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Google search volumes for portfolio management: performances and asset concentration

  • S.I.: Recent Developments in Financial Modeling and Risk Management
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Abstract

Google search volumes have proven to be useful in portfolio management. The basic idea is that high search volumes are related to bad news and risk increase. This paper shows additional evidence about the use of Google search volumes in risk management, for the Dow Jones Industrial Average index components from 2004 to 2017. To overcome the (time-series and cross-section) limitations Google imposes on data download, a renormalization procedure is presented, to obtain a multivariate sample of volumes, which preserve their relative magnitude. The results indicate that the volume normalization is relevant for portfolio performances. Renormalized Google search volumes yield poor results when they penalize the portfolio diversification. Instead, if the portfolio diversification can be kept to an acceptable level, the renormalized Google search volumes contribute to improving risk-adjusted performances.

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Notes

  1. See https://trends.google.com/trends/.

  2. Potentially, the situation can be even more severe. Consider, for example, the joint download of “Pfizer” and “The Home Depot”, where the GI of “Pfizer” is flattened to 1, losing all relative and dynamical information.

  3. Kristoufek (2015) proposes a comparison between the use of web search volumes in-sample and out-of-sample. In this paper, we only consider the so called out-of-sample analysis, which is appropriate to build an asset allocation rule.

  4. Recall that the Gini coefficient is a concentration measure widely used in economics and statistics, principally to analyze income inequalities (see Gini 2005). The Gini coefficient ranges from 0 (perfect equality: all the individuals have the same income) to 1 (maximum concentration: 1 individual earns all the income in the economy). In this paper, we use the Gini coefficient as a portfolio concentration measure. This way, we have an indicator of the diversification of the considered portfolios. Here and in the following, we report the Gini coefficient of the average portfolio weights on the considered period: \(\mathrm {Gini}\left( \frac{1}{T}\sum _{t=1}^T w_t\right) \), where \(w_t\) is the vector of the portfolio weights at time \(t\).

  5. Remark that the Gini coefficient of the weights of common stock indexes is around 0.5 or below.

  6. The qualitative behavior of the indicators for \(k=4,5,6\) is similar to the one with \(k=3\). For the sake of interest and space, we do not report the results for such values of \(k\), although they are available upon request.

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Acknowledgements

We would like to thank two anonymous Referees for their useful suggestions that helped us to improve the paper.

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Authors and Affiliations

  1. Department of Economics and Management, University of Pavia, Via S. Felice, 5, 27100, Pavia, Italy

    Mario Maggi

  2. DIEC Department of Economics, University of Genoa, Genoa, Italy

    Pierpaolo Uberti

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  1. Mario Maggi
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  2. Pierpaolo Uberti
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Correspondence to Mario Maggi.

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Maggi, M., Uberti, P. Google search volumes for portfolio management: performances and asset concentration. Ann Oper Res 299, 163–175 (2021). https://doi.org/10.1007/s10479-019-03424-7

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