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Dancing with Trump in the Stock Market: A Deep Information Echoing Model

Published: 05 July 2020 Publication History

Abstract

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’s 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’s market-related tweets can influence the market movement—that is, some of Trump’s 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’s 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’s 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’s tweets contain indicative information for short-term market trends. Furthermore, we find that Trump’s 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.

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Published In

cover image ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology  Volume 11, Issue 5
Survey Paper and Regular Paper
October 2020
325 pages
ISSN:2157-6904
EISSN:2157-6912
DOI:10.1145/3409643
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 July 2020
Accepted: 01 May 2020
Revised: 01 April 2020
Received: 01 December 2019
Published in TIST Volume 11, Issue 5

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Author Tags

  1. Stock market prediction
  2. Trump
  3. Twitter
  4. deep learning
  5. information echoing

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  • Research-article
  • Research
  • Refereed

Funding Sources

  • National Natural Science Foundation of China (NSFC)
  • National Key R8D Program of China

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  • (2024)A financial anomaly prediction approach using semantic space of news flow on twitterDecision Analytics Journal10.1016/j.dajour.2024.10042210(100422)Online publication date: Mar-2024
  • (2024)Using dynamic semantic structure of news flow to enhance financial forecasting: a twelve-year study on twitter news channelsMultimedia Tools and Applications10.1007/s11042-024-20274-zOnline publication date: 25-Sep-2024
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  • (2023)Stock Price Trends Prediction Based on the Classical Models with Key Information Fusion of OntologiesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/359259922:5(1-22)Online publication date: 9-May-2023
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