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Source of Overconfidence in Investor Trading Behavior
Last registered on November 17, 2020


Trial Information
General Information
Source of Overconfidence in Investor Trading Behavior
Initial registration date
November 17, 2020
Last updated
November 17, 2020 7:11 AM EST
Primary Investigator
Maastricht University
Other Primary Investigator(s)
PI Affiliation
UC Berkeley Haas
PI Affiliation
Additional Trial Information
In development
Start date
End date
Secondary IDs
Investor overconfidence helps to make sense of many stylized facts in financial markets, such as excessive trading volume and asset price volatility. The primary goal of this project is to study a process in which investors become overconfident about their ability to invest. Our hypothesis is that investors update their beliefs about their ability mainly by observing realized gains and losses (rather than paper gains and losses). We design and conduct experiments to test this hypothesis.

The learning process described above has the potential to give rise to investor overconfidence. The disposition effect, an empirically robust pattern documented by prior research, says that investors have higher propensities to realize gains than to realize losses. In conjunction with the hypothesis that investors learn about their ability to trade by observing realized gains and losses, the disposition effect implies that investors tend to become overconfident after a sequence of investment choices.

Our study includes two experiments. In Experiment 1, subjects first go through an investment task that asks them to make investment choices over multiple periods. After each period, the computer sells a stock from subjects’ portfolios. Between subjects, we exogenously manipulate whether the computer sells winning stocks (Selling Gains treatment) or losing stocks (Selling Losses treatment). After this investment task, we elicit subjects’ beliefs about their investment ability to test whether our treatments affect subjects’ level of confidence. We then ask subjects to perform a second investment task to investigate whether the treatments from the previous task influence subjects’ subsequent investment choices.

Experiment 2 has the same design as Experiment 1, except that subjects do not make active investment choices in the first investment task. Rather, subjects get stocks randomly assigned to their portfolio and merely observe price changes of these stocks. In this setting, our treatment task (the first investment task) does not depend on subjects’ own investment ability. Experiment 2 tests whether the manipulation of gain or loss realizations in such a passive setting affects subjects’ beliefs about the quality of the observed assets as well as their subsequent investment choices.

The results from these two experiments will contribute to a large literature in economics that studies the role of overconfidence in financial decision making.
External Link(s)
Registration Citation
Gödker, Katrin, Lawrence J. Jin and Terrance Odean. 2020. "Source of Overconfidence in Investor Trading Behavior." AEA RCT Registry. November 17. https://doi.org/10.1257/rct.6751-1.0.
Experimental Details
We conduct online experiments with Prolific’s US participant pool.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
The study has two outcome variables. The first outcome variable is subjects’ confidence level (obtained from Part 2, Belief Elicitation). In Experiment 1, this variable is subjects’ confidence level about their own ability. In Experiment 2, it is subjects’ confidence level about the stock type.

The second outcome variable is subjects’ excessive investment volume in Investment Task 2 (Part 3) of each experiment.
Primary Outcomes (explanation)
The variable of subjects’ excessive investment volume computes how often a subject has bought additional shares with a fee that is suboptimally high from a Bayesian perspective.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
In our experiments, we exogenously manipulate whether subjects sell winning stocks or losing stocks. We then test whether the manipulation of gain or loss realizations affects subjects’ level of confidence as well as their subsequent investment choices.
Experimental Design Details
Please see the attached pre-analysis file.
Randomization Method
Randomization is done by the program.
Randomization Unit
Randomization is done at the individual level.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
There are no clusters.
Sample size: planned number of observations
400 experiment subjects.
Sample size (or number of clusters) by treatment arms
100 subjects per treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Committee for the Protection of Human Subjects (IRB) at Caltech
IRB Approval Date
IRB Approval Number
IRB Name
Committee for Protection of Human Subjects (CPHS) at UC Berkeley
IRB Approval Date
IRB Approval Number
IRB Name
Ethical Review Committee Inner City Faculties (ERCIC) of Maastricht University
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)