Stock price nowcasting and forecasting with deep learning

Chuanzhi Fan · X. D. Zhang

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

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.

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References

2014Stock Price Prediction Using the ARIMA Model 2004A hybrid ARIMA and support vector machines model in stock price forecasting 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies 2018Time series forecasting using artificial neural networks methodologies: A systematic review 2021A Survey of Forex and Stock Price Prediction Using Deep Learning 2011Stock index forecasting based on a hybrid model 2019Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data 2008Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs)

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