Using Google Trends and Baidu Index to analyze the impacts of disaster events on company stock prices. Issue 2 (6th November 2019)

• Record Type: Journal Article
• Authors: Liu, Ying; Peng, Geng; Hu, Lanyi; Dong, Jichang; Zhang, Qingqing
• Is Part Of: Industrial management & data systems. Volume 120:Issue 2(2020)
• Page Start: 350
• Page End: 365
• Publication Date: 2019-11-06
• Subjects: Stock market -- AR-GARCH -- Crash incidents -- Search volume index; Industrial management -- Periodicals; Electronic data processing -- Periodicals; Business -- Periodicals; Industrial management -- Great Britain -- Periodicals; 658.05
• DOI: 10.1108/IMDS-03-2019-0190
• Languages: English
• ISSNs: 0263-5577

Abstract

Abstract : Purpose: With the ascendance of information technology, particularly through the internet, external information sources and their impacts can be readily transferred to influence the performance of financial markets within a short period of time. The purpose of this paper is to investigate how incidents affect stock prices and volatility using vector error correction and autoregressive-generalized auto regressive conditional Heteroskedasticity models, respectively. Design/methodology/approach: To characterize the investors' responses to incidents, the authors introduce indices derived using search volumes from Google Trends and the Baidu Index. Findings: The empirical results indicate that an outbreak of disasters can increase volatility temporarily, and exert significant negative effects on stock prices in a relatively long time. In addition, indices derived from different search engines show differentiation, with the Google Trends search index mainly representing international investors and appearing more significant and persistent. Originality/value: This study contributes to the existing literature by incorporating open-source data to analyze how catastrophic events affect financial markets and effect persistence.