Skip to main content
Log in

RETRACTED ARTICLE: Google Search Intensity and the Investor Attention Effect: A Quantile Regression Approach

  • Original Article
  • Published:
Journal of Quantitative Economics Aims and scope Submit manuscript

This article was retracted on 03 September 2021

This article has been updated

Abstract

This paper investigates whether the investor attention effect caused by the Google stock search can be used to forecast stock returns. The evolving literature on Google search investor attention effect suggests that high Google search volumes can predict high returns for the first 1–2 weeks, but with a subsequent price reversal. We use a more recent data set that covers the period from 2012 to 2017 in the Indian stock market and employ the quantile regression approach as it alleviates some of the statistical problems to find that high Google search volumes lead to positive returns. Indeed, the high Google search volumes predict positive and significant returns in the subsequent third, fourth and fifth weeks. The Google search volume index performs as a better predictor of the direction as well as the magnitude of the excess returns. The findings infer that the signals from the search volume data could be of benefit construction of profitable trading strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Change history

Notes

  1. Quantile regression analyses can be easily implemented using commercial statistics software such as Eviews, Stata and SAS. Koenker and Hallock (2001) provide a brilliant introduction to quantile regression.

  2. In the OLS method, the conditional mean relationship is the estimation of the mean value of the dependent variable given a fixed value of each explanatory variable. On the contrary, in the QReg method, given a fixed value of each explanatory variable, various percentiles of the distribution of the dependent variables, e.g., 10, 25, 50, 75, and 90% percentiles are estimated. Koenker and Hallock’s (2001) provide an excellent survey on quantile regression and a discussion of the intuition behind the class of estimators.

  3. The NIFTY 50 is a diversified 50 stock index accounting for 12 sectors of the economy. It is used for a variety of purposes such as benchmarking fund portfolios, index based derivatives and index funds. The NIFTY 50 Index represents about 62.9% of the free float market capitalization of the stocks listed on NSE as on March 31, 2017. The total traded value of NIFTY 50 index constituents for the last 6 months ending March 2017 is approximately 43.8% of the traded value of all stocks on the NSE. (source: https://www.nseindia.com/products/content/equities/indices/nifty_50.htm.

  4. The data on the online search intensity is obtained from Google Trends (http://www.google.co.in/trends/).

  5. The results of the Slope Equality test are not reported here in the interest of space. But they are available on request for verification.

References

  • Bank, M., M. Larch, and G. Peter. 2011. Google search volume and its influence on liquidity and returns of German stocks. Financial Markets and Portfolio Management 253: 239–264.

    Article  Google Scholar 

  • Baker, M., and J. Wurgler. 2006. Investor sentiment and the cross-section of stock returns. The Journal of Finance 61: 1645–1680.

    Article  Google Scholar 

  • Barber, B.M., and T. Odean. 2008. All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies 21: 785–818.

    Article  Google Scholar 

  • Barnes, M., & Hughes, A.W. (2002). A quantile regression analysis of the cross section of stock market returns. Available at SSRN: http://ssrn.com/abstract=458522. Accessed 11 Feb 2018.

  • Bijl, Laurens, Glenn Kringhaug, Peter Molnár, and Eirik Sandvik. 2016. Google searches and stock returns. International Review of Financial Analysis 45: 150–156.

    Article  Google Scholar 

  • Bollen, J., H. Mao, and X. Zeng. 2011. Twitter mood predicts the stock market. Journal of Computational Science 2: 1–8.

    Article  Google Scholar 

  • Breitung, J. 2000. The local power of some unit root tests for panel data. In Nonstationary panels, panel cointegration, and dynamic panels, advances in econometrics ed. Baltagi B.H., vol. 15, pp. 161–178.

  • Buchinsky, Moshe. 1998. Recent advances in quantile regression models: A practical guideline for empirical research. Journal of Human Resources 33 (1): 88–126.

    Article  Google Scholar 

  • Campbell, J.Y., S.J. Grossman, and J. Wang. 1993. Trading volume and serial correlation in stock returns. Quarterly Journal of Economics 108: 905–939.

    Article  Google Scholar 

  • Challet, D., and A. B. H. Ayed. 2013. Predicting financial markets with Google Trends and not so random keywords. Cornell University. Available at https://arxiv.org/abs/1307.4643. Accessed 11 Feb 2018.

  • Conrad, J.S., A. Hameed, and C. Niden. 1994. Volume and autocovariance in short-horizon individual security returns. The Journal of Finance 49: 1305–1329.

    Article  Google Scholar 

  • Corsi, F. 2009. A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7: 174–196. https://doi.org/10.1093/jjfinec/nbp001.

    Article  Google Scholar 

  • Cooper, T.E. 1999. Filter rules based on price and volume of individual security overreaction. Review of Financial Studies 12: 901–935.

    Article  Google Scholar 

  • Da, Z., J. Engelberg, and P. Gao. 2011. In search of attention. The Journal of Finance 665: 1461–1499.

    Article  Google Scholar 

  • Fama, E.F. 1965. The behavior of stock-market prices. The Journal of Business 38: 34–105.

    Article  Google Scholar 

  • Fama, E.F. 1976. Foundations of finance: Portfolio decisions and securities prices. AZ: Basic Books.

    Google Scholar 

  • Fehle, F., S. Tsyplakov, and V. Zdorovtsov. 2005. Can companies influence investor behavior through advertising? Super Bowl commercials and stock returns. European Financial Management 11: 625–647.

    Article  Google Scholar 

  • Glosten, L.R., R. Jagannathan, and D.E. Runkle. 1993. On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance 48: 1779–1801.

    Article  Google Scholar 

  • Huberman, G., and T. Regev. 2001. Contagious speculation and a cure for cancer: A non-event that made stock prices soar. The Journal of Finance 56: 387–396.

    Article  Google Scholar 

  • Joseph, K., M.B. Wintoki, and Z. Zhang. 2011. Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from an online search. International Journal of Forecasting 27: 1116–1127.

    Article  Google Scholar 

  • Kahneman, Daniel. 1973. Attention and effort. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Kim, Y.H., & Meschke, F. (2011). CEO interviews on CNBC. Working Paper Extracted from https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=1745085. Accessed 11 Feb 2018.

  • Koenker, R., and G. Bassett. 1978. Regression quantiles. Econometrica 46 (1): 33–50.

    Article  Google Scholar 

  • Koenker, Roger, and Gilbert Bassett Jr. 1982. Robust tests for heteroskedasticity based on regression quantiles. Econometrica 50 (1): 43–62.

    Article  Google Scholar 

  • Koenker, Roger W., and Kevin F. Hallock. 2001. Quantile regression. Journal of Economic Perspectives 15 (4): 143–156.

    Article  Google Scholar 

  • Levin, A., C.F. Lin, J. Chu, and S. Chia. 2002. Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics 108: 1–24.

    Article  Google Scholar 

  • Merton, R. 1987. A simple model of capital market equilibrium with incomplete information. The Journal of Finance 423: 483–510.

    Article  Google Scholar 

  • Moat, H.S., C. Curme, A. Avakian, D.Y. Kenett, H.E. Stanley, and T. Preis. 2013. Quantifying Wikipedia usage patterns before stock market moves. Scientific Reports 3: 1801.

    Article  Google Scholar 

  • Newey, W., and J. Powell. 1987. Asymmetric least squares estimation and testing. Econometrica 55 (4): 819–847. https://doi.org/10.2307/1911031.

    Article  Google Scholar 

  • Poon, S.H., and C.W. Granger. 2003. Forecasting volatility in financial markets: A review. Journal of Economic Literature 41: 478–539.

    Article  Google Scholar 

  • Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google trends. Scientific Reports, 3. https://doi.org/10.1038/srep01684. Available at https://www.nature.com/articles/srep01684. Accessed 11 Feb 2018.

  • Preis, T., D. Reith, and H.E. Stanley. 2010. Complex dynamics of our economic life ondifferent scales: Insights from search engine query data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368 (1933): 5707–5719. https://doi.org/10.1098/rsta.2010.0284.

    Article  Google Scholar 

  • Takeda, F., and H. Yamazaki. 2006. Stock price reactions to public TV programs on listed Japanese companies. Economics Bulletin 13 (7): 1–7.

    Google Scholar 

  • Tetlock, P.C. 2007. Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance 62: 1139–1168.

    Article  Google Scholar 

  • Vlastakis, N., and R.N. Markellos. 2012. Information demand and stock market volatility. Journal of Banking & Finance 366: 1808–1821.

    Article  Google Scholar 

  • Ying, Qianwei, Dongmin Kong, and Danglun Luo. 2015. Investor attention, institutional ownership, and stock return: Empirical evidence from China. Emerging Markets Finance and Trade 51: 672–685.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vighneswara Swamy.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s40953-021-00251-1

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Swamy, V., Dharani, M. RETRACTED ARTICLE: Google Search Intensity and the Investor Attention Effect: A Quantile Regression Approach. J. Quant. Econ. 18, 403–423 (2020). https://doi.org/10.1007/s40953-019-00185-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40953-019-00185-9

Keywords

JEL Classification

Navigation