Asset Allocation System Using Historical Cluster Sampling
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Solution Overview
Problem
Conventional retirement investment strategies, such as the glide path, rely on assumptions of normally distributed market and inflation rates, which may not accurately reflect real-world risks, potentially failing to account for anomalous events and thus inadequately managing uncertainties like market volatility, savings shortfall, longevity risk, and inflation risk.
Innovation Solution
A computer system simulates investment outcomes by randomly sampling historical data in multi-year clusters to generate a wide range of asset allocation scenarios, allowing for the construction of a glide path that better reflects real-world risks and reduces exposure to investment strategy failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional glide path strategies assume normally distributed market returns and inflation rates, then the asset allocation model is simple and easy to implement, but it fails to accurately reflect real-world risks including anomalous events
Solution Approach 1:
The patent changes the fundamental parameter assumptions from normal distribution to historical multi-year cluster sampling. Instead of assuming returns follow a bell curve, the system samples actual historical sequences of multi-year periods (2, 3, 4, or 5 years) including anomalous events, thereby transforming the input parameters to better reflect real-world market behavior while maintaining computational feasibility through Monte Carlo simulation methods
2Reliability
If the system uses historical multi-year cluster sampling to simulate investment outcomes, then the accuracy of risk reflection improves, but the computational complexity and time required for simulations increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing historical market data into multi-year cluster sequences before the actual simulation begins. Historical returns and inflation data are segmented into discrete multi-year periods and stored in a ready-to-sample format, which reduces computational complexity during the simulation phase while maintaining high accuracy in risk reflection
Solution Approach 2:
The patent creates simplified copies of historical market conditions through multi-year cluster sampling. Instead of analyzing complete historical datasets, the system generates representative copies of historical periods (2-5 year sequences) that capture the essential risk characteristics including anomalous events, thereby reducing computational complexity while preserving accuracy
3Reliability
If the glide path shifts portfolio toward conservative fixed-income assets as investor ages, then the protection against market volatility improves, but the potential for investment growth and overcoming inflation risk decreases
Solution Approach 1:
The patent applies dynamics by making the asset allocation strategy adaptive rather than static. The glide path dynamically adjusts allocations based on simulated outcomes from multiple multi-year cluster scenarios, allowing the portfolio to respond to different historical market conditions. This dynamic approach optimizes the balance between volatility protection and growth potential at each age, rather than following a rigid conventional glide path
Data Source
AI summary
A computer system is provided for selecting an asset allocation for an investment portfolio intended to produce a target payout starting at a target date. The computer system is programmed to receive inputs that may relate to the target payout, the target date and a nominal rate of contributions to the investment portfolio. The computer system calculates at least one thousand simulated investment outcomes based on assumed investment return rates and assumed inflation rates. The assumed investment return rates and assumed inflation rates are randomly selected in multi-year clusters from historical data. The computer system selects the asset allocation based on relative performance of the assumed asset allocations in the simulated investment outcomes.