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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2017: Intelligent Data Engineering and Automated Learning – IDEAL 2017 pp 580–591Cite as

Finding Sentiment in Noise: Non-linear Relationships Between Sentiment and Financial Markets

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

Sentiment analysis, especially in economics and finance, start-ed with a great fanfare in the late 1990’s when (negative) sentiment proxies were introduced in econometric schemes used in finance for forecasting price returns. Intuitively, the relationship between returns and the proxies suggests a polar opposition - high negative sentiment low returns and vice versa. The parametric (vector autoregression) analysis has suggested a linear relationship between two variables especially in ostensibly well-regulated and stable markets like New York Stock Exchange. We have examined this return-sentiment relationship over a period of many years in stable (NYSE), quasi-stable (Copenhagen Stock Exchange and the Chicago Board of Trade’s commodity markets) and the emergent markets (Shanghai Stock Exchange). A non-parametric method (locally weighted regression) with variations across the board has graphically shown the relationship between sentiment and returns in a non-linear fashion. Our study is largely based on newspaper report (Shanghai and Copenhagen), opinion pieces (New York and Chicago) and blogosphere (Chicago) - totalling 39.2 million tokens regressed with time series comprising 15,871 data points. Evidence from both models shows that negative sentiment actively influences stock and commodity markets respectively. The analytical conclusions determine that sentiment represents a meaningful performance in predicting momentary market-wide drops in valuation.

Z. Zhao—The research leading to these results has received funding from the EU FP7 Slandail project under grant agreement no. 607691. In this study we used the text analysis system Rocksteady, developed as part of the Faireachain project for monitoring, evaluating and predicting the behaviour of markets and communities (20092011). Support for Rocksteady’s development was provided by Trinity College, University of Dublin and Enterprise Ireland (Grant IP-2009-0595).

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Notes

  1. 1.

    A percentage of the total number of data points in the data set.

References

  1. Black, F.: Noise. J. Finan. 41(3), 529–543 (1986)

    Article  Google Scholar 

  2. Robert, J.S.: Irrational Exuberance. Princeon University, Princeton (2015)

    Google Scholar 

  3. Hirshleifer, D.: Investor psychology and asset pricing. J. Finan. 56(4), 1533–1597 (2001)

    Article  Google Scholar 

  4. Klibanoff, P., Lamont, O., Wizman, T.A.: Investor reaction to salient news in closed-end country funds. J. Finan. 53(2), 673–699 (1998)

    Article  Google Scholar 

  5. Stone, P.J., Dunphy, D.C., Smith, M.S., Olgilvie, D.M., with associates: The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Cambridge (1966)

    Google Scholar 

  6. Sims, C.A.: Macroeconomics and reality. Econometrica: J. Econometric Soc. 1–48 (1980)

    Google Scholar 

  7. Cleveland, W.S.: Lowess: a program for smoothing scatterplots by robust locally weighted regression. Am. Stat. 35(1), 54–54 (1981)

    Article  Google Scholar 

  8. Hardie, I., MacKenzie, D.: Assembling an economic actor: the agencement of a hedge fund. Sociol. Rev. 55(1), 57–80 (2007)

    Article  Google Scholar 

  9. MacKenzie, D.: Aftermath of a typhoon. New Sci. 220(2943), 6–7 (2013)

    Article  Google Scholar 

  10. Aitken, M.J., Harris, F.H.D.B., Ji, S.: A worldwide examination of exchange market quality: greater integrity increases market efficiency. J. Bus. Ethics 132(1), 147–170 (2015)

    Article  Google Scholar 

  11. Hansen, J.L.: The trinity of market regulation: disclosure, insider trading and market manipulation. Int. J. Discl. Gov. 1(1), 82–96 (2003)

    Article  Google Scholar 

  12. Hansen, J.L.: Mad in a hurry: the swift and promising adoption of the eu market abuse directive. Eur. Bus. L. Rev. 15, 183 (2004)

    Google Scholar 

  13. Kanas, A.: Volatility spillovers between stock returns and exchange rate changes: international evidence. J. Bus. Finan. Account. 27(3–4), 447–467 (2000)

    Article  Google Scholar 

  14. Xu, J.: Modeling shanghai stock market volatility. Ann. Oper. Res. 87, 141–152 (1999)

    Article  MATH  Google Scholar 

  15. Lee, C.F., Chen, G.-M., Rui, O.M.: Stock returns and volatility on china’s stock markets. J. Financ. Res. 24(4), 523–543 (2001)

    Article  Google Scholar 

  16. Wang, P.: Restructuring to repair legitimacy-a contingency perspective. Corp. Governance Int. Rev. 18(1), 64–82 (2010)

    Article  Google Scholar 

  17. Harris, Z.S.: Distributional structure. Word 10(2–3), 146–162 (1954)

    Article  Google Scholar 

  18. Newey, W.K., West, K.D.: A simple, positive semi-definite, heteroscedastic and autocorrelation consistent covariance matrix. Econometrica 55, 703–709 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  19. Balestra, P., Nerlove, M.: Pooling cross section and time series data in the estimation of a dynamic model: the demand for natural gas. Econometrica: J. Econometric Soc. 585–612 (1966)

    Google Scholar 

  20. Da, Z., Engelberg, J., Gao, P.: The sum of all fears investor sentiment and asset prices. Rev. Financ. Stud. 28(1), 1–32 (2014)

    Article  Google Scholar 

  21. Whaley, R.E.: The investor fear gauge. J. Portfolio Mgmt. 26(3), 12–17 (2000)

    Article  Google Scholar 

  22. Ahmad, K., Hutson, E., Kearney, C., Liu, S.: Media pessimism and stock returns: a time-varying analysis at the firm level (2014)

    Google Scholar 

  23. Engle, R.: Risk and Volatility. New York University, New York (2003)

    Google Scholar 

  24. Yu, J.: Disagreement and return predictability of stock portfolios. J. Financ. Econ. 99(1), 162–183 (2011)

    Article  Google Scholar 

  25. Baker, M., Wurgler, J.: Investor sentiment and the cross-section of stock returns. J. Finan. 61(4), 1645–1680 (2006)

    Article  Google Scholar 

  26. Kelly, S., Ahmad, K.: The impact of news media and affect in financial markets. In: Jackowski, K., Burduk, R., Walkowiak, K., Woźniak, M., Yin, H. (eds.) IDEAL 2015. LNCS, vol. 9375, pp. 535–540. Springer, Cham (2015). doi:10.1007/978-3-319-24834-9_62

    Chapter  Google Scholar 

  27. Kelly, S., News, Sentiment, and Financial Markets: A computational system to evaluate the influence of text sentiment on financial assets. Ph.D. thesis, Trinity College Dublin (2016)

    Google Scholar 

  28. Tetlock, P.C.: Giving content to investor sentiment: the role of media in the stock market. J. Finan. 62(3), 1139–1168 (2007)

    Article  Google Scholar 

  29. Tetlock, P.C., Saar-Tsechansky, M., Macskassy, S.: More than words: quantifying language to measure firms’ fundamentals. J. Finan. 63(3), 1437–1467 (2008)

    Article  Google Scholar 

  30. Garcia, D.: Sentiment during recessions. J. Finan. 68(3), 1267–1300 (2013)

    Article  Google Scholar 

  31. Zhao, Z., Ahmad, K.: Qualitative and quantitative sentiment proxies: interaction between markets. In: Jackowski, K., Burduk, R., Walkowiak, K., Woźniak, M., Yin, H. (eds.) IDEAL 2015. LNCS, vol. 9375, pp. 466–474. Springer, Cham (2015). doi:10.1007/978-3-319-24834-9_54

    Chapter  Google Scholar 

  32. Zhao, Z., Ahmad, K.: A computational account of investor behaviour in chinese and us market. Int. J. Econ. Behav. Organ. 3(6), 78–84 (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Trinity College Dublin, Dublin, Ireland

    Zeyan Zhao, Stephen Kelly & Khurshid Ahmad

Authors
  1. Zeyan Zhao
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  2. Stephen Kelly
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  3. Khurshid Ahmad
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Corresponding author

Correspondence to Zeyan Zhao .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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Zhao, Z., Kelly, S., Ahmad, K. (2017). Finding Sentiment in Noise: Non-linear Relationships Between Sentiment and Financial Markets. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_63

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_63

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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