AEA RCT Registry

Field	Before	After
Trial Status	in_development	completed
Trial End Date	April 30, 2023	December 31, 2022
Last Published	February 28, 2022 05:07 PM	April 17, 2023 06:05 AM
Study Withdrawn		No
Intervention Completion Date		December 31, 2022
Data Collection Complete		Yes
Final Sample Size: Number of Clusters (Unit of Randomization)		30
Final Sample Size: Total Number of Observations		600 markets
Final Sample Size (or Number of Clusters) by Treatment Arms		20 for each treatment
Is there a restricted access data set available on request?		No
Program Files		No
Data Collection Completion Date		December 31, 2022
Is data available for public use?		No
Intervention End Date	April 30, 2023	December 31, 2022

Field	Before	After
Paper Abstract		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.
Paper Citation		Corgnet, Brice and DeSantis, Mark and Siemroth, Christoph, Algorithmic Trading, Price Efficiency and Welfare: An Experimental Approach (April 14, 2023). Available at SSRN: https://ssrn.com/abstract=4419304.
Paper URL		https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4419304

Fields Changed