Source of Overconfidence in Investor Trading Behavior

Last registered on November 17, 2020

Pre-Trial

Trial Information

General Information

Title
Source of Overconfidence in Investor Trading Behavior
RCT ID
AEARCTR-0006751
Initial registration date
November 17, 2020
Last updated
November 17, 2020, 7:11 AM EST

Locations

Region

Primary Investigator

Affiliation
Maastricht University

Other Primary Investigator(s)

PI Affiliation
UC Berkeley Haas
PI Affiliation
Caltech

Additional Trial Information

Status
In development
Start date
2020-11-22
End date
2020-12-31
Secondary IDs
Abstract
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

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

Interventions

Intervention(s)
We conduct online experiments with Prolific’s US participant pool.
Intervention Start Date
2020-11-22
Intervention End Date
2020-12-31

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?
No

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

Institutional Review Boards (IRBs)

IRB Name
Committee for the Protection of Human Subjects (IRB) at Caltech
IRB Approval Date
2020-10-17
IRB Approval Number
20-1041
IRB Name
Committee for Protection of Human Subjects (CPHS) at UC Berkeley
IRB Approval Date
2020-10-26
IRB Approval Number
2020-09-13681
IRB Name
Ethical Review Committee Inner City Faculties (ERCIC) of Maastricht University
IRB Approval Date
2020-10-18
IRB Approval Number
ERCIC_212_28_09_2020
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials