Ensemble Active Management Portfolio Generation
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Solution Overview
Problem
Conventional active portfolio management techniques face challenges in simultaneously maximizing expected returns and minimizing the risk of significant relative performance distribution tails, often resulting in subpar performance due to non-diversified biases and the dilutive effect of beta anchors.
Innovation Solution
The method involves using ensemble techniques to combine data from multiple actively managed investment portfolios, extracting high conviction predictive security selections, and generating a dynamic Ensemble Active Management (EAM) portfolio that reduces the need for beta anchors, thereby enhancing returns and managing tail risk effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If beta anchors are added to reduce tail risk, then reliability is improved, but productivity deteriorates due to performance dilution
Solution Approach 1:
The patent segments the portfolio into multiple independent predictive engines, each generating predictions separately. This segmentation allows the system to capture diverse predictive signals without requiring large beta anchors for risk management, as the segmented approaches naturally diversify tail risk through their independent prediction mechanisms.
Solution Approach 2:
The patent merges multiple independent predictive engines into an ensemble portfolio that combines their predictions. By merging diverse predictive approaches (fundamental analysis, technical analysis, quantitative models), the system achieves both tail risk reduction through diversification and maintained expected returns through complementary predictive signals.
2Productivity
If a single predictive engine is used to maximize expected return, then productivity is improved, but reliability deteriorates due to non-diversified biases and tail risk
Solution Approach 1:
The patent divides the predictive function into multiple independent segments (different predictive engines with distinct methodologies). Each engine operates independently with its own biases and strengths, segmenting the overall predictive capability to reduce concentration risk while maintaining or enhancing expected returns through diverse signal sources.
Solution Approach 2:
The patent creates a composite predictive system by combining multiple predictive engines with different characteristics (fundamental, technical, quantitative). This composite approach leverages the strengths of each individual engine while offsetting their respective weaknesses and biases, achieving both high expected returns and reduced tail risk through diversification.
3Reliability
If beta anchors constitute a large portion of the portfolio, then reliability is improved through risk management, but productivity deteriorates due to performance penalty
Solution Approach 1:
The patent segments risk management across multiple independent predictive engines rather than concentrating it in large beta anchors. Each engine independently manages its own predictive risk, and the ensemble combination provides natural diversification that reduces tail risk without requiring dominant beta anchor positions that would dilute expected returns.
Solution Approach 2:
The predictive engines are designed to be self-sufficient in managing their own risk profiles through their independent prediction methodologies. Each engine naturally hedges its own tail risk through its specific approach (e.g., fundamental analysis provides inherent value protection, technical analysis provides momentum protection), eliminating the need for external beta anchors that would penalize performance.
Data Source
AI summary
According to at least one aspect, data of a plurality of existing portfolios is accessed that each include time series data of securities and associated weights. At least two existing portfolios are associated with a reference portfolio comprising reference time series data of reference securities and associated reference weights. Portfolio data is determined for at least two existing portfolios by determining, for each of a plurality of securities, difference data based on a difference between the weight of the security for the existing portfolio at a specific time period and a reference weight of the security at the specific time period, and determining a ranking for each of the plurality of securities based on the difference data. An ensemble portfolio is determined, based on the portfolio data and using an ensemble technique, comprising new time series data indicative of a new set of securities and associated new weights.