AI and ML in Stock Analysis
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This paper presents an investigation into the role of artificial intelligence (AI) in enhancing stock analysis and investment decision-making. The deep integration of AI in financial markets has enabled rapid and more precise analysis, in contrast to traditional methods that generally suffer from human biases and lesser data handling capacity. Studies indicate that AI is capable of working with huge datasets, minimizing the influence of emotions in decision-making, and blending both figures and market news to give a more extensive view of the changes in stock prices. The research was based on stock data of Apple Inc. (AAPL) for the years 2019-2024, collected from Yahoo Finance. This data was structured and analyzed through a machine learning algorithm (linear regression), with 80% of the data allocated for training and 20% for testing. Besides, the sentiment behind financial news was examined and recorded as positive, negative, or neutral to observe the impact of news on stock prices. Result-wise, it is evident that the AI model came up with estimations that were very close to the real figures as demonstrated by low error numbers and a quite high R score of 0.91. A descriptive statistical summary revealed that the mean closing price was $154.32, and volatility of daily returns was at a moderate level. Sentiment analysis showed that good news dominated (46.7%) the story, followed by news that was quite neutral or negative. In conclusion, these results provide strong evidence that AI contributes significantly to stock analysis enhancement by making it more accurate, less biased, and capable of handling enormous volumes of data. Being non reliable during sudden market changes makes it a support tool and not a replacement.
Keywords
Deep Learning Algorithms, Market Analysis, Patterns, Quantitative & Qualitative Data, Support Tool