Self-Verification Mechanisms for Investment Decisions

Last registered on March 01, 2021

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Pre-Trial

Trial Information

General Information

Title

Self-Verification Mechanisms for Investment Decisions

RCT ID

AEARCTR-0007061

Initial registration date

January 18, 2021

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

January 19, 2021, 6:20 AM EST

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

Last updated

March 01, 2021, 3:44 AM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

Germany

Region

Primary Investigator

Name

Israel Waichman

Affiliation

Bard College Berlin

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Till Requate

PI Affiliation

University of Kiel

Contact Investigator

PI Name

Aurel Stenzel

PI Affiliation

University of Kiel

Contact Investigator

Additional Trial Information

Status

Completed

Start date

2021-01-19

End date

2021-02-28

Keywords

Behavior, Finance & Microfinance, Lab

Additional Keywords

self-verification; investment; experiment; communication

JEL code(s)

C9; D7; G2; O4

Secondary IDs

Abstract

The situation between investors and startups is typically characterized by asymmetric information inducing adverse selection and thus suboptimal investments. To mitigate inefficiencies, investors often engage in costly verification processes referred to as due diligence. Additionally, latest technological innovations help to considerably reduce transaction costs. In this project, we suggest a novel “startup investment game” experiment and a series of treatments to test for the effects of costly verification and self-verification. Depending on the results, a policy conclusion could be to support platforms based on the recent self-verification technologies.

External Link(s)

Registration Citation

Citation

Requate, Till, Aurel Stenzel and Israel Waichman. 2021. "Self-Verification Mechanisms for Investment Decisions." AEA RCT Registry. March 01. https://doi.org/10.1257/rct.7061

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Our workhorse is an investment game. We implement five treatments with different verification mechanisms: (i) Baseline, (ii) Costly Noisy Verification, (iii) Costly Verification, (iv) Costly Self-Verification, and (v) Self-Verification

Intervention Start Date

2021-01-19

Intervention End Date

2021-02-28

Primary Outcomes

Primary Outcomes (end points)

Amount invested in the firm (conditional on success probability)

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Success probability communicated to the investor
Verification (yes / no)

Secondary Outcomes (explanation)

Experimental Design

Our workhorse is an investment game. We implement five treatments with different verification mechanisms: Baseline, Costly Noisy Verification, Costly Verification, Costly Self-Verification, Self-Verification”

The experiment will be conducted online using Otree (Chen et al., 2016) with participant pool from an economics department experimental lab.

Experimental Design Details

The design is explained in detail in a pdf document attached to this preregistration. The document will become available at the completion of the study.

Randomization Method

All participants are recruited from the same participant pool of an experimental economics lab.
We randomized the experimental sessions using a computer (4-5 sessions per treatment for a total of 20-25 sessions).
In each session, individual rules (investor, firm) are randomly determined by the Otree program.

Randomization Unit

Experimental session

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

Between 4 and 5 experimental sessions per treatment for 5 treatments:
A total of of 20-25 experimental sessions.

Sample size: planned number of observations

4 or 5 sessions per treatment for 5 treatments. Each session with about 20 participants: Thus, a total of 400-500 participants, where each pair of participants is an independent observations. Hence, the number of independent observations: 200-250 (i.e., between 40 and 50 per treatment for 5 treatments ).

Sample size (or number of clusters) by treatment arms

Baseline treatment´with 4-5 sessions (80-100 participants);
Costly Noisy Verification treatment with 4-5 sessions (80-100 participants);
Costly Verification treatment with 4-5 sessions (80-100 participants);
Costly Self-Verification treatment with 4-5 sessions (80-100 participants);
Self-Verification treatment with 4-5 sessions (80-100 participants)

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

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