Can Google Trends Affect Sentiment of Individual Investors? The Case of the United States

• Autorzy: Aleksandra Rutkowska , Agata Kliber
• Języki publikacji: EN

Abstrakty

EN

As the empirical studies show, investor sentiment is a significant factor in financial markets. The large-scale development of the technology has led to widespread access to information in real time (also to individual investors), which in turn has also led to the inflow of Big Data to market analysis. One of the sources of such data is the ability to track the phrases searched for in the web search engines. In our research we verify whether investor sentiment is affected by, among others, a daily Google keyword search called "Google Trends". We consider measures of US investors' sentiment calculated from survey studies - the AAII index. We investigate changes of sentiment and its volatility, which can be interpreted as nervousness of the market participants. We estimate a set of GARCH models with explanatory variables in conditional mean and variance. We confirm that negative keyword searches are connected with the decline of the investor confidence. The overall effect of a negative search is stronger than positive. Older searches have a weaker influence on investor sentiment than new ones - no lagged search proved to be significant.(original abstract)

Słowa kluczowe

EN

Behavioural finance; Financial markets; Individual investors; GARCH model

PL

Finanse behawioralne; Rynki finansowe; Inwestor indywidualny; Model GARCH

• Czasopismo: Mathematical Economics
• Rocznik: 2018
• Numer: nr 14 (21)
• Strony: 51--70

Twórcy

autor

Aleksandra Rutkowska

• Poznań University of Economics and Business, Poland

autor

Agata Kliber

• Poznań University of Economics and Business, Poland

Bibliografia

• Antweiler W., Frank, M.Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance. Vol. 59. No. 3, pp. 1259-1294.
• BankM., Larch M., Peter G. (2011). Google search volume and its influence on liquidity and returns of German stocks, Financial Markets and Portfolio Management. Vol. 25. No. 3, pp. 239-264.
• Baker M., Wurgler J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance. Vol. 61. No. 4, pp. 1645-1680.
• Bijl L., Kringhaug G., Molnár P., Sandvik E. (2016). Google searches and stock returns. International Review of Financial Analysis. Vol. 45, pp. 150-156.
• Bollen J., Mao H., Zeng X. (2011). Twitter mood predicts the stock market. Journal of Computational Science. Vol. 2. No. 1, pp. 1-8.
• Bollerslev T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics. Vol. 31. No. 3, pp. 307-327.
• Brown G.W., Cliff M.T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance. Vol. 11. No. 1, pp. 1-27.
• Brown G.W., Cliff M.T. (2005). Investor sentiment and asset valuation. The Journal of Business. Vol. 78. No. 2, pp. 405-440.
• Bukovina J. (2016). Social Media and Capital Markets. An Overview. Procedia-Social and Behavioral Sciences. Vol. 220, pp. 70-78.
• Campbell J.Y., Hentschel L. (1992). No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics. Vol. 31. No. 3, pp. 281-318.
• Da Z., Engelberg J., Gao P. (2011). In search of attention. The Journal of Finance. Vol. 66. No. 5, pp. 1461-1499.
• De Long J.B., Shleifer A., Summers L.H.,Waldmann R.J. (1990). Noise trader risk in financial markets. Journal of Political Economy. Vol. 98. No. 4, pp. 703-738.
• Dimpfl T., Jank S. (2016). Can internet search queries help to predict stock market volatility? European Financial Management. Vol. 22. No. 2, pp. 171-192.
• Fisher K.L., Statman M. (2000). Investor sentiment and stock returns. Financial Analysts Journal. Vol. 56. No. 2, pp.16-23.
• Hibbert A.M., Daigler R.T., Dupoyet B. (2008). A behavioral explanation for the negative asymmetric return-volatility relation. Journal of Banking & Finance. Vol. 32. No. 10, pp. 2254-2266.
• Joseph K., Wintoki M.B., Zhang Z. (2011). Forecasting abnormal stock returns and trading volume using investor sentiment: evidence from online search. International Journal of Forecasting. Vol. 27. No. 4, pp. 1116-1127
• Kim S.H., Kim D. (2014). Investor sentiment from internet message postings and the predictability of stock returns. Journal of Economic Behavior & Organization. Vol. 107, pp. 708-729.
• Laurent S. (2017). G@RCH 7.0 Help, file:///C:/program files/oxmetrics7/doc/g@rch/index. html.
• Meinusch A., Tillmann P. (2017). Quantitative Easing and Tapering Uncertainty: Evidence from Twitter. Joint Discussion Paper Series in Economics. No. 09-2015.
• Oliveira N., Cortez P., Areal N. (2017). The impact of microblogging data for stock market prediction: using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Systems with Applications. Vol. 73, pp. 125-144.
• Piñeiro-Chousa J.R., López-Cabarcos M.Á., Pérez-Pico A.M. (2016). Examining the influence of stock market variables on microblogging sentiment. Journal of Business Research. Vol. 69. No. 6, pp. 2087-2092.
• Preis T., Reith D., Stanley H.E. (2010). Complex dynamics of our economic life on different scales: Insights from search engine query data. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. Vol. 368. No. 1933, pp. 5707-5719.
• Preis T., Moat H.S., Stanley H.E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific reports 3, srep01684.
• Schmeling M. (2009). Investor sentiment and stock returns: Some international evidence. Journal of Empirical Finance. Vol. 16. No. 3, pp. 394-408.
• Siganos A., Vagenas-Nanos E., Verwijmeren P. (2014). Facebook's daily sentiment and international stock markets. Journal of Economic Behavior & Organization. Vol. 107, pp. 730-743.
• Sun A., Lachanski M., Fabozzi F.J. (2016). Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction. International Review of Financial Analysis. Vol. 48, pp. 272-281.
• Tang W., Zhu L. (2017). How security prices respond to a surge in investor attention: Evidence from Google Search of ADRs. Global Finance Journal. Vol. 33, pp. 38-50.
• Tversky A., Kahneman D. (1975). Judgment under Uncertainty: Heuristics and biases. In: Utility, Probability, and Human Decision Making. Springer Netherlands, pp. 141-162.

Identyfikatory

DOI

10.15611/me.2018.14.05