AI and Stock prices and trend predictions
2 viewsAbstract
This project explores the use of artificial intelligence (AI) techniques for stock price prediction, a task known for its complexity and uncertainty. Financial markets are influenced by multiple factors, including economic conditions, investor behavior, and unexpected events, making accurate forecasting challenging. The study begins by reviewing existing literature, highlighting the transition from traditional statistical models to more advanced machine learning and deep learning approaches. The methodology involves collecting historical stock data, preprocessing it to ensure quality, and selecting relevant features such as price indicators and trading volume. Several models were implemented, including Linear Regression, machine learning algorithms, and a deep learning model (LSTM), in order to compare their performance. The dataset was divided into training and testing sets, and model performance was evaluated using standard metrics such as MSE, RMSE, and MAE. The results showed that while all models were able to capture general trends, more advanced models, especially LSTM, provided more accurate predictions. However, the findings also confirmed that stock markets remain inherently unpredictable. In conclusion, AI can significantly improve stock prediction compared to traditional methods, but it should be used as a decision-support tool rather than a guaranteed forecasting solution.
Keywords
Artificial Intelligence, Deep Learning, Machine Learning, stock price prediction, Time Series Analysis