Predicting abnormal trading behavior from internet rumor propagation:a machine learning approach  

作　　者：Li‑Chen Cheng Wei‑Ting Lu Benjamin Yeo 

机构地区：[1]Department of Information and Finance Management,National Taipei University of Technology,Taipei 100,Taiwan [2]Albers School of Business and Economics,Seattle University,90112th Ave,Seattle,WA 98122,USA

出　　处：《Financial Innovation》2023年第1期56-78,共23页金融创新（英文）

基　　金：supported by the National Science and Technology Council,Taiwan,under grants MOST 108-2410-H-027-020,MOST 109-2410-H-027-009-MY2 and MOST 111-2410-H-027-011-MY3.

摘　　要：In 2021,the abnormal short-term price fluctuations of GameStop,which were triggered by internet stock discussions,drew the attention of academics,financial analysts,and stock trading commissions alike,prompting calls to address such events and maintain market stability.However,the impact of stock discussions on volatile trading behavior has received comparatively less attention than traditional fundamentals.Furthermore,data mining methods are less often used to predict stock trading despite their higher accuracy.This study adopts an innovative approach using social media data to obtain stock rumors,and then trains three decision trees to demonstrate the impact of rumor propagation on stock trading behavior.Our findings show that rumor propagation outperforms traditional fundamentals in predicting abnormal trading behavior.The study serves as an impetus for further research using data mining as a method of inquiry.

关 键 词：Fake news RUMORS Data mining Social media Classification Machine learning GameStop Reddit 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]