Investment Strategy Rule Generation via Simulation
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Solution Overview
Problem
Current investment forecasting methods derive investment models from past market trends using machine learning, but these models have accuracy lower than 100%, leading to potential investment risks and asset allocation issues, as they do not adjust proportions based on future market forecasts.
Innovation Solution
An investment strategy rule generation method and device that generates an investment strategy model based on historical market trends with less than 100% accuracy, performs investment simulations across multiple time points, and calculates total investment returns for candidate rules, selecting the best rule for market entry and holding strategies to optimize asset allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are used to derive investment models from past market trends, then investment forecasting can be automated, but the forecasting accuracy remains below 100% leading to investment risks
Solution Approach 1:
The patent segments the investment decision-making process into multiple candidate rules (e.g., different market conditions, different investment strategies) and evaluates each rule's investment return separately through simulation. This allows the system to handle uncertainty by considering multiple possibilities rather than relying on a single inaccurate prediction, thus resolving the contradiction between automation and reliability.
Solution Approach 2:
The patent performs preliminary investment simulations and evaluations on multiple candidate rules using historical data before actual investment decisions are made. By pre-calculating and comparing the investment returns of different rules through simulation, the system identifies the optimal rule in advance, thereby improving reliability while maintaining automation in the actual investment process.
2Ease of operation
If fixed proportions are maintained among investment targets, then asset allocation is simple to manage, but the allocation does not adapt to future market forecasting
Solution Approach 1:
The patent transforms static fixed-proportion asset allocation into a dynamic system where investment proportions are adjusted based on simulated performance of multiple candidate rules. The system dynamically determines the optimal investment rule and corresponding asset allocation strategy through simulation and comparison, allowing the allocation to adapt to different market conditions while maintaining operational simplicity through automated rule selection.
Solution Approach 2:
The patent changes the parameters of asset allocation by evaluating multiple candidate rules with different investment proportions and strategies through simulation. Instead of maintaining fixed proportions, the system selects the rule that optimizes investment returns under simulated market conditions, thereby achieving adaptability while keeping the implementation straightforward through rule-based parameter adjustment.
3Reliability
If multiple candidate investment rules are simulated and evaluated, then the best investment strategy can be selected, but the calculation complexity increases
Solution Approach 1:
The patent extracts and isolates the complex simulation and evaluation process into a separate preliminary stage that operates independently from the actual investment decision-making. By performing all heavy calculations, simulations, and rule comparisons in advance using historical data, the system reduces the complexity during real-time operation to simply selecting from pre-evaluated rules, thus maintaining high reliability without overwhelming calculation complexity during execution.
Data Source
AI summary
An investment strategy rule generation method including the following steps is provided. Firstly, an investment strategy rule generator generates an investment strategy model according to an investment history trend. Then, a total investment return of each of N candidate investment rules is obtained by the investment strategy rule calculator, wherein each of the N candidate investment rules includes a candidate market direction rule. The obtaining step includes: performing an investment simulation in each of multiple time points in a time window of a time interval. Then, the total investment return under the operation of the investment simulations over the time interval is calculated by the investment strategy rule calculator. Then, the candidate investment rule corresponding to the best of the total investment returns is used as an investment strategy rule of the investment strategy model by the investment strategy rule calculator.