Marketing Letters

, Volume 23, Issue 1, pp 1–12 | Cite as

The relationship between online chatter and firm value

Article

Abstract

The visible trace of online communications has given rise to research on their effect on firm outcomes. The literature has established a link between online communication about a product and the product’s sales and price performance. On the assumption that financial markets understand this link, we conjecture financial markets consider the amount of online communication, or chatter, about a firm to be an indication of the firm’s performance in the marketplace. Our results confirm this conjecture. The relationship between stock returns and chatter are robust to alternative specifications of the model and to alternative measures of stock returns. We also investigate the issues of reverse causality and omitted variable bias driving a spurious relationship between stock returns and chatter. The data are not consistent with any of these alternative explanations for our results.

Keywords

Online word of mouth Value relevance Financial markets 

References

  1. Antweiler, W., & Frank, M. (2004). Is all that talk just noise? The information content of internet stock message boards. Journal of Finance, 59, 1259–1293.CrossRefGoogle Scholar
  2. Chevalier, J. A., & Mayzlin, D. (2006). The effect of work of mouth on sales: Online book reviews. Journal of Marketing Research, 43(August), 345–354.CrossRefGoogle Scholar
  3. Chintagunta, P. K., Gopinath, S., & Venkataraman, S. (2010). The effects of online user reviews on movie box-office performance: accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5), 944–957.CrossRefGoogle Scholar
  4. Conchar, M. P., Crask, M. R., & Zinkhan, G. M. (2005). Market valuation models of the effect of advertising and promotional spending: A review and meta-analysis. Journal of the Academy of Marketing Science, 33(4), 445–460.CrossRefGoogle Scholar
  5. Das, S., & Chen, M. (2007). Yahoo! for Amazon: Sentiment extraction from small talk on the web. Santa Clara University working paper.Google Scholar
  6. Day, R. L., & Landon, E. L. (1976). Collecting comprehensive complaint data by survey research. In B. B. Anderson (Ed.), Advances in consumer research, 3 (pp. 263–268). Atlanta: Association for Consumer Research.Google Scholar
  7. Dellarocas, C., Zhang, M., & Awad, N. F. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive Marketing, 21(4), 23–45.CrossRefGoogle Scholar
  8. Dhar, V., & Chang, E. (2009). Does chatter matter? The impact of user-generated content on music sales. Journal of Interactive Marketing, 23, 300–307.CrossRefGoogle Scholar
  9. Duan, W., Gu, B., & Whinston, A. (2008). The dynamics of online word-of-mouth and product sales—an empirical investigation of the movie industry. Journal of Retailing, 84(2), 233–242.CrossRefGoogle Scholar
  10. Fama, E., & French, K. (1992). The cross-section of expected stock returns. Journal of Finance, 47(2), 427–465.CrossRefGoogle Scholar
  11. Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545–560.CrossRefGoogle Scholar
  12. Godes, D., Mayzlin, D., Chen, Y., Das, S., Dellarocas, C., Pfeiffer, B., et al. (2005). The firm’s management of social interactions. Marketing Letters, 16(3/4), 415–428.Google Scholar
  13. Hirschman, A. O. (1970). Exit, voice, and loyalty responses to decline in firms, organizations and states. Cambridge: Harvard University Press.Google Scholar
  14. Ingram, M. (2011). The Verizon iPhone: What the web is saying. Gigaom.com (January 11, 2011, 1:42 PDT).Google Scholar
  15. Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(July), 74–89.CrossRefGoogle Scholar
  16. Luo, X. (2007). Consumer negative voice and firm-idiosyncratic stock returns. Journal of Marketing, 71(July), 75–88.CrossRefGoogle Scholar
  17. Luo, X. (2009). Quantifying the long-term impact of negative word of mouth on cash flows and stock prices. Marketing Science, 28(1), 148–165.CrossRefGoogle Scholar
  18. Mizik, N., & Jacobson, R. (2004). Stock return response modeling. In C. Moorman & D. Lehmann (Eds.), Assessing marketing strategy performance (pp. 29–46). Boston: Marketing Science Institute.Google Scholar
  19. Onishi, H., & Manchanda, P. (2010). Marketing activity, blogging and sales. Working Paper: University of Michigan, MI.Google Scholar
  20. Shin, H. S., Hanssens, D. M., & Gajula, B. (2008). The impact of positive vs. negative online buzz on retail prices. UCLA working paper.Google Scholar
  21. Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. (2002). Bayesian measure of model complexity and fit. Journal of Royal Statistics Society, 64(4), 583–639.CrossRefGoogle Scholar
  22. Srinivasan, S., & Hanssens, C. M. (2009). Marketing and firm value: Metrics, methods, findings and future directions. Journal of Marketing Research, XLV1(3), 293–312.CrossRefGoogle Scholar
  23. Tumarkin, R., & Whitelaw, R. (2001). News or noise? Internet postings and stock prices. Financial Analysts Journal, 57(3), 41–51.CrossRefGoogle Scholar
  24. Yung, Y., Thissen, D., & McLeod, L. (1999). On the relationship between the higher-order factor model and the hierarchical factor model. Psychometrika, 64, 112–128.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.McCombs School of BusinessUniversity of Texas at AustinAustinUSA

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