Predicting Stock Prices Using Artificial Intelligence
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The quick evolution of financial markets has increased the complexity of predicting stock prices, which makes traditional methods less effective. In this context, artificial intelligence and machine learning techniques have emerged as powerful tools and alternatives for financial prediction. This study examines the effectiveness of AI-based models in forecasting stock prices compared to traditional statistical approaches. Using historical stock market data collected from reliable and verified financial databases, this research applies quantitative methods to analyze patterns, relationships and directions within the data. Machine learning algorithms are also used to identify hidden structures that are often missed by traditional models. The findings suggest that AI-based models can improve prediction accuracy in today’s world, but under certain conditions especially when large datasets are available. However, results also highlight some limitations related to market volatility, trends and the unpredictability of external factors. The study shows that AI should be used as a complementary tool rather than a replacement for traditional financial analysis. These findings provide valuable insights, especially for investors and financial analysts who are seeking to improve their decision-making amidst uncertain market factors..
Keywords
Artificial Intelligence, financial markets, forecasting accuracy, Machine Learning, stock price prediction