Algorithmic trader experiment

Last registered on February 28, 2022
View Trial History

Pre-Trial

Trial Information

General Information

Title
Algorithmic trader experiment
RCT ID
AEARCTR-0009001
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.

Locations

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Primary Investigator

Name
Christoph Siemroth
Affiliation
University of Essex
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Brice Corgnet
PI Affiliation
PI Name
Mark DeSantis
PI Affiliation

Additional Trial Information

Status
In development
Start date
2022-03-03
End date
2023-04-30
Keywords
Finance & Microfinance
Additional Keywords
lab experiment, algorithmic trading
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
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

Citation
Corgnet, Brice, Mark DeSantis and Christoph Siemroth. 2022. "Algorithmic trader experiment." AEA RCT Registry. February 28. https://doi.org/10.1257/rct.9001
Sponsors & Partners

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2022-03-03
Intervention End Date
2023-04-30

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?
Yes

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
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Chapman University Institutional Review Board (CU IRB)
IRB Approval Date
2022-02-07
IRB Approval Number
0910H017
Analysis Plan

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information