Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network

Abstract

Stock price prediction is crucial but also challenging in any trading system in stock markets. Currently, family of recurrent neural networks (RNNs) have been widely used for stock prediction with many successes. However, difficulties still remain to make RNNs more successful in a cluttered stock market. Specifically, RNNs lack power to retrieve discerning features from a clutter of signals in stock information flow. Making it worse, by RNN a single long time cell from the market is often fused into a single feature, losing all the information about time which is essential for temporal stock prediction. To tackle these two issues, we develop in this paper a novel hybrid neural network for price prediction, which is named frequency decomposition induced gate recurrent unit (GRU) transformer, abbreviated to FDGRU-transformer or FDG-trans). Inspired by the success of frequency decomposition, in FDG-transformer we apply empirical model decomposition to decompose the complete ensemble of cluttered data into a trend component plus several informative and independent mode components. Equipped with the decomposition, FDG-transformer has the capacity to extract the discriminative insights from the cluttered signals. To retain the temporal information in the observed cluttered data, FDG-transformer utilizes hybrid neural network of GRU, long short term memory (LSTM) and multi-head attention (MHA) transformers. The integrated transformer network is capable of encoding the impact of different weights from each past time step to the current one, resulting in the establishment of a time series model from a deeper fine-grained level. We appy the developed FDG-transformer model to analyze Limit Order Book data and compare the results with that obtained from other state-of-the-art methods. The comparison shows that our model delivers effective price forecasting. Moreover, an ablation study is conducted to validate the importance and necessity of each component in the proposed model.

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

“…DL hybrids, such as CNN-LSTM, can extract hierarchical features and comprehend sequential dependencies, making them suitable for time-series forecasting like stock prices. 76,77 AMs 81,82 and transformers 85,86 have demonstrated promise in investigating sequential data by allocating varying levels of importance to different time steps, which is pivotal in financial time-series data.…”

“…Hybrid DL approaches frequently combine DL techniques with traditional methods [71][72][73][74][75] or DL architectures with each other, such as CNN-LSTM, LSTM or BiLSTM with attention mechanisms (AMs), transformer models, and graph convolutional neural network (GraphCNN). [76][77][78][79][80][81][82][83][84][85][86][87][88][89][90] These hybrid DL models prove to be efficient in identifying complex patterns and relationships in data due to the high capacity and adaptability of DL architectures, especially in applications like SPF. Chandar 71 proposed a new method for stock trading by combining technical indicators and CNNs, termed TI-CNN.…”

“…This method used BiLSTM, an improved transformer model (MTRAN-TCN), and TCN (temporal convolutional network), aiming to explore the individual benefits of each model. Li and Qian 86 introduced a novel hybrid neural network—the FDG-transformer—specifically developed for predicting stock prices.…”

Literature Review: Machine Learning in Stock Predictions

“…It is designed to capture long-range dependencies and patterns in sequences, making it suitable for tasks like stock forecasting. GRU was specifically designed as a way to address some of the limitations of traditional RNNs, such as the vanishing gradient problem [18] [19]. GRU has a more simple architecture than LSTM, and is equally as viable when it comes to being used for stock predictions.…”

“…GRU has a more simple architecture than LSTM, and is equally as viable when it comes to being used for stock predictions. This is due to the fact that it is capable of yielding strong results [18] as well as outperforming other high-achieving models [19]. This is, in part, due to its fast training speed and efficiency at capturing short-term dependencies within sequences.…”