Can We Predict the Financial Markets Based on Google's Search Queries?

Abstract

In this paper we look into the interaction of Google's search queries and several aspects of international equity markets. Using a novel methodology for selecting words and a VAR modeling approach, we study whether the search queries of finance related words can have an impact on market uncertainty, log returns and traded volume of four different english speaking countries. Our overall main results are positive, we find several words that have a robust impact over the explained variables. Particularly, the most robust result we find is that an increase of the search queries including the word "stock" predicts an increase of volatility and decrease of index prices in the next week. With the support of this finding, we investigate the performance of a market timing strategy based on the search frequency of this word and compare it against random words from the Word-Net database and a naive buy and hold strategy. The results of this empirical application are positive, indicating the potential of this new set of data in predicting financial markets.

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