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. 2 Textmasterformate durch Klicken bearbeiten Agenda 1. Motivation and Theoretical Background 2. Data 3. Research Design and Methodology 4. Analysis and Findings 5.Conclusions Outperforming the Benchmark: Identifying Investment Strategies for the US REIT Market using Google Trends

3. 3 Textmasterformate durch Klicken bearbeiten Can Google Trends data be used to capture the information gathering process and predict short-term market movements in the US REIT Market? Motivation and Theoretical 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 gathering process lies between an event and a financial transaction Motivation and Research Question

4. 4 Textmasterformate durch Klicken bearbeiten Relationship between Google Trends Data and Financial Markets Motivation and Theoretical Background 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. 5 Textmasterformate durch Klicken bearbeiten Relationship between Google Trends Data and the Real Estate Market Motivation and Theoretical Background 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 studies demonstrate Google‘s predictive abilities for the real estate market on both a state and national level

6. 6 Textmasterformate durch Klicken bearbeiten Google Data Data Search Volume Indices (SVI) derived from Google Trends (http://www.google.com/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. 7 Textmasterformate durch Klicken bearbeiten Research Design and Methodology Covering different aspects of real estate  real estate  reits  affordable housing  properties+property  real estate management  real estate broker  … Real Estate Representing the mood, circumstances, desires and fears of Google users  hate  happy  energy  conflict  cash  health  … General Sentiment Covering financial topics  fed  bonds  derivatives  dividend  currency  investor  … Finance

8. 8 Textmasterformate durch Klicken bearbeiten Measuring Search Volume Change Research Design and Methodology Determing buy/sell signal Average week t-3 week t-2 week t-1 week t downward trend

9. 9 Textmasterformate durch Klicken bearbeiten Measuring Search Volume Change Research Design and Methodology Determing buy/sell signal Average week t-3 week t-2 week t-1 week t upward trend

10. 10 Textmasterformate durch Klicken bearbeiten Definition of Search Volume (SV) Change Research Design and Methodology 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. 11 Textmasterformate durch Klicken bearbeiten Positive vs. Negative Correlation Research Design and Methodology 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. 12 Textmasterformate durch Klicken bearbeiten Reinvestment Strategy  First Trade: Monday, February 20, 2006  Last Trade: Monday, December 30, 2013  Reinvestment assumption Absolute Investment Performance (AIP): Conventional Strategies  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.) Methodology Research Design and Methodology

13. 13 Textmasterformate durch Klicken bearbeiten Empirical Results..

14. 14 Textmasterformate durch Klicken bearbeiten Empirical Results

15. 15 Textmasterformate durch Klicken bearbeiten Empirical Results..

16. 16 Textmasterformate durch Klicken bearbeiten Main Findings Findings and Conclusion  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 based on Google search data are able to outperform the market particularly during volatile market phases

17. 17 Textmasterformate durch Klicken bearbeiten Identifying Investment Strategies for the US REIT Market using Google Trends Outperforming the Benchmark Thanks for listening. Feel free to ask any questions

18. 18 Textmasterformate durch Klicken bearbeiten Backup Slides

19. 19 Textmasterformate durch Klicken bearbeiten Data (Sub-)Categories unmistakably allocating Real Estate Real Estate Listings Topics Capture all different spellings Property Job (Quotation Subject) Search Terms risk dividend Type of Search Term Logical keywords & synonyms real estate reits Google Sets Google Labs project similar terms based on starting values real estate properties reit Top ‘related [search] terms‘ suggested by Google Trends Finding Process of search terms

20. 20 Textmasterformate durch Klicken bearbeiten The role of real estate Analysis and Findings  Why do real estate related searches have a better sense of prediction?  Graff (2001, p. 104): “[…] the investment value of an asset equals the present value of future net cash flows expected from the asset”. Investors gather information about potential future cash flows Reviewing the property market as a whole REIT income is bound to be highly correlated to the property markets  REIT investors gather information about the underlying assets of their stocks  Property-specific GTIS exploit this revealed set of information  Real estate related terms, that are less specific and capture interest on a larger scale, i.e. are more general, perform more successfully.

21. 21 Textmasterformate durch Klicken bearbeiten Capital Market Data MSCI US REIT Index: Free float-adjusted market capitalization index 85% of the US REIT universe Exposure to all investment and property sectors: Diversified REITs, Industrial REITs, Mortgage REITs, Office REITs, Residential REITs, Retail REITs, Specialized REITs All generating a majority of their revenue and income from real estate rental and leasing operations Weekly index prices starting on Monday, January 2, 2006 and ending on Monday, December 30, 2013

22. 22 Textmasterformate durch Klicken bearbeiten Relationship between Google Trends Data and Financial Markets (I) Motivation and Theoretical Background Preis, T., Reith, D. and Stanley, H.E. (2010), “Complex dynamics of our economic life on different scales: insights from search engine query data”, Philosophical Transactions of the Royal Society A, Vol. 368, pp. 5707-5719. Bank, M., Larch, M. and Peter, G. (2011), “Google search volume and its influence on liquidity and returns of German stocks”, Financial Markets and Portfolio Management, Vol. 25 No. 3, pp. 229-64. Vlastakis, N. and Markellos, R.N. (2012), “Information demand and stock market volatility”, Journal of Banking & Finance, Vol. 36, pp. 1808-1821. Dimpfl, T., Jank, S. (2011), “Can internet search queries help to predict stock market volatility?”, CFR Working Paper, No. 11-15, pp. 1-32. Latoeiro, P., Ramos S. B. and Veiga, H. (2013), “Predictability of stock market activity using Google search queries”, Working Paper 13-06, Statistics and Econometrics Series 05. Da, Z., Engelberg, J. and Gao, P. (2011), “In search of fundamentals”, Working paper, University of Notre Dame and University of North Carolina at Chapel Hill.

23. 23 Textmasterformate durch Klicken bearbeiten Conclusions  This field of research only stands at the beginning in its approach to predict financial markets  Buy-/Sell-Decision is most probably influenced by diverse factors (e.g. economic and political news), but: price of a stock is still determined by demand and supply in between the happening of an event and a financial transaction conducted by a person lays a process of information gathering.  Ever-increasing use of the internet (smart phones, tablets and laptops) is improving the predictive power  Internet has become the main source for the information gathering process Gauging this process could be of high relevance for the prediction of people’s decisions and the movement of the market. Whilst the analysis proposed is focused on the real estate market (REITs), we believe that this approach can also be applied to other tradable asset classes.