Algorithmic trader experiment

Last registered on April 17, 2023


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.

Last updated
April 17, 2023, 6:05 AM EDT

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


Primary Investigator

University of Essex

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

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. 2023. "Algorithmic trader experiment." AEA RCT Registry. April 17.
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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
In a round, a group of 8 traders receive their endowments in units of an asset and cash in ECUs. They then trade for 100 sec via a double auction. A trader either has a high type, who has an asset valuation of 10 ECUs more than the low type, or the low type. At some time during trading, a public news release either shifts the asset value upwards or downwards.

Each session has 1 practice round which does not count, then 20 rounds of trading that do count. In each round, endowments are reset, and new random variables are drawn (asset value realizations, news time); in fact, these random variables have been pre-drawn in matlab and we use the same sequence of realizations in all sessions and treatments to reduce noise. We pay the payoff of one of these 20 rounds, chosen at random.

7 of the 8 traders receive 4 units of the asset as initial endowments. 1 trader, the high endowment trader, receives 12 units of the asset. 5 traders with an endowment of 4 have the high valuation type, and all others, including the high endowment trader, have the low type. Trader types and endowments are randomly assigned in the practice round, and then constant throughout the experiment. In the trading algorithm treatments, the algo is always the high endowment trader.

Three treatments: Control, MO-Algo, LO-Algo, see above.

Within each treatment, we either start with 10 rounds where the public news release time is known and communicated before trading starts (e.g., news is released 40 sec after trading starts), or with 10 rounds where the public news release time is not precisely known (it is only known that it occurs in the interval 40 sec to 60 sec after trading started). The last 10 rounds will have the other regime. Each order is used for 5 sessions, making 10 sessions per treatment. Hence, we vary whether the precise news time is known within-subject. But the trading algorithms are between-subject.
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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Post Trial Information

Study Withdrawal

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Is the intervention completed?
Intervention Completion Date
December 31, 2022, 12:00 +00:00
Data Collection Complete
Data Collection Completion Date
December 31, 2022, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
600 markets
Final Sample Size (or Number of Clusters) by Treatment Arms
20 for each treatment
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

We develop a novel experimental paradigm to study the causal impact of trading algorithms on informational efficiency, liquidity, and welfare. In our design, public information about the asset value is revealed during trading, which gives algorithms a reaction speed advantage. We distinguish market-order (aggressive) and limit-order (passive) algorithms, which replace human traders from the baseline markets. Relative to human-only markets, limit-order algorithms can improve welfare, although human traders do not benefit, as the surplus is captured by the algorithms. Market-order algorithms do not significantly change welfare, though they do lower human traders' profits. Both types of algorithms improve price efficiency, lower volatility, and increase the share of profits for unsophisticated human traders. Our results offer unique evidence that non-exploitative algorithms can enhance welfare and be beneficial to unsophisticated traders.
Corgnet, Brice and DeSantis, Mark and Siemroth, Christoph, Algorithmic Trading, Price Efficiency and Welfare: An Experimental Approach (April 14, 2023). Available at SSRN:

Reports & Other Materials