EXPLORING DEEP LEARNING AND GAN MODELS FOR LEVERAGING STOCK PRICES PREDICTION: A NOVEL ADVERSARIAL FRAMEWORK FOR FINANCIAL TIME SERIES FORECASTING

Abstract

Predicting stock prices is one of the hardest things to do in finance analytics since financial markets are naturally volatile, non-linear, and highly interdependent. Conventional machine learning algorithms often fail to recognise the latent patterns and temporal correlations present in stock market data. This research presents an innovative deep learning system that employs Generative Adversarial Networks (GANs) specifically tailored for stock price prediction by integrating adversarial training with advanced feature engineering methodologies.
Our suggested model, the Financial Adversarial Prediction Network (FAPN), has two generators. One is for short-term price changes and the other is for long-term trend prediction. The discriminator network is augmented with an attention mechanism to improve the differentiation between authentic and synthetic price series. To help make more accurate predictions, technical indicators, news sentiment analysis, and macroeconomic data are all given as multi-modal inputs. The model has another loss function that combines adversarial loss with finance-specific indicators like risk-adjusted returns and directional correctness.

Over the course of ten years, extensive testing involving five prominent stock indices (S&P 500, NASDAQ, Dow Jones, FTSE 100, and Nikkei 225) demonstrates that the new strategy significantly outperforms the previous one. Our FAPN model can predict the average direction of movement with 78.4% accuracy, which is better than the best methods available today, like LSTM (65.2%), ARIMA (58.7%), and traditional GAN approaches (71.3%). The model also does better on the Mean Absolute Percentage Error (MAPE), with an average of 2.34% compared to 4.12% for LSTM and 5.67% for classical statistical methods. Risk-adjusted performance measurements show a 34% increase in the Sharpe ratio compared to previous approaches. The proposed framework enhances the current standards in financial forecasting and elucidates the application of adversarial learning in compound time series forecasting jobs. We have made progress in the areas of novel architectural innovations, strong evaluation procedures, and useful applications for algorithmic trading systems. The tests demonstrate that GANs, when meticulously designed for financial applications, can significantly enhance predictive performance while sacrificing computing efficiency suitable for real-time trading operations.