Enhancing Risk Mitigation In Business Investments Using Deep Reinforcement Learning

Abstract

Deep reinforcement learning (DRL) has emerged as a game-changing technology in financial decision-making, offering new opportunities for enterprise risk management and investment management. Common risk assessment approaches such as static credit scoring, regression-based models, and traditional time series models like Long Short-Term Memory (LSTM) suffer from serious drawbacks in their ability to capture the dynamic, non-linear, and volatile nature of today's financial markets. This paper introduces a new framework named Adaptive Deep Reinforcement Learning Risk Mitigation (ADRL-RM), which combines Proximal Policy Optimization (PPO), Deep Q-Network (DQN), and multi-source data fusion—encompassing financial indicators (34 features), macroeconomic signals, market indices, and BERT-extracted news sentiment—to provide real-time, context-aware risk assessment for business investment portfolios. The architecture utilizes a Bidirectional LSTM (BiLSTM) encoder and Temporal Pattern Attention (TPA) for feature extraction; state representations are passed to an actor-critic module that jointly optimizes the risk-aversion parameters (λ ∈ [0,1]) as well as rebalancing horizons (h ∈ [1,20] days). Empirical results on U.S. sector ETFs (n=12) and Dow Jones Industrial Average (DJIA) components (n=28) from 2003 to 2023 show that ADRL-RM outperforms baselines: PPO Mean-Variance achieves an annualized return of 15.7%, a Sharpe ratio of 0.887 (vs. 0.286 tangency; ΔSR=0.601, p<0.001HAC-adjusted), and a max drawdown of 30.7% (a 25% reduction). The accuracy of enterprise risk classification is 81.4%, recall (high risk) is 87.6%, and RMSE is 139.29 (R^2=0.91). Ablation results include additive improvements, such as BiLSTM-TPA (+4.3% accuracy), sentiment fusion (+2.1% recall), and PPO (+3.1% accuracy compared to supervised models). The framework offers a management-friendly tool, converting AI signals into interpretable risk ratings (High/Medium/Low) and dynamic allocations, effectively supporting institutional investors.