How Algorithmic Trading Undermines Efficiency in Capital Markets

Yesha Yadav

Vanderbilt University - Law School

February 24, 2014

Vanderbilt Law Review, Vol. 68, No. 1607, 2015
Vanderbilt Law and Economics Research Paper No. 14-8

This Article argues that the rise of algorithmic trading profoundly challenges the foundation on which much of today’s securities regulation framework rests: the understanding that securities’ prices objectively reflect available information in the market. The Efficient Capital Markets Hypothesis (ECMH) has long provided the theoretical touchstone undergirding central pillars of securities regulation. Mandatory disclosure, evidentiary presumptions in anti-fraud litigation and regulation driving the design of modern exchanges all look to the ECMH for theoretical validation. It is easy to understand why. Laws that make markets better at interpreting information can also improve their ability to allocate capital across the real economy.

Theory and regulation have failed to keep pace with markets where traders rely on pre-programmed algorithms to execute trades. This Article makes two claims. First, complex algorithms foster a separation between the trader and her ability to fully control the operation of the algorithm. Algorithms can execute many thousands of trades in milliseconds, crunching vast quantities of data and dynamically interacting with other traders in the process. This intelligence makes it difficult for a trader to fully predict how an algorithm might behave ex ante and near-impossible for her to track and control its activities in real-time. Secondly, though markets have traditionally relied on informed fundamental traders to decode complexity, these actors now possess reduced incentives to perform this function in algorithmic markets. Fundamental traders routinely see their gains diminished by faster, automated counterparts, able to front-run trades and to derive maximal benefit from the research of others. In arguing that algorithmic trading is transforming how markets process and interpret information, this Article shows that conventional assumptions in securities law doctrine and policy also break down. With these insights, this Article, offers a new framework to thoroughly reevaluate the centrality of efficiency economics in regulatory design.

Number of Pages in PDF File: 65

Keywords: market efficiency, Efficient Capital Markets Hypothesis, ECMH, mandatory disclosure, algorithms, algorithmic trading, securities, equities, Rule 10b-5, integrated disclosure, high frequency trading, spoofing, manipulation, exchanges, microstructure, arbitrage, materiality, damages, behavioral econom

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Date posted: February 26, 2014 ; Last revised: July 31, 2016

Suggested Citation

Yadav, Yesha, How Algorithmic Trading Undermines Efficiency in Capital Markets (February 24, 2014). Vanderbilt Law Review, Vol. 68, No. 1607, 2015; Vanderbilt Law and Economics Research Paper No. 14-8. Available at SSRN: https://ssrn.com/abstract=2400527 or http://dx.doi.org/10.2139/ssrn.2400527

Contact Information

Yesha Yadav (Contact Author)
Vanderbilt University - Law School ( email )
131 21st Avenue South
Nashville, TN 37203-1181
United States

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