Algorithmic trader experiment

Last registered on February 28, 2022


Trial Information

General Information

Algorithmic trader experiment
Initial registration date
February 28, 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
February 28, 2022, 5:07 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

University of Essex

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
We will run a lab experiment, where human traders can trade multiple units of an asset in a double auction. Some time during trading, a public news release shifts the asset value either up ("good news") or down ("bad news"). Trade is possible before, during, or after news release. There are two types of traders, one type values the asset more by 10 ECUs than the other type, for any realization of the asset value. Hence, this is a private value setting, and there are gains from trade. All traders are initially endowed with multiple units of the asset and with cash.

We have three main treatments: Control, with only human traders. MO-Algo, where an algorithm that only uses market orders is active. LO-Algo, where an algorithm that only uses limit orders is active. In the algo treatments, the trading algorithm replaces a human trader from the control treatment, so the endowments and number of traders in the market remain constant.

Within treatments, we vary whether the public news release happens at a fixed and known point in time (e.g., in the middle of the trading window), or whether the news release happens some time in a time window (but the traders do not know the exact time until it happens).
External Link(s)

Registration Citation

Corgnet, Brice, Mark DeSantis and Christoph Siemroth. 2022. "Algorithmic trader experiment." AEA RCT Registry. February 28.
Sponsors & Partners

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Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Main outcome variables on market level:

- Welfare, as measured by sum of profits of all traders
- Price efficiency, as measured by share of transaction prices between the willingnesses to pay of both trader types,
- Liquidity, as measured by a time-weighted bid-ask spread.

On an individual level, the main outcome is the individual trader profit in a round.
Primary Outcomes (explanation)
More detail in the pre-analysis plan.

Secondary Outcomes

Secondary Outcomes (end points)
- Welfare, excluding the high endowment trader (which is the algorithmic trader in the algo treatments)
- Number of trades
- Number of trades within 1 sec of news release

On an individual level, the secondary outcomes are the number of trades in a round by that trader and the share of trades implying an expected loss given the information at the time of trading by that trader (a measure of traders' mistakes).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
See hidden.
Experimental Design Details
Not available
Randomization Method
We use MS excel's random number generator to assign treatments to sessions.
Randomization Unit
We randomize by trader group/session.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
10 per treatment, 30 overall (we have 3 treatments). A cluster is a group, consisting of 8 traders.
Sample size: planned number of observations
On market level, an observation is one round. 3 treatments, 10 groups per treatment, each with 20 rounds. 3*10*20=600 rounds, 200 rounds per treatment.
Sample size (or number of clusters) by treatment arms
10 groups, 10*8=80 subjects in control
10 groups, 10*7=70 subjects in LO-Algo
10 groups, 10*7=70 subjects in MO-Algo
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We aim for a power of 0.8 and an alpha of 0.05. We have 20 rounds per cluster (group), so this is 20 observations on market level per cluster. The main outcome variable of interest is welfare, measured as the sum of payoffs of all traders. Relative to the intial endowments, welfare can increase up to 200 ECU or can decrease up to 200 ECU, depending on the number and kinds of trades made. Suppose welfare is uniformly distributed in [0,80], i.e., between 0% and 40% of maximum gains. This implies an SD of about 25. We assume the intracluster correlation to be 0.5. In a previous experiment (Corgnet, DeSantis and Porter (2020)) it was lower than that, sometimes it is higher, so this is our best guess. Under these conditions, how many clusters per treatment would we need if we want to be able to detect a treatment difference of 25? According to Stata, we need 9 clusters per treatment, but we will make it 10 to be sure. The command: power twomeans 0 25, m1(20) m2(20) sd(25) rho(0.5) cluster

Institutional Review Boards (IRBs)

IRB Name
Chapman University Institutional Review Board (CU IRB)
IRB Approval Date
IRB Approval Number
Analysis Plan

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