en pl
en pl


Show issue
Year 08/2020 
Issue 33

The role of need for structure in technical analysis and how identifying information in price movements raises traders’ confidence

Łukasz Markiewicz
Kozminski University

Marcin Czupryna
Cracow University of Economics

Elżbieta Kubińska
Cracow University of Economics

08/2020 (33) Decyzje

DOI 10.7206/DEC.1733-0092.141


Technical analysis (TA) is a tool believed to support investor’s investment decisions. Even if research has demonstrated that TA cannot be used to make systematic profits over a long time period, it could potentially bring psychological payoffs to its users in the form of enhancing their confidence. In an experimental study we show that: (1) chartists demonstrate
overconfidence in TA usage, believing that they are better than they actually are in TA formation recognition, and that; (2) the act of naming an observed trend as a TA formation brings extra confidence to the chartist, regardless of whether this is a real TA sequence or a random sequence. Thus, both naming an existing TA formation as a TA formation and naming a random sequence as a TA formation result in greater confidence. Also, irrespective of the high popularity of TA among investors, there are marked individual differences in TA followers. In a questionnaire study, we demonstrate that declared positive attitudes toward TA correlate positively with high need for (cognitive) closure (as measured by the Need for Cognitive Closure Scale; NFCS), specifically, desire for predictability.


  1. Aronson, D. (2011). Evidence-based technical analysis: applying the scientific method and statistical inference to trading signals (Vol. 274): John Wiley & Sons. [Google Scholar]
  2. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(i01), 1–48. doi:10.18637/jss.v067.i01 [Google Scholar]
  3. Burns, B. D., & Corpus, B. (2004). Randomness and Inductions From Streaks: “Gambler’s Fallacy” Versus “Hot Hand”. Psychonomic Bulletin and Review, 11(1), 179–184. [Google Scholar]
  4. Campbell, J.Y., Lo, A.W.-C., & MacKinlay, A. C. (1997). The econometrics of financial markets (Vol. 2): Princeton University Press, Princeton, NJ. [Google Scholar]
  5. Charlebois, M., & Sapp, S. (2007). Temporal Patterns in Foreign Exchange Returns and Options. Journal of Money, Credit and Banking, 39(2–3), 443–470. doi:10.1111/j.0022-2879.2007.00032.x [Google Scholar]
  6. Fama, E. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 383–417. [Google Scholar]
  7. Fama, E., & Blume, M. (1966). Filter Rules and Stock-Market Trading. The Journal of Business, 39(1), 226–241. Retrieved from http://www.jstor.org/stable/2351744 [Google Scholar]
  8. Hsu, P.-H., Hsu, Y.-C., & Kuan, C.-M. (2010). Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias. Journal of Empirical Finance, 17(3), 471–484. doi:http://dx.doi.org/10.1016/j.jempfin.2010.01.001 [Google Scholar]
  9. [Google Scholar]
  10. Iacus, S.M. (2009). Simulation and inference for stochastic differential equations: with R examples: Springer Science & Business Media. [Google Scholar]
  11. Kossowska, M., Hanusz, K., & Trejtowicz, M. (2012). Skrócona wersja Skali Potrzeby Poznawczego Domknięcia. Dobór pozycji i walidacja skali. Psychologia Społeczna, 71(20), 89–99. [Google Scholar]
  12. Kounios, J., & Beeman, M. (2009). The Aha! Moment:The Cognitive Neuroscience of Insight. Current Directions in Psychological Science, 18(4), 210–216. doi:10.1111/j.1467-8721.2009.01638.x [Google Scholar]
  13. Kourtidis, D., Šević, Ž., & Chatzoglou, P. (2011). Investors’ trading activity: A behavioural perspective and empirical results. The Journal of Socio-Economics, 40(5), 548–557. doi:http://dx.doi. org/10.1016/j.socec.2011.04.008 [Google Scholar]
  14. Kruglanski, A.W. (1989). Lay epistemics and human knowledge: Cognitive and motivational bases: Springer. [Google Scholar]
  15. Kubińska, E., Czupryna, M., Markiewicz, Ł., & Czekaj, J. (2018). Technical analysis gives you courage, but not money – on the relationship between technical analysis usage, overconfidence and investment performance. Argumenta Oeconomica, 1(40). [Google Scholar]
  16. Kubińska, E., & Markiewicz, Ł. (2013). Wpływ nadmiernej pewności siebie na ryzyko portfela inwestycyjnego. In A. S. Barczak & P. Tworek (Eds.), Zastosowanie metod ilościowych w zarządzaniu ryzykiem w działalności inwestycyjnej (pp. 375–385). Katowice: Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach. [Google Scholar]
  17. Larrick, R., Burson, K., & Soll, J. (2007). Social comparison and confidence: When thinking you’re better than average predicts overconfidence (and when it does not). Organizational Behavior and Human Decision Processes, 102(1), 76–94. [Google Scholar]
  18. Lo, A.W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. New York: Bloomberg Press. [Google Scholar]
  19. Lo, A.W., & Hasanhodzic, J. (2010). The evolution of technical analysis: financial prediction from Babylonian tablets to Bloomberg terminals. New York: Bloomberg Press. [Google Scholar]
  20. Lo, A.W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 55(4), 1705–1765. doi:10.1111/0022-1082.00265 [Google Scholar]
  21. Lopes, L.L., & Oden, G. C. (1987). Distinguishing between random and nonrandom events. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13(3), 392–400. doi:10.1037/02787393.13.3.392 [Google Scholar]
  22. Markiewicz, Ł., & Markiewicz-Żuchowska, A. (2012). Skłonności poznawcze sędziego wpływające na wysokość wymierzonej kary. Decyzje, 18, 49–81. [Google Scholar]
  23. Markiewicz, Ł., & Weber, E. U. (2013). DOSPERT’s Gambling Risk-Taking Propensity Scale Predicts Excessive Stock Trading. Journal of Behavioral Finance, 14(1), 65–78. doi:10.1080/154275762000 [Google Scholar]
  24. [Google Scholar]
  25. Mateus, A., & Caeiro, F. (2014). An R implementation of several randomness tests. Paper presented at the International conference of computational methods in sciences and engineering 2014 (iccmse 2014). [Google Scholar]
  26. Moore, D.A. (2007). Not so above average after all: When people believe they are worse than average and its implications for theories of bias in social comparison. Organizational Behavior and Human Decision Processes, 102(1), 42–58. Retrieved from http://www.sciencedirect.com/science/article/B6WP2-4M93P6S-3/2/af3bb54d1d3d6eb467fe8197957ccd69 [Google Scholar]
  27. Moore, D.A., & Healy, P.J. (2008). The trouble with overconfidence. Psychological review, 115(2), 502–517. [Google Scholar]
  28. Murphy, J.J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications: Penguin. [Google Scholar]
  29. R Core Team. (2016). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org/. [Google Scholar]
  30. Rustichini, A., DeYoung, C.G., Anderson, J.E., & Burks, S.V. (2016). Toward the integration of personality theory and decision theory in explaining economic behavior: An experimental investigation. Journal of Behavioral and Experimental Economics, 64, 122–137. doi:http://dx.doi. org/10.1016/j.socec.2016.04.019 [Google Scholar]
  31. Schmitz, C. (2015). LimeSurvey: An Open Source survey tool. Hamburg, Germany: LimeSurvey Project Team. Retrieved from http://www.limesurvey.org [Google Scholar]
  32. Sternberg, R.J., & Davidson, J.E. (1995). The nature of insight. Cambridge, MA, US: The MIT Press. [Google Scholar]
  33. Sturm, R.R. (2014). A Turning Point Method For Measuring Investor Sentiment. Journal of Behavioral Finance, 15(1), 30–42. doi:10.1080/15427560.2014.877464 [Google Scholar]
  34. Tyszka, T., Markiewicz, Ł., Kubińska, E., Gawryluk, K., & Zielonka, P. (2017). A belief in trend reversal requires access to cognitive resources. Journal of Cognitive Psychology, 29(2), 202–216. doi:10.1080/20445911.2016.1245195 [Google Scholar]
  35. Tyszka, T., Zielonka, P., Dacey, R., & Sawicki, P. (2008). Perception of randomness and predicting uncertain events. Thinking & Reasoning, 14(1), 83–110. doi:10.1080/13546780701677669 [Google Scholar]
  36. Weber, E.U. (2004). Perception matters: Psychophysics for economists. The psychology of economic decisions, 2, 163–176. [Google Scholar]
  37. Weber, E.U., Siebenmorgen, N., & Weber, M. (2005). Communicating Asset Risk: How Name Recognition and the Format of Historic Volatility Information Affect Risk Perception and Investment [Google Scholar]
  38. Decisions. Risk Analysis, 25(3), 597–609. doi:10.1111/j.1539-6924.2005.00627.x [Google Scholar]
  39. Webster, D. M., & Kruglanski, A. W. (1994). Individual differences in need for cognitive closure. Journal of Personality and Social Psychology, 67(6), 1049–1062. doi:10.1037/0022-3514.67.6.1049 [Google Scholar]
  40. Williams, J.J., & Griffiths, T.L. (2013). Why are people bad at detecting randomness? A statistical argument. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(5), 1473– 1490. doi:10.1037/a0032397 [Google Scholar]
  41. Wojciszke, B. (2009). Dane i pseudodane w procesie postrzegania ludzi. In M. Kofta & T. Szustrowa [Google Scholar]
  42. (Eds.), Złudzenia, które pozwalają żyć: szkice ze społecznej psychologii osobowości: praca zbiorowa: Wydawnictwo Naukowe PWN. [Google Scholar]
  43. Zaleśkiewicz, T., Gąsiorowska, A., Stasiuk, K., Maksymiuk, R., & Bar-Tal, Y. (2016). Efekt aktywnej rekomendacji czy efekt konfirmacyjny? Mechanizm zniekształceń poznawczych w ocenie autorytetu epistemicznego na przykładzie ekspertów z dziedziny finansów. Psychologia Ekonomiczna (8), 59–74. [Google Scholar]
  44. Zielonka, P. (2002). How financial analysts perceive macroeconomic, political news and technical analysis signals. Financial Counseling and Planning, 13(1), 87–97. [Google Scholar]
  45. Zielonka, P. (2004). Technical analysis as the representation of typical cognitive biases. International Review of Financial Analysis, 13(2), 217–225. Retrieved from http://www.sciencedirect.com/ science/article/B6W4W-4BYF8YP-1/2/22eadef1501f5a0c1eb575f303539691 [Google Scholar]
  46. Zielonka, P., Białaszek, W. (2020). Technical analysis momentum and contrarian signals as a representation of common cognitive biases. Journal of Management and Business Administration. Central Europe, 3(2), 217–225 [Google Scholar]

Full metadata record

Cite this record

APA style

Markiewicz, Łukasz & Czupryna, Marcin & Kubińska, Elżbieta (2020). The role of need for structure in technical analysis and how identifying information in price movements raises traders’ confidence. (2020). The role of need for structure in technical analysis and how identifying information in price movements raises traders’ confidence. Decyzje, (33), 75-96. https://doi.org/10.7206/DEC.1733-0092.141 (Original work published 08/2020AD)

MLA style

Markiewicz, Łukasz and Czupryna, Marcin and Kubińska, Elżbieta. “The Role Of Need For Structure In Technical Analysis And How Identifying Information In Price Movements Raises Traders’ Confidence”. 08/2020AD. Decyzje, no. 33, 2020, pp. 75-96.

Chicago style

Markiewicz, Łukasz and Czupryna, Marcin and Kubińska, Elżbieta. “The Role Of Need For Structure In Technical Analysis And How Identifying Information In Price Movements Raises Traders’ Confidence”. Decyzje, Decyzje, no. 33 (2020): 75-96. doi:10.7206/DEC.1733-0092.141.