The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Temporal Relational Ranking for Stock Prediction
Open accessAbstract
Stock prediction aims to predict the future trends of a stock in order to help investors make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction. However, most existing deep learning solutions are not optimized toward the target of investment, i.e., selecting the best stock with the highest expected revenue. Specifically, they typically formulate stock prediction as a classification (to predict stock trends) or a regression problem (to predict stock prices). More importantly, they largely treat the stocks as independent of each other. The valuable signal in the rich relations between stocks (or companies), such as two stocks are in the same sector and two companies have a supplier-customer relation, is not considered. In this work, we contribute a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. Our RSR method advances existing solutions in two major aspects: (1) tailoring the deep learning models for stock ranking, and (2) capturing the stock relations in a time-sensitive manner. The key novelty of our work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution , which jointly models the temporal evolution and relation network of stocks. To validate our method, we perform back-testing on the historical data of two stock markets, NYSE and NASDAQ. Extensive experiments demonstrate the superiority of our RSR method. It outperforms state-of-the-art stock prediction solutions achieving an average return ratio of 98% and 71% on NYSE and NASDAQ, respectively.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Neural Collaborative Filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052569 · Cited by 7,385
Self-citationVrandečić, D., & Krötzsch, M. (2014) · Communications of the ACM · doi:10.1145/2629489 · Cited by 3,367
Wavelets on graphs via spectral graph theory
Hammond, D. K., Vandergheynst, P., & Gribonval, R. (2010) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2010.04.005 · Cited by 2,173
Unsupervised Learning of Video Representations using LSTMs
Srivastava, N., Mansimov, E., & Salakhutdinov, R. (2015) · arXiv · doi:10.48550/arxiv.1502.04681 · Cited by 1,861
Learning with Hypergraphs: Clustering, Classification, and Embedding
Zhou, D., Huang, J., & Schölkopf, B. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0205 · Cited by 1,328
Bao, W., Yue, J., & Rao, Y. (2017) · PLoS ONE · doi:10.1371/journal.pone.0180944 · Cited by 1,225
Textual analysis of stock market prediction using breaking financial news
Schumaker, R. P., & Chen, H. (2009) · ACM Transactions on Information Systems · doi:10.1145/1462198.1462204 · Cited by 805
Text mining for market prediction: A systematic review
Nassirtoussi, A. K., Aghabozorgi, S. R., Wah, T. Y., & Ngo, D. C. L. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.06.009 · Cited by 628
Research works printed but not linked: 21
- Adebiyi Ayodele Ariyo, 2014, Aderemi Oluyinka Adewumi, and Charles Korede Ayo
- Aggarwal Charu C., 2013, Reddy
- Alberg John, 2017, Lipton
- Antoine Bordes Nicolas Usunier Alberto Garcia-Duran Jason Weston and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In NIPS. 2787--2795. Antoine Bordes Nicolas Usunier Alberto Garcia-Duran Jason Weston and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In NIPS. 2787--2795.
- Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2014. Spectral networks and locally connected networks on graphs. In ICLR. https://iclr.cc/archive/2014/conference-proceedings/. Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2014. Spectral networks and locally connected networks on graphs. In ICLR. https://iclr.cc/archive/2014/conference-proceedings/.
- Kyunghyun Cho Bart van Merriënboer ÇaÄlar Gülçehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder--decoder for statistical machine translation. In EMNLP. ACL 1724--1734. Kyunghyun Cho Bart van Merriënboer ÇaÄlar Gülçehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder--decoder for statistical machine translation. In EMNLP. ACL 1724--1734.
- Michaël Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS. 3844--3852. Michaël Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS. 3844--3852.
- Ding Xiao, Deep learning for event-driven stock prediction (IJCAI)
- Dixon Matthew, 2016, Classification-based financial markets prediction using deep neural networks. Algorithmic Finance Preprint
- Claire Donnat Marinka Zitnik David Hallac and Jure Leskovec. 2017. Spectral graph wavelets for structural role similarity in networks. arXiv preprint arXiv:1710.10321. Claire Donnat Marinka Zitnik David Hallac and Jure Leskovec. 2017. Spectral graph wavelets for structural role similarity in networks. arXiv preprint arXiv:1710.10321. · arXiv
- Diederik, 2015, Kingma and Jimmy Ba
- Thomas, 2017, Kipf and Max Welling
- Alex, 2012, Krizhevsky
- Lo Andrew W., A Non-random Walk Down Wall Street
- Maas Andrew L., 2013, Rectifier nonlinearities improve neural network acoustic models, ICML, 30, 3
- Musgrave Gerald L., 1997, A random walk down Wall Street, Bus. Econ., 32, 74
- 10.5555/1331268.1331274 · DOI
- Richard Socher Danqi Chen Christopher D. Manning and Andrew Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In NIPS. 926--934. Richard Socher Danqi Chen Christopher D. Manning and Andrew Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In NIPS. 926--934.
- Weimer Markus, 2007, Smola
- Xu Yan, 2015, International Symposium on Knowledge Acquisition and Modeling (KAM). Atlantis Press, 197--198
- Zhu Xiaojin, 2003, Lafferty
Cited by
Sorted by citations
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401063 · Cited by 5,300
Self-citationApplications of deep learning in stock market prediction: Recent progress
Jiang, W. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115537 · Cited by 809
Cao, Y., Wang, X., He, X., Hu, Z., & Chua, T. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313705 · Cited by 677
Self-citationA Survey of Forex and Stock Price Prediction Using Deep Learning
Hu, Z., Zhao, Y., & Khushi, M. (2021) · Applied System Innovation · doi:10.3390/asi4010009 · Cited by 406
Representation learning for dynamic graphs: A survey
Kazemi, S. M., Goel, R., Jain, K., Kobyzev, I., Sethi, A., Forsyth, P., & Poupart, P. (2019) · arXiv · doi:10.48550/arxiv.1905.11485 · Cited by 338
Metadata
- Authors
- Feng, F., He, X., Wang, X., Luo, C., Liu, Y., & Chua, T.-S.
- Affiliations
- National University of Singapore (SG)
- University of Science and Technology of China (CN)
- Tsinghua University (CN)
- ORCID
- Wang, Xiang
- Venue
- ACM Transactions on Information Systems
- Published
- 2019-03-05
- Volume
- 37
- Issue
- 2
- Pages
- 1-30
- ISSN
- 1046-8188 (ISSN-L)
- 1558-2868
- OpenAlex
- W2893230400
- Cited by
- 553
- References
- 31
- Citations by year
- 2026: 95 · 2025: 111 · 2024: 88 · 2023: 87 · 2022: 78 · 2021: 51 · 2020: 28 · 2019: 15
- FWCI
- 30.77
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Transactions on Information Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- National University of Singapore (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation, Prime Minister? Office, Singapore under its IRC@Singapore Funding Initiative
- 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
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- stock price prediction, stock ranking, time evolution, investment decision support, backtesting, New York Stock Exchange, NASDAQ
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 ›
Are you an author, or do you see a problem with this page? Request a review
