RETRACTED ARTICLE: Google Search Intensity and the Investor Attention Effect: A Quantile Regression Approach
Vighneswara Swamy () and
M. Dharani ()
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M. Dharani: ICFAI Foundation for Higher Education
Journal of Quantitative Economics, 2020, vol. 18, issue 2, No 7, 403-423
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.
Keywords: Stock returns; Google searches; Investor attention/sentiment; Trading strategies; Quantile regression (search for similar items in EconPapers)
JEL-codes: G11 G12 G14 (search for similar items in EconPapers)
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