Investor confidence in delegated decisions

Last registered on November 08, 2022
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Pre-Trial

Trial Information

General Information

Title
Investor confidence in delegated decisions
RCT ID
AEARCTR-0010379
Initial registration date
November 08, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
November 08, 2022, 3:59 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

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Primary Investigator

Name
Marten Laudi
Affiliation
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Other Primary Investigator(s)

PI Name
Katrin Gödker
PI Affiliation
Bocconi University
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Additional Trial Information

Status
In development
Start date
2022-11-11
End date
2023-11-11
Keywords
Behavior, Finance & Microfinance
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We employ a series of laboratory experiments in the context of delegated investments to make three key contributions. First, we explore belief patterns when investment decisions are delegated. Second, we investigate underlying mechanisms. Third, we explore how learning in a delegated setting affects subsequent investor behavior.
External Link(s)

Registration Citation

Citation
Gödker, Katrin and Marten Laudi. 2022. "Investor confidence in delegated decisions." AEA RCT Registry. November 08. https://doi.org/10.1257/rct.10379-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We employ a between-subject design. The experimental setting features six risky assets with equal purchase prices. Participants are invested in the asset for 10 periods. In a pre-study, we sample participants, who we call asset managers throughout. In the main study, participants are randomly allocated to one of five treatments.

1. TREATMENT CHOICE: Participants select one asset they invest in.
2. TREATMENT RANDOM DELEGATION: Each participant gets an asset manager randomly allocated. The asset manager selects one asset to invest in on behalf of the participant.
3. TREATMENT CHOICE DELEGATION: Each participant selects her own asset manager based on noisy information about the quality of the manager. The chosen asset manager selects one asset to invest in on behalf of the participant.
4. TREATMENT OBSERVED DELEGATION: We show each participants the price path of an asset that was selected by a randomly drawn asset manager for another participant.
5. TREATMENT RANDOM: Participants are randomly assigned an asset they are invested in.
Intervention Start Date
2022-11-11
Intervention End Date
2023-11-11

Primary Outcomes

Primary Outcomes (end points)
Participants’ beliefs about the fundamental value of stocks that they hold.
Primary Outcomes (explanation)
Does delegation influence belief formation about stock quality? We calculate the difference between participants’ estimate of the fundamental quality of their stock and the normative Bayesian posterior. We compare deviations from normative posteriors between CHOICE, CHOICE DELEGATION, RANDOM DELEGATION, and RANDOM. We expect investors to extrapolate and hence update beliefs more strongly to recent price movements when they have selected stocks themselves, compared to when it was selected by an asset manager.

Secondary Outcomes

Secondary Outcomes (end points)
Beliefs about asset managers; Subsequent behavior
Secondary Outcomes (explanation)
1. Beliefs about asset managers: In our study, asset managers can exert costly effort to increase their clients’ payoff. Per default, each asset manager does not have any informative signals about the quality of the available assets. Yet, the asset manager can buy historical information, which costs a fixed amount per additional signal (i.e., additional price level). Among participants in the delegation treatments, we elicit beliefs about how many previous price levels their asset managers have purchased. We compare these beliefs between delegation treatments and expect clients to form more positive beliefs when they have selected an asset manager themselves.

2. Subsequent behavior:
2.1. How do investor beliefs drive risk taking? The first behavioral outcome variable is the level of risk taking. After the first block, participants have the option to invest again in the same asset they were invested in before. They are invested for the next 10 investment periods of the second block. However, this time they can decide how much (from 0% to 100%) of their experimental endowment they want to allocate to the asset. The amount not invested in the risky asset will be invested in a risk-free asset with a small but certain return after 10 periods.

2.2. How do investor beliefs drive switching? The second behavioral outcome variable is switching behavior. After the first block, participants invest again for the next 10 investment periods of the second block. Depending on the treatment group, participants can switch their manager or switch the asset they were invested in. Participants in the RANDOM DELEGATION and CHOICE DELEGATION treatment can decide to either keep their asset manager of block 1 or to switch and get a new manager randomly allocated (RANDOM DELEGATION) / select a new manager (CHOICE DELEGATION). Participants in the RANDOM and CHOICE treatment can decide to either invest in the asset they were invested in before (block 1) or to switch and get a new asset randomly allocated (RANDOM) / select a new asset (CHOICE).

Experimental Design

Experimental Design
Between-subject design. Lab experiment.
Experimental Design Details
Not available
Randomization Method
Block randomization
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
30 asset managers; at least 50 per treatment arm, depending on budget constraints.
Sample size: planned number of observations
30 asset managers; at least 50 per treatment arm, depending on budget constraints.
Sample size (or number of clusters) by treatment arms
30 asset managers; at least 50 per treatment arm, depending on budget constraints.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Ethical Review Committee Inner City Faculties at Maastricht University
IRB Approval Date
2021-09-17
IRB Approval Number
ERCIC_283_25_08_2021
Analysis Plan