  We study power-law correlations properties of the Google search queries for
Dow Jones Industrial Average (DJIA) component stocks. Examining the daily data
of the searched terms with a combination of the rescaled range and rescaled
variance tests together with the detrended fluctuation analysis, we show that
the searches are in fact power-law correlated with Hurst exponents between 0.8
and 1.1. The general interest in the DJIA stocks is thus strongly persistent.
We further reinvestigate the cross-correlation structure between the searches,
traded volume and volatility of the component stocks using the detrended
cross-correlation and detrending moving-average cross-correlation coefficients.
Contrary to the universal power-law correlations structure of the related
Google searches, the results suggest that there is no universal relationship
between the online search queries and the analyzed financial measures. Even
though we confirm positive correlation for a majority of pairs, there are
several pairs with insignificant or even negative correlations. In addition,
the correlations vary quite strongly across scales.
