Gan Based Framework of Decision Support for Stock Market Forecasting

Stock market forecasting is a complex and challenging task due to the highly dynamic, nonlinear, and unpredictable nature of financial markets. Investors and traders continuously seek reliable tools that can help them make informed decisions while minimizing financial risk. Traditional statistical and machine learning approaches often struggle to capture sudden market fluctuations and hidden patterns present in stock price movements. To address these limitations, this paper presents a GAN-based framework for decision support in stock market forecasting. The proposed framework employs Generative Adversarial Networks (GANs) to learn complex market behavior from historical stock data and technical indicators. The generator network predicts future stock price movements, while the discriminator network evaluates the realism of these predictions by comparing them with actual market data. Through this adversarial learning process, the system improves forecasting accuracy and robustness. In addition to prediction, the framework provides decision support in the form of trend interpretation and buy–sell–hold signals, assisting users in practical investment decision-making. The proposed system aims to offer a reliable, intelligent, and data-driven solution for stock market forecasting.