Attention-Based Event Relevance Model for Stock Price Movement Prediction

Liu, Jian; Chen, Yubo; Liu, Kang; Zhao, Jun

doi:10.1007/978-981-10-7359-5_5

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…Liu et al [26] predicted stock price movements using a novel end-to-end attentionbased event model. They proposed the ATT-ERNN model to exploit implicit correlations between world events, including the effect of event counts and short-term, medium-term, and long-term influence, as well as the movement of stock prices.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions

et al. 2021

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The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. Most studies propose machine learning models to predict stock prices. However, constructing trading strategies is helpful for traders to avoid making mistakes and losing money. We propose an automatic trading framework using LSTM combined with deep Q-learning to determine the trading signal and the size of the trading position. This is more sophisticated than traditional price prediction models. This study used price data from the Taiwan stock market, including daily opening price, closing price, highest price, lowest price, and trading volume. The profitability of the system was evaluated using a combination of different states of different stocks. The profitability of the proposed system was positive after a long period of testing, which means that the system performed well in predicting the rise and fall of stocks.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions

et al. 2021

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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%

“…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%

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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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“…Nelson et al [8] were the first to apply Vanilla LSTM [9] for stock price prediction and proved its effectiveness as its distinguished ability to capture long-term dependencies in input sequences. Combined with LSTM, some other frameworks [10][11][12][13][14][15][16] are also investigated to promote price prediction accuracy. In [10], to discover stock price patterns, the K-means algorithm is firstly used to cluster stock price subsequences, then a multibranch LSTM model is constructed which makes the final prediction based on the learned k clusters.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method

et al. 2020

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Stock trend prediction refers to predicting future price trend of stocks for seeking profit maximum of stock investment. Although it has aroused broad attention in stock markets, it is still a tough task not only because the stock markets are complex and easily volatile but also because real short-term stock data is so limited that existing stock prediction models could be far from perfect, especially for deep neural networks. As a kind of time-series data, the underlying patterns of stock data are easily influenced by any tiny noises. us, how to augment limited stock price data is an open problem in stock trend prediction, since most data augmentation schemes adopted in image processing cannot be brutally used here. To this end, we devise a simple yet effective time-sensitive data augmentation method for stock trend prediction. To be specific, we augment data by corrupting highfrequency patterns of original stock price data as well as preserving low-frequency ones in the frame of wavelet transformation. e proposed method is motivated by the fact that low-frequency patterns without noisy corruptions do not hurt the true patterns of stock price data. Besides, a transformation technique is proposed to recognize the importance of the patterns at varied time points, that is, the information is time-sensitive. A series of experiments carried out on a real stock price dataset including 50 corporation stocks verify the efficacy of our data augmentation method.

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Attention-Based Event Relevance Model for Stock Price Movement Prediction

Cited by 10 publications

References 26 publications

A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions

A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions

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

Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method

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