Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis

ArXiv ID: 2402.15994 “View on arXiv”

Authors: Unknown

Abstract

Portfolio management issues have been extensively studied in the field of artificial intelligence in recent years, but existing deep learning-based quantitative trading methods have some areas where they could be improved. First of all, the prediction mode of stocks is singular; often, only one trading expert is trained by a model, and the trading decision is solely based on the prediction results of the model. Secondly, the data source used by the model is relatively simple, and only considers the data of the stock itself, ignoring the impact of the whole market risk on the stock. In this paper, the DQN algorithm is introduced into asset management portfolios in a novel and straightforward way, and the performance greatly exceeds the benchmark, which fully proves the effectiveness of the DRL algorithm in portfolio management. This also inspires us to consider the complexity of financial problems, and the use of algorithms should be fully combined with the problems to adapt. Finally, in this paper, the strategy is implemented by selecting the assets and actions with the largest Q value. Since different assets are trained separately as environments, there may be a phenomenon of Q value drift among different assets (different assets have different Q value distribution areas), which may easily lead to incorrect asset selection. Consider adding constraints so that the Q values of different assets share a Q value distribution to improve results.

Keywords: Deep Reinforcement Learning (DRL), Portfolio Management, Asset Allocation, Deep Q-Network (DQN), Quantitative Trading

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper uses standard deep reinforcement learning (DQN) without advanced derivations, keeping math complexity moderate, but it includes detailed empirical backtesting with specific datasets, performance tables, and transaction cost analysis, demonstrating high empirical readiness.

  flowchart TD
    A["Research Goal:<br>Optimize Portfolio Management &<br>Risk Assessment in Digital Assets"] --> B["Key Methodology: Deep Q-Network DQN"]
    B --> C{"Computational Process:<br>Single Asset Training"}
    C --> D["Outcome:<br>Performance Exceeds Benchmark"]
    C --> E["Issue:<br>Q-value Drift Across Assets"]
    E --> F["Proposed Improvement:<br>Constrain Q-values to<br>Shared Distribution"]
    F --> G["Final Outcome:<br>Enhanced DRL for Portfolio Management<br>Adapting to Financial Complexity"]
    D --> G