Predicting financial markets with Google Trends and not so random keywords

TL;DR

This paper investigates whether Google Trends data can predict financial market returns, highlighting biases, the importance of keyword selection, and demonstrating that some finance-related keywords can lead to profitable trading strategies.

Contribution

It critically evaluates the predictive power of Google Trends keywords for financial markets, revealing that only certain keywords associated with specific assets yield consistent profits.

Findings

Random finance keywords do not outperform random non-finance keywords.

Certain targeted keywords on specific assets produce robust profitable strategies.

Biases in backtesting can significantly affect the evaluation of predictive models.

Abstract

We check the claims that data from Google Trends contain enough data to predict future financial index returns. We first discuss the many subtle (and less subtle) biases that may affect the backtest of a trading strategy, particularly when based on such data. Expectedly, the choice of keywords is crucial: by using an industry-grade backtesting system, we verify that random finance-related keywords do not to contain more exploitable predictive information than random keywords related to illnesses, classic cars and arcade games. We however show that other keywords applied on suitable assets yield robustly profitable strategies, thereby confirming the intuition of Preis et al. (2013)