Case Law On Criminal Responsibility In Algorithmic Manipulation Of Digital Financial Markets
1. United States v. Michael Coscia (2015) – The First “Spoofing” Conviction
Court: U.S. District Court, Northern District of Illinois; affirmed by the U.S. Court of Appeals, 7th Circuit (2017).
Citation: United States v. Coscia, 866 F.3d 782 (7th Cir. 2017)
Facts:
Michael Coscia operated Panther Energy Trading LLC and used specially designed trading algorithms to place large orders on futures exchanges (CME, ICE) with no intent to execute them. These large orders created a false impression of demand and price movement. Before others could react, the algorithm would cancel the orders and execute smaller trades in the opposite direction, profiting from the artificial market movement.
Legal Issue:
Whether using an algorithm to place and cancel orders rapidly (a common practice in HFT) constituted criminal “spoofing” under §4c(a)(5)(C) of the Commodity Exchange Act (CEA) and wire fraud.
Holding:
The 7th Circuit upheld Coscia’s conviction. The court found that:
The algorithm was programmed with manipulative intent — to create false market signals.
Even though algorithms execute trades automatically, intent can be imputed to the programmer or operator.
“Spoofing” is distinct from legitimate order cancellations because the orders here were never intended to be executed.
Significance:
Established that algorithmic manipulation constitutes a criminal act if the underlying code reflects an intent to mislead the market.
Clarified that mens rea can be proven through design and usage of algorithms, not just direct manual actions.
2. United States v. Navinder Sarao (2016) – The “Flash Crash” Case
Court: U.S. District Court, Northern District of Illinois
Citation: United States v. Navinder Singh Sarao, No. 15-cr-75 (N.D. Ill. 2016)
Facts:
Sarao, a London-based trader, used an automated trading program to place large sell orders on E-mini S&P 500 futures contracts and quickly cancel them. His actions contributed to the 2010 Flash Crash, when U.S. markets lost nearly $1 trillion in minutes before rebounding.
Legal Issue:
Whether Sarao’s use of an algorithm to manipulate market prices constituted wire fraud and spoofing under U.S. law, despite his operations being based in the U.K.
Holding:
Sarao pleaded guilty to one count of spoofing and one count of wire fraud. The court found:
He intentionally designed his trading system to manipulate prices.
Geographic distance did not exempt him from U.S. jurisdiction because his trades directly affected U.S. markets.
Significance:
Reinforced the extraterritorial reach of U.S. financial crime laws in digital markets.
Demonstrated that algorithmic manipulation has global consequences, and the human operator remains criminally liable.
Sarao’s defense that “the computer did it” was rejected — algorithms are extensions of the trader’s intent.
3. CFTC v. Oystacher and 3Red Trading (2018)
Court: U.S. District Court, Northern District of Illinois
Citation: CFTC v. Oystacher, No. 15-cv-09196 (N.D. Ill. 2018)
Facts:
Igor Oystacher and his firm 3Red Trading used high-frequency trading strategies to place and cancel large numbers of orders to mislead market participants about supply and demand. The CFTC alleged pattern-based spoofing using algorithmic trading software.
Legal Issue:
Whether repeated algorithmic cancellation of orders constituted a violation of the CEA, and whether intent could be inferred from trading patterns rather than direct evidence.
Holding:
The case was settled with Oystacher and 3Red agreeing to pay $2.5 million and accept trading restrictions. The court noted:
The pattern of algorithmic activity was sufficient circumstantial evidence of intent.
Reliance on algorithmic trading does not insulate a trader from liability.
Corporate entities can bear vicarious liability for the design and deployment of manipulative algorithms.
Significance:
Demonstrated that corporate entities can be held responsible for the actions of their algorithmic systems and employees.
Intent in algorithmic contexts can be inferred from trading data patterns, not just explicit communications.
4. SEC v. Athena Capital Research (2019)
Court: U.S. Securities and Exchange Commission Administrative Proceeding
Citation: In re Athena Capital Research, LLC, SEC Release No. 86153 (June 2019)
Facts:
Athena Capital Research engaged in a strategy known as “marking the close” using algorithms that placed thousands of rapid trades at the end of the trading day to influence closing prices. These trades distorted the prices of certain NASDAQ securities.
Legal Issue:
Whether algorithmic trading that manipulates end-of-day prices constitutes securities fraud under Section 9(a)(2) and 10(b) of the Securities Exchange Act of 1934.
Holding:
Athena settled with the SEC, admitting to manipulative trading and paying $1 million in fines. The SEC found:
The algorithm’s code intentionally targeted the market’s closing window to create artificial prices.
Manipulative intent was embedded in the algorithm’s design parameters.
Significance:
Reinforced that algorithmic trading strategies can constitute securities manipulation if they intentionally affect market prices.
Highlighted regulatory focus on closing auction manipulations, a common digital market tactic.
Established the precedent that algorithmic design decisions reflect corporate intent.
5. FCA v. Barclays Bank PLC (U.K., 2015) – Algorithmic FX Manipulation
Court: U.K. Financial Conduct Authority (FCA) Enforcement Action
Citation: FCA Final Notice to Barclays Bank PLC, May 2015
Facts:
Barclays traders, using algorithmic trading systems, coordinated to manipulate the foreign exchange (FX) benchmark rates. Algorithms were used to submit large orders timed to move benchmark rates, coordinated with other traders via chat rooms.
Legal Issue:
Whether the bank’s use of algorithmic systems in collusion with others amounted to market manipulation under U.K. law (Financial Services and Markets Act 2000).
Holding:
The FCA imposed a fine of £284 million on Barclays. The regulator held that:
The bank failed to control or supervise algorithmic trading strategies that distorted the market.
Corporate responsibility attached because the manipulation was systemic and facilitated by technology.
Significance:
Established corporate criminal and regulatory liability for algorithmic misconduct.
Highlighted the importance of algorithmic oversight and internal controls in financial institutions.
Reinforced that algorithmic misconduct can trigger both individual and corporate sanctions.
Overall Legal Principles Emerging
| Principle | Explanation | Key Cases |
|---|---|---|
| Intent can be embedded in code | Programming an algorithm to mislead or manipulate satisfies criminal intent. | Coscia, Athena Capital |
| Human accountability persists | Algorithmic autonomy does not absolve the trader or firm from liability. | Sarao, Oystacher |
| Corporate liability | Firms are liable for the acts of their employees or systems if they fail to prevent manipulation. | Barclays, Oystacher |
| Pattern-based inference of intent | Courts can infer manipulative intent from trading behavior and data patterns. | Oystacher, Athena |
| Extraterritorial enforcement | Cross-border algorithmic manipulation affecting U.S. markets can be prosecuted under U.S. law. | Sarao |
Conclusion
The body of case law demonstrates a clear judicial trend: algorithmic manipulation is treated as a human-directed crime, not a technological accident. Courts and regulators worldwide have affirmed that designing, deploying, or supervising manipulative algorithms constitutes a form of criminal and corporate misconduct under financial market laws.
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