{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/temporal-relational-ranking-for-stock","title":"Temporal Relational Ranking for Stock Prediction","arxiv_id":"1809.09441","date":"2018-09-25","proceeding":null,"authors":["Fuli Feng","Xiangnan He","Xiang Wang","Cheng Luo","Yiqun Liu","Tat-Seng Chua"],"abstract":"Stock prediction aims to predict the future trends of a stock in order to\nhelp investors to make good investment decisions. Traditional solutions for\nstock prediction are based on time-series models. With the recent success of\ndeep neural networks in modeling sequential data, deep learning has become a\npromising choice for stock prediction.\n  However, most existing deep learning solutions are not optimized towards the\ntarget of investment, i.e., selecting the best stock with the highest expected\nrevenue. Specifically, they typically formulate stock prediction as a\nclassification (to predict stock trend) or a regression problem (to predict\nstock price). More importantly, they largely treat the stocks as independent of\neach other. The valuable signal in the rich relations between stocks (or\ncompanies), such as two stocks are in the same sector and two companies have a\nsupplier-customer relation, is not considered.\n  In this work, we contribute a new deep learning solution, named Relational\nStock Ranking (RSR), for stock prediction. Our RSR method advances existing\nsolutions in two major aspects: 1) tailoring the deep learning models for stock\nranking, and 2) capturing the stock relations in a time-sensitive manner. The\nkey novelty of our work is the proposal of a new component in neural network\nmodeling, named Temporal Graph Convolution, which jointly models the temporal\nevolution and relation network of stocks. To validate our method, we perform\nback-testing on the historical data of two stock markets, NYSE and NASDAQ.\nExtensive experiments demonstrate the superiority of our RSR method. It\noutperforms state-of-the-art stock prediction solutions achieving an average\nreturn ratio of 98% and 71% on NYSE and NASDAQ, respectively.","url_abs":"http://arxiv.org/abs/1809.09441v1","url_pdf":"http://arxiv.org/pdf/1809.09441v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"temporal-relational-ranking-for-stock","repo_url":"https://github.com/hennande/Temporal_Relational_Stock_Ranking","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"AGPL-3.0"}},{"paper_slug":"temporal-relational-ranking-for-stock","repo_url":"https://github.com/HFrost0/TGC_torch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"temporal-relational-ranking-for-stock","repo_url":"https://github.com/fulifeng/Temporal_Relational_Stock_Ranking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"AGPL-3.0"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"relation-network","task_name":"Relation Network"},{"task_slug":"stock-market-prediction","task_name":"Stock Market Prediction"},{"task_slug":"stock-prediction","task_name":"Stock Prediction"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.09441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.09441"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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