Temporal Relational Ranking for Stock Prediction

By Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, Tat-seng Chua. ACM Trans. Inf. Syst., 2018

545 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b278f34cde7fb0e94e271ecfa7b0a0ed84c4d866

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Citations in the corpus

Representation Learning for Dynamic Graphs: A Survey (2019, background): “(2018) and Feng et al. (2018a) develop models that take such changes into account for DTDGs.”
A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, methodology): “proposed a novel LSTM model that combined with stock relational model; it had a great performance compared with its benchmarks [59].”
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (2020, background): “Owing to its interpretability and efficiency, it quickly becomes a prevalent formulation of GNNs and is being widely used [11, 29, 47].”
Applications of deep learning in stock market prediction: recent progress (2020, methodology): “Both bi-directional LSTM (BiLSTM) and bi-directional GRU (BGRU) have been used for stock market prediction.”
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences (2019, background): “suffer from the data sparsity issue and cold-start problem. Content-based methods deal with the issues by introducing various side information, such as the contextual reviews [9, 25], relational data [10, 36] and knowledge graphs [6]. Another advantage of additional contents is the improved explaina”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, background): “For the case where the attribute values can change over time as well, Seo et al. [201] and Feng et al. [72] develop models that take such changes into account for DTDGs. Developing models for attributed CTDGs where the attribute values can change over time is an interesting direction for future…”