The phenomenon of Anchoring bias refers to the influence of arbitrary numbers in decision-making under uncertainty. Humans are affected by anchors on a daily basis, especially when confronted with quantitative tasks. However, basic anchoring effects appear even when individuals are not expected to compare the value to a certain estimation task. Many researchers tried to figure out the reasons for the anchoring bias. Kahnemann et alii conducted three studies and concluded anchoring bias as the disability of adjustment processes. In an experimental setting the anchoring effects are examined by first showing the participant an arbitrary number, then comparing it to a certain targeted value and finally giving an own estimation. The anchoring bias appears in the last step as the estimation is inevitably biased toward the initial anchor. An alternative explanation is found in the studies conducted by Mussweiler et al. They suggest that the anchoring effect is rather a combination of insufficient adjustment and selective accessibility.
This paper aims to combine both explanations and test four hypotheses in an experimental setting related to financial markets. The first assumption to be tested is whether a comparative task yields to higher anchoring bias when an anchor is provided or self-generated. The second hypothesis to be proved is whether the anchoring bias can mitigate by giving explanations on the comparative task answers. Thirdly, the paper assumes a positive correlation between motivation and cognitive capacity influence the estimates significantly. Finally, this paper extents the research by asking whether risk-aversion is correlated to the anchoring bias.
Table of Contents
1 Introduction
2 Background and Related Literature
2.1 Anchoring bias in Financial Markets
3 Hypotheses Development
4 Data and Research Design
4.1 Experimental setup
4.1.1 Data analysis and theoretical results
5 General Discussion and model limitations
5.1.1 Incentivizing
5.1.2 Choice of non-professionals
5.2 Conversational inferences
5.3 Research Design and Forecast Errors
6 Conclusion and Outlook
7 Appendix
8 Publication bibliography
Objectives & Core Topics
This paper examines the influence of anchoring bias on financial market behavior, specifically targeting retail investors. The central research objective is to investigate how pre-calibrated versus self-generated anchors impact adjustment processes, while testing whether decision-making can be mitigated through logical explanation, and whether cognitive capacity or risk-aversion correlate with the intensity of the anchoring bias.
- Comparison of pre-calibrated and self-generated anchoring effects
- Mitigation of anchoring bias through justifying decision processes
- Impact of cognitive capacity and motivation on estimation accuracy
- Correlation between individual risk-aversion and susceptibility to anchors
- Behavioral patterns of retail investors in financial market forecasting
Excerpt from the book
1 Introduction
The phenomenon of Anchoring bias refers to the influence of arbitray numbers in decision making under uncertainty. Humans are affected by anchors on a daily basis especially when confronted with quantitative tasks. However basic anchoring effects appear even when individuals are not expected to compare the value to a certain estimation task (Wilson et al. 1996). Many researchers tried to figure out the reasons for the anchoring bias. Kahnemann et al. (1974, 1995, 1996) conducted three studies and concluded anchoring bias as the disability of adjustment processes. In an experimental setting the anchoring effects are examined by first showing the participant an arbirary number, then comparing it to a certain targeted value and finally giving an own estimation. The anchoring bias appears in the last step as the estimation is inevitably biased toward the initial anchor. An alternative explanation is found in the studies conducted by Mussweiler et al. (2000, 2001). They suggest that the anchoring effect is rather a combination of insufficient adjustment and selective accessibility.
This paper aims to combine both of these explanations and test four hypotheses in an experimental setting related to financial markets. The first assumption to be tested is whether a comparative task yields to higher anchoring bias when an anchor is provided or self-generated. The second hypothesis to be proved is whether the anchoring bias can mitigate by giving explanations on the comparative task answers. Thirdly, the paper assumes a positive correlation between motivation and cognitive capacity influence the estimates significantly. Finally, this paper extents the research by asking whether risk-aversion is correlated to the anchoring bias.
Summary of Chapters
1 Introduction: Provides an overview of the anchoring bias phenomenon and outlines the research objectives, including the four central hypotheses tested within an experimental financial market setting.
2 Background and Related Literature: Reviews foundational research on anchoring heuristics, starting with Tversky and Kahneman, and discusses various studies on plausible/implausible anchors and their application in different fields.
2.1 Anchoring bias in Financial Markets: Explores how anchoring affects market participants, analysts, and stakeholders, specifically regarding earnings forecasts and the presentation of performance measures.
3 Hypotheses Development: Details the motivation behind the study, focusing on non-professional retail investors, and defines the four hypotheses regarding anchor types, decision justification, and individual cognitive/risk traits.
4 Data and Research Design: Proposes a laboratory experiment structure to test the hypotheses, describing the use of calibration and estimation groups, as well as the implementation of various experimental conditions.
4.1 Experimental setup: Explains the laboratory methodology, participant selection criteria (undergraduates), and the specific structure of the tasks given to the calibration and estimation groups.
4.1.1 Data analysis and theoretical results: Outlines the statistical approach, including the use of the Anchoring Index, t-statistics, and ANOVA to validate the results of the proposed experiment.
5 General Discussion and model limitations: Critically evaluates potential issues in the research design, such as the effect of incentivization and the implications of using non-professional subjects.
5.1.1 Incentivizing: Discusses the conflicting evidence regarding whether financial incentives effectively reduce anchoring bias, specifically in the context of self-generated anchors.
5.1.2 Choice of non-professionals: Addresses the limitations of focusing exclusively on uninformed subjects and acknowledges the role of expertise in reducing anchoring bias.
5.2 Conversational inferences: Explores how subjects interpret anchors as hints from the experimenter and how this social inference influences the resulting bias.
5.3 Research Design and Forecast Errors: Connects the experimental design to existing literature on financial forecast errors and the reliance on historical market data.
6 Conclusion and Outlook: Summarizes the study’s contributions to behavioral economics and suggests that this thesis provides a groundwork for future experimental research in financial decision-making.
7 Appendix: Presents summary statistics for calibration and anchored estimates derived from the foundational Jacowitz and Kahnemann (1995) study.
8 Publication bibliography: Lists the academic sources and empirical studies referenced throughout the paper.
Keywords
Anchoring bias, Behavioral Economics, Financial Markets, Heuristics, Retail Investors, Decision Making, Cognitive Capacity, Risk-Aversion, Forecast Error, Experimental Design, Adjustment Process, Self-generated Anchors, Calibration, Rationality, Market Participants
Frequently Asked Questions
What is the core focus of this research paper?
The paper focuses on the anchoring bias—a cognitive heuristic—and its impact on decision-making under uncertainty, specifically within the context of financial market forecasting by retail investors.
What are the primary themes discussed in the work?
Key themes include the differences between pre-calibrated and self-generated anchors, the role of cognitive capacity and motivation, the mitigation of bias through justifying decisions, and the influence of risk-aversion.
What is the central research goal or question?
The goal is to test four specific hypotheses in a lab-based experimental setting to see how retail investors behave when faced with different anchoring conditions and to determine if specific interventions can reduce the resulting bias.
Which scientific methods are utilized?
The author proposes a quantitative laboratory experiment involving calibration and estimation groups, utilizing statistical methods such as the Anchoring Index, t-statistics, linear regression, and ANOVA for data analysis.
What topics are covered in the main body?
The main body covers the theoretical background of anchoring, the development of the hypotheses, a proposed experimental research design, an analysis of potential limitations, and a discussion of financial forecast errors.
What defines the core terminology of this paper?
The work is characterized by terms such as anchoring bias, behavioral finance, cognitive heuristic, retail investor activity, and adjustment processes.
How does the author define the difference between plausible and implausible anchors?
The author distinguishes them based on the knowledge required for the task; plausible values rely on task-related exemplar knowledge, whereas implausible anchors demand broader category knowledge.
Why does the author choose to focus on non-professionals instead of finance experts?
The paper focuses on retail investors (laymen) because their participation in stock markets has increased significantly, and they are often more susceptible to cognitive biases due to limited market knowledge.
How does the "Anchoring Index" (AI) function in this study?
The AI quantitatively evaluates the bias by calculating the difference in median estimates between high and low-anchored groups, divided by the difference in the anchors themselves.
- Citation du texte
- Pascal Hlavka (Auteur), 2021, The Effects of Anchoring Bias on Behavior in Financial Markets, Munich, GRIN Verlag, https://www.grin.com/document/1172421
