Reinforcement Learning for Financial Consolidation: A2C Model with Sentiment and Temporal Data

Financial markets present inherent challenges due to their complex, non-linear dynamics and susceptibility to various external factors, such as macroeconomic indicators, investor sentiment, and market volatility. Traditional stock trading models, relying heavily on historical price data, often fall short in capturing the complexities of market behavior. This paper proposes an advanced financial trading system based on the Advantage Actor-Critic (A2C) reinforcement learning framework. The model integrates Long Short-Term Memory (LSTM) networks to capture temporal dependencies in stock prices, as well as sentiment analysis to incorporate market psychology into trading decisions. Combining these elements, the model optimizes cumulative portfolio returns and effectively manages risk, as evidenced by enhanced Sharpe ratios and reduced volatility. A special reward function with built-in volatility penalties and entropy regularization also helps make sure that decisions are fair by encouraging exploration and stopping solutions that aren't the best. Numerous tests demonstrate that the proposed model outperforms the standard Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) approaches in numerous financial domains. These results show that combining advanced reinforcement learning models with outside data sources could help people make better decisions in real-life financial markets. This implies that tasks such as combining finances, creating budgets, making predictions, and integrating ERP systems could utilize the model.