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Do investors post messages differently from mobile devices? The correlation between mobile Internet messages posting and stock returns

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Abstract

Mobile Internet has become a popular channel for investors to share their information and ideas about investment. This paper investigates the relation between frequency of mobile Internet messages and subsequent stock returns. We find that firms with higher proportion of mobile Internet messages on average earn a significant return premium even after controlling for well-known risk factors. Moreover, the marginal effect of mobile Internet messages is more pronounced among stocks in weaker information environments (i.e., higher fraction of individual ownership and lower analysts following). Further results suggest this correlation is more likely to be driven by “noise” rather than “information.” We also provide evidence that the lack of liquidity can explain the persistence of the correlation between mobile Internet messages and stock returns. Our findings highlight the importance for financial market participants to consider the peer-based opinions from mobile Internet.

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Notes

  1. During our 2014.1.1–2015.3.31 sample period, if you reward other users, you need at least one “Snowball Money” (0.1 RMB).

  2. Rewards from Snowball Finance (xueqiu.com) mean that users can sponsor others based on the quality of messages by “Snowball money” (One “Snowball Money” is worth 0.1 RMB).

  3. We repeated the analysis with the Second Level-CSRC Industry Classification Standard and obtained similar results.

  4. In contrast, the following papers argue that greater idiosyncratic volatility captures more value-relevant information and less noise (Wurgler 2000; Durnev et al. 2003; Piotroski and Roulstone 2004; Bakke and Whited 2010).

References

  • Amihud Y (2002) Illiquidity and stock returns: cross-section and time-series effects. J Financ Mark 5:31–56

    Article  Google Scholar 

  • Ang A, Hodrick RJ, Xing Y, Zhang X (2006) The cross-section of volatility and expected returns. J Finance 61:259–299

    Article  Google Scholar 

  • Antweiler W, Frank MZ (2004) Is all that talk just noise? The information content of internet stock message boards. J Finance 59:1259–1294

    Article  Google Scholar 

  • Bakke TE, Whited TM (2010) Which firms follow the market? An analysis of corporate investment decisions. Rev Financ Stud 23:1941–1980

    Article  Google Scholar 

  • Bartram SM, Brown G, Stulz RM (2012) Why are US stocks more volatile? J Finance 67:1329–1370

    Article  Google Scholar 

  • Black F (1986) Noise. J Finance 41:529–543

    Article  Google Scholar 

  • Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2:1–8

    Article  Google Scholar 

  • Brown JR, Ivkovic Z, Smith PA, Weisbenner S (2008) Neighbors matter: causal community effects and stock market participation. J Finance 63:1509–1531

    Article  Google Scholar 

  • Carhart MM (1997) On persistence in mutual fund performance. J Finance 52:57–82

    Article  Google Scholar 

  • Chan K, Hameed A (2006) Stock price synchronicity and analyst coverage in emerging markets. J Financ Econ 80:115–147

    Article  Google Scholar 

  • Chan LKC, Jegadeesh N, Lakonishok J (1996) Momentum strategies. J Finance 51:1681–1713

    Article  Google Scholar 

  • Chen H, De P, Hu Y, Hwang BH (2014) Wisdom of crowds: the value of stock opinions transmitted through social media. Rev Financ Stud 27:1367–1403

    Article  Google Scholar 

  • Cohen L, Frazzini A, Malloy C (2008) The small world of investing: board connections and mutual fund returns. J Polit Econ 116:951–979

    Article  Google Scholar 

  • Cohen L, Frazzini A, Malloy C (2010) Sell-side school ties. J Finance 65:1409–1437

    Article  Google Scholar 

  • Duflo E, Saez E (2002) Participation and investment decisions in a retirement plan: the influence of colleagues’ choices. J Pub Econ 85:121–148

    Article  Google Scholar 

  • Durnev A, Morck R, Yeung B, Zarowin P (2003) Does greater firm-specific return variation mean more or less informed stock pricing? J Account Res 41:797–836

    Article  Google Scholar 

  • Fama E, French KR (1993) Common risk factors in the returns on stocks and bonds. J Financ Econ 33:3–56

    Article  Google Scholar 

  • Fang L, Peress J (2009) Media coverage and the cross-section of stock returns. J Finance 64:2023–2052

    Article  Google Scholar 

  • Hong H, Kubik JD, Stein JC (2004) Social interaction and stock-market participation. J Finance 59:137–163

    Article  Google Scholar 

  • Hong H, Kubik JD, Stein JC (2005) Thy neighbor’s portfolio: word-of-mouth effects in the holdings and trades of money managers. J Finance 60:2801–2824

    Article  Google Scholar 

  • Ivkovic Z, Weisbenner S (2007) Information diffusion effects in individual investors’ common stock purchases: covet thy neighbors’ investment choices. Rev Financ Stud 20:1327–1357

    Article  Google Scholar 

  • Jegadeesh N, Titman S (1993) Returns to buying winners and selling losers: implications for stock market efficiency. J Finance 48:65–91

    Article  Google Scholar 

  • Kelly M, Grada CO (2000) Market contagion: evidence from the panics of 1854 and 1857. Am Econ Rev 90:1110–1124

    Article  Google Scholar 

  • Lee C, Shleifer A, Thaler RH (1991) Investor sentiment and the closed-end fund puzzle. J Finance 46:75–109

    Article  Google Scholar 

  • Li B, Rajgopal S, Venkatachalam M (2014) R2 and idiosyncratic risk are not interchangeable. Account Rev 89:2261–2295

    Article  Google Scholar 

  • Merton RC (1987) A simple model of capital market equilibrium with incomplete information. J Finance 42:483–510

    Article  Google Scholar 

  • Newey WK, West KD (1987) A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55:703–708

    Article  Google Scholar 

  • Newswire PR (2015) Highpower international sponsors and featured at third Xueqiu Investment carnival in Beijing. http://www.prnewswire.com/news-releases/highpower-international-sponsors-and-featured-at-third-xueqiu-investment-carnival-in-beijing-300110935.html

  • Piotroski JD, Roulstone DT (2004) The influence of analysts, institutional investors, and insiders on the incorporation of market, industry, and firm-specific information into stock prices. Account Rev 79:1119–1151

    Article  Google Scholar 

  • Roberts JA, Pirog SF III (2013) A preliminary investigation of materialism and impulsiveness as predictors of technological addictions among young adult. J Behav Addict 2:56–62

    Article  Google Scholar 

  • Shiller RJ, Pound J (1989) Survey evidence on diffusion of interest and information among investors. J Econ Behav Organ 12:47–66

    Article  Google Scholar 

  • Shive S (2010) An epidemic model of investor behavior. J Financ Quant Anal 45:169–198

    Article  Google Scholar 

  • Sprenger TO, Tumasjan A, Sandner PG, Welpe IM (2014) Tweets and trades: the information content of stock microblogs. Eur Financ Manag 20:926–957

    Article  Google Scholar 

  • Tetlock PC, Saar-Tsechansky M, Macskassy S (2008) More than words: quantifying language to measure firms’ fundamentals. J Finance 63:1437–1467

    Article  Google Scholar 

  • Tirunillai S, Tellis GJ (2012) Does chatter really matter? Dynamics of user-generated content and stock performance. Mark Sci 31:198–215

    Article  Google Scholar 

  • Wurgler J (2000) Financial markets and the allocation of capital. J Financ Econ 58:187–214

    Article  Google Scholar 

  • Xu Y, Malkiel BG (2003) Investigating the behavior of idiosyncratic volatility. J Bus 76:613–645

    Article  Google Scholar 

  • Yang J, Counts S (2010) Predicting the speed, scale, and range of information diffusion in Twitter. In: International conference on weblogs and social media, Washington, DC, pp 355–358

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Acknowledgements

We would like to thank Diefeng Peng, Daniel Houser, and Erte Xiao for helpful comments and suggestions. We also thank Ganggang Guo for outstanding assistance with the stock messages collection process from Snowball Finance platform. Besides, we thank China Scholarship Council (CSC), Project (71673306, 71301169, 71501193, 71372063) supported by the National Natural Science Foundation of China, Project of the Ministry of Education of China(14YJC630133) and Project (17CGL056) supported by the National Social Science Foundation of China.

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Correspondence to Lixing Mei.

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Mei, L., Rao, Y., Wang, M. et al. Do investors post messages differently from mobile devices? The correlation between mobile Internet messages posting and stock returns. Int Rev Econ 66, 423–452 (2019). https://doi.org/10.1007/s12232-019-00329-6

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