This project analyzes mortgage trading strategies using Power BI, focusing on loan evaluation, market pricing, and trade profitability. The study examines whole loan trading vs. securitization, applies risk metrics like Loan-to-Value (LTV) and Debt-to-Income (DTI) ratios, and evaluates trade execution efficiency.
Please find the datasets here.
Data Cleaning & Transformation – Standardized loan data, calculated risk ratios, and created trade status indicators using Power Query.
Loan Amortization & Balance Calculation – Modeled repayment schedules and principal balances with DAX functions.
Trade Execution Analysis – Compared whole loan bids vs. securitized pricing, identified profitable trades, and optimized bid selection.
Profitability & Benchmarking – Analyzed trade margins, loan revenue, and weighted average price to maximize profitability.
Power BI Dashboard – Interactive visuals showcasing loan performance, trade execution, and risk insights.
Please see the report for detailed steps.
- Power BI – Data visualization, DAX calculations, Power Query transformations.
- DAX (Data Analysis Expressions) – Financial modeling, trade profitability analysis.
- Financial Modeling – Mortgage trading strategies, risk assessment, and market benchmarking.
- Integrate real-time financial market data for dynamic pricing analysis.
- Apply machine learning models to predict trade premiums.
- Expand risk assessment by incorporating economic indicators.
- Gandhar Ravindra Pansare
