Application of Transformers in Financial Analysis: Forecasting Stock Prices and Trading Volumes

The accurate forecasting of stock prices and trading volumes is critical for financial market participants, including investors, traders, and analysts. Traditional methods such as ARIMA and GARCH have been widely used, but they often struggle with capturing complex patterns in financial time series data. This paper explores the application of Transformer models, originally developed for natural language processing, in the domain of financial analysis. We provide a comprehensive overview of the Transformer architecture and its advantages over conventional models. Using historical stock market data, we build and train a Transformer model to predict future stock prices and trading volumes. Our results demonstrate that Transformers can effectively capture intricate temporal dependencies and deliver more accurate forecasts compared to traditional methods. The findings highlight the potential of Transformer models to enhance decision-making processes in financial markets. Future research directions and practical implications of implementing Transformers in real-world financial systems are also discussed.