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Valuable information in early sales proxies: The use of Google search ranks in portfolio optimization
Author(s) -
Kupfer Alexander,
Zorn Josef
Publication year - 2019
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2547
Subject(s) - portfolio , metric (unit) , volume (thermodynamics) , portfolio optimization , ranking (information retrieval) , rank (graph theory) , project portfolio management , computer science , search engine optimization , econometrics , process (computing) , sample (material) , online search , search engine , business , economics , information retrieval , financial economics , marketing , mathematics , project management , chemistry , physics , management , chromatography , quantum mechanics , combinatorics , operating system
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%.
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