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.
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Notes
Among the papers listed in Table 1, some find an effect for the overall volume of rating or mentions while others find an effect for the valence of ratings or the sentiment of online mentions.
Further details regarding the model, including the Markov chain Monte Carlo algorithm used to estimate its parameters, are available from the authors on request.
We consider two alternative model formulations. First, we re-estimate equation system 1 including the Fama and French (1992) factors which capture market capitalization and book-to-price effects. Not surprisingly, these additional factors, designed to account for differences across firms, had no effect on our within-firm analysis. We also estimated models with a linear, a second-order, and a third-order polynomial trend in the stock return regression. In all cases, the 95% HPD intervals on the trend coefficients span zero. Results for these two alternative model specifications, omitted for the sake of brevity, are available from the authors upon request.
We also considered two alternative measures of weekly abnormal returns, the weekly cumulative return (CAR) and the weekly compounded abnormal return (CPAR). The empirical results are robust to these alternative measures. The results using CAR and CPAR are available from the authors on request.
As noted, the extant literature has documented a relationship between sales and chatter. While a full analysis of the sales–chatter relationship is beyond the scope of this paper, we note that a positive correlation exists between weekly sales and weekly counts of posts, sites, and authors, ranging from 0.18 to 0.22.
To rule out the omitted variable bias argument, it is sufficient to show that the omitted variable is not related to stock return.
We also experimented with windows of differing sizes around each of the launch announcements. Some of the announcements were in adjacent weeks; thus, the windows for one announcement often overlap with subsequent announcements. Using windows instead of a simple dummy, we find no significant effect of announcements in any of the models.
These results also hold true for analyses using CAR and CPAR as the dependent variable.
These results also hold true for analyses using CAR and CPAR as the dependent variable.
References
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.
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.
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.
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.
Das, S., & Chen, M. (2007). Yahoo! for Amazon: Sentiment extraction from small talk on the web. Santa Clara University working paper.
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.
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.
Dhar, V., & Chang, E. (2009). Does chatter matter? The impact of user-generated content on music sales. Journal of Interactive Marketing, 23, 300–307.
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.
Fama, E., & French, K. (1992). The cross-section of expected stock returns. Journal of Finance, 47(2), 427–465.
Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545–560.
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.
Hirschman, A. O. (1970). Exit, voice, and loyalty responses to decline in firms, organizations and states. Cambridge: Harvard University Press.
Ingram, M. (2011). The Verizon iPhone: What the web is saying. Gigaom.com (January 11, 2011, 1:42 PDT).
Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(July), 74–89.
Luo, X. (2007). Consumer negative voice and firm-idiosyncratic stock returns. Journal of Marketing, 71(July), 75–88.
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.
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.
Onishi, H., & Manchanda, P. (2010). Marketing activity, blogging and sales. Working Paper: University of Michigan, MI.
Shin, H. S., Hanssens, D. M., & Gajula, B. (2008). The impact of positive vs. negative online buzz on retail prices. UCLA working paper.
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.
Srinivasan, S., & Hanssens, C. M. (2009). Marketing and firm value: Metrics, methods, findings and future directions. Journal of Marketing Research, XLV1(3), 293–312.
Tumarkin, R., & Whitelaw, R. (2001). News or noise? Internet postings and stock prices. Financial Analysts Journal, 57(3), 41–51.
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.
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McAlister, L., Sonnier, G. & Shively, T. The relationship between online chatter and firm value. Mark Lett 23, 1–12 (2012). https://doi.org/10.1007/s11002-011-9142-5
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DOI: https://doi.org/10.1007/s11002-011-9142-5