Dancing with Trump in the Stock Market

Kun Yuan, Guannan Liu, Junjie Wu, Hui Xiong

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Article number62
JournalACM Transactions on Intelligent Systems and Technology
Volume11
Issue number5
DOIs
StatePublished - Sep 2020

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Artificial Intelligence

Keywords

  • Stock market prediction
  • Trump
  • Twitter
  • deep learning
  • information echoing

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