Technical analysis as the representation of typical cognitive biases

Zielonka, Piotr

doi:10.1016/j.irfa.2004.02.007

Cited by 41 publications

(19 citation statements)

References 9 publications

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“…Results of this study found anchoring to have a positive influence on speculative buying behavior. This concurs with findings by Galinsky and Mussweiler (2001) and Zielonka (2004).…”

Section: Results Of the Estimated Pls Structural Modelsupporting

confidence: 93%

“…They also demonstrate that whether they are the buyer or the seller, the party who makes the first offer obtains a better outcome. Zielonka (2004) carried out an experiment involving financial analysts and found out that certain historical peaks and lows in a security's price can serve as mental anchors in technical analysis. This means that anchoring and adjustment can influence the judgment and the speculating behavior of an individual.…”

Section: Anchoring and Adjustmentmentioning

confidence: 99%

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Behavioral Finance And Speculative Behavior Of Investors: Evidence From Saudi Stock Market

Alsedrah¹

2018

The European Proceedings of Social and Behavioural Sciences

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This study examines the profile of individual stock traders in Saudi stock market and attempts to determine the behavioral finance factors affecting the speculative behavior of investors as well as to discover the strongest predictors. Using questionnaire survey method, 130 individual traders in Saudi stock market were identified for this study. The survey is carried out in the biggest brokerage firms in Dammam, Riyadh, and Jeddah. It appears that more than 50% of the individual stock traders are in the age group of between 30-39 years and most of them are inexperienced traders. The results from the Partial Least Square method show that anchoring, confirmation, representativeness and overconfidence heuristics have a significant relationship with speculative behavior. Anchoring appears to be the main predictor of speculative behavior, followed by confirmation, representativeness, and overconfidence. In contrast, availability, loss aversion, and regret aversion does not play a significant role in the speculative behavior of individual traders. The behavior factors introduced in this study explain 34% of individuals' speculative behavior. The findings provide important implications to Capital Market Authority in Saudi to develop appropriate programs and strategies to help market participants to make better and wiser buying decisions that can aggregate to increase market efficiency rather than just to speculate on the price movement of the stock market.

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“…Results of this study found anchoring to have a positive influence on speculative buying behavior. This concurs with findings by Galinsky and Mussweiler (2001) and Zielonka (2004).…”

Section: Results Of the Estimated Pls Structural Modelsupporting

confidence: 93%

Section: Anchoring and Adjustmentmentioning

confidence: 99%

Behavioral Finance And Speculative Behavior Of Investors: Evidence From Saudi Stock Market

Alsedrah¹

2018

The European Proceedings of Social and Behavioural Sciences

View full text Add to dashboard Cite

show abstract

“…Lee, Gleason, and Mathur (2001) apply the moving average and channel trading rules to 13 Latin currencies and find that some trading rules may be more suitable for certain types of currencies. Zielonka (2004) justifies the popularity of technical analysis with its relation to the typical cognitive biases.…”

Section: Introductionmentioning

confidence: 98%

Is the Swedish stock market efficient? Evidence from some simple trading rules

Metghalchi

Chang

Marcucci

2008

International Review of Financial Analysis

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“…In his paper, Zielonka [2004] claimed that in spite of the zero efficiency for stock market forecasting, technical analysis seems to be the most popular analytical tool used by practitioners.…”

Section: Literature Overviewmentioning

confidence: 99%

Should We Rely on Forecasts of Prices or Returns? The Short Term Approach

Majerowska¹

2017

Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

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Summary:Investors make their decisions on the basis of the information coming from the market. The main features of assets are prices and investment risk. The rates of return are calculated based on the prices. For modelling the returns, capital asset pricing models can be applied; for the prices, methods of technical analysis could be taken into account. The purpose of this paper is to evaluate both approaches. First -financial modelling of the assets' returns, and the second -the analysis of the assets' prices. In order to verify the effectiveness of the forecasting processes, forecasts and ex-post type forecasting errors were calculated. The empirical analysis is based on the stock prices of ten food companies traded on the Warsaw Stock Exchange. The traditional CAPM and the extension of the CAPM by the GARCH(1,1) process are in use. As the technical analysis tool for price modelling, three period moving averages are calculated. The obtained results allow indicating the superiority of modelling the returns, in terms of short-term forecasting. Unfortunately, the hypothesis about the advantage of the application of GARCH for modelling, and then for forecasting, must be rejected.Keywords: modelling returns, CAPM with GARCH, moving average. Streszczenie: Inwestorzy podejmują decyzje na podstawie informacji pochodzących z rynku. Głównymi cechami aktywów, branymi przez nich pod uwagę, są ceny i ryzyko inwestycji. W oparciu o ceny obliczane są stopy zwrotu. Przyszłe ruchy cen można przewidywać z pomocą narzędzi analizy technicznej. Do modelowania stóp zwrotu wykorzystuje się głów-nie modele wyceny aktywów kapitałowych. Celem niniejszej pracy jest ocena zastosowania obu podejść: modelowania ekonometrycznego stóp zwrotu aktywów i analizy cen aktywów oraz sprawdzenie skuteczności obu podejść do prognozowania. Analiza empiryczna obejmuje dzienne notowania akcji dziesięciu spółek sektora spożywczego, notownych na Giełdzie Papierów Wartościowych w Warszawie. Dokonano porównania tradycyjnego podejścia do wyceny aktywów za pomocą modelu CAPM z modelem uwzględniającym zmienność wariancji GARCH(1,1). Narzędziem analizy technicznej do modelowania cen jest trzyokresowa średnia ruchoma. Wyniki wskazują na przewagę modelowania stóp zwrotu w kontekście pro- 188Ewa Majerowska gnozowania nad narzędziem analizy technicznej. Nie pozwalają jednak na wskazanie przewagi uwzględnienia zmienności wariancji w przypadku prognozowania krótkookresowego.Słowa kluczowe: modelowanie stóp zwrotu, CAPM z efektem GARCH, średnia ruchoma.

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Technical analysis as the representation of typical cognitive biases

Cited by 41 publications

References 9 publications

Behavioral Finance And Speculative Behavior Of Investors: Evidence From Saudi Stock Market

Behavioral Finance And Speculative Behavior Of Investors: Evidence From Saudi Stock Market

Is the Swedish stock market efficient? Evidence from some simple trading rules

Should We Rely on Forecasts of Prices or Returns? The Short Term Approach

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