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A Study on Big Data Based Investment Strategy Using Internet Search Trends

인터넷 검색추세를 활용한 빅데이터 기반의 주식투자전략에 대한 연구

  • Kim, Minsoo (Department of Systems Management and Engineering, Pukyong National University) ;
  • Koo, Pyunghoi (Department of Systems Management and Engineering, Pukyong National University)
  • 김민수 (부경대학교 시스템경영공학부) ;
  • 구평회 (부경대학교 시스템경영공학부)
  • Received : 2013.10.01
  • Accepted : 2013.12.09
  • Published : 2013.12.31

Abstract

Together with soaring interest on Big Data, now there are vigorous reports that unearth various social values lying underneath those data from a number of application areas. Among those reports many are using such data as Internet search histories from Google site, social relationships from Facebook, and transactional or locational traces collected from various ubiquitous devices. Many of those researches, however, are conducted based on the data sets that are accumulated over the North American and European areas, which means that direct interpretation and application of social values exhibited by those researches to the other areas like Korea can be a disturbing task. This research has started from a validation study against Korean environment of the former paper which says an investment strategy that exploits up and down of Google search volume on a carefully selected set of terms shows high market performance. A huge difference between North American and Korean environment can be eye witnessed via the distinction in profit rates that are exhibited by the corresponding set of search terms. Two sets of search terms actually presented low correlation in their profit rates over two financial markets. Even in an experiment which compares the profit rates with two different investment periods with the same set of search terms showed no such meaningful result that outperforms the market average. With all these results, we cautiously conclude that establishing an investment strategy that exploits Internet search volume over a specified word set needs more conscious approach.

Keywords

References

  1. 료스케 지음 천재성 옮김, 빅 데이터 비즈니스, 도서출판 더 숲, 2012.
  2. 송민정, 빅 데이터가 만드는 비즈니스 미래지도, 한스 미디어, 2012.
  3. 윤형중 지음, 이제는 빅 데이터 시대, e비즈북스, 2012.
  4. 한국정보화진흥원, "신가치창출 엔진, 빅 데이터의 새로운 가능성과 대응 전략", IT and Future Strategy, 제18호, 2011.
  5. Bollen, J., H. Mao, and X.J. Zeng, "Twitter mood predicts the stock market," Journal of Computational Science, Vol.2, No.1(2011), pp.1-8. https://doi.org/10.1016/j.jocs.2010.12.007
  6. Bordino, I., S. Battiston, G. Caldarelli, M. Cristelli, and Ukkonen, "Web search queries can predict stock market volumes," PLoS One, Vol.7, No.7(2012), pp.1-17.
  7. Choi, H. and H. Varian, "Predicting Initial Claims for Unemployment Insurance Using Google Trends," Technical Report, Google., 2009.
  8. Choi, H. and H. Varian, "Predicting the present with Google Trends," The Economic Record, Vol.88(2012), pp.2-9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
  9. Cooper, C., K. Mallon, S. Leadbetter, L. Pollack, and L. Peipins, "Cancer Internet Search Activity on a Major Search Engine, United States 2001-2003," Journal of Medical Internet Research, Vol.7, No.3(2005), e36. https://doi.org/10.2196/jmir.7.3.e36
  10. Ettredge, M., J. Gerdes, and G. Karuga, "Using Web-based search data to predict macroeconomic statistics," Communications of the ACM, Vol.48, No.11(2005), pp.87-92.
  11. Ginsberg, J., M.H. Mohebbi, R.S. Patel, L. Brammer, M.S. Smolinski, and L. Brilliant, "Detecting influenza epidemics using search engine query data," Nature, Vol.457(2009), pp.1012-1014. https://doi.org/10.1038/nature07634
  12. Goel, S., J.M. Hofman, S. Lahaie, D.M. Pennock, and D.J. Watts, "Predicting consumer behavior with Web search," Proceedings of the National Academy of Sciences, Vol.7, No.41(2010), pp.17486-17490.
  13. McLaren, L. and R. Shanbhogue, "Using internet search data as economic indicator," Quarterly Bulletin, Vol.Q2(2011), pp.134-140.
  14. Moat, H.S., C. Curme, A. Avakian, D.Y. Kenett, E. Stanley, and T. Preis, "Quantifying Wikipedia usage patterns before stock market moves," Scientific Report, Vol.3(2013), pp. 01801 : 1-5.
  15. Polgreen, P.M., Y. Chen, D.M. Pennock, and F.D. Nelson, "Using Internet Searches for Influenza Surveillance," Healthcare Epidemiology, Vol.47(2008), pp.1443-1448.
  16. Preis, T., D. Reith, and H.E. Stanley, "Complex dynamics of our economic life on different scales : insights from search engine query data," Philosophical Transactions of the Royal Society, Vol.368(2010), pp.5707-5719. https://doi.org/10.1098/rsta.2010.0284
  17. Preis, T., Moat, H.S., Stanley, H.E. and Bishop, S.R., "Quantifying the Advantage of Looking Forward," Scientific Report, Vol.2 (2012), pp.00350 : 1-2. https://doi.org/10.1038/srep00350
  18. Preis, T., H.S. Moat, and H.E. Stanley, "Quantifying trading behavior in financial markets using Google Trends," Scientific Report, Vol.3(2013), pp.01684 : 1-5.