing portfolio management.

Long-Term Performance Classification:

Objective: Build a classification model to categorize IPOs based on their long-term performance (positive or negative) after listing.
Approach: Utilize machine learning classification algorithms to identify patterns and features indicative of successful or underperforming IPOs.
Benefits: Investors can use the classification model to assess the potential long-term performance of an IPO, aiding in investment strategy planning.