Theory of Mind in Learning to Forecast Experiments

Last registered on July 07, 2021

Pre-Trial

Trial Information

General Information

Title
Theory of Mind in Learning to Forecast Experiments
RCT ID
AEARCTR-0007836
Initial registration date
June 30, 2021
Last updated
July 07, 2021, 11:04 AM EDT

Locations

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Primary Investigator

Affiliation
Radboud University

Other Primary Investigator(s)

PI Affiliation
Nanyang Technological University
PI Affiliation
Dongbei University of Finance and Economics

Additional Trial Information

Status
In development
Start date
2021-09-10
End date
2021-09-24
Secondary IDs
Abstract
Learning to forecast experiments aim to find evidence on whether subjects can learn a rational expectations equilibrium. Experimental results often report substantial swings in prices due to adaptive learning processes or trend extrapolation strategies. We aim to explore whether Theory of Mind - in our case, the degree to which participants correctly assess information and signals in markets - helps to explain deviations from rational expectations equilibrium and the existence of price swings. We hypothesize that groups consisting of subjects who score high in a Theory of Mind test get closer to the rational expectations equilibrium showing lower price swings than groups consisting of subjects who score low. In line with Corgnet et al. (2018), we assume that high Theory of Mind subjects better assess the precision of signals in the markets and thus are better at making forecasts. To test our research question, we compare the price deviation from the fundamental value and the coordination of price forecasts in markets with different Theory of Mind levels, elicited via the eye gaze test (Baron-Cohen et al. 1997)
External Link(s)

Registration Citation

Citation
Bao, Te, Sascha Füllbrunn and Jichuan Zong. 2021. "Theory of Mind in Learning to Forecast Experiments." AEA RCT Registry. July 07. https://doi.org/10.1257/rct.7836-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
hidden
Intervention Start Date
2021-09-10
Intervention End Date
2021-09-24

Primary Outcomes

Primary Outcomes (end points)
The independent variable ToM has levels 1) individual ToM score, 2) average ToM group score, and 3) categorization in top (=1), middle upper (=2), middle lower (=3), and bottom (=4). The main learning to forecast measurement (dependent variable) contains the deviation of prices from the rational expectation equilibrium measured by the relative absolute deviation (RAD) and the scale of price fluctuation, i.e. the amplitude of price. Further measures from the learning to forecast experiment literature will apply.
Primary Outcomes (explanation)
The dependent variables are taken from Hommes, C., Sonnemans, J., Tuinstra, J., & Velden, H. van de. (2005). Coordination of expectations in asset pricing experiments. Review of Financial Studies, 18(3), 955–980.

We test whether groups with higher ToM scores reduce price deviation from fundamental value, price amplitude, and standard deviations of price expectations.

Secondary Outcomes

Secondary Outcomes (end points)
In addition, we consider subjects characteristics as controls.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
hidden
Experimental Design Details
Not available
Randomization Method
Students are invited from a subject pool and randomly register for a session.
Randomization Unit
With the student group, we apply the ToM test to every person. We rank the 24 student subjects by their ToM score and cluster them in for cohorts. We then compare the forecasting behaviour of such groups (see analysis plan) as a standard approach considered in other experimental studies on the LtF environment. Hence, we consider a so-called quasi-experiment.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
In total, we consider six sessions with 24 subjects, i.e. six times four groups.
Sample size: planned number of observations
four group level observation for each session
Sample size (or number of clusters) by treatment arms
6*24=144 student subjects
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Considering a Wilcoxon signed-rank test with six observations (Diff=Top - Bottom=0) the minimum detectable effect size is 1.22 (assuming a power of 80% and significance levels of 5%. However, even doubling the sample size would yield 0.79.
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB of Dongbei University of Finance and Economics
IRB Approval Date
2021-05-03
IRB Approval Number
F20210503
Analysis Plan

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