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.

Many investors still rely on their emotions as their primary guide for asset allocation, blindly following one or two news sources that may or may not be an actual synthesis of the market, without any mathematical formulation to support them, even if slightly. This work aims to conduct a Systematic Review using Kitchenham’s protocol to understand the state of the art regarding the combination of Portfolio Optimization techniques and Artificial Intelligence in the context of trading assets, with a special focus on Sentiment Analysis techniques. This was achieved through the definition of search keywords used in 4 different major research databases and selection through inclusion and exclusion criteria. A total of 384 articles published between 2019–2025 were identified, among which 27 articles were selected that fit all the selection criteria. The research questions address the specific techniques employed for optimization, with the most proliferated being Deep Reinforcement Learning.

In this work, we explore machine learning to ob-tain sentiment indicators from financial market text messages collected from Twitter and news in Portuguese. A statistical analysis was carried out with Sperman’s correlation coefficient between sentiment indicators and actual variables in the Brazilian financial market. For sentiment analysis, the MaxEnt and CNN models provided F1-scores of 85% and 96%, for tweets and news, respectively. A result shown a moderate correlation (Cohen’s scale) between some variables such as, amount of tweets and sentiment of the news; market variation and tweet sentiment; amount of retweets published and sentiment of the news; trading volume and tweet sentiment. Moreover, a very large correlation was identified between the amount of negative tweets and retweets, leading to the belief that pessimism is often propagated.

Microblogs have increasingly been used by the crowd to post their thoughts and speeches about everything. Thus, one of the themes is the stock market that is exploited by many researchers. Although obtaining indicators of stock market dynamics through online social networking has been gaining the attention of academia and the business world, there are many questions to be analyzed about their effectiveness. This work presents the development of a simulator for buying and selling stocks based on microblog data. Therefore, we collected tweets about the Brazilian stock exchange market, produced indicators using sentiment analysis and performed a set of heuristics for decision making. The first technique is the composition of the decision-making strategy for buying and selling stocks composed of pure logic, tweets volume thresholds, profit objective and technical analysis in the stock exchange. The contribution of this work is a decision-making architecture using Twitter's data as an index of future expectation about the social mood that may change the market behavior. As a result, it has pointed to attractive profits for Brazilian market actions and many issues that can be analyzed and improved. Our study showed that it is possible to obtain market dynamics information on the Twitter social network and this information could be used to compose stock buying and selling strategies.