A Deep Reinforcement Learning Approach for Portfolio Optimization of Brazilian Assets Using Fundamental and Sentiment Indicators

The Brazilian capital market presents unique challenges for portfolio optimization due to its volatility and information asymmetries. This paper proposes a Deep Reinforcement Learning (DRL) framework that integrates fundamental indicators, Portuguese-language news sentiment (via Gemini Pro), and market data (prices, volume). Five DRL algorithms (A2C, PPO, DDPG, TD3, SAC) were trained and evaluated across three feature scenarios using performance metrics such as Sharpe Ratio, Annual Return, and Maximum Drawdown. News sentiment classification and entity extraction were performed using Gemini Pro LLM. Statistical validation over 44 executions, including Shapiro-Wilk and Kruskal-Wallis tests, showed no significant differences among DRL methods. However, all DRL approaches outperformed the Ibovespa index and uniform Buy-and-Hold benchmark, highlighting the value of combining DRL with localized and diverse data sources for portfolio optimization in emerging markets.