Deep Reinforcement Learning for Robust Goal-Based Wealth Management

ArXiv ID: 2307.13501 “View on arXiv”

Authors: Unknown

Abstract

Goal-based investing is an approach to wealth management that prioritizes achieving specific financial goals. It is naturally formulated as a sequential decision-making problem as it requires choosing the appropriate investment until a goal is achieved. Consequently, reinforcement learning, a machine learning technique appropriate for sequential decision-making, offers a promising path for optimizing these investment strategies. In this paper, a novel approach for robust goal-based wealth management based on deep reinforcement learning is proposed. The experimental results indicate its superiority over several goal-based wealth management benchmarks on both simulated and historical market data.

Keywords: Reinforcement Learning, Goal-Based Investing, Deep Reinforcement Learning, Wealth Management, Wealth/Portfolio Management

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper utilizes advanced mathematical concepts like Markov Decision Processes, dynamic programming, and stochastic calculus, indicating high math complexity. It also demonstrates empirical rigor through backtesting on historical market data, comparing against benchmarks, and detailing simulation and training procedures.

  flowchart TD
    A["Research Goal: Robust Goal-Based Wealth Management"] --> B["Methodology: Deep Reinforcement Learning DRL"]
    B --> C["Data: Simulated & Historical Market Data"]
    C --> D["Computational Process: Sequential Decision-Making"]
    D --> E["Training: Optimization of Investment Strategies"]
    E --> F["Outcomes: Superior Performance vs Benchmarks"]