Glide Path Asset Allocation via Nash Equilibrium Optimization
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Solution Overview
Problem
Current asset allocation methods for retirement plans, particularly target date funds, fail to account for individual differences in risk tolerance, current balances, and future earnings, leading to inefficient portfolio management and lack of theoretical substance in glide path evolution, resulting in inadequate risk management tools for practitioners.
Innovation Solution
A method for allocating assets that selects glide periods, determines risk tolerance levels, and generates a glide path representing a series of time-dependent investment portfolios using computer-implemented optimizations, incorporating demographic data and risk analysis to create a Nash equilibrium solution for funding future financial commitments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If target date funds use a standardized glide path based only on age and time to retirement, then the asset allocation is simple to implement, but it fails to account for individual differences in risk tolerance, current balances, and future earnings
Solution Approach 1:
The patent applies local quality by transitioning from a uniform glide path to customized glide paths tailored to specific participant segments. Different asset allocation strategies are assigned to different participant groups based on their unique characteristics (risk tolerance, balance, earnings), allowing each local segment to receive appropriate treatment rather than a one-size-fits-all approach
Solution Approach 2:
The patent segments the participant population into distinct groups based on multiple criteria including risk tolerance, current account balance, and future earnings potential. This segmentation enables the creation of multiple customized glide paths, each optimized for specific participant segments, thereby resolving the contradiction between implementation simplicity and individualized adaptation
2Ease of manufacture
If existing glide path models are used, then the asset allocation follows conventional approaches, but the theoretical substance and rigor of glide path evolution is insufficient
Solution Approach 1:
The patent fundamentally changes the parameters and variables incorporated into glide path construction. Instead of relying solely on age and time to retirement, the invention introduces additional critical parameters including risk tolerance measurements, current account balance levels, and future earnings projections. These parameter changes create a more theoretically robust and comprehensive glide path framework
3Reliability
If comprehensive risk management frameworks are implemented to maximize safety of benefits and minimize costs, then the quality of retirement planning improves, but the complexity of the system increases
Solution Approach 1:
The patent introduces dynamic elements to the asset allocation system by creating glide paths that adapt to changing participant characteristics and market conditions. The system dynamically adjusts asset allocations based on updated risk tolerance measurements, balance changes, and earnings information, allowing the framework to respond flexibly to varying conditions while maintaining comprehensive risk management
Solution Approach 2:
The patent adds another dimension to traditional glide path construction by incorporating multiple participant-specific variables simultaneously (risk tolerance, balance, earnings) rather than relying on a single dimension like age. This multi-dimensional approach enhances the comprehensiveness and reliability of the risk management framework while systematically managing the associated complexity
Data Source
AI summary
Methods are provided for allocating assets of defined benefit or defined contribution investing plans that yield the highest post-employment standard of living given a level of risk acceptable to plan participants, and that minimize saving rates and risk. The Nash equilibrium glide path representing a series of time-dependent investment portfolios including multiple asset classes is generated, taking into account demographic data for plan participants and at least one selected risk tolerance level, and utilizing computer-implemented optimization. Multiple moments of a stochastic present value of future cash flows are calculated, and a matching distribution is selected. Risk analysis of the glide path and financial commitments utilizes this matching distribution.