Thursday, January 5, 2012

Internet Search Data Federal Reserve; The G20 and Thailand

The conclusion is very important I list it first just like a "Abstract".

Economists are always looking for ways to improve their forecasts—to make their crystal ball a bit less cloudy. We find that Internet search counts possess useful information, not available in other variables, to now-cast or forecast the trajectory of some financial market data. While this predictive power is by no means universal—as we observe above, for a number of markets, Internet search data do not provide explanatory power beyond that of more traditional forecasting methods—the basic message is of a useful addition to the economist’s toolkit.

The Appendix 

Several research studies use Internet search data. Hyunyoung Choi and Hal Varian, both of Google, have established the usefulness of search data to predict upcoming economic data releases for U.S. retail sales, auto sales, home sales, and initial jobless claims, as well as visitor statistics for Hong Kong (2009). Chamberlin (2010) of the U.K. Office for National Statistics examines search data’s correlation with British retail sales, property transactions, car registrations, and foreign trips.

    A couple of papers have looked at the housing market. Wu and Brynjolfsson (2009) find that search data foreshadow U.S. housing sale and prices. Webb (2009) finds a strong correlation between the keyword “foreclosure” and actual foreclosures in the United States. McLaren and Shanbhogue (2011) of the Bank of England look at several markets, but find the strongest contribution of search data in a model forecasting U.K. house prices.

    Regarding unemployment, Askitas and Zimmermann (2009) show strong correlations between search data and German unemployment. D’Amuri (2009) of the Bank of Italy finds that an Internet-search-based measure is superior to other leading indicators in predicting Italian unemployment. D’Amuri and Marcucci (2009) find that augmenting models of the U.S. unemployment rate with an Internet job-search indicator outperforms traditional forecasting methods and the Survey of Professional Forecasters. Suhoy (2009) of the Bank of Israel finds search data to be a good predictor of labor market conditions in that country.

    Several papers examine the usefulness of search data in the area of U.S. consumer confidence and spending. Della Penna and Huang (2009) develop a query-based consumer confidence measure that leads those of the University of Michigan and the Conference Board. Schmidt and Vosen (2010) find that search data outperform these two consumer confidence indexes in forecasting private consumption. Similarly, Kholodilin, Podstawski, and Siliverstovs (2010) show that an Internet-search-based forecasting model outperforms several benchmark models of private consumption.

    While most of the academic work to date has focused on economic data, search data have also been used in stock market analysis, although only one group of researchers find that the data can predict prices. Andrade, Bian, and Burch (2010) use search data to identify peak interest in stock investing in a study of the sharp run-up in Chinese stock prices in 2007. Preis, Reith, and Stanley (2010) find that searches for specific company names correlate with transaction volumes for those companies’ shares. Vlastakis and Markellos (2010) use search data as an indicator for information demand on specific stocks and find that this leads not only trading volume but also volatility. Da, Engelberg, and Gao (2010a) construct an “investor attention” index using search data and find that it predicts higher stock prices over a two-week horizon, followed by a reversal over a one-year time frame. They also conclude (2010b) that searches 

for a firm’s most popular products are better than analyst forecasts at predicting earnings surprises and the subsequent market reaction.

The views expressed in this post are those of the author(s) and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author(s).

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