Stock Price Nowcasting and Forecasting with Deep Learning

Abstract Recent studies have improved stock price forecasting with the emerging deep learning models. Despite advancements in deep learning, stock price prediction faces significant challenges. Existing studies predominantly focus on forecasting future prices, with limited attention to nowcasting, which predicts current or near-future market states. Additionally, most methods use univariate data, neglecting the valuable interactions between multiple financial variables. This study addresses these challenges by evaluating both forecasting and nowcasting approaches using deep learning. We incorporate multivariate inputs, including opening price, high, low, close, volume, inday-change and trend, to enhance the predictive power of our models. We implement and compare several deep learning methods: Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), Convolutional Neural Networks combined with LSTM (CNN-LSTM), and the transformer-based Patch Time Series Transformer (PatchTST). Our experimental results reveal that the standard LSTM model achieves superior performance compared to the more recent PatchTST and CNN-LSTM models. Specifically, models perform better in nowcasting scenarios, likely due to smaller price fluctuations over shorter periods. Furthermore, our analysis shows that including variables such as opening prices, highest prices, and lowest prices enhances predictive accuracy, whereas trading volume tends to reduce performance. These findings suggest that deep learning models are more effective for real-time or near-term stock price prediction and highlight the importance of multivariate inputs in developing robust prediction models. This study provides valuable insights for enhancing the accuracy and reliability of stock price forecasts, with significant implications for financial analysts and investors.