Google trends stock prediction12/29/2023 J Finance 66(1):67–97įang L, Peress J (2009) Media coverage and the cross-section of stock returns. Clust Comput 21:1–14Įngelberg JE, Parsons CA (2011) Causal impact of media in financial markets. J Finance 66:1461–1499ĭeng S, Liu P (2018) The impact of attention heterogeneity on stock market in the era of big data. J Netw Theory Finance 3:1–20ĭa Z, Engelberg J, Gao P (2011) In search of attention. PNAS 111:11600–11605Ĭurme C, Zhuo YD, Moat HS, Preis T (2017) Quantifying the diversity of news around stock market moves. Econ Rec 88(S1):2–9Ĭurme C, Preis T, Stanley HE, Moat HS (2014) Quantifying the semantics of search behavior before stock market moves. J Financ Econ 70(2):223–260Ĭhoi H, Varian H (2012) Predicting the present with Google Trends. J Econ Dyn Control 31(6):1938–1970Ĭhan WS (2003) Stock price reaction to news and no-news: drift and reversal after headlines. Wiley, Chichester, pp 2154–2160īoswijk HP, Hommes CH, Manzan S (2007) Behavioral heterogeneity in stock prices. In: Armitage P, Colton T (eds) Encyclopedia of biostatistics, vol 3. J Comput Sci 2:1–8īorgan Ø (1998) Kaplan-Meier estimator. Mimeoīollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. Rev Financ Stud 21:786–818īiaias B, Bossaerts P, Spatt C (2003) Equilibrium asset pricing under heterogeneous information. Financ Mark Portf Manag 25:239īarber BM, Odean T (2008) All that glitters: the effect of attention and news on the buying behavior of individual and institutional investors. J Finance Data Sci 4:120–137īank M, Larch M, Peter G (2011) Google search volume and its influence on liquidity and returns of German stocks. Appl Econ Q 55(2):107–120Ītkins A, Niranjan M, Gerding E (2018) Financial news predicts stock market volatility better than close price. Rev Finance 13:401–465Īskitas N, Zimmermann KF (2009) Google econometrics and unemployment forecasting. Int Rev Financ Anal 45:39–46Īlbuquerque R, Vega C (2009) Economic news and international stock market co-movement. Using the terms that are persistently found to be Granger causal with the index, we propose several generalized linear models for forecasting the probability of positive or negative directional movements, and propose a trade strategy from the generated forecasts, resulting in a 40% outperformance of a traditional buy-and-hold strategy in our testing period.Īckert LF, Jiang L, Lee HS (2016) Influential investors in online stock forums. We hypothesize that while Google Trends is a valid measure of investor attention, the signals derived from changes in search volume is conditional upon the sentiment inherent to the search terms. We find that the directional movement of the S&P 500 from changes in the search volume series is dependent on the specific term being searched for, and by extension, the sentiment of the term itself. We apply the Kaplan–Meier estimator to quantify the level of persistence in lagged correlations between the search volume series and the directional movements in the S&P 500. While past studies have proposed using Google Trends as an effective proxy for investor attention, we re-evaluate this idea in the context of a Granger causal framework. In the following paper, we seek to evaluate the predictive capabilities of internet search data.
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