Valuable information in early sales proxies: The use of Google search ranks in portfolio optimization

Alexander Kupfer and Josef Zorn

Journal of Forecasting, 2019, vol. 38, issue 1, 1-10

Abstract: We extract information on relative shopping interest from Google search volume and provide a genuine and economically meaningful approach to directly incorporate these data into a portfolio optimization technique. By generating a firm ranking based on a Google search volume metric, we can predict future sales and thus generate excess returns in a portfolio exercise. The higher the (shopping) search volume for a firm, the higher we rank the company in the optimization process. For a sample of firms in the fashion industry, our results demonstrate that shopping interest exhibits predictive content that can be exploited in a real‐time portfolio strategy yielding robust alphas around 5.5%.

Date: 2019
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https://doi.org/10.1002/for.2547

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:38:y:2019:i:1:p:1-10

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