Please use this identifier to cite or link to this item: https://doi.org/10.1145/3309547
Title: Temporal Relational Ranking for Stock Prediction
Authors: FENG FULI 
HE XIANGNAN 
WANG XIANG 
LUO CHENG 
LIU YIQUN 
CHUA TAT SENG 
Keywords: Stock prediction
Learning to rank
Graph-based learning
Issue Date: 1-Mar-2019
Citation: FENG FULI, HE XIANGNAN, WANG XIANG, LUO CHENG, LIU YIQUN, CHUA TAT SENG (2019-03-01). Temporal Relational Ranking for Stock Prediction. ACM Transactions on Information Systems 37 (2). ScholarBank@NUS Repository. https://doi.org/10.1145/3309547
Rights: Attribution-ShareAlike 4.0 International
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.
Source Title: ACM Transactions on Information Systems
URI: https://scholarbank.nus.edu.sg/handle/10635/168269
DOI: 10.1145/3309547
Rights: Attribution-ShareAlike 4.0 International
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