Power-law correlations in finance-related Google searches, and their cross-correlations with volatility and traded volume: Evidence from the Dow Jones Industrial components

TL;DR

This study reveals that Google search queries for DJIA stocks exhibit strong, persistent power-law correlations, but their cross-correlations with volatility and traded volume are inconsistent and vary across scales.

Contribution

It demonstrates the presence of power-law correlations in Google searches for DJIA stocks and investigates their complex, non-universal relationships with financial measures.

Findings

Search queries show strong power-law persistence with Hurst exponents between 0.8 and 1.1.

Cross-correlations between searches and financial measures are not universal and vary across pairs and scales.

Some pairs exhibit positive correlations, while others show insignificant or negative relationships.

Abstract

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.