The Issue with Measuring Legal Risks of Algorithmic TradingPosted on Nov 21, 2022
I. The Basics of Algorithmic Trading
Trading in modern financial markets is dominated by algorithms. While human traders once were at the center of the world’s markets, only 10% of all trades today are executed by humans. Trading algorithms are designed to maximize profit, but the extreme speed at which these algorithms execute trades means that a single line of code can have major effects on the market. The lack of human oversight of these high frequency trades means that algorithmic trading has inherent risk. This risk can spread throughout the market as other algorithms react to errors and execute trades on false assumptions. For example, the Flash Crash of 2010, where the S&P 500, Dow Jones Industrial Average, and Nasdaq collapsed and rebounded rapidly (within approximately 36 minutes), has been blamed on automated execution of trading. Additionally, algorithms’ ability to execute trades quickly and efficiently makes them a prime tool for market manipulation.
Aside from the consequences that a lack of risk management can have on the market, algorithmic trading has become a popular tool for market manipulation as the sheer number of trades an algorithm can execute has immense power to distort and deceive a market. High frequency trades are perfect tools for manipulation methods such as spoofing and layering, in which manipulators “place and cancel orders to deceive others into buying or selling stocks at artificial prices” by instantaneously executing and cancelling orders to reach their desired goal.
II. Legal Standards for Market Manipulation
Preventing market manipulation remains at the core of legal regulation of the securities and commodities markets. The Securities Exchange Act Section 10(b) and corresponding Rule 10b-5 makes it “unlawful for any person… to employ any device, scheme, or artifice to defraud.” The 2010 Dodd-Frank Act makes it “unlawful for any person to engage in any trading, practice, or conduct ... that is of the character of, or is commonly known to the trade as, ‘spoofing’ (bidding or offering with the intent to cancel the bid or offer before execution).”
While these laws facially appear to cover most cases of fraud, prosecution of crimes under these laws hinges upon the intent of the trader to manipulate the markets. Proving that a trader acted intentionally or recklessly to distort the market is difficult. This difficulty is only exacerbated as algorithms become the primary tool for executing trades.
Courts and the SEC have struggled to prove intent to defraud, especially looking at instances where trades were executed using algorithms. In United States v. Coscia, the primary evidence of Mr. Coscia’s intent was testimony from his program designer that “Mr. Coscia asked that the programs act "[l]ike a decoy," which would be "[u]sed to pump [the] market” … to “get a reaction from other algorithms.”” In an SEC proceeding against Athena Capital, the SEC looked at the algorithms that were used to manipulate prices as well as communications between managers running these algorithms to substantiate its allegations that Athena willfully violated Section 10(b) of the Exchange Act and Rule 10b-5.
Algorithmic trading is a cheaper, more efficient tool to execute trades on a high level, but its benefits do not come without their own risks. The lack of human ability to oversee all trades executed by an algorithm makes algorithmic trading both the perfect tool to manipulate markets outside the vision of regulatory agencies, but also immensely susceptible to such market manipulation.
Legal attempts to regulate market manipulation rely on proof of intent. However, as technology evolves, machine learning and AI will no doubt be used as even more efficient tools than pre-programmed algorithms. Artificial intelligence, motivated solely by profit maximization, can autonomously learn to manipulate markets. The intent requirements of the law, already difficult to prove for a pre-programmed algorithm, will be even more difficult to prove for an instance of artificial intelligence. Not only will scienter be difficult to prove, but also assigning culpability for any resulting market manipulation will be nearly impossible. The law, already seemingly ill-equipped to deal with the presence of technology in the markets, needs to evolve to deal with these situations at the pace which technology evolves, and traders find new ways to manipulate markets.
 Evelyn Cheng, Just 10% of Trading Is Regular Stock Picking, JPMorgan Estimates, CNBC (Sept. 13, 2017), https://www.cnbc.com/2017/06/13/death-of-the-human-investorjust-10-percent-of-trading-is-regular-stock-picking-jpmorgan-estimates.html.
 Algorithmic Trading Briefing Note, New York Federal Reserve (April 2015), https://www.newyorkfed.org/medialibrary/media/newsevents/news/banking/2015/SSG-algorithmic-trading-2015.pdf
 Andrei A. Kirilenko et al., The Flash Crash: High-Frequency Trading in an Electronic Market, J. Fin. (Jan. 6, 2017, forthcoming) https://ssrn.com/abstract=1686004.
 Megan Shearer et al., Machine Learning, Algorithmic Trading, and Manipulation, CLS Blue Sky Blog (September 19, 2022), https://clsbluesky.law.columbia.edu/2022/09/19/machine-learning-algorithmic-trading-and-manipulation/.
 Gina-Gail S. Fletcher, Deterring Algorithmic Manipulation, 74 Vand. L. Rev. 259, 262 (2021).
 Chris Montagnio, Litigation, Professional Perspective - Compliance Tips to Identify Manipulative Securities Trading, Bloomberg Finance L.P. (September 2022).
 Supra note 6 at 263.
 17 CFR § 240.10b-5 - Employment of manipulative and deceptive devices.
 Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010, Pub. L. No. 111-203, 124 Stat. 1376, 1913 (2012) (codified at 12 U.S.C. § 5301).
 Gina-Gail S. Fletcher, Legitimate Yet Manipulative: The Conundrum of Open-Market Manipulation, 68 DUKE L.J. 479, 485 (2018).
 Supra note 6 at 263.
 See United States v. Coscia, 866 F.3d 782 (7th Cir. 2017); Myun-Uk Choi v. Tower Rsch. Cap. LLC, No. 14-CV-9912 (KMW), 2022 BL 342136 (S.D.N.Y. Sept. 27, 2022); United States v. Bases, 549 F. Supp. 3d 822 (N.D. Ill. 2021); United States v. Vorley, No. 18 CR 00035, 2021 BL 97906, (N.D. Ill. Mar. 18, 2021).
 United States v. Coscia, 866 F.3d 782, 788 (7th Cir. 2017).
 In the Matter of Athena Capital Research, LLC, Exchange Act Release No. 73369, Investment Advisors Act Release No. 3950 (Oct. 16, 2014).
 Supra note 5.