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Can Google Trends search queries contribute to risk diversification?

  • Ladislav Kristoufek

Portfolio diversification and active risk management are essential parts of financial analysis which became even more crucial (and questioned) during and after the years of the Global Financial Crisis. We propose a novel approach to portfolio diversification using the information of searched items on Google Trends. The diversification is based on an idea that popularity of a stock measured by search queries is correlated with the stock riskiness. We penalize the popular stocks by assigning them lower portfolio weights and we bring forward the less popular, or peripheral, stocks to decrease the total riskiness of the portfolio. Our results indicate that such strategy dominates both the benchmark index and the uniformly weighted portfolio both in-sample and out-of-sample.

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File URL: http://arxiv.org/pdf/1310.1444
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Paper provided by arXiv.org in its series Papers with number 1310.1444.

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Date of creation: Oct 2013
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Publication status: Published in Scientific Reports 3:2713, 2013
Handle: RePEc:arx:papers:1310.1444
Contact details of provider: Web page: http://arxiv.org/

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  1. Jordi Mondria & Thomas Wu & Yi Zhang, 2008. "The Determinants of International Investment and Attention Allocation: Using Internet Search Query Data," Working Papers tecipa-326, University of Toronto, Department of Economics.
  2. Michael S. Drake & Darren T. Roulstone & Jacob R. Thornock, 2012. "Investor Information Demand: Evidence from Google Searches Around Earnings Announcements," Journal of Accounting Research, Wiley Blackwell, vol. 50(4), pages 1001-1040, 09.
  3. Torsten Schmidt & Simeon Vosen, 2009. "Forecasting Private Consumption: Survey-based Indicators vs. Google Trends," Ruhr Economic Papers 0155, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  4. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, 09.
  5. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
  6. Yan Carrière-Swallow & Felipe Labbé, 2010. "Nowcasting With Google Trends in an Emerging Market," Working Papers Central Bank of Chile 588, Central Bank of Chile.
  7. Dror Y. Kenett & Matthias Raddant & Thomas Lux & Eshel Ben-Jacob, 2011. "Evolvement of uniformity and volatility in the stressed global financial village," Kiel Working Papers 1739, Kiel Institute for the World Economy.
  8. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
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