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Cited by 562Stock Market Forecasting MethodsFinancial Markets and Investment StrategiesTime Series Analysis and Forecasting

Temporal Relational Ranking for Stock Prediction

Open access
Feng, Fuli, He, Xiangnan, Wang, Xiang, Luo, Cheng, Liu, Yiqun, Chua, Tat-Seng·2019·ACM Transactions on Information Systems
DOI 10.1145/3309547
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Abstract

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.

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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
Publisher
Association for Computing Machinery (ACM)
Published
2019-03-05
Volume
37
Issue
2
Pages
1-30
ISSN
  • 1046-8188 (ISSN-L)
  • 1558-2868
DOI
10.1145/3309547
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
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
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

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