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Technol."],"published-print":{"date-parts":[[2020,10,31]]},"abstract":"<jats:p>It is always deemed crucial to identify the key factors that could have significant impact on the stock market trend. Recently, an interesting phenomenon has emerged that some of President Trump\u2019s posts in Twitter can surge into a dominant role on the stock market for a certain time period, although studies along this line are still in their infancy. Therefore, in this article, we study whether and how this new-rising information can help boost the performance of stock market prediction. Specifically, we have found that the echoing reinforced effect of financial news with Trump\u2019s market-related tweets can influence the market movement\u2014that is, some of Trump\u2019s tweets directly impact the stock market in a short time, and the impact can be further intensified when it echoes with other financial news reports. Along this line, we propose a deep information echoing model to predict the hourly stock market trend, such as the rise and fall of the Dow Jones Industrial Average. In particular, to model the discovered echoing reinforced impact, we design a novel information echoing module with a gating mechanism in a sequential deep learning framework to capture the fused knowledge from both Trump\u2019s tweets and financial news. Extensive experiments have been conducted on the real-world U.S. stock market data to validate the effectiveness of our model and its interpretability in understanding the usability of Trump\u2019s posts. Our proposed deep echoing model outperforms other baselines by achieving the best accuracy of 60.42% and obtains remarkable accumulated profits in a trading simulation, which confirms our assumption that Trump\u2019s tweets contain indicative information for short-term market trends. Furthermore, we find that Trump\u2019s tweets about trade and political events are more likely to be associated with short-term market movement, and it seems interesting that the impact would not degrade as time passes.<\/jats:p>","DOI":"10.1145\/3403578","type":"journal-article","created":{"date-parts":[[2020,7,5]],"date-time":"2020-07-05T21:17:01Z","timestamp":1593983821000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Dancing with Trump in the Stock Market"],"prefix":"10.1145","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7237-4587","authenticated-orcid":false,"given":"Kun","family":"Yuan","sequence":"first","affiliation":[{"name":"Beihang University, Beijing, China"}]},{"given":"Guannan","family":"Liu","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7650-3657","authenticated-orcid":false,"given":"Junjie","family":"Wu","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Rutgers University, Newark, NJ USA"}]}],"member":"320","published-online":{"date-parts":[[2020,7,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.2307\/2491464"},{"key":"e_1_2_1_2_1","article-title":"Forecasting with Twitter data","volume":"5","author":"Arias Marta","year":"2014","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0180944"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2010.12.007"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961194"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3233\/AF-170211"},{"volume-title":"Ljung","year":"2015","author":"Box George E. 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