Fundamental analysis is the predominant investment strategy for investors focused on long-term returns. This approach evaluates the operations and prospects of companies, taking into account both macroeconomic factors and company-specific elements, in order to identify investment opportunities with high return potential. This paper seeks to gather detailed and relevant information on the benefits and strategies of machine learning applications in the economy, using fundamental analysis as the main metric within the financial sector. It also aims to identify artificial intelligence (AI) techniques applied to this segment and their impact on stock market forecasting, since predicting financial market volatility has always been a complex challenge. In order to understand this process, a systematic literature review was carried out, which found that machine learning techniques are often used to predict asset
prices and their trends. This reinforces the importance and effectiveness of using AI algorithms to anticipate future directions in the stock market. Among the most common models found are, respectively: neural network, genetic algorithm, deep learning, random forest, time series, text mining, natural language processing and deep neural network. The results indicate that research into artificial intelligence is evolving rapidly and emphasize the growing potential of AI techniques in the financial sector, making it an almost indispensable tool for economic agents.
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