Security Risk Scoring Using Default Probability and Capital Stack
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Solution Overview
Problem
Conventional systems for analyzing securities fail to properly account for the likelihood of company default and the investor's position on the capital stack, leading to inefficient decision-making and avoidance of high-risk securities that could offer significant returns.
Innovation Solution
A system and method that retrieves public ratings and additional information to calculate an expected return and proprietary score for securities, considering the probability of default and net recovery, providing a comprehensive output screen for informed investment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional systems analyze securities using public information from various sources, then the analysis process becomes more comprehensive, but the decision-making process becomes complicated and inefficient due to the need to synthesize diverse information
Solution Approach 1:
The system segments the complex security analysis process into distinct functional modules: data collection from multiple public sources, data cleaning and standardization, risk assessment using machine learning models, return calculation, and investment recommendation generation. Each module processes specific information types independently, then integrates results through structured APIs, transforming the synthesis of diverse information into a systematic workflow that maintains comprehensiveness while improving efficiency.
2Reliability
If conventional systems avoid securities identified as likely to default, then investment risk is reduced, but potential enormous returns from high-risk securities are missed
Solution Approach 1:
The system changes the parameter for risk assessment from binary classification (safe/unsafe) to a continuous risk spectrum with quantitative probability estimates. By calculating specific default probabilities and capital stack positions, the system enables investors to make informed decisions about high-risk securities based on their risk tolerance rather than avoiding them automatically. The machine learning models provide nuanced risk metrics that allow high-risk securities with high potential returns to be properly evaluated and included in portfolios when appropriate.
3Device complexity
If conventional systems fail to account for probability of default and capital stack position, then the analysis is simpler, but the likelihood of company default and investor position are not properly evaluated
Solution Approach 1:
The system introduces machine learning models as intermediary components that bridge the gap between raw public data and accurate risk assessment. These models process complex patterns from financial statements, news articles, and market data to generate precise probability of default estimates and capital stack position calculations. The intermediaries translate diverse unstructured information into standardized risk metrics, achieving high measurement precision without requiring the entire system to become overly complex.
4Adaptability or versatility
If investors manually synthesize diverse information from multiple sources, then the analysis can be customized, but the process is time-consuming and prone to human error
Solution Approach 1:
The system implements self-service through automated data collection, cleaning, and analysis processes that execute without human intervention. The machine learning models automatically process new securities and update existing analyses in real-time. The system self-updates its knowledge base by continuously monitoring public filings and market data, eliminating the need for manual information gathering and synthesis while maintaining the ability to customize analyses through configurable parameters and user-defined criteria.
Data Source
AI summary
A method for analyzing investment opportunities includes receiving an identifier of a security of interest. The method further includes identifying an address associated with the security of interest based on public filings by an issuer of the security of interest. The method also includes retrieving public ratings of, and additional public information regarding, the security of interest by using the identifier and the identified address of the security of interest, the method also includes calculating an expected return for the security of interest based on the public ratings and the additional public information. The method also includes generating a proprietary score of the security of interest based on the public ratings, the additional public information, and the expected return.