Derivatives Portfolio Management Using Linear Markov Simulation
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Solution Overview
Problem
Conventional financial inventory management of securities and derivatives lacks a data-driven approach, failing to account for frictions and restrictions, especially in environments with abundant data and computation power, and struggles with complex financial instruments.
Innovation Solution
A data-driven method using a processor to identify hedging instruments, obtain historical market data, assess optimized portfolio values through market model simulation functions based on Linear Markov Representation, and determine potential actions considering trading restrictions and risk aversion, even for derivatives without public market prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional quantitative finance methods and individual analysts are used for financial inventory management, then decisions can be made with limited data and computation power, but the approach fails to account for frictions and restrictions and cannot handle complex financial instruments effectively
Solution Approach 1:
The patent replaces conventional quantitative finance methods and manual analyst decision-making with a machine learning-based automated system. The ML model processes historical market data, portfolio information, and trading restrictions to generate optimized trading decisions, substituting the mechanical analyst workflow with an automated computational system that can handle complex derivatives effectively
Solution Approach 2:
The system transforms the decision-making approach by changing from traditional finance models to data-driven statistical frameworks. It incorporates multiple parameters including historical market data, portfolio constraints, risk preferences, and transaction costs into the ML model, enabling adaptive optimization for complex financial instruments while systematically managing the complexity through structured data processing
2Reliability
If a data-driven statistical framework is implemented for scalable decision making, then frictions and restrictions can be accounted for, but the system requires abundant data availability and computation power
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing historical market data in structured formats before actual trading decisions are needed. The ML model is trained in advance on historical data to learn optimal trading strategies, so that when real-time decisions are required, the system can quickly query and apply pre-learned patterns rather than performing heavy computation from scratch
Solution Approach 2:
The patent uses historical market data as copies of past market conditions to train the ML model and simulate trading scenarios. By creating synthetic training datasets that replicate historical market behavior, the system can extensively train the model offline using abundant computation power, then deploy the trained model for efficient real-time decision-making with minimal computational resources
3Ease of operation
If classic quantitative finance methods are used, then the system is simple to implement, but it does not account for trading frictions, restrictions, and risk preferences in optimized decision making
Solution Approach 1:
The ML-based system serves multiple functions within a single unified framework: it processes historical market data, evaluates portfolio constraints, incorporates risk preferences, optimizes trading decisions, and accounts for transaction costs. This multi-functional approach replaces multiple separate conventional methods with a single adaptable system that handles diverse trading scenarios and constraints simultaneously
Solution Approach 2:
The system transitions from static conventional finance models to dynamic adaptive decision-making. The ML model continuously learns from historical data and can adjust its predictions based on changing market conditions, portfolio states, and constraint parameters. This dynamic capability allows the system to adapt to various frictions and restrictions without requiring complete redesign for each scenario
Data Source
AI summary
A method and a computing apparatus for managing a portfolio of securities and derivatives are provided. The method includes: identifying a plurality of potential trades based on the portfolio of securities and derivatives; obtaining historical market data that relates to the identified plurality of potential trades; assessing a respective optimal value of each of the at least one security that relates to a corresponding one of the potential trades; and determining trades to be executed from among the identified plurality of potential trades. The assessments of the respective optimal values are based on the obtained historical market data, and also on additional information that relates to each security, using statistical methods. The determination of trades to be executed is based on the corresponding optimal values.