Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction
Abstract
Stock trend prediction, aiming at predicting future price trend of stocks, plays a key role in seeking maximized profit from the stock investment. Recent years have witnessed increasing efforts in applying machine learning techniques, especially deep learning, to pursue more promising stock prediction. While deep learning has given rise to significant improvement, human investors still retain the leading position due to their understanding on stock intrinsic properties, which can imply invaluable principles for stock prediction. In this paper, we propose to extract and explore stock intrinsic properties to enhance stock trend prediction. Fortunately, we discover that the repositories of investment behaviors within mutual fund portfolio data form up a gold mine to extract latent representations of stock properties, since such collective investment behaviors can reflect the professional fund managers' common beliefs on stock intrinsic properties. Powered by extracted stock properties, we further propose to model the dynamic market state and trend using stock representations so as to generate the dynamic correlation between the stock and the market, and then we aggregate such correlation with dynamic stock indicators to achieve more accurate stock prediction. Extensive experiments on real-world stock market data demonstrate the effectiveness of stock properties extracted from collective investment behaviors in the task of stock prediction.
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Matrix Factorization Techniques for Recommender Systems
Koren, Y., Bell, R. M., & Volinsky, C. (2009) · Computer · doi:10.1109/mc.2009.263 · Cited by 11,232 · see citing works
Clustering by Passing Messages Between Data Points
Frey, B. J., & Dueck, D. (2007) · Science · doi:10.1126/science.1136800 · Cited by 6,733 · see citing works
Deep learning with long short-term memory networks for financial market predictions
Fischer, T. G., & Krauß, C. (2017) · European Journal of Operational Research · doi:10.1016/j.ejor.2017.11.054 · Cited by 3,000 · see citing works
Learning to Rank for Information Retrieval
Liu, T. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000016 · Cited by 1,575 · see citing works
Self-citationPreferences for Stock Characteristics As Revealed by Mutual Fund Portfolio Holdings
FALKENSTEIN, E. G. (1996) · The Journal of Finance · doi:10.1111/j.1540-6261.1996.tb05204.x · Cited by 1,058 · see citing works
Stock market's price movement prediction with LSTM neural networks
Nelson, D. M. Q., Pereira, A. C. M., & de Oliveira, R. A. (2017) · 2017 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966019 · Cited by 951 · see citing works
(1999) · Choice Reviews Online · doi:10.5860/choice.36-4016 · Cited by 773 · see citing works
Predicting stock market index using fusion of machine learning techniques
Patel, J., Shah, S. S., Thakkar, P., & Kotecha, K. V. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.10.031 · Cited by 611 · see citing works
Grossberg, S. (2013) · Scholarpedia · doi:10.4249/scholarpedia.1888 · Cited by 602 · see citing works
Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction
Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014) · Journal of Applied Mathematics · doi:10.1155/2014/614342 · Cited by 581 · see citing works
A Bayesian regularized artificial neural network for stock market forecasting
Ticknor, J. L. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.04.013 · Cited by 518 · see citing works
Recurrent neural network and a hybrid model for prediction of stock returns
Rather, A. M., Agarwal, A., & Sastry, V. N. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.12.003 · Cited by 506 · see citing works
Non-negative tensor factorization with applications to statistics and computer vision
Shashua, A., & Hazan, T. (2005) · International Conference on Machine Learning · doi:10.1145/1102351.1102451 · Cited by 502 · see citing works
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns
Zhang, L., Aggarwal, C. C., & Qi, G.-J. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098117 · Cited by 476 · see citing works
Recurrent neural networks: design and applications
Stoianov, I. (1999) · Cited by 442 · see citing works
Deep learning for stock prediction using numerical and textual information
Akita, R. M., Yoshihara, A., Matsubara, T., & Uehara, K. (2016) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis.2016.7550882 · Cited by 420 · see citing works
On Mutual Fund Investment Styles
Chan, L. K. C., Chen, H., & Lakonishok, J. (2002) · Review of Financial Studies · doi:10.1093/rfs/15.5.1407 · Cited by 387 · see citing works
Technical analysis of stock trends
Edwards, R. D., Magee, J. H., & Bassetti, W. H. C. (1997) · Choice Reviews Online · doi:10.5860/choice.35-2224 · Cited by 365 · see citing works
Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction
Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.09.029 · Cited by 326 · see citing works
Text Mining using Non-Negative Matrix Factorizations
Pauca, V. P., Shahnaz, F., Berry, M. W., & Plemmons, R. J. (2004) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972740.45 · Cited by 311 · see citing works
Nassirtoussi, A. K., Aghabozorgi, S. R., Wah, T. Y., & Ngo, D. C. L. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.08.004 · Cited by 277 · see citing works
Exploiting Topic based Twitter Sentiment for Stock Prediction
Si, J., Mukherjee, A., Liu, B., Li, Q., Li, H., & Deng, X. (2013) · doi:10.13140/2.1.3604.7043 · Cited by 210 · see citing works
Rogers, A., Boyd-Graber, J., & Okazaki, N. (2023) · doi:10.18653/v1/2023.acl-short · Cited by 208 · see citing works
Investment Analysis and Portfolio Management.
Baxter, N. D., Cohen, J. B., & Zinbarg, E. D. (1969) · The Journal of Finance · doi:10.2307/2325368 · Cited by 188 · see citing works
Stock price prediction using neural networks: A project report
Schöneburg, E. (1990) · Neurocomputing · doi:10.1016/0925-2312(90)90013-h · Cited by 173 · see citing works
Laboissiere, L. A., Fernandes, R. A. S., & Lage, G. G. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.06.005 · Cited by 168 · see citing works
Investment Analysis and Portfolio Management
Chandra, P. (2004) · Cited by 145 · see citing works
Bisoi, R., & Dash, P. K. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.01.039 · Cited by 120 · see citing works
Simultaneous optimization of artificial neural networks for financial forecasting
Kim, K.-J., & Ahn, H. (2011) · Applied Intelligence · doi:10.1007/s10489-011-0303-2 · Cited by 62 · see citing works
Technical Analysis of Stock Trends
Hooper, C. M., Thompson, C., Laver-Bradbury, C., & Gale, C. K. (2012) · CRC Press eBooks · doi:10.1201/b14301 · Cited by 61 · see citing works
Stock Market Forecasting Techniques: A Survey
Sharma, S., Chouhan, M., & Jyoti, G. (2014) · Cited by 53 · see citing works
Can Online Emotions Predict the Stock Market in China?
Zhou, Z., Zhao, J., & Xu, K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48740-3_24 · Cited by 51 · see citing works
Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression
Li, L., Leng, S., Yang, J., & Yu, M. (2016) · Mathematical Problems in Engineering · doi:10.1155/2016/1285768 · Cited by 36 · see citing works
Stock market forecasting using recurrent neural network
Gao, Q. (2016) · MOspace Institutional Repository (University of Missouri) · Cited by 33 · see citing works
Flagged first, then by citations
3 of the 37 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts
Yoo, J., Soun, Y., Park, Y.-C., & Kang, U. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467297 · Cited by 152 · see citing works
XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification
Fauvel, K., Lin, T., Masson, V., Fromont, É., & Termier, A. (2021) · Mathematics · doi:10.3390/math9233137 · Cited by 146 · see citing works
Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction
Xiang, S., Cheng, D., Shang, C., Zhang, Y., & Liang, Y. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557089 · Cited by 140 · see citing works
Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
Lin, H., Zhou, D., Liu, W., & Bian, J. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467358 · Cited by 84 · see citing works
Self-citationZhao, Y., Du, H., Liu, Y., Wei, S., Chen, X., Zhuang, F., Li, Q., & Kou, G. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3220520 · Cited by 85 · see citing works
Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction
Ye, J., Zhao, J., Ye, K., & Xu, C. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412695 · Cited by 79 · see citing works
Reinforcement Learning for Quantitative Trading
Sun, S., Wang, R., & An, B. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3582560 · Cited by 91 · see citing works
A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist
Zhang, W., Zhao, L., Xia, H., Sun, S., Sun, J., Qin, M., Li, X., Zhao, Y., Zhao, Y., Cai, X., et al. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671801 · Cited by 74 · see citing works
“For instance, if a past product launch significantly boosted a company’s stock, a recent launch might have a similar effect 3 .”
Self-citationMulti-Granularity Residual Learning with Confidence Estimation for Time Series Prediction
Hou, M., Xu, C., Li, Z., Liu, Y., Liu, W., Chen, E., & Bian, J. (2022) · Proceedings of the ACM Web Conference 2022 · doi:10.1145/3485447.3512056 · Cited by 59 · see citing works
Self-citationShi, Y., Wang, Y., Qu, Y., & Chen, Z. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-01817-6 · Cited by 56 · see citing works
Stock Volatility Prediction by Hybrid Neural Network
Wang, Y.-J., Liu, H., Guo, Q., Xie, S., & Zhang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2949074 · Cited by 44 · see citing works
Graph-Based Stock Recommendation by Time-Aware Relational Attention Network
Gao, J., Ying, X., Xu, C., Wang, J., Zhang, S., & Li, Z. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3451397 · Cited by 43 · see citing works
Accurate Stock Movement Prediction with Self-supervised Learning from Sparse Noisy Tweets
Soun, Y., Yoo, J., Cho, M.-Y., Jeon, J., & Kang, U. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020720 · Cited by 40 · see citing works
Time-aware Graph Relational Attention Network for Stock Recommendation
Ying, X., Xu, C., Gao, J., Wang, J., & Li, Z. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412160 · Cited by 36 · see citing works
Deep Portfolio Optimization via Distributional Prediction of Residual Factors
Imajo, K., Minami, K., Ito, K., & Nakagawa, K. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16095 · Cited by 33 · see citing works
Hou, M., Xu, C., Liu, Y., Liu, W., Bian, J., Wu, L., Li, Z., Chen, E., & Liu, T.-Y. (2021) · Proceedings of the 30th ACM International Conference on Information & Knowledge Management · doi:10.1145/3459637.3482483 · Cited by 35 · see citing works
Self-citationYang, Z., Zhao, T., Wang, S., & Li, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122134 · Cited by 32 · see citing works
Sun, S., Xue, W., Wang, R., He, X., Zhu, J., Li, J., & An, B. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557283 · Cited by 39 · see citing works
Enhancing stock movement prediction with market index and curriculum learning
Yang, J., Zhang, W., Zhang, X., Zhou, J., & Zhang, P. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118800 · Cited by 21 · see citing works
Talent Demand Forecasting with Attentive Neural Sequential Model
Zhang, Q., Zhu, H., Sun, Y., Liu, H., Zhuang, F., & Xiong, H. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467131 · Cited by 20 · see citing works
Stock Ranking Prediction Using List-Wise Approach and Node Embedding Technique
Saha, S., Gao, J., & Gerlach, R. (2021) · IEEE Access · doi:10.1109/access.2021.3090834 · Cited by 22 · see citing works
Efficient Continuous Space Policy Optimization for High-frequency Trading
Han, L., Ding, N., Wang, G., Cheng, D., & Liang, Y. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599813 · Cited by 19 · see citing works
Zhou, F., Zhang, Q., Zhu, Y., & Li, T. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119020 · Cited by 19 · see citing works
A representation learning framework for stock movement prediction
Feng, W., Ma, X., Li, X., & Zhang, C. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110409 · Cited by 16 · see citing works
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Sun, S., Wang, X., Xue, W., Lou, X., & An, B. (2023) · Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3580305.3599424 · Cited by 16 · see citing works
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Uddin, A., Tao, X., & Yu, D. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482413 · Cited by 15 · see citing works
Wu, Q., Brinton, C. G., Zhang, Z., Pizzoferrato, A., Liu, Z., & Cucuringu, M. (2021) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3490354.3494409 · Cited by 13 · see citing works
Zhang, T., Li, Y., Jin, Y., & Li, J. (2020) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2002.08245 · Cited by 13 · see citing works
DTSMLA: A dynamic task scheduling multi-level attention model for stock ranking
Du, Y., Xie, L., Liao, S., Chen, S., Wu, Y., & Xu, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122956 · Cited by 12 · see citing works
A Dynamic Dual-Graph Neural Network for Stock Price Movement Prediction
Du, K., Mao, R., Xing, F., & Cambria, E. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650440 · Cited by 11 · see citing works
Putra, A. H. P. K., Rivera, K. M., & Pramukti, A. (2023) · Golden Ratio of Mapping Idea and Literature Format · doi:10.52970/grmilf.v3i1.398 · Cited by 9 · see citing works
Digger-Guider: High-Frequency Factor Extraction for Stock Trend Prediction
Liu, Y., Xu, C., Hou, M., Liu, W., Bian, J., Liu, Q., & Liu, T. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3424475 · Cited by 8 · see citing works
Self-citationUnraveling asset pricing with AI: A systematic literature review
Chen, Y., Zhang, L., Xie, Z., Zhang, W., & Li, Q. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112978 · Cited by 8 · see citing works
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Wang, M., Chen, F., Guo, J., & Jia, W. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191523 · Cited by 5 · see citing works
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Liu, H., Hu, B., Zhou, Y., & Zhou, Y. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3547141 · Cited by 4 · see citing works
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Wang, M., Wang, S., Guo, J., & Jia, W. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-02006-1 · Cited by 3 · see citing works
Time horizon-aware modeling of financial texts for stock price prediction
Feng, F., Wang, X., He, X., Ng, R., & Chua, T. (2021) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3490354.3494416 · Cited by 3 · see citing works
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ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend ForecastingPreprint
Tang, H., Wu, L., Liu, W., & Bian, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.06289 · Cited by 3 · see citing works
Self-citationTriayudi, A., Fitri, I., Sumiati, S., & Iksal (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-0248-4_20 · Cited by 2 · see citing works
E2EAI: End-to-End Deep Learning Framework for Active Investing
Wei, Z., Dai, B., & Lin, D. (2023) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3604237.3626848 · Cited by 2 · see citing works
Chen, Y.-S., Kao, C.-L. M., Liu, P.-H., & Tseng, V. S. M. (2024) · Computational Economics · doi:10.1007/s10614-024-10673-7 · Cited by 2 · see citing works
Correlation Matters: A Stock Price Predication Model Based on the Graph Convolutional Network
Xin, C., Han, Q., & Pan, G. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5588-2_20 · Cited by 2 · see citing works
Pleno-Alignment Framework for Stock Trend Prediction
Luo, Y., Zheng, J., Yang, Z., Chen, N., & Wu, D. O. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3561811 · Cited by 2 · see citing works
Bilateral Autotrading Framework for Stock Prediction
Zhou, Q., Liu, H., Li, W., Mo, T., & Wu, B. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533435 · Cited by 2 · see citing works
Multi-Relational Graph Convolution Network for Stock Movement Prediction
Zhou, Z., Zhang, L., Zha, R., Hao, Q., Xu, T., Wu, D., & Chen, E. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892482 · Cited by 3 · see citing works
Zhaoyan, J., Yan, H., Siping, Y., Chen, C., & Su, Q. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-56725-5_12 · Cited by 1 · see citing works
Self-citationTime-Lag and Edge Features Incorporated Graph Attention Network for Stock Movement Prediction
Lai, J., Chen, M., & Cai, J. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228010 · Cited by 1 · see citing works
Prediction of Nikkei VI increase for reducing investment risk using Yahoo! JAPAN stock BBS
Ueda, K., Sasaki, K., Suwa, H., Ogawa, Y., Umehara, E., Yamashita, T., Tsubouchi, K., & Yasumoto, K. (2021) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3498851.3498940 · Cited by 1 · see citing works
Understanding Financial Information Seeking Behavior from User Interactions with Company Filings
Ariannezhad, M., Yahya, M., Meij, E., Schelter, S., & de Rijke, M. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3487553.3524636 · Cited by 1 · see citing works
A Hybrid Approach to Similar Stock Recommender with Taxonomy and Behavior Data
Mao, Y., Chen, M., Dorn, B., & He, Y. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020418 · Cited by 1 · see citing works
Hybrid Heterogeneous Graph Neural Networks for Fund Performance Prediction
Hao, S., Dai, L., Zhang, L., Zhang, S., Wang, C., Qin, C., & Xiong, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40286-9_25 · Cited by 1 · see citing works
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Xu, H., Li, J., Wang, P., Yin, X., & Xue, W. (2023) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3587716.3587814 · Cited by 1 · see citing works
MERGE: Multi-view Relationship Graph Network for Event-Driven Stock Movement Prediction
Liu, C., Xu, T., Liu, Q., Zheng, Z., Peng, J., & Chen, E. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-7238-4_15 · Cited by 1 · see citing works
Mining for the Preference of Funds based on Subgraph Embedding of Fund-Stock Networks
Li, S., Wang, X., & Jiang, X. (2020) · International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) · doi:10.1109/ccci49893.2020.9256660 · Cited by 1 · see citing works
Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal TransportPreprint
Lin, H., Zhou, D., Liu, W., & Bian, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.12950 · Cited by 1 · see citing works
Self-citationFine-Grained Double-View Link Prediction Within the Dynamic Interaction Network
Pang, J., & Ke, W. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-08333-4_21
A Novel Interpretable Stock Selection Algorithm for Quantitative Trading
Li, Z., Lin, W., Wang, J. Z., Peng, P., Lin, J., Chang, V. I. C., & Pan, J. (2022) · International Journal of Grid and High Performance Computing · doi:10.4018/ijghpc.301589
Zhang, K., & Cai, J. (2022) · International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) · doi:10.1109/icicml57342.2022.10009834
Spatial-Temporal Graph Neural Networks Fusing Multiple Data
Xu, H., & Xue, W. (2022) · IEEE International Conference on Frontiers Technology of Information and Computer (ICFTIC) · doi:10.1109/icftic57696.2022.10075229
Putri, S. S., Alhakim, A., Nuraini, R., & Chandra, Y. U. (2023) · International Conference on Industrial Engineering and Operations Management (IEOM) · doi:10.46254/an12.20220619
Analysis of SOE Banking Stock Investment as Knowledge for Millenial Generation
Idries, F. A., Wulandari, N. D., & Ruscitasari, Z. (2023) · JEM17 Jurnal Ekonomi Manajemen · doi:10.30996/jem17.v8i1.8660
EGHAN: Event Graph Enhanced Hypergraph Attention Network for Stock Movement Forecasting
Chen, L., Xue, Z., Shao, X., Li, Y., Ou, S., & Li, B. (2023) · International Conference on Data-driven Optimization of Complex Systems (DOCS) · doi:10.1109/docs60977.2023.10294474
Stock trend prediction based on feature embedding and stock similarity
Song, G., Meng, Q., Li, C., Su, Z., & Zhang, W. (2023) · IEEE Smart World Congress (SWC) · doi:10.1109/swc57546.2023.10448794
Td-Hcn: A Trend-Driven Hypergraph Convolutional Network for Stock RecommendationPreprint
Fang, L., Zhang, C., Ma, X., Zhao, T., Guo, Q., & Li, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4806890
ForumPFN: Online Forum Post Fusion Network for Volatility Index Movement Prediction
Ueda, K., Suwa, H., Umehara, E., Ogawa, Y., Yamashita, T., Tsubouchi, K., & Yasumoto, K. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825644
Uddin, A. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761049
Yuxue, W., & Shuanghong, G. (2026) · Computational Economics · doi:10.1007/s10614-025-11289-1
MDMF: A Market-Mainline-Driven Multi-Feature Fusion Model for Stock Trend Forecasting
Shi, Z.-C., Zhao, Y., Zhang, Y., & Wu, H.-F. (2026) · Applied Sciences · doi:10.3390/app16031648
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Luo, Y., Zheng, J., Chen, N., & Wu, D. O. (2026) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2026.3663488
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Xu, Q., Huan, M., Gao, T., Wang, J., Jia, L., & Qi, D. (2026) · International Review of Economics & Finance · doi:10.1016/j.iref.2026.105681
CMLF Stock Return Prediction Model Enhanced with Fine-Grained Statistical Feature Fusion
Chen, H. (2026) · ITM Web of Conferences · doi:10.1051/itmconf/20268903008
MIST-Net: A market–identity–spatial–temporal network for stock return prediction
Liu, H.-Y., Lü, X., Jia, X., Yu, J.-J., & Tian, H. (2026) · Engineering Analysis with Boundary Elements · doi:10.1016/j.enganabound.2026.107087
- Authors
- Chen, C., Zhao, L., Bian, J., Xing, C., & Liu, T.-Y.
- Affiliations
- Tsinghua University (CN)
- Microsoft Research Asia (China) (CN)
- Venue
- Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2019-07-25
- Pages
- 2376-2384
- OpenAlex
- W2952277250
- Cited by
- 73
- References
- 37
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 8 · 2023: 19 · 2022: 14 · 2021: 15 · 2020: 5 · 2019: 1
- FWCI
- 12.64
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Funders
- NSFC (91646202)
- National Key R&D Program of China (SQ2018YFB140235)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Business, Management and Accounting › Accounting › Financial Analysis and Corporate Governance
- Keywords
- stock trend prediction, investment behavior, stock market correlation, stock price prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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