Retail Loan Portfolio Covariance Analysis via Time Series Decomposition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing portfolio optimization and economic capital calculation methods face challenges in accurately predicting correlations between product segments due to contamination from marketing plans and product lifecycles, leading to unreliable forecasts and skewed capital calculations.
Innovation Solution
The method employs Dual-time Dynamics (DtD) decomposition to isolate maturation, vintage quality, and exogenous trends from vintage-level time series data, creating a steady-state scenario that removes marketing and seasonal impacts, allowing for accurate correlation forecasting between product segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If historical averages are used for portfolio optimization, then the calculation process is simple, but the predictions become unreliable and dangerous
Solution Approach 1:
The patent segments the time series data into distinct components: permanent level, cyclic component, and transient component. This segmentation allows each component to be analyzed and forecasted separately, improving prediction reliability while maintaining computational tractability through the structured decomposition approach.
Solution Approach 2:
The patent performs preliminary decomposition of historical data into permanent, cyclic, and transient components before forecasting. This preliminary action separates the reliable permanent trends from the noisy transient fluctuations, enabling more reliable predictions while avoiding the complexity of modeling all components simultaneously.
2Quantity of substance
If marketing plans and product lifecycle effects are included in correlation calculations, then the analysis captures all available data, but false correlations are introduced
Solution Approach 1:
The patent extracts and removes the transient component from the time series data, which contains marketing plan and product lifecycle effects. By taking out this component before correlation analysis, the method eliminates false correlations while preserving the complete data set for other purposes, thus improving measurement precision without losing data completeness.
Solution Approach 2:
The patent introduces the transient component as an intermediary that mediates between the complete historical data and the correlation analysis. By identifying and separating this intermediary component, the method allows complete data to be used for forecasting while preventing it from contaminating correlation calculations.
3Productivity
If portfolio diversification is pursued based on apparent high Sharpe Ratio segments, then return optimization is achieved, but true diversification benefit is lost due to high correlation
Solution Approach 1:
The patent performs preliminary forecasting of the permanent component for each product segment before calculating correlations and optimizing the portfolio. This preliminary action removes transient noise that creates false differentiation between segments, allowing true diversification benefits to be identified and realized while maintaining return optimization.
Data Source
AI summary
The present invention relates to a method that allows managers of retail portfolios to compute performance time series that have been cleaned of marketing impacts, lifecycles, management actions, and seasonality, leaving only the performance changes due to the environment. These normalized series can be used to compute the necessary covariance matrices for portfolio optimization or computing portfolio-level economic capital. The invention applies to any retail product or segment where vintage-level performance time series are being stored.