Valuable information in early sales proxies: The use of Google search ranks in portfolio optimization. (19th September 2018)

• Record Type: Journal Article
• Authors: Kupfer, Alexander; Zorn, Josef
• Is Part Of: Journal of forecasting. Volume 38:Number 1(2019)
• Page Start: 1
• Page End: 10
• Publication Date: 2018-09-19
• Subjects: asset ranking -- entropy pooling -- Google search volume -- Google trends -- Kullback–Leibler divergence -- portfolio optimization; Forecasting -- Periodicals; Forecasting -- Mathematical models -- Periodicals; 003.2
• DOI: 10.1002/for.2547
• Languages: English
• ISSNs: 0277-6693

Abstract

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%.