Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network

By Dang Lien Minh, Abolghasem Sadeghi-Niaraki, Huynh Duc Huy, Kyungbok Min, Hyeonjoon Moon. 2018

225 citations recorded by the source corpus.

Financial news has been proven to be a crucial factor which causes fluctuations in stock prices. However, previous studies heavily relied on analyzing shallow features and ignored the structural relation among words in a sentence. Several sentiment analysis studies have tried to point out the relationship between investors’ reaction and news events. However, the sentiment dataset was usually constructed from the lingual dataset which is unrelated to the financial sector and led to poor performance. This paper proposes a novel framework to predict the directions of stock prices by using both financial news and sentiment dictionary. The original contributions of this paper include the proposal of a novel two-stream gated recurrent unit network and Stock2Vec—a sentiment word embedding trained on financial news dataset and Harvard IV-4. Two main experiments are conducted: the first experiment predicts S&P 500 index stock price directions using the historical S&P 500 prices and the articles crawled from Reuters and Bloomberg, and the second experiment forecasts the price trends of VN-index using VietStock news and stock prices from cophieu68. Results show that: 1) two-stream GRU outperforms state-of-the-art models; 2) Stock2Vec is more efficient in dealing with financial datasets; and 3) applying the model, a simulation scenario proves that our model is effective for the stock sector.

Subfields: Electrical and Electronic Engineering, Finance, Management Science and Operations Research, Model Architectures, Time-Series & Forecasting

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Citations in the corpus

Deep Learning for Financial Applications : A Survey (2020, methodology): “Since financial text mining is overtaking the other topics in an accelerated fashion, new data models like Stock2Vec [168] can be enhanced for better and more representative models.”
Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…RNN Liu et al. (2018) HCAN BoW (Joachims, 1998), Fast- Text (Joulin et al., 2017), Structured-Event (Ding et al., 2014), IAN (Ma et al., 2017) Lien Minh et al. (2018) two-stream GRU LSTM, GRU Siami-Namini et al. (2018) LSTM ARIMA Tsang et al. (2018) LSTM WT+SAEs+LSTM (Bao et al., 2017) Xu &…”
A Survey on Internet of Things and Cloud Computing for Healthcare (2019, background): “The rise of portable devices, artificial intelligence (AI) [6], and cloud computing ensures a firm foundation for the evolution of IoT in the healthcare sector to revolutionize every aspect of human lives.”
A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, methodology): “…five of the other deep learning methods models used the multivariable input and only one paper used candlestick charts alone as input. used LSTM based model;paper [46], [47], [87] used DNN based model; paper [54], [55] used RNN based model and paper [68], [70],[86] used other deep learning method.”
Stock price prediction based on deep neural networks (2019, methodology): “Other hyperparameters were similarly set based on the grid searching method and the relevant literature [18, 19] as follows: time step, 10; initial learning rate, 0.”