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Karim F. S. Rochdi | Marian Alexander Dietzel ERES 2014 Annual Meeting, Bucharest

Outperforming the Benchmark: Identifying Investment Strategies for the US REIT Market using Google Trends. Karim F. S. Rochdi | Marian Alexander Dietzel ERES 2014 Annual Meeting, Bucharest. Agenda.

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Karim F. S. Rochdi | Marian Alexander Dietzel ERES 2014 Annual Meeting, Bucharest

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  1. Outperforming the Benchmark: Identifying Investment Strategies for the US REIT Market using Google Trends Karim F. S. Rochdi | Marian Alexander Dietzel ERES 2014 Annual Meeting, Bucharest

  2. Agenda Outperforming the Benchmark: Identifying Investment Strategies for the US REIT Market using Google Trends 1. Motivation andTheoretical Background Data Research Design andMethodology Analysis andFindings 5. Conclusions

  3. Motivation andTheoretical Background Increasing use of the internet (smart phones, tablets and computers) Internet has become the main source for information gathering process Buy-/Sell-Decision is influenced by diverse factors (e.g. economic and political news) Price of a stock is determined by demand and supply Information gatheringprocess liesbetween an event and a financial transaction Motivation and Research Question Can Google Trends databeusedtocapturetheinformationgatheringprocessandpredictshort-term marketmovementsin theUS REIT Market?

  4. Motivation andTheoretical Background Relationshipbetween Google Trends Data and Financial Markets Da, Z., Engelberg, J. and Gao, P. (2011), “In Search of Attention”, The Journal of Finance, Vol. 66 No. 5, pp. 1461-99. Drake, M. S., Roulstone, D. T. and Thornock, J. R. (2012), “Investor Information Demand: Evidence from Google Searches Around Earning Announcements”, Journal of Accounting Research, Vol. 50 No. 4, pp. 1001-40. Da, Z., Engelberg, J. and Gao, P. (2013). “The sum of all fears: investor sentiment and asset prices”. SSRN eLibrary. Preis, T., Moat, H.S. and Stanley, E. (2013), “Quantifying Trading Behavior in Financial Markets Using Google Trends”, Nature - Scientific Reports, Vol. 3 No. 1684, pp. 1-6. Kristoufek, L. (2013), “Can Google Trends search queries contribute to risk diversification?”, Nature - Scientific Reports, Vol. 3 No. 2713, pp. 1-5.  Main empirical findings are that Google Trends data are significantly related to trading activity, stock liquidity, volatility, earnings surprises and market movements.

  5. Motivation andTheoretical Background Relationshipbetween Google Trends Data andthe Real Estate Market Beracha, E. and Wintoki, J. (2012), “Predicting Future Home Price Changes Using Current Google Search Data,” Journal of Real Estate Research, forthcoming. Hohenstatt, R., Kaesbauer, M. and Schaefers, W. (2011), “’Geco’ and its Potential for Real Estate Research: Evidence from the U.S. Housing Market”, Journal of Real Estate Research, Vol. 33 No. 4., pp. 471-506. Hohenstatt, R. and Kaesbauer, M. (2013), “GECO’s Weather Forecast’ for the U.K. Housing Market: To What Extent Can We Rely on Google ECOnometrics?”, Journal of Real Estate Research, forthcoming. Wu, L. and Brynjolfsson, E. (2009), “The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales”, Working papers, Wharton School, University of Pennsylvania.  The studiesdemonstrateGoogle‘spredictiveabilitiesforthereal estatemarketon both a stateand national level

  6. Data Google Data Search Volume Indices (SVI) derived from Google Trends (http://www.google.com/trends/) Normalized values, scaled measured between 0 and 100 The weekly data covers search queries conducted from Sunday to Saturday. Google Trends makes the newest weekly data available with an approximate two day delay.

  7. Research Design andMethodology Cluster Formation Real Estate General Sentiment Finance • Covering different aspectsof real estate • real estate • reits • affordablehousing • properties+property • real estatemanagement • real estatebroker • … • Representing the mood, circumstances, desires and fears of Google users • hate • happy • energy • conflict • cash • health • … • Coveringfinancial topics • fed • bonds • derivatives • dividend • currency • investor • …

  8. Research Design andMethodology Measuring Search Volume Change Determingbuy/sellsignal Average downwardtrend weekt-3 weekt-2 weekt-1 weekt

  9. Research Design andMethodology Measuring Search Volume Change Determingbuy/sellsignal Average upwardtrend weekt-3 weekt-2 weekt-1 weekt

  10. Research Design andMethodology Definition of Search Volume (SV) Change where: t = week of observation, SV = Search Volume Da, Z., Engelberg, J. and Gao, P. (2011) Finding: Search queries conducted two weeks prior, have a predictive ability for the capital market Drake et al. (2012) Finding: Information demand through the internet starts increasing, on average, about two weeks prior to earnings announcements

  11. Research Design andMethodology Positive vs. Negative Correlation Positively correlated (from 2006 – 2008) see Da et al. (2011) and Barber and Odean (2007) Upward trend: buy signal(long position) Downward trend: sell signal (short position) Negatively correlated (from 2006 – 2008) see Preis et al. (2013) and Simon (1955) Downward trend: buy signal (long position) Upward trend: sell signal (short position)

  12. Research Design andMethodology Methodology ReinvestmentStrategy First Trade: Monday, February 20, 2006 Last Trade: Monday, December 30, 2013 Reinvestment assumption Absolute Investment Performance (AIP): ConventionalStrategies Buy-and-Hold-Strategy: 1.58 % (0.20 % p. a.) Random Strategy (purely random signals): 72.27 % (7.04 % p. a.) Momentum Strategy: -53.5 % (-9.13 % p. a.)

  13. EmpiricalResults Performance Ranking (Top 15) .. .. .. .. .. .. .. .. ..

  14. EmpiricalResults Performance of GTIS (properties+property)

  15. EmpiricalResults Performance Measures (subperiods) .. .. .. .. .. .. .. .. ..

  16. FindingsandConclusion Main Findings 85 GTIS outperform the market (buy-and-hold) Best GTIS “properties+property” achieves a performance of 2,181.6 % (47.9 % p.a.) 26 GTIS have a lower risk exposure than the buy-and-hold strategy despite higher returns GTIS with the highest hit rates are not automatically the best performers The Top 12 search terms are strictly real estate related (overall) Strong performance of real estate GTIS during the crisis (09/15/2008 - 02/21/2011) Significant positive correlation between search relevance and investment performance (Kendall’s τ = 0.417, z-stat = 4.274; Spearman’s ρ = 0.580, t-stat = 4.929)  Investment strategies basedon Google search data are able to outperform the market particularly during volatile market phases

  17. Outperformingthe Benchmark Identifying Investment Strategies for the US REIT Market using Google Trends Thanks for listening. Feel free to ask any questions

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