Why do flows affect prices? Evidence from an Experiment on the U.S. Stock Market

Last registered on June 06, 2022


Trial Information

General Information

Why do flows affect prices? Evidence from an Experiment on the U.S. Stock Market
Initial registration date
December 14, 2021

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
December 17, 2021, 10:12 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
June 06, 2022, 3:20 PM EDT

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



Primary Investigator

Purdue University

Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
In this experiment, I randomly purchase and sell quantities of publicly-traded U.S. stocks available on the New York, NASDAQ, and American Stock Exchanges.
External Link(s)

Registration Citation

Gallen, Trevor. 2022. "Why do flows affect prices? Evidence from an Experiment on the U.S. Stock Market." AEA RCT Registry. June 06. https://doi.org/10.1257/rct.8550
Experimental Details


I purchase and sell random quantities of stocks on the online Brokerage, TD Ameritrade.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The key outcome variables are (1) price and (2) volume from time of trade onward.
Primary Outcomes (explanation)
For my "naive" experimental results (pure "treatment mean vs control mean")
(1) Price is normalized to be in percentage terms, and is divided by the price one second before the trade was entered.
(2) Volume is normalized to be the ten-minute moving average of volume as a fraction of average daily volume

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
I randomly purchase and sell stocks on the U.S. stock market.
Experimental Design Details
Not available
Randomization Method
The randomization is done on a computer in Python, and has two parts: first, I randomly select stocks to be traded. I then randomize the order in which the treatment and control trades would be performed.
Randomization Unit
Individual stock is the unit of randomization.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
45 baseline treatment days, and 15 quantity treatment days (these are not the unit of randomization, see below for # observations).
Sample size: planned number of observations
Approximately 120,000 for the baseline treatment (18000 treatment, 100,000 control).
Sample size (or number of clusters) by treatment arms
Total expected sample size for treatment in baseline is 20,000, for control is 110000. In quantity treatment 1900 and 8000, respectively.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
See pre-analysis plan for details. I expect to be able to detect a 0.0176% effect on average price 10 minutes after trading.

Institutional Review Boards (IRBs)

IRB Name
Purdue University Human Research Protection Program System
IRB Approval Date
IRB Approval Number
Initial - IRB-2021-1353 ("Purdue’s HRPP has determined that the research does not qualify as Human Subjects Research")
Analysis Plan

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