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Complexity and Under-vs. Overreaction in Expectation Formation
Last registered on September 09, 2019

Pre-Trial

Trial Information
General Information
Title
Complexity and Under-vs. Overreaction in Expectation Formation
RCT ID
AEARCTR-0004316
Initial registration date
June 15, 2019
Last updated
September 09, 2019 9:08 PM EDT
Location(s)
Primary Investigator
Affiliation
Shanghai University of Finance and Economics
Other Primary Investigator(s)
PI Affiliation
Humboldt University of Berlin
Additional Trial Information
Status
Completed
Start date
2019-05-22
End date
2019-08-31
Secondary IDs
Abstract
We experimentally study how under- and overreaction to new information is affected by complexity. Our hypothesis is that people are more likely to underreact to news when information is complex and difficult to process, leading to context dependence in expectation formation. In our experiment, subjects predict future values of variable A. In the simple treatment, A follows an AR(1), and subjects only observe past values of A. In the complex treatment, subjects additionally observe a leading indicator B, with A and B jointly generated by a bivariate VAR(1). Our experimental design ensures that the predictability and persistence of A are kept constant across treatments. We investigate how under- and overreaction to new information varies with complexity.
External Link(s)
Registration Citation
Citation
He, Simin and Simas Kucinskas. 2019. "Complexity and Under-vs. Overreaction in Expectation Formation." AEA RCT Registry. September 09. https://doi.org/10.1257/rct.4316-6.0.
Former Citation
He, Simin and Simas Kucinskas. 2019. "Complexity and Under-vs. Overreaction in Expectation Formation." AEA RCT Registry. September 09. https://www.socialscienceregistry.org/trials/4316/history/53158.
Experimental Details
Interventions
Intervention(s)
In a between-subjects design, subjects are randomly assigned to either a simple or complex information treatment. In the simple treatment, subjects observe past values of variable A, and are asked to predict future values of A. In the complex treatment, subjects observe past values of A and an additional variable B, and are asked to predict future values of A.

In our secondary intervention, we vary the persistence of A (as well as B in the complex treatment), and the informativeness of B in predicting A (in the complex for treatment).
Intervention Start Date
2019-05-22
Intervention End Date
2019-08-31
Primary Outcomes
Primary Outcomes (end points)
Subjects’ one-step-ahead forecasts of the future realizations of variable A.
Primary Outcomes (explanation)
By using the forecasts made by subjects, we will construct the following variables:
• Forecast accuracy (captured by the scores earned in the experiment);
• Over- and underreaction to (i) variable A; (ii) variable B; and (iii) overall reaction level to both variables;
• Perceived persistence and correlation levels of subjects.
Secondary Outcomes
Secondary Outcomes (end points)
Subjects’ response times for each forecast
Secondary Outcomes (explanation)
Response time can be used as a proxy for cognitive effort as well as complexity.
Experimental Design
Experimental Design
In the experiment, subjects observe the past realizations of a time-series variable (named “A”) and predict its future values. There are two types of informational treatments. In the simple treatment, A follows an AR (1), and subjects observe past values of A. In the complex treatment, subjects additionally observe a leading indicator, named “B,” with A and B jointly generated by a bivariate VAR (1). The VAR (1) process is parametrized so that the predictability and persistence of A both remain fixed across the two treatments.

A key feature of the experimental design is that we keep the predictability of A constant across all treatments. In other words, by incorporating all the available information, a rational agent would make equally accurate predictions in both simple and complex treatments.

Besides the complexity of information, we also vary the persistence of A (and B, they are always equal in our design) and the correlation between A and B. This allows us to study how the effect of information complexity depends on the informativeness of each variable.

In total there are six treatments, two simple treatments and four complex. In the two simple treatments, the persistence of A is either low or high. In the four complex treatments, the persistence of A, and the correlation between A and B, is either low or high.
Experimental Design Details
In the experiment, subjects first observe the initial 40 realizations of A (and B if applicable). They are asked to forecast the next realization of variable A for 40 rounds. They make one prediction at a time. After they make one prediction, they are shown the actual realization of A (as well as B in complex treatments) for that round. After they make all 40 predictions, they finish the experiment by completing a questionnaire. We incentivize the subjects with a base payment $1.00 and a bonus payment. The bonus payment depends on the subject’s prediction accuracy. The exact formula for the accuracy score is 100*max {0, 1 - |D|/10}, where D is the difference between the actual value and the prediction. In each round, subjects earn a score according to this formula. In the end, subjects are paid with bonus $1.00 plus (total earned points of 40 rounds)/500. A subject can only receive this payment after completing the entire experiment.
Randomization Method
Block randomization with a pseudo-random random number generator. As we have six treatment arms in total, we randomize in blocks of six.
Randomization Unit
Individual-level randomization
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
We aim to collect about 150-170 subjects in each of the 6 treatment arms.
Sample size: planned number of observations
About 1000 subjects, recruited via Amazon Mechanical Turk.
Sample size (or number of clusters) by treatment arms
150-170 subjects per treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan
Analysis Plan Documents
Analysis Plan for “Complexity and Under- vs. Overreaction in Expectation Formation”

MD5: 920e7f3c2a1399cb0777b4714d672a34

SHA1: 2c5ee47b2b9c04ff31ff3d7914001220ea217546

Uploaded At: June 15, 2019

Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
July 03, 2019, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
July 03, 2019, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
978 subjects
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
978 subjects (39120 predictions).
Final Sample Size (or Number of Clusters) by Treatment Arms
657 subjects in complex treatments, 321 in simple treatments.
Data Publication
Data Publication
Is public data available?
Yes
Program Files
Program Files
Yes
Reports and Papers
Preliminary Reports
Relevant Papers