Multi-factor Based Stock Price Prediction Using Hybrid Neural Networks with Attention Mechanism

Li, Chen; Zhang, Xu; Qaosar, Mahboob; Ahmed, Shamim; Alam, Kazi Md. Rokibul; Morimoto, Yasuhiko

doi:10.1109/dasc/picom/cbdcom/cyberscitech.2019.00176

Cited by 22 publications

(18 citation statements)

References 17 publications

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“…Experiments on Chinese stock market index CSI300 showed the superiority of MFNN to traditional machine learning models, statistical models, CNN, RNN, and LSTM in terms of the accuracy, profitability, and stability. In fact, a more commonly used hybrid model is the CNN-LSTM model [14][15][16][17][18][19][20]. For example, in [14], the authors found that the CNN-LSTM model is superior to LSTM and CNN in stock price movement prediction.…”

Section: Model Enhancementmentioning

confidence: 99%

“…For example, in [14], the authors found that the CNN-LSTM model is superior to LSTM and CNN in stock price movement prediction. In [17], Li et al added an attention mechanism to the CNN-LSTM model and further improved its scalability and prediction accuracy. Similarly, Zhou et al [18] developed a generic framework by using LSTM and CNN for adversarial training to predict stock price direction in the high-frequency stock market and achieved significant results.…”

Section: Model Enhancementmentioning

confidence: 99%

“…CNN [8], RNN [10], and LSTM [11] were commonly used deep learning models in predicting the stock price movement. In addition, constructing hybrid models is a popular way to enhance the performance of model, such as SVM-ANN model [12], CNN-SVM model [13], and CNN-LSTM model [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, we proposed a CNN-LSTM model for stock price movement forecast. Compared with other CNN-LSTM models [14][15][16][17][18][19][20], the main difference between them and our proposed hybrid model lies on the CNN-based feature extraction module. eir feature extraction modules mainly aimed at extracting features from one-dimensional or two-dimensional input variables, while ours was aimed at three-dimensional input tensor.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Yang

Zhai

Tao

2020

Mathematical Problems in Engineering

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The prediction of stock price movement direction is significant in financial studies. In recent years, a number of deep learning models have gradually been applied for stock predictions. This paper presents a deep learning framework to predict price movement direction based on historical information in financial time series. The framework combines a convolutional neural network (CNN) for feature extraction and a long short-term memory (LSTM) network for prediction. We specifically use a three-dimensional CNN for data input in the framework, including the information on time series, technical indicators, and the correlation between stock indices. And in the three-dimensional input tensor, the technical indicators are converted into deterministic trend signals and the stock indices are ranked by Pearson product-moment correlation coefficient (PPMCC). When training, a fully connected network is used to drive the CNN to learn a feature vector, which acts as the input of concatenated LSTM. After both the CNN and the LSTM are trained well, they are finally used for prediction in the testing set. The experimental results demonstrate that the framework outperforms state-of-the-art models in predicting stock price movement direction.

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Section: Model Enhancementmentioning

confidence: 99%

Section: Model Enhancementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Yang

Zhai

Tao

2020

Mathematical Problems in Engineering

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“…CNN-LSTM has also been utilized in stock price forecasting with combination of attention mechanism, a form of summation with weights, for improving regression capability of the CNN-LSTM model and yielded good results on two evaluation datasets [34]. The used datasets are JQData which the authors collected and Pingan Bank; each contains data from April 1 st , 2017 until March 30 th , 2019 and each was split for 90% training and 10% testing data.…”

Section: Cnn-lstm For Stock Price Forecastingmentioning

confidence: 99%

Stock Price Forecasting: Review and Experiment on Time Series Data Processing

2020

IJATCSE

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Stock price forecasting may present a huge benefit for economy sector as the ability to predict future stock prices may help related parties prepare for the predicted or forecasted condition. Many artificial intelligence-related approaches have been proposed, and this paper provides reviews of time series processing in general and stock price forecasting progresses for newcomers to get acquainted with. In addition, experimentation of stock price forecasting on several existing approaches from time series processing in general are provided to gain insight on which approach may perform better and which may merit further research on stock price forecasting task.

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Volatility forecasting for stock market index based on complex network and hybrid deep learning model

Song,

Lei,

Tang

et al. 2023

Journal of Forecasting

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The existing literature on the volatility forecasting less considered the co‐movement among stock markets from the spatial dimension. This paper builds the hybrid convolutional neural networks (CNNs)–gated recurrent unit (GRU) model for volatility forecasting under high frequency financial data based on transaction information and the topological characteristics constructed through the complex network of multi‐market symbol patterns. The hybrid neural network CNN‐GRU combines the advantages of CNN automatically extracting features for the input indicators and GRU processing long and short‐term serially dependent features, which can better improve the forecasting accuracy. The empirical results show that with the integration of topological characteristics as the indicators based on complex network, the deep learning model has a significant improvement of one‐step and multi‐step volatility forecasting accuracy for the China's and the US stock markets. The research in this paper provides a complete index system from the spatial dimension and a more accurate and robust volatility forecasting method under the high‐frequency financial data.

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Multi-factor Based Stock Price Prediction Using Hybrid Neural Networks with Attention Mechanism

Cited by 22 publications

References 17 publications

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Stock Price Forecasting: Review and Experiment on Time Series Data Processing

Volatility forecasting for stock market index based on complex network and hybrid deep learning model

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