Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance

被引:397
|
作者
Tirunillai, Seshadri [1 ]
Tellis, Gerard J. [2 ]
机构
[1] Univ Houston, CT Bauer Coll Business, Houston, TX 77204 USA
[2] Univ So Calif, Marshall Sch Business, Los Angeles, CA 90089 USA
关键词
user-generated content (UGC); stock returns; online word of mouth; vector autoregression (VAR); computational text processing; WORD-OF-MOUTH; MARKET RESPONSE; TRADING VOLUME; FIRM VALUE; IMPACT; SALES; RETURNS; REVIEWS; INFORMATION; MODELS;
D O I
10.1287/mksc.1110.0682
中图分类号
F [经济];
学科分类号
02 ;
摘要
This study examines whether user-generated content (UGC) is related to stock market performance, which metric of UGC has the strongest relationship, and what the dynamics of the relationship are. We aggregate UGC from multiple websites over a four-year period across 6 markets and 15 firms. We derive multiple metrics of UGC and use multivariate time-series models to assess the relationship between UGC and stock market performance. Volume of chatter significantly leads abnormal returns by a few days (supported by Granger causality tests). Of all the metrics of UGC, volume of chatter has the strongest positive effect on abnormal returns and trading volume. The effect of negative and positive metrics of UGC on abnormal returns is asymmetric. Whereas negative UGC has a significant negative effect on abnormal returns with a short "wear-in" and long "wear-out," positive UGC has no significant effect on these metrics. The volume of chatter and negative chatter have a significant positive effect on trading volume. Idiosyncratic risk increases significantly with negative information in UGC. Positive information does not have much influence on the risk of the firm. An increase in off-line advertising significantly increases the volume of chatter and decreases negative chatter. These results have important implications for managers and investors.
引用
下载
收藏
页码:198 / 215
页数:18
相关论文
共 50 条
  • [21] A Solution for Navigating User-Generated Content
    Uusitalo, Severi
    Eskolin, Peter
    Belimpasakis, Petros
    2009 8TH IEEE INTERNATIONAL SYMPOSIUM ON MIXED AND AUGMENTED REALITY - SCIENCE AND TECHNOLOGY, 2009, : 219 - 220
  • [22] Extraversion as a stimulus for user-generated content
    Pagani, Margherita
    Goldsmith, Ronald E.
    Hofacker, Charles F.
    JOURNAL OF RESEARCH IN INTERACTIVE MARKETING, 2013, 7 (04) : 242 - 256
  • [23] Generative AI in User-Generated Content
    Hua, Yiqing
    Niu, Shuo
    Cai, Jie
    Chilton, Lydia B.
    Heuer, Hendrik
    Wohn, Donghee Yvette
    EXTENDED ABSTRACTS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2024, 2024,
  • [24] (How) Does User-Generated Content Impact Content Generated by Professionals? Evidence from Local News
    Sen, Ananya
    Grad, Tom
    Ferreira, Pedro
    Claussenb, Joerg
    MANAGEMENT SCIENCE, 2024, 70 (09) : 6045 - 6068
  • [25] Editorial: Online User Behavior and User-Generated Content
    Saura, Jose Ramon
    Dwivedi, Yogesh K.
    Palacios-Marques, Daniel
    FRONTIERS IN PSYCHOLOGY, 2022, 13
  • [26] Do different kinds of user-generated content in online brand communities really work?
    Estrella-Ramon, Antonia
    Ellis-Chadwick, Fiona
    ONLINE INFORMATION REVIEW, 2017, 41 (07) : 954 - 968
  • [27] The institutionalization of YouTube: From user-generated content to professionally generated content
    Kim, Jin
    MEDIA CULTURE & SOCIETY, 2012, 34 (01) : 53 - 67
  • [28] Analyzing the effect of user-generated content on studio performance: A combined approach
    Liu, Yang
    MANAGERIAL AND DECISION ECONOMICS, 2024, 45 (04) : 2228 - 2248
  • [29] Does Money Talk? The Impact of Monetary Incentives on User-Generated Content Contributions
    Liu, Yuewen
    Feng, Juan
    INFORMATION SYSTEMS RESEARCH, 2021, 32 (02) : 394 - 409
  • [30] Fast Food Data: Where User-Generated Content Works and Where It Does Not
    Folch, David C.
    Spielman, Seth E.
    Manduca, Robert
    GEOGRAPHICAL ANALYSIS, 2018, 50 (02) : 125 - 140