Python Financial Modeling & Analysis Expert
Budget: $10 – $30 AUD
need someone skilled in building models to analyse data in python to check the maths in a project i have regarding investors and issuers and bond parameters.
task:
I'm seeking a data scientist with expertise in financial modeling and machine learning. The project involves creating a model to forecast potential investors for new bond issuances based on their historical investment behavior. I have a dataset with hundreds of columns with some relevant historical data. Key Responsibilities: - Analyze historical investment data: Utilize features such as credit rating, coupon type, seniority, tenor, and issuance size to understand past investment patterns. - Model new bond issuances: The model should take into account parameters of new bond issuances including credit rating, coupon type, tenor, seniority, and issuance size. - Prioritize based on issuance size: The model should particularly focus on the size of the issuance. Outputs: - A list of interested investors for each new bond issuance. - A score for each investor based on their likelihood of interest. - An order of investors based on their score. - An allocation number indicating how much of the issuance each investor should receive.
task:
I'm seeking a data scientist with expertise in financial modeling and machine learning. The project involves creating a model to forecast potential investors for new bond issuances based on their historical investment behavior. I have a dataset with hundreds of columns with some relevant historical data. Key Responsibilities: - Analyze historical investment data: Utilize features such as credit rating, coupon type, seniority, tenor, and issuance size to understand past investment patterns. - Model new bond issuances: The model should take into account parameters of new bond issuances including credit rating, coupon type, tenor, seniority, and issuance size. - Prioritize based on issuance size: The model should particularly focus on the size of the issuance. Outputs: - A list of interested investors for each new bond issuance. - A score for each investor based on their likelihood of interest. - An order of investors based on their score. - An allocation number indicating how much of the issuance each investor should receive.