Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction

Li, Wei; Bao, Ruihan; Harimoto, Keiko; Chen, Deli; Xu, Jingjing; Su, Qi

doi:10.24963/ijcai.2020/626

Cited by 88 publications

(34 citation statements)

References 8 publications

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“…(x) CNN-BiLSTM-AM: this paper proposes a CNN-BiLSTM-AM method to predict the stock closing price of the next day, which is composed of convolutional neural networks (CNN), bidirectional long-short-term memory (BiLSTM), and an attention mechanism (AM) [18]. (xi) LSTM-RGCN: this paper proposes a more practical objective to predict the overnight stock movement between the previous close price and the open price by making use of the connection among stocks, which models the connection among stocks with their correlation matrix [19]. (xii) FOCUS: this paper proposes a fuzzy 8-momentum inverse uncertain feature system for the 9-classification and quantification of stock features.…”

Section: Complexitymentioning

confidence: 99%

Incorporating Transformers and Attention Networks for Stock Movement Prediction

Liu

et al. 2022

Complexity

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Predicting stock movements is a valuable research field that can help investors earn more profits. As with time-series data, the stock market is time-dependent and the value of historical information may decrease over time. Accurate prediction can be achieved by mining valuable information with words on social platforms and further integrating it with actual stock market conditions. However, many methods still cannot effectively dig deep into hidden information, integrate text and stock prices, and ignore the temporal dependence. Therefore, to solve the above problems, we propose a transformer-based attention network framework that uses historical text and stock prices to capture the temporal dependence of financial data. Among them, the transformer model and attention mechanism are used for feature extraction of financial data, which has fewer applications in the financial field, and effective analysis of key information to achieve an accurate prediction. A large number of experiments have proved the effectiveness of our proposed method. The actual simulation experiment verifies that our model has practical application value.

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Section: Complexitymentioning

confidence: 99%

Incorporating Transformers and Attention Networks for Stock Movement Prediction

Liu

et al. 2022

Complexity

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“…Recent SMP studies take stock relations into consideration [Chen et al 2018;Cheng and Li 2021;Li et al 2020a;Sawhney et al 2020b]. For instance, Chen et al [2018] proposed to incorporate corporation relationship via graph convolutional neural networks for stock price prediction.…”

Section: Stock Relations Modelingmentioning

confidence: 99%

“…presented a multi-task recurrent neural network (RNN) with high-order Markov random fields (MRFs) to predict stock price movement direction using stock's historical records together with its correlated stocks. Li et al [2020a] proposed a LSTM Relational Graph Convolutional Network (LSTM-RGCN) model, which models the connection among stocks with their correlation matrix. Ye et al [2020] encoded multiple relationships among stocks into graphs based on financial domain knowledge and utilized GCN to extract the cross effect based on these pre-defined graphs for stock prediction.…”

Section: Stock Relations Modelingmentioning

confidence: 99%

“…3.1.1 Bi-typed Entities. Most existing SMP methods solely learn from company relationships in market [Kim et al 2019;Li et al 2020a;Ye et al 2020]. In fact, in most stock markets there are also significant associated executives for listed companies, with rich associative information about these companies [Cai et al 2016 3.1.2 Hybrid-relations.…”

Section: Bi-typed Hybrid-relational Market Knowledge Graph (Mkg)mentioning

confidence: 99%

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Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

Yu¹,

Du²,

Liu³

et al. 2022

Preprint

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Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose DANSMP, a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed DANSMP is capable of improving stock prediction with the constructed MKG.

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“…The efficiency of the LSTM models and Transformer models are significantly reduced when the sequence length is reduced. [26][27][28][29]. The sequence length used in the CNN model is considerably lesser than the ones used for LSTM and transformer models.…”

Section: Related Workmentioning

confidence: 99%

Stock market prediction using deep learning algorithms

Mukherjee

Sadhukhan²,

Sarkar³

et al. 2021

CAAI Trans on Intel Tech

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The Stock Market is one of the most active research areas, and predicting its nature is an epic necessity nowadays. Predicting the Stock Market is quite challenging, and it requires intensive study of the pattern of data. Specific statistical models and artificially intelligent algorithms are needed to meet this challenge and arrive at an appropriate solution. Various machine learning and deep learning algorithms can make a firm prediction with minimised error possibilities. The Artificial Neural Network (ANN) or Deep Feedforward Neural Network and the Convolutional Neural Network (CNN) are the two network models that have been used extensively to predict the stock market prices. The models have been used to predict upcoming days' data values from the last few days' data values. This process keeps on repeating recursively as long as the dataset is valid. An endeavour has been taken to optimise this prediction using deep learning, and it has given substantial results. The ANN model achieved an accuracy of 97.66%, whereas the CNN model achieved an accuracy of 98.92%. The CNN model used 2-D histograms generated out of the quantised dataset within a particular time frame, and prediction is made on that data. This approach has not been implemented earlier for the analysis of such datasets. As a case study, the model has been tested on the recent COVID-19 pandemic, which caused a sudden downfall of the stock market. The results obtained from this study was decent enough as it produced an accuracy of 91%. K E Y W O R D S artificial neural network, convolutional neural network, nifty, stock marketThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction

Cited by 88 publications

References 8 publications

Incorporating Transformers and Attention Networks for Stock Movement Prediction

Incorporating Transformers and Attention Networks for Stock Movement Prediction

Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

Stock market prediction using deep learning algorithms

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