The Relationship between Internet Search Volumes and Stock Price Changes: An Empirical Study on KOSDAQ Market

개별 기업에 대한 인터넷 검색량과 주가변동성의 관계: 국내 코스닥시장에서의 산업별 실증분석

Jeon, Saemi;Chung, Yeojin;Lee, Dongyoup

  • Received : 2016.04.07
  • Accepted : 2016.05.16
  • Published : 2016.06.30


As the internet has become widespread and easy to access everywhere, it is common for people to search information via online search engines such as Google and Naver in everyday life. Recent studies have used online search volume of specific keyword as a measure of the internet users' attention in order to predict disease outbreaks such as flu and cancer, an unemployment rate, and an index of a nation's economic condition, and etc. For stock traders, web search is also one of major information resources to obtain data about individual stock items. Therefore, search volume of a stock item can reflect the amount of investors' attention on it. The investor attention has been regarded as a crucial factor influencing on stock price but it has been measured by indirect proxies such as market capitalization, trading volume, advertising expense, and etc. It has been theoretically and empirically proved that an increase of investors' attention on a stock item brings temporary increase of the stock price and the price recovers in the long run. Recent development of internet environment enables to measure the investor attention directly by the internet search volume of individual stock item, which has been used to show the attention-induced price pressure. Previous studies focus mainly on Dow Jones and NASDAQ market in the United States. In this paper, we investigate the relationship between the individual investors' attention measured by the internet search volumes and stock price changes of individual stock items in the KOSDAQ market in Korea, where the proportion of the trades by individual investors are about 90% of the total. In addition, we examine the difference between industries in the influence of investors' attention on stock return. The internet search volume of stocks were gathered from "Naver Trend" service weekly between January 2007 and June 2015. The regression model with the error term with AR(1) covariance structure is used to analyze the data since the weekly prices in a stock item are systematically correlated. The market capitalization, trading volume, the increment of trading volume, and the month in which each trade occurs are included in the model as control variables. The fitted model shows that an abnormal increase of search volume of a stock item has a positive influence on the stock return and the amount of the influence varies among the industry. The stock items in IT software, construction, and distribution industries have shown to be more influenced by the abnormally large internet search volume than the average across the industries. On the other hand, the stock items in IT hardware, manufacturing, entertainment, finance, and communication industries are less influenced by the abnormal search volume than the average. In order to verify price pressure caused by investors' attention in KOSDAQ, the stock return of the current week is modelled using the abnormal search volume observed one to four weeks ahead. On average, the abnormally large increment of the search volume increased the stock return of the current week and one week later, and it decreased the stock return in two and three weeks later. There is no significant relationship with the stock return after 4 weeks. This relationship differs among the industries. An abnormal search volume brings particularly severe price reversal on the stocks in the IT software industry, which are often to be targets of irrational investments by individual investors. An abnormal search volume caused less severe price reversal on the stocks in the manufacturing and IT hardware industries than on average across the industries. The price reversal was not observed in the communication, finance, entertainment, and transportation industries, which are known to be influenced largely by macro-economic factors such as oil price and currency exchange rate. The result of this study can be utilized to construct an intelligent trading system based on the big data gathered from web search engines, social network services, and internet communities. Particularly, the difference of price reversal effect between industries may provide useful information to make a portfolio and build an investment strategy.


Internet Search Volume;Investors' attention;Price pressure;Stock Price;KOSDAQ


  1. Ahn, S., "An Empirical Study on the Volatility Decomposition In Korea Stock Market - The analysis of the Return Effect and the Volatility Decomposition in the Industry and the Period", Dongguk University, 2012
  2. Barber, B. M., and T. Odean, "All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," Review of Financial Studies, Vol.21, No.2(2008), 787-818.
  3. Berkman, H., P. D. Koch, L. Tuttle, and Y. J. Zhang., "Paying Attention: Overnight Returns and the Hidden Cost of Buying at the Open," Journal of Financial and Quantitative Analysis Vol.47, No.4(2012), 715-741.
  4. Bordino, I., S. Battiston, G. Caldarelli, M. Cristelli, A. Ukkonen, and L. Weber, "Web search queries can predict stock market volumes," PLOS One, Vol.7, No.7(2012), 1-17.
  5. Chang, Y. B., Y. Kwon., and W. Cho, "Attention to the Internet : The Impact of Active Information Search on Investment Decisions", Journal of Intelligence and Information Systems, Vol.21, No.3(2015), 117-129.
  6. Chemmanur, T. and A. Yan, "Advertizing, Attention, and Stock Returns," Technical Report, Boston College and Fordham University, 2009
  7. Choi, H., "Investor attention and stock return reversals : evidence from the KOSDAQ market," Yonsei University, 2014.
  8. Choi, H.Y. and H. Varian, "Predicting the Present with Google Trends," Economic Record, Vol.88, No.s1(2012), 2-9.
  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.
  10. Da, Z., J. Engelberg, and P. Gao, "In Search of Attention," Journal of Finance, Vol.66, No.5(2011), 1461-1499.
  11. Engelberg, J. E., and C. A. Parsons. "The causal impact of media in financial markets." The Journal of Finance, Vol.66, No.1(2011), 67-97.
  12. 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), 87-92.
  13. Gervais, S., R. Kaniel, and D. H. Mingelgrin, "The high-volume return premium," Journal of Finance, Vol.56, No.3(2001), 877-919.
  14. 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, No.7232 (2009), 1012-1014.
  15. Jeong, J. S., D. S. Kim, and J. W. Kim, "Influence analysis of Internet buzz to corporate performance : Individual stock price prediction using sentiment analysis of online news", Journal of Intelligence and Information Systems, Vol.21, No.4(2015), 37-51.
  16. Jung, J. and H. Kim., "Effects of the Oil Shock on the Korean Domestic Stock Market Classified by the Types of Industry," Review of business & economics, Vol.24, No.6(2011), 3589-3610.
  17. Kahneman, D., Attention and Effort, Prentice-Hall, New Jersey, 1973.
  18. Kang, I., "study on the relationship between volatility of industrial stock market and volatility of exchange rate," Korean Journal of Business Administration, Vo.25, No.3 (2013), 1703-1724.
  19. Kim, B. C., "Using Internet Search Trends Analysis to Monitor and Predict Suicide Risk in Korea", Korean Journal of Communication Studies, Vol.23, No.2(2015), 99-120.
  20. Kim, D. and J. S. Yu, "A Dynamic Relationship Between Internet Search Activity, Housing Price, and Trading Volume", Korean Appraisal Review, Vol.24, No.2(2014), 125-140.
  21. Kim, Y., "An Empirical Study on the Lead Relationship between Industries and Market Returns in the Korean Stock Market," Korea Industrial Economics Association, Vol.20, No.5(2007), 1903-1926.
  22. Kim, Y., N. Kim, and S. R. Jeong, "Stock-Index Invest Model Using News Big Data Opinion Mining", Journal of Intelligence and Information Systems, Vol.18, No.2(2012), 143-156.
  23. Koo, P., and M. Kim, "A Study on the Relationship between Internet Search Trends and Company's Stock Price and Trading Volume," The Journal of Society for e-Business Studies, Vol.20, No.2(2015), 1-14.
  24. Kwon, C., S. Hwang, and J. Jung, "Application of Web Query Information for Forecasting Korean Unemployment Rate", Journal of the Korea Society for Simulation, Vol.24, No.2(2015), 31-39
  25. Lou, D., "Attracting Investor Attention through Advertising," Technical Report, London School of Economics and Political Science, 2008.
  26. Park J. and D. H. Hwang,"Short-run Overreaction to Large Stock Price Changes and Investors' Trading Behavior," Korean Journal of Financial Management, Vol.29, No.1(2012), 33-55.
  27. Park, J. W., M. H. Kim., and J. H. Kim, "IT Stock Bubble: Evidence from Korean Stock Market," The Review of Business History, Vol.46, No.0(2008), 9-41.
  28. Polgreen, P.M., Y. Chen, D.M. Pennock, and F.D. Nelson, "Using Internet Searches for Influenza Surveillance," Healthcare Epidemiology, Vol.47, No.11(2008), 1443-1448.
  29. Preis, T., H. S. Moat, and H. E. Stanley, "Quantifying trading behavior in financial markets using Google Trends", Scientific reports, Vol.3, No.1684(2013).
  30. Preis, T., H. S. Moat, H. E. Stanley, and S. R. Bishop, "Quantifying the Advantage of Looking Forward," Scientific Report, Vol.2, No.350(2012).
  31. Seasholes, M.S., and G. Wu, 2007, "Predictable Behavior, Profits, and Attention," Journal of Empirical Finance, Vol.14, No.5(2007), 590-610.
  32. Song, J., Y. Lee, and G. Park, "Sector Investment Strategy with the Black-Litterman Mode," Korean Management Science Review, Vol.29, No.1(2012), 57-71
  33. Yu E., Y. Kim, N. Kim, and S. R. Jeong, "Predicting the Direction of the Stock Index by Using a Domain-Specific Sentiment Dictionary", Journal of Intelligence and Information Systems, Vol.19, No.1(2013), 95-110..
  34. Yuan, Y., "Attention and Trading," Technical Report. University of Pennsylvania, 2008.
  35. Yun, J. S., S. G. Yoon, and C. H. Hong, "Momentum and Contrarian Strategies and Behavior of Foreign Investors in Korean Stock Market," International Area Studies Review, Vol.12, No.3(2008), 195-216.