A Hybrid Convolutional Recurrent (CNN-GRU) Model for Stock Price Prediction

Jaiswal, Rashi; Singh, Brijendra

doi:10.1109/csnt54456.2022.9787651

Cited by 24 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lu [15] et al make full use of the advantages of deep learning algorithms by combining CNN with LSTM (CNN-LSTM) to predict the stock closing price of the next day's forecasting. Rashi [16] et al propose the CNN-GRU model and the experiments show that their model has better prediction performance than CNN-LSTM on their data sets [17].…”

Section: Related Workmentioning

confidence: 99%

Stock price prediction in Chinese stock markets based on CNN-GRU-attention model

Huang

et al. 2022

International Symposium on Artificial Intelligence and Robotics 2022

View full text Add to dashboard Cite

Stock price prediction is a hot topic and has attracted the sufficient attention of both regulatory authorities and financial institutions. Because the fluctuation of stock prices is the result of many different factors, it is not easy to make stock price prediction. Traditional prediction solutions are mainly using simple linear models based on statistical and econometric models, these solutions are difficult to support nonstationary time series data. With the development of deep learning, some newly models can not only support non-linear data, but also retain useful information for better forecasting the stock prices. This paper aims to construct a CNN-GRU-Attention based model for price prediction in Chinese stock markets. First, the convolutional and pooling layers of CNN are used to extract features of factor correlation information from the input data; then, the output of feature matrix is used as input for the GRU model to forecast correlation; finally, the Attention mechanism is used to focus on the important characteristics of stock prices and optimize model structure. We collect multi-dimensional stock data of the China SSE 50 index from 2011 to 2021 as our dataset and conduct a set of experiments to compare the performance, which measured in terms of their Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and R squared (R²) score. The proposed model is superior to other models: MAPE decreased by 11.23%, RMSE decreased by 5.71% and R²score improved by 0.41%, which shows that the CNN-GRU-Attention model outperforms state-of-the-art approaches in forecasting stock price.

show abstract

Section: Related Workmentioning

confidence: 99%

Stock price prediction in Chinese stock markets based on CNN-GRU-attention model

Huang

et al. 2022

International Symposium on Artificial Intelligence and Robotics 2022

View full text Add to dashboard Cite

show abstract

“…Gong et al apply a CNN-based model to contruct a three-category prediction model to forecast AAPL, showing that the performance of the model can be improved using technical indicators 14 . Recently, some novel hybrid models combining CNN and LSTM are proposed [15][16][17][18] , which can capture key features to improve the forecasting accuracy.…”

Section: Related Workmentioning

confidence: 99%

Price movement prediction using deep learning: a case study of the China futures market

Wang

2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

View full text Add to dashboard Cite

Financial time series prediction has always been a tricky problem due to the uncertainty in the market. It has attracted attention from industry to academia. In recent years, deep learning has shown excellent performance in many different fields. More and more researchers try to apply deep learning on financial markets. In this paper, the complete modeling process of price movement prediction is introduced. Based on high frequency data Limit Order Books, an improved deep learning model combining the local feature extraction ability of Convolutional Neural Network (CNN) with the sequential feature extraction ability of Long Short-Term Memory (LSTM) is proposed and evaluated on RB dominant contracts in the China futures market. Based on the experimental results, it is concluded that our model’s performance on prediction is better than that of single CNN and LSTM models. Through back testing, trading based on the predicted results of the proposed model can yield significantly more returns than other models.

show abstract

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

Zhang,

Sjarif,

Ibrahim

2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Accurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a notable rise in research related to applying deep learning models to price forecasting, resulting in a rapid accumulation of new knowledge. Therefore, we conducted a literature review of relevant studies over the past 3 years with a view to aiding researchers and practitioners in the field. This review delves deeply into deep learning‐based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. In particular, detailed information is provided on advanced models for price forecasting, such as Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and deep quantum neural networks (DQNNs). The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting, extending from point prediction to interval prediction using deep learning models, scrutinizing the reliability and validity of decomposition ensembles, and exploring the influence of data volume on model performance.This article is categorized under: Technologies > Prediction Technologies > Artificial Intelligence

show abstract

A Hybrid Convolutional Recurrent (CNN-GRU) Model for Stock Price Prediction

Cited by 24 publications

References 22 publications

Stock price prediction in Chinese stock markets based on CNN-GRU-attention model

Stock price prediction in Chinese stock markets based on CNN-GRU-attention model

Price movement prediction using deep learning: a case study of the China futures market

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

Contact Info

Product

Resources

About