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Central European Management Journal

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Year 06/2020 
Volume 28 
Issue 2

Don’t fight the tape! Technical Analysis Momentum and Contrarian Signals as Common Cognitive Biases

Piotr Zielonka
Warsaw University of Life Sciences

Wojciech Białaszek
SWPS University of Social Sciences and Humanities
https://orcid.org/0000-0002-4672-4376

Paweł Biedrzycki
Kozminski University

Bartłomiej Dzik
Kozminski University

06/2020 28 (2) Central European Management Journal

DOI 10.7206/cemj.2658-0845.24

Abstract

Purpose: Stock market participants use technical analysis to seek trends in stock price charts
despite its doubtful efficiency. We tested whether technical analysis signals represent typical and
common cognitive biases associated with the continuation or reversal of the trend.

Methodology: We compared investors’ opinions about the predictive power of technical analysis
signals grouped into five conditions: real technical analysis signals associated with trend continuation
(real momentum signals) or trend reversal (real contrarian signals), fake momentum or fake
contrarian signals, and fluctuation signals.

Findings: Investors assigned larger predictive power to real and fake signals associated with trend
continuation than to signals associated with trend reversal. Fake signals, which represented cognitive
biases, elicited similar predictions about trend continuation or reversal to real technical analysis
signals.

Originality: Market players assess momentum signals to have greater predictive power than contrarian signals and neutral signals to have the least predictive power. These results are independent of whether technical analysis signals were well-known to investors or made up by experimenters. The hardwired propensity of our brains to detect patterns combined with the non-natural environment of the stock market creates the illusion of expertise that is not easy to dispel.

References

  1. Bar-Hillel, M. and Wagenaar, W. A. (1991). The Perception of Randomness. Advances in Applied Mathematics, 12(4), 428–454. https://doi.org/10.1016/0196-8858(91)90029-I [Google Scholar]
  2. Barret, J.L. (2000). Exploring the natural foundations of religion. Trends in Cognitive Sciences, 4(1), 29–34. https://doi.org/10.1016/S1364-6613(99)01419-9 [Google Scholar]
  3. De Bondt, W.F.M. (1993). Betting on trends: Intuitive forecasts of financial risk and return. International Journal of Forecasting, 9, 355–371. [Google Scholar]
  4. Epstein, R.A. (1977). The Theory of Gambling and Statistical Logic. London, UK: Academic Press. [Google Scholar]
  5. Fama, E.F. (1991). Efficient Capital Markets: II. The Journal of Finance, 46(5), 1575–1617, https://doi.org/10.1111/j.1540-6261.1991.tb04636.x [Google Scholar]
  6. Gilovich, T., Vallone, R. and Tversky, A. (1985). The hot hand in basketball: On the misperception of random sequences. Cognitive Psychology, 17(3), 295–314, https://doi.org/10.1016/0010-0285(85)90010-6 [Google Scholar]
  7. Hahn, U. and Warren, P.A. (2009) Perceptions of Randomness: Why Three Heads Are Better Than Four. Psychological Review, 116(2), 454–461. https://doi.org/10.1037/a0015241 [Google Scholar]
  8. Henrich, J. (2015).The Secret of Our Success How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter. Princeton, Princeton University Press. [Google Scholar]
  9. Kubińska, E., Czupryna, M., Markiewicz, Ł., and Czekaj, J. (2018). Technical analysis gives you courage, but not money – on the relationship between technical analysis usage, overconfidence and investment performance. Argumenta Oeconomica, 40(1), 317–344. https://doi.org/10.15611/aoe.2018.1.14 [Google Scholar]
  10. List, J.A. (2003). Neoclassical Theory Versus Prospect Theory: Evidence from the Marketplace. Econometrica, 72(2), 615–625. https://doi.org/10.3386/w9736 [Google Scholar]
  11. Lo, A.W. and Hasanhodzic, J. (2010). The Evolution of Technical Analysis: Financial Prediction from Babylonian Tablets to Bloomberg Terminals. Hoboken, NJ: Wiley. [Google Scholar]
  12. Martin, C.F., Bhui, R., Bossaerts, P., Matsuzawa, T. and Camerer, C. (2014). Chimpanzee choice rates in competitive games match equilibrium game theory predictions. Scientific Reports, 4(5182), 1–6. https://doi.org/10.1038/srep05182 [Google Scholar]
  13. Malkiel, B.G. and Fama, E.F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.1111/j.1540-6261.1970.tb00518.x [Google Scholar]
  14. Menkhoff, L. (2010). The Use of Technical Analysis by Fund Managers: International Evidence. Journal of Banking & Finance, 34(11), 2573–2586. https://doi.org/10.1016/j.jbankfin.2010.04.014 [Google Scholar]
  15. Miller, J.B. and Sanjurjo, A. (2019). Is It a Fallacy to Believe in the Hot Hand in the NBA Three-Point Contest? IGIER Working Paper No. 548. https://doi.org/10.2139/ssrn.2611987 [Google Scholar]
  16. Quattrone, G.A. and Tversky, A. (1995). Self-deception and the voter’s illusion. In: J. Elster, (ed.). The multiple self, 35–58. New York, NY: Cambridge University Press. [Google Scholar]
  17. Roney, C.J.R. and Trick, L.M. (2009). Sympathetic magic and perceptions of randomness: The hot hand versus the gambler's fallacy. Thinking & Reasoning, 15(2), 197–210, https://doi.org/10.1080/13546780902847137 [Google Scholar]
  18. Scheibehenne, B., Wilke, A. and Todd, P.M. (2011). Expectations of clumpy resources influence predictions of sequential events. Evolution and Human Behavior, 32(5), 326–333. https://doi.org/10.1016/j.evolhumbehav.2010.11.003 [Google Scholar]
  19. Shafir, E., Simonson, I. and Tversky, A. (1993). Reason-based choice. Cognition, 49(1–2), (1993), 11–36, https://doi.org/10.1016/0010-0277(93)90034-S [Google Scholar]
  20. Tversky, A. and Kahneman, D. (1971) Belief in the law of small numbers. Psychological Bulletin, 76(2), 105–110, https://doi.org/10.1037/h0031322 [Google Scholar]
  21. Tyszka, T., Markiewicz, Ł., Kubińska, E., Gawryluk, K. and Zielonka, P. (2017). A belief in trend reversal requires access to cognitive resources. Journal of Cognitive Psychology, 29(2), 1–15, doi:10.1080/20445911.2016.1245195 [Google Scholar]
  22. Tyszka, T., Zielonka, P., Dacey, R. and Sawicki, P. (2008). Perception of randomness and predicting uncertain events. Thinking & Reasoning, 14(1), 83–110. https://doi.org/10.1080/13546780701677669 [Google Scholar]
  23. Wilke, A., and Clark Barrett, H. (2009). The hot hand phenomenon as a cognitive adaptation to clumped resources. Evolution and Human Behavior, 30(3), 161–169. https://doi.org/10.1016/j.evolhumbehav.2008.11.004 [Google Scholar]
  24. Your Chinese Astrology (2019). 2019 & 2020 Chinese Baby Gender Prediction Chart, https://www.yourchineseastrology.com/calendar/baby-gender-predictor.htm (11.11.2019). [Google Scholar]
  25. Zielonka, P. (2004). Technical analysis as the representation of typical cognitive biases. International Review of Financial Analysis, 13, 217–225, https://doi.org/10.1016/j.irfa.2004.02.007 [Google Scholar]

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APA style

Don’t fight the tape! Technical Analysis Momentum and Contrarian Signals as Common Cognitive Biases. (2020). Don’t fight the tape! Technical Analysis Momentum and Contrarian Signals as Common Cognitive Biases. Central European Management Journal, 28(2), 98-110. https://doi.org/10.7206/cemj.2658-0845.24 (Original work published 06/2020AD)

MLA style

“Don’T Fight The Tape! Technical Analysis Momentum And Contrarian Signals As Common Cognitive Biases”. 06/2020AD. Central European Management Journal, vol. 28, no. 2, 2020, pp. 98-110.

Chicago style

“Don’T Fight The Tape! Technical Analysis Momentum And Contrarian Signals As Common Cognitive Biases”. Central European Management Journal, Central European Management Journal, 28, no. 2 (2020): 98-110. doi:10.7206/cemj.2658-0845.24.