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Big Data Finance and Financial Markets

  • Dehua ShenEmail author
  • Shu-Heng Chen
Chapter
Part of the Computational Social Sciences book series (CSS)

Abstract

Financial markets are always the most aggressive adopters of new information technologies. The recent boom in big data has enhanced the effect of information diffusion in financial markets since the physical cost of participation has been reduced and interactions among investors have become more efficient. In this chapter, we provide an overview of the current state of the art related to the utilization of big data in financial markets. To start with, we introduce the concept of financial big data from the perspective of complementing our understanding of the predictability and dynamics of financial markets as well as illustrating the changing landscape from conventional media to big data in academic research. Secondly, we summarize the medium effects of financial big data on the efficient market hypothesis and the market dynamics, respectively. Thirdly, we further probe into the underlying mechanisms as to why financial big data exhibits superior predictability and explanatory power for the market dynamics. Finally, this chapter outlines the challenges and promising avenues for future research.

Keywords

Big data Financial markets Information technologies Social media Efficient market hypothesis Market dynamics 

Notes

Acknowledgement

The first author is grateful for the research support in the form of National Natural Science Foundation of China (Grant number: 71701150 and 71320107003), whereas the second author is grateful for the research support in the form of Ministry of Science and Technology (MOST) Grants, Taiwan, MOST 106-2410-H-004-006-MY2.

References

  1. Ahern, K. R., & Sosyura, D. (2014). Who writes the news? Corporate press releases during merger negotiations. Journal of Finance, 69(1), 241–291.Google Scholar
  2. Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. Journal of Finance, 59(3), 1259–1294.Google Scholar
  3. Bagnoli, M., Beneish, M. D., & Watts, S. G. (1999). Whisper forecasts of quarterly earnings per share. Journal of Accounting and Economics, 28(1), 27–50.Google Scholar
  4. Bank, M., Larch, M., & Peter, G. (2011). Google search volume and its influence on liquidity and returns of German stocks. Financial Markets and Portfolio Management, 25(3), 239–264.Google Scholar
  5. Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49(3), 307–343.Google Scholar
  6. Batsell, J. (1998). Gossip central—internet message boards can leave some stocks hanging by a thread. Seattle Times, September 14.Google Scholar
  7. Bennett, J. (1998). Traffic on financial web pages rises when the market falls. Dow Jones News Service, September 1.Google Scholar
  8. Blankespoor, E., Miller, G. S., & White, H. D. (2014). The role of dissemination in market liquidity: Evidence from firms’ use of Twitter™. Accounting Review, 89(1), 79–112.Google Scholar
  9. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.Google Scholar
  10. Chan, W. S. (2003). Stock price reaction to news and no-news: Drift and reversal after headlines. Journal of Financial Economics, 70(2), 223–260.Google Scholar
  11. Chen, H., De, P., Hu, Y., & Hwang, B.-H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. Review of Financial Studies, 27(5), 1367–1403.Google Scholar
  12. Chen, S.-H., Chang, C.-L., & Du, Y.-R. (2012). Agent-based economic models and econometrics. Knowledge Engineering Review, 27(Special Issue 02), 187–219.Google Scholar
  13. Chen, S. H., & Venkatachalam, R. (2017). Agent-based modelling as a foundation for big data. Journal of Economic Methodology, 24(4), 362–383.Google Scholar
  14. Clarkson, P. M., Joyce, D., & Tutticci, I. (2006). Market reaction to takeover rumour in internet discussion sites. Accounting & Finance, 46(1), 31–52.Google Scholar
  15. Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223–236.zbMATHGoogle Scholar
  16. Cutler, D. M., Poterba, J. M., & Summers, L. H. (1989). What moves stock prices? Journal of Portfolio Management, 15(3), 4–12.Google Scholar
  17. Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. Journal of Finance, 66(5), 1461–1499.Google Scholar
  18. Da, Z., Engelberg, J., & Gao, P. (2015). The sum of all FEARS investor sentiment and asset prices. Review of Financial Studies, 28(1), 1–32.Google Scholar
  19. Dang, T. L., Moshirian, F., & Zhang, B. (2015). Commonality in news around the world. Journal of Financial Economics, 116(1), 82–110.Google Scholar
  20. Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and security market under- and overreactions. Journal of Finance, 53(6), 1839–1885.Google Scholar
  21. Das, S. R., & Chen, M. Y. (2007). Yahoo! for Amazon: Sentiment extraction from small talk on the web. Management Science, 53(9), 1375–1388.Google Scholar
  22. De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703–738.Google Scholar
  23. Dimpfl, T., & Jank, S. (2016). Can internet search queries help to predict stock market volatility? European Financial Management, 22(2), 171–192.Google Scholar
  24. Drake, M. S., Roulstone, D. T., & Thornock, J. R. (2012). Investor information demand: Evidence from Google searches around earnings announcements. Journal of Accounting Research, 50(4), 1001–1040.Google Scholar
  25. Dzielinski, M. (2012). Measuring economic uncertainty and its impact on the stock market. Finance Research Letters, 9(3), 167–175.Google Scholar
  26. Einav, L., & Levin, J. (2014). The data revolution and economic analysis. Innovation Policy and the Economy, 14(1), 1–24.Google Scholar
  27. Engelberg, J. E., & Parsons, C. A. (2011). The causal impact of media in financial markets. Journal of Finance, 66(1), 67–97.Google Scholar
  28. Fama, E. F. (1970). Efficient capital market: A review of theory and empirical work. Journal of Finance, 25(2), 383–417.Google Scholar
  29. Fang, L., & Peress, J. (2009). Media coverage and the cross-section of stock returns. Journal of Finance, 64(5), 2023–2052.Google Scholar
  30. Goldstein, A. (1998). Money messages: Electronic message boards are a good way to get investing facts and fiction. Dallas Morning News, August 3.Google Scholar
  31. Griffin, J. M., Hirschey, N. H., & Kelly, P. J. (2011). How important is the financial media in global markets? Review of Financial Studies, 24(12), 3941–3992.Google Scholar
  32. Grullon, G., Kanatas, G., & Weston, J. P. (2004). Advertising, breadth of ownership, and liquidity. Review of Financial Studies, 17(2), 439–461.Google Scholar
  33. Gurun, U. G., & Butler, A. W. (2012). Don’t believe the hype: Local media slant, local advertising, and firm value. Journal of Finance, 67(2), 561–598.Google Scholar
  34. Hanke, M., & Hauser, F. (2008). On the effects of stock spam e-mails. Journal of Financial Markets, 11(1), 57–83.Google Scholar
  35. Harmon, A. (1998). The market turmoil: Investors on line. New York Times, September 1.Google Scholar
  36. Hayek, F. A. (1945). The use of knowledge in society. American Economic Review, 35(4), 519–530.Google Scholar
  37. Irresberger, F., Mühlnickel, J., & Weiß, G. N. F. (2015). Explaining bank stock performance with crisis sentiment. Journal of Banking & Finance, 59, 311–329.Google Scholar
  38. Jin, X., Shen, D., & Zhang, W. (2016). Has microblogging changed stock market behavior? Evidence from China. Physica A: Statistical Mechanics and its Applications, 452, 151–156.Google Scholar
  39. Jones, A. L. (2006). Have internet message boards changed market behavior? Info, 8(5), 67–76.MathSciNetGoogle Scholar
  40. Joseph, K., Babajide Wintoki, M., & Zhang, Z. (2011). Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search. International Journal of Forecasting, 27(4), 1116–1127.Google Scholar
  41. Klibanoff, P., Lamont, O., & Wizman, T. A. (1998). Investor reaction to salient news in closed-end country funds. Journal of Finance, 53(2), 673–699.Google Scholar
  42. Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541–1578.Google Scholar
  43. Liang, B. (1999). Price pressure: Evidence from the “dartboard” column. Journal of Business, 72(1), 119–134.Google Scholar
  44. Maremount, M. (1998). Predeal trading in U.S. Surgical puts spotlight on cyberinvestors. Wall Street Journal, May 28.Google Scholar
  45. Medill, G. (1998). Chicago firm wants to know what Yahoo! left messages. Chicago Daily Herald, October 12.Google Scholar
  46. Meschke, F. (2003). CEO interviews on CNBC, AFA 2003 Washington, DC Meetings.Google Scholar
  47. Mitchell, M. L., & Mulherin, J. H. (1994). The impact of public information on the stock market. Journal of Finance, 49(3), 923–950.Google Scholar
  48. Sabherwal, S., Sarkar, S. K., & Zhang, Y. (2011). Do internet stock message boards influence trading? Evidence from heavily discussed stocks with no fundamental news. Journal of Business Finance & Accounting, 38(9–10), 1209–1237.Google Scholar
  49. Saxton, G. D. (2012). New media and external accounting information: A critical review. Australian Accounting Review, 22(3), 286–302.Google Scholar
  50. Shen, D., Zhang, W., Xiong, X., Li, X., & Zhang, Y. (2016). Trading and non-trading period Internet information flow and intraday return volatility. Physica A: Statistical Mechanics and its Applications, 451, 519–524.Google Scholar
  51. Siganos, A., Vagenas-Nanos, E., & Verwijmeren, P. (2014). Facebook’s daily sentiment and international stock markets. Journal of Economic Behavior & Organization, 107(Part B), 730–743.Google Scholar
  52. Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99–118.Google Scholar
  53. Solomon, D. H. (2012). Selective publicity and stock prices. Journal of Finance, 67(2), 599–638.Google Scholar
  54. Solomon, D. H., Soltes, E., & Sosyura, D. (2014). Winners in the spotlight: Media coverage of fund holdings as a driver of flows. Journal of Financial Economics, 113(1), 53–72.Google Scholar
  55. Sun, L., Najand, M., & Shen, J. (2016). Stock return predictability and investor sentiment: A high-frequency perspective. Journal of Banking & Finance, 73, 147–164.Google Scholar
  56. Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. Journal of Finance, 62(3), 1139–1168.Google Scholar
  57. Tetlock, P. C. (2010). Does public financial news resolve asymmetric information? Review of Financial Studies, 23(9), 3520–3557.Google Scholar
  58. Tetlock, P. C. (2011). All the news that’s fit to reprint: Do investors react to stale information? Review of Financial Studies, 24(5), 1481–1512.Google Scholar
  59. Tetlock, P. C., Saar-Tsechansky, M., & Macskassy, S. (2008). More than words: Quantifying language to measure firms’ fundamentals. Journal of Finance, 63(3), 1437–1467.Google Scholar
  60. Tumarkin, R., & Whitelaw, R. F. (2001). News or noise? Internet postings and stock prices. Financial Analysts Journal, 57(3), 41–51.Google Scholar
  61. Vlastakis, N., & Markellos, R. N. (2012). Information demand and stock market volatility. Journal of Banking & Finance, 36(6), 1808–1821.Google Scholar
  62. Vozlyublennaia, N. (2014). Investor attention, index performance, and return predictability. Journal of Banking & Finance, 41, 17–35.Google Scholar
  63. Wysocki, P. (1998). Cheap talk on the web: The determinants of postings on stock message boards. University of Michigan Business School Working Paper (98025).Google Scholar
  64. Zhang, W., Li, X., Shen, D., & Teglio, A. (2016). Daily happiness and stock returns: Some international evidence. Physica A: Statistical Mechanics and its Applications, 460, 201–209.Google Scholar
  65. Zhang, W., Shen, D., Zhang, Y., & Xiong, X. (2013). Open source information, investor attention, and asset pricing. Economic Modelling, 33(0), 613–619.Google Scholar
  66. Zhang, Y., Feng, L., Jin, X., Shen, D., Xiong, X., & Zhang, W. (2014). Internet information arrival and volatility of SME PRICE INDEX. Physica A: Statistical Mechanics and its Applications, 399(0), 70–74.Google Scholar
  67. Zhang, Y., Song, W., Shen, D., & Zhang, W. (2016). Market reaction to internet news: Information diffusion and price pressure. Economic Modelling, 56, 43–49.Google Scholar
  68. Zhang, Y., & Swanson, P. E. (2010). Are day traders bias free?—Evidence from internet stock message boards. Journal of Economics and Finance, 34(1), 96–112.Google Scholar
  69. Zhang, Y., Swanson, P. E., & Prombutr, W. (2012). Measuring effects on stock returns of sentiment indexes created from stock message boards. Journal of Financial Research, 35(1), 79–114.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Management and EconomicsTianjin UniversityTianjinChina
  2. 2.AI-ECON Research Center, Department of EconomicsNational Chengchi UniversityTaipeiTaiwan

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