Can Artificial Intelligence Really Predict Stock Prices Better? A Case Study of Apple Inc.
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The integration of technology in financial decision-making has transitioned from traditional statistical analysis to advanced computational modeling. This study evaluates the efficacy of Artificial Intelligence (AI) in predicting stock market movements, using Apple Inc. as a primary case study. While traditional methods like fundamental and technical analysis often struggle with the volatility and complexity of modern markets, AI offers the ability to identify intricate patterns within large datasets. Using a quantitative methodology, this research compares a traditional 5-day Moving Average against an AI-based predictive trend analysis over a 30-day period of Apple’s historical closing prices. The results indicate that while the moving average provides stability, it lags behind sudden market shifts. Conversely, the AI-driven approach demonstrated greater responsiveness to recent trends and recorded a lower average error margin. The findings support the hypothesis that AI- based models provide superior predictive accuracy, though the study acknowledges risks such as data overfitting.
Keywords
Artificial Intelligence (AI), financial markets, Machine Learning, stock price prediction