East Asian Economic Review

Korea Institute for International Economic Policy (대외경제정책연구원)
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Can Big Data Help Predict Financial Market Dynamics?: Evidence from the Korean Stock Market

  • Pyo, Dong-Jin (Macroprudential Supervision Department, The Financial Supervisory Service)
  • Received : 2017.03.22
  • Accepted : 2017.06.05
  • Published : 2017.06.30
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Abstract

This study quantifies the dynamic interrelationship between the KOSPI index return and search query data derived from the Naver DataLab. The empirical estimation using a bivariate GARCH model reveals that negative contemporaneous correlations between the stock return and the search frequency prevail during the sample period. Meanwhile, the search frequency has a negative association with the one-week- ahead stock return but not vice versa. In addition to identifying dynamic correlations, the paper also aims to serve as a test bed in which the existence of profitable trading strategies based on big data is explored. Specifically, the strategy interpreting the heightened investor attention as a negative signal for future returns appears to have been superior to the benchmark strategy in terms of the expected utility over wealth. This paper also demonstrates that the big data-based option trading strategy might be able to beat the market under certain conditions. These results highlight the possibility of big data as a potential source-which has been left largely untapped-for establishing profitable trading strategies as well as developing insights on stock market dynamics.

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References

  1. Andrei, D. and M. Hasler. 2015. "Investor Attention and Stock Market Volatility," Review of Financial Studies, vol. 28, no. 1, pp. 33-72. https://doi.org/10.1093/rfs/hhu059
  2. Aouadi, A., Arouri, M. and F. Teulon. 2013. "Investor Attention and Stock Market Acitivity: Evidence from France," Economic Modelling, vol. 35, pp. 674-681. https://doi.org/10.1016/j.econmod.2013.08.034
  3. Bollen, J., Mao, H. and X. Zeng. 2011. "Twitter Mood Predicts the Stock Market," Journal of Computational Science, vol. 2, no. 1, pp. 1-8. https://doi.org/10.1016/j.jocs.2010.12.007
  4. Bollerslev, T. 1990. "Modelling the Coherence in Short-Run Nominal Exchange Rates: a Multivariate Generalized ARCH Model," Review of Economics and Statistics, vol. 72, no. 3, pp. 498-505. https://doi.org/10.2307/2109358
  5. Choi, H. and H. Varian. 2009. "Predicting Initial Claims for Unemployment Benefits," mimeo.
  6. Choi, H. and H. Varian. 2012. "Predicting the Present with Google Trends," Economic Record, vol. 88, no. S1, Special Issue, pp. 2-9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
  7. Da, Z., Engelberg, J. and P. Gao. 2010. "In Search of Earnings Predictability," mimeo.
  8. Da, Z., Engelberg, J. and P. Gao. 2011. "In Search of Attention," Journal of Finance, vol. 66, no. 5, pp. 1461-1499. https://doi.org/10.1111/j.1540-6261.2011.01679.x
  9. Da, Z., Engelberg, J. and P. Gao. 2014. "The Sum of All Fears: Investor Sentiment and Asset Prices," Review of Financial Studies, vol. 28, no. 1, pp. 1-32. https://doi.org/10.1093/rfs/hhu072
  10. De Bondt, W. F. M. and R. Thaler. 1985. "Does the Stock Market Overract?," Journal of Finance, vol. 40, no. 3, pp. 793-805. https://doi.org/10.1111/j.1540-6261.1985.tb05004.x
  11. Dodds, P. S., Harris, K. D., Kloumann, I. M., Bliss, C. A. and C. M. Danforth. 2011. "Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter," PLoS ONE, vol. 6, no. 12.
  12. Einav, L. and J. D. Levin. 2013. "The Data Revolution and Economic Analysis," NBER Working Paper, no. 19035.
  13. Engle, R. 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, vol. 20, no. 3, pp. 339-350. https://doi.org/10.1198/073500102288618487
  14. Gandelman, N. and R. Hernandez-Murillo. 2014. "Risk Aversion at the Country Level," Federal Reserve Bank of St. Louis Working Paper, 2014-005B.
  15. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S. and L. Brilliant. 2009. "Detecting Influenza Epidemics using Search Engine Query Data," Nature, vol. 457, no. 7232, pp. 1012-1014. https://doi.org/10.1038/nature07634
  16. Kloᵦner, S., Becker, M. and R. Friedman. 2012. "Modeling and Measuring Intraday Overreaction of Stock Prices," Journal of Banking & Finance, vol. 36, no. 4, pp. 1152-1163. https://doi.org/10.1016/j.jbankfin.2011.11.005
  17. Moat, H. S., Curme, C., Avakian, A., Kenett, D. Y., Stanley, H. E. and T. Preis. 2013. "Quantifying Wikipedia Usage Patterns Before Stock Market Moves," Scientific Reports, vol. 3, no. 1801.
  18. Preis, T., Moat, H. S. and H. E. Stanley. 2013. "Quantifying Trading Behavior in Financial Markets Using Google Trends," Scientific Reports, vol. 3, no. 1684.
  19. Rubin, A. and E. Rubin. 2010. "Informend Investors and the Internet," Journal of Business Finance and Accounting, vol. 37, no. 7-8, pp. 841-865. https://doi.org/10.1111/j.1468-5957.2010.02187.x
  20. Varian, H. 2014. "Big Data: New Tricks for Econometrics," Journal of Economics Perspectives, vol. 28, no. 2, pp. 3-28.
  21. Vlastakisa, N. and R. N. Markellos. 2012. "Information Demand and Stock Market Volatility," Journal of Banking and Finance, vol. 36, no. 6, pp. 1808-1821. https://doi.org/10.1016/j.jbankfin.2012.02.007
  22. Vosen, S. and T. Schmidt. 2011. "Forecasting Private Consumption: Survey-based Indicators vs. Google Trends," Journal of Forecasting, vol. 30, no. 6, pp. 565-578. https://doi.org/10.1002/for.1213
  23. Vozlyublennaia, N. 2014. "Investor Attention, Index Performance, and Return Predictability," Journal of Banking and Finance, vol. 41, pp. 17-35. https://doi.org/10.1016/j.jbankfin.2013.12.010
  24. Wu, L. and E. Brynjolfsson. 2013. "The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales," mimeo.