DEEP LEARNING IN FINANCIAL ANALYSIS: APPLICATION OF LSTM AND GANs FOR STOCK PRICE PREDICTION

Stock price prediction is a crucial aspect of financial analytics, helping investors make informed decisions. This study examines traditional forecasting methods, such as technical analysis (moving averages SMA, EMA) and statistical models (ARIMA, exponential smoothing). Their advantages and limitations are analyzed, particularly the challenges in capturing complex market patterns. To improve prediction accuracy, the use of modern machine learning approaches is proposed, specifically Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs). The GAN architecture and its ability to model market dynamics even with limited historical data are described. The research is based on real stock market data (AAPL stock prices), and the results are compared with ARIMA and LSTM methods, confirming the effectiveness of the proposed approach.