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Google Searches and Stock Returns

Last Updated on 10 February, 2024 by Rejaul Karim

In this compelling examination featured in the “Google Searches and Stock Returns”, researchers Laurens Bijl, Glenn Kringhaug, Peter Molnár, and Eirik Sandvik from Norwegian University of Science and Technology (NTNU) delve into the intriguing relationship between Google searches and stock returns.

The study seeks to determine whether data gleaned from Google Trends holds predictive power for stock returns. Contrary to previous findings that associated high Google search volumes with subsequent positive returns, this research, covering the period from 2008 to 2013, uncovers a nuanced shift.

The investigation reveals that elevated Google search volumes now precede negative returns, challenging prior assertions. To further probe the practical implications, the researchers assess a trading strategy grounded in selling stocks associated with high Google search volumes and buying those with infrequent searches.

The strategy proves profitable when not accounting for transaction costs, emphasizing the importance of considering these costs in real-world applications.

This study contributes fresh insights to the intersection of technology and finance, offering a contemporary perspective on the intricate dynamics between Google searches and stock returns. As markets evolve, understanding these relationships becomes pivotal for investors seeking informed decision-making strategies.

Abstract Of Paper

We investigate whether data from Google Trends can be used to forecast stock returns. Previous studies have found that high Google search volumes predict high returns for the first one to two weeks, with subsequent price reversal. By using a more recent dataset that covers the period from 2008 to 2013 we find that high Google search volumes lead to negative returns. We also examine a trading strategy based on selling stocks with high Google search volumes and buying stocks with infrequent Google searches. This strategy is profitable when the transaction cost is not taken into account but is not profitable if we take into account transaction costs.

Original paper – Download PDF

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Author

Laurens Bijl
Norwegian University of Science and Technology (NTNU)

Glenn Kringhaug
Norwegian University of Science and Technology (NTNU)

Peter Molnár
University of Stavanger

Eirik Sandvik
Norwegian University of Science and Technology (NTNU)

The pursuit of predicting stock returns has witnessed a paradigm shift with the advent of technology. The research paper “Google Searches and Stock Returns” by Laurens Bijl, Glenn Kringhaug, Peter Molnár, and Eirik Sandvik, published in 2016, delves into the intriguing intersection of online search behavior and stock market movements.

This comprehensive exploration aims to uncover the nuanced dynamics, implications, and temporal variations associated with Google search volumes and their predictive power in forecasting stock returns. At the core of this study is the challenge to conventional wisdom.

The researchers question whether data from Google Trends, specifically search volumes, can reliably forecast stock returns. This question is not merely academic; it holds profound implications for the development of trading strategies and offers a unique lens through which to understand how technological advancements shape financial markets.

The temporal dimension, spanning from 2008 to 2013, adds a layer of complexity and relevance to the findings. Let’s take a look at the methodology used in the study and the key findings that this study helped discover.

Methodology Used in The Study

The methodology adopted in this research is robust, drawing on data from Wharton Research Data Services (WRDS) and Google Trends. The dataset encompasses daily open prices, volumes, dividends, and shares outstanding for S&P 500 companies from 2007 to 2013. Google search volume (GSV) data, a key variable, is analyzed from 2008 to 2013.

The researchers employ a regression model that incorporates excess returns, weekly volatility, and detrended log volume to unravel the intricate relationship between Google search volumes and stock returns.

Key Findings of The Study

The key findings of the study are as follows.

1. Contrary Findings on Google Search Volumes and Returns

The most noteworthy and paradigm-shifting finding is the contradiction to prior research that suggested a positive correlation between high Google search volumes and subsequent stock returns. In this study, high Google search volumes are unexpectedly associated with negative returns.

This counterintuitive result challenges established notions and prompts a reassessment of the presumed positive relationship.

2. Trading Strategy Implications

The study delves into the practical implications of crafting a trading strategy based on Google search volumes. While the strategy appears profitable when transaction costs are excluded, it loses its profitability when these costs are considered.

This finding underscores the importance of factoring in transaction costs when translating predictive insights into real-world trading strategies.

3. Temporal Dynamics in the Relationship

The researchers keenly observe the temporal dynamics in the relationship between Google search volumes and stock returns. They posit that the information embedded in Google searches may be assimilated into the market more rapidly over time, leading to a shifting dynamic in stock returns.

This temporal perspective adds nuance and complexity to the understanding of how external factors influence market behavior.

4. Exploring Coherence with Prior Research

The study positions itself within the context of prior research by Da et al. (2011) and Joseph et al. (2011), which found a positive correlation between high Google search volumes and positive returns in the short term, followed by subsequent reversals.

However, the dataset used in this research (2008-2013) presents a distinct narrative, suggesting that the relationship between Google search volumes and stock returns evolves over time, challenging the temporal consistency of prior findings.

5. Robustness Across Different Metrics

To validate the findings, the research rigorously tests the robustness of its model across different metrics. Multiple definitions of returns, including ordinary weekly return and excess returns calculated in various ways, consistently yield similar and significant results. This strengthens the hypothesis that Google search volumes do indeed possess predictive power in forecasting stock returns.

Summary and conclusion

In conclusion, the paper “Google Searches and Stock Returns” significantly contributes to the evolving discourse on predicting stock returns by unraveling the intricacies of Google search data. The findings, contrary to conventional wisdom, open new avenues for understanding the relationship between online search behavior and stock market dynamics.

As technology continues to shape financial markets, this research underscores the need for adaptive strategies and a nuanced comprehension of the impact of transaction costs on trading strategies based on predictive insights. The temporal analysis further enriches the understanding of how information flows and influences market behavior over time, emphasizing the need for a dynamic approach in navigating the complexities of financial markets.

In this study by Laurens Bijl, Glenn Kringhaug, Peter Molnár, and Eirik Sandvik, we delve into the potential of Google Trends data for predicting stock returns. Contrary to previous findings, our analysis, using a dataset spanning 2008 to 2013, reveals that elevated Google search volumes precede negative returns in subsequent periods.

Furthermore, we assess a trading strategy grounded in selling stocks with high Google search volumes and buying those with infrequent searches. While proving profitable without considering transaction costs, the strategy loses its profitability when these costs are factored in.

This nuanced insight highlights the need to carefully account for transaction costs when evaluating the effectiveness of trading strategies based on Google search data, challenging prior assumptions about the predictive power of online search behavior in the stock market.

Related Reading:

Share Buybacks and Abnormal Returns

The Relation between Momentum and Drift: Industry-Level Evidence from Equity Real Estate Investment Trusts (REITs)

FAQ

– What is the key finding of the study regarding the relationship between Google searches and stock returns?

The study challenges prior findings by revealing that, contrary to earlier research, high Google search volumes are associated with negative stock returns in subsequent periods. This conclusion is drawn from an analysis of a dataset spanning from 2008 to 2013.

– How does the profitability of the trading strategy based on Google search volumes change when transaction costs are considered?

The study assesses a trading strategy involving selling stocks with high Google search volumes and buying stocks with infrequent searches. While the strategy is profitable without considering transaction costs, it loses its profitability when these costs are factored in. This highlights the importance of accounting for transaction costs when evaluating the effectiveness of such trading strategies.

– What is the significance of the study’s findings for investors and decision-making strategies in the evolving landscape of technology and finance?

The study contributes fresh insights to the intersection of technology and finance, emphasizing the need for investors to reevaluate assumptions about the predictive power of online search behavior in the stock market. Understanding the intricate dynamics between Google searches and stock returns becomes pivotal for informed decision-making strategies in the evolving markets.

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