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How laws need to change to catch up with algorithmic stock trading

Date:
April 29, 2014
Source:
Vanderbilt University
Summary:
High speed algorithms have so revolutionized the design and functioning of our stock markets that they are fast tearing up the rule book in how these markets are regulated, according to a researcher.

High speed algorithms have so revolutionized the design and functioning of our stock markets that they are fast tearing up the rule book in how these markets are regulated, according to a Vanderbilt Law School researcher.

"Algorithms able to execute tens of thousands of trades in just fractions of a second are responsible for more than 70 percent of all equity trading volume in the United States," said Yesha Yadav, assistant professor of law at Vanderbilt Law School. "Securities regulation has not kept pace with this profound transformation underway in public markets."

Many of our most basic securities laws depend on prices to reflect available information in the market. But, high-speed algorithmic markets are transforming how this information is received and processed in the market. It is not just a matter of the extraordinary speeds at which algorithms can trade, but more importantly, in the amount of control that human traders have to review and correct errors in information flows. Although algorithms are making markets extremely fast at reacting to news, we have also seen instances of markets going haywire when algorithms trade on the back of incorrect or over-hyped information received through Twitter feeds or the news media, Yadav argues in "Beyond Efficiency in Securities Regulation," a recent working paper made available on SSRN.

"More problematically, traders also benefit where their algorithms deploy strategies that confound other market participants, or that make it more difficult for others to trade," Yadav writes. "Sending out false signals through dummy trades, or flooding the market with inexplicable but canceled orders can disrupt information flows to the benefit of the strategic algorithmic trader."

One possible consequence to the prevalence of algorithmic stock trading may be that some traders will opt out of the system and take their assets elsewhere, feeling they cannot compete. The market relies on fundamentally informed traders to provide high-quality insights into what prices mean. But, where informed investors systematically lose to their computerized counterparts, they may eventually lose the incentive to take full part in the market, or they may invest less in research. This impact might not be obvious today. However, as markets develop and evolve, it is a pressing question for the next 5 years.

"Automated markets have little respect for traditional paradigms that have governed markets over the last half-century," Yadav writes. "As markets change, so must the laws and theories that regulate them. Without this, regulation must become a bystander to the inevitability of innovation."

Yadav, who worked as legal counsel with the World Bank before joining Vanderbilt's law faculty in 2011, researches international finance and securities regulation, notably with respect to the evolving response of regulatory policy to innovations in financial engineering, market microstructure and globalization.


Story Source:

The above story is based on materials provided by Vanderbilt University. Note: Materials may be edited for content and length.


Journal Reference:

  1. Yesha Yadav. Beyond Efficiency in Securities Regulation. SSRN Electronic Journal, 2014; DOI: 10.2139/ssrn.2400527

Cite This Page:

Vanderbilt University. "How laws need to change to catch up with algorithmic stock trading." ScienceDaily. ScienceDaily, 29 April 2014. <www.sciencedaily.com/releases/2014/04/140429125508.htm>.
Vanderbilt University. (2014, April 29). How laws need to change to catch up with algorithmic stock trading. ScienceDaily. Retrieved October 23, 2014 from www.sciencedaily.com/releases/2014/04/140429125508.htm
Vanderbilt University. "How laws need to change to catch up with algorithmic stock trading." ScienceDaily. www.sciencedaily.com/releases/2014/04/140429125508.htm (accessed October 23, 2014).

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