Predictive Model for Bond Investors
Budget: $10 – $30 AUD
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.
Ideal candidates will have a strong background in data science, finance, and machine learning, with experience in building predictive models. Familiarity with bond market and investment patterns is a plus.
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.
Ideal candidates will have a strong background in data science, finance, and machine learning, with experience in building predictive models. Familiarity with bond market and investment patterns is a plus.