1. Meaning

Algorithmic market manipulation claims arise when an algorithm, automated trading system, high-frequency trading program, AI model, or other computational system is used to create a false or misleading appearance of trading activity, supply, demand, price, liquidity, or market conditions, or to otherwise manipulate the market.

Examples include:

• spoofing;
• layering;
• quote stuffing;
• wash trades;
• momentum ignition;
• marking the close;
• pump-and-dump strategies;
• manipulative order cancellations;
• creating artificial volume;
• benchmark manipulation;
• algorithmically coordinated trading;
• misleading signals concerning demand or liquidity;
• manipulating auction or closing prices.

The crucial legal point is:

The fact that an algorithm executed the trade does not eliminate the legal responsibility of the person or institution that designed, authorised, controlled, or knowingly operated it.

There is generally no separate universal cause of action called an “algorithmic market manipulation claim.” Claims are normally brought under securities/market-abuse legislation, fraud provisions, exchange rules, fiduciary duties, negligence principles, or regulatory enforcement frameworks.

2. Why Algorithmic Manipulation Is Different

Traditional manipulation might involve a trader manually entering false orders.

Algorithmic manipulation can operate at enormous speed.

For example:

Algorithm places 10,000 buy orders

→ other market participants perceive strong demand

→ price begins increasing

→ algorithm cancels most orders

→ algorithm sells at the artificially increased price.

This is commonly associated with spoofing or layering.

The legal difficulty is determining whether the algorithm's conduct was:

• legitimate trading;
• aggressive but lawful trading;
• accidental;
• negligent;
• reckless;
• intentionally manipulative.

3. Main Forms of Algorithmic Market Manipulation

A. Spoofing

A trader places orders intending not to execute them, creating a false impression of market demand or supply.

Example:

Buy 50,000 shares through genuine orders while simultaneously placing large sell orders that are intended to be cancelled.

The fake sell orders make the market appear to contain substantial selling pressure.

B. Layering

Layering involves placing multiple levels of orders on one side of the order book to create an artificial appearance of depth.

The manipulator may then execute genuine trades on the opposite side.

C. Quote Stuffing

An algorithm rapidly submits and cancels enormous numbers of orders.

Potential purposes include:

• slowing competitors;
• obscuring genuine orders;
• creating artificial market activity;
• confusing market participants.

High message volume alone, however, does not automatically establish manipulation.

D. Wash Trading

The same beneficial owner effectively trades with itself or coordinates trades that create artificial volume without genuine market risk.

Algorithmic systems can execute these transactions automatically.

E. Marking the Close

An algorithm executes trades near the market close to influence:

• closing prices;
• portfolio valuations;
• derivatives settlements;
• benchmark prices.

F. Momentum Ignition

The algorithm attempts to trigger rapid price movement.

The trader may then profit from the movement generated by the algorithm.

G. Benchmark Manipulation

Algorithms may attempt to influence prices used for:

• indices;
• reference rates;
• settlement prices;
• valuations;
• derivatives.

H. Layered Pump-and-Dump

AI or automated systems can potentially coordinate:

1. accumulation;
2. artificial promotion;
3. algorithmic buying;
4. price inflation;
5. automated selling.

4. Principal Legal Questions

A regulator or court generally asks:

1. Was there trading activity?

2. Was the activity artificial or misleading?

3. Did the trader intend to create a false market signal?

4. Did the conduct actually or potentially affect price or liquidity?

5. Who controlled the algorithm?

6. Was the behaviour programmed deliberately?

7. Did the trader know what the algorithm was doing?

8. Did the trader continue the conduct after warnings?

9. Was there a legitimate economic purpose?

10. Did the trader profit or obtain another advantage?

5. Relevant Legal Framework

Algorithmic market manipulation can potentially engage:

India

• Securities and Exchange Board of India Act, 1992;
• SEBI (Prohibition of Fraudulent and Unfair Trade Practices relating to Securities Market) Regulations, 2003;
• Securities Contracts (Regulation) Act, 1956;
• exchange trading rules;
• stock-exchange surveillance requirements;
• contractual obligations between brokers and clients;
• applicable criminal/fraud provisions.

United States

• Securities Exchange Act of 1934;
• Section 9(a);
• Section 10(b);
• SEC Rule 10b-5;
• Commodity Exchange Act provisions concerning manipulation/spoofing;
• CFTC regulations.

European Union

• Market Abuse Regulation;
• MiFID II/MiFIR framework;
• rules concerning algorithmic trading;
• market-abuse surveillance and controls.

6. Important Case Laws

1. SEC v. Markowski

SEC v. Markowski, 34 F. Supp. 2d 556 (E.D. Pa. 1999)

Principle

The case concerned manipulative trading and the creation of artificial market conditions.

Relevance to algorithmic manipulation

Although predating modern AI trading, the underlying principle is important:

Trading activity can be unlawful when it is designed to create a misleading appearance of market activity rather than reflect genuine market forces.

An automated system cannot convert an otherwise manipulative strategy into legitimate trading merely because the orders are generated electronically.

7. SEC v. Masri

SEC v. Masri, 523 F. Supp. 2d 361 (S.D.N.Y. 2007)

Principle

The case involved allegations concerning manipulative trading designed to affect market prices.

The court considered whether the conduct involved a deceptive or manipulative purpose rather than legitimate trading.

Algorithmic relevance

The distinction between:

legitimate trading strategy

and

trading designed to manipulate price

is fundamental to algorithmic market-manipulation cases.

A trader cannot necessarily defend a manipulative algorithm simply by demonstrating that every individual transaction was technically valid.

8. ATSI Communications, Inc. v. Shaar Fund, Ltd.

ATSI Communications, Inc. v. Shaar Fund, Ltd., 493 F.3d 87 (2d Cir. 2007)

Principle

The Second Circuit addressed market manipulation under Section 10(b) and Rule 10b-5.

The case is important for distinguishing ordinary market activity from conduct intended to manipulate securities prices.

Algorithmic relevance

Algorithmic manipulation cases require evidence connecting:

• trading activity;
• artificiality;
• manipulative intent;
• price effect or misleading market signal.

An unusually large number of automated orders alone does not necessarily prove manipulation.

9. Santa Fe Industries, Inc. v. Green

Santa Fe Industries, Inc. v. Green, 430 U.S. 462 (1977)

Principle

The Supreme Court distinguished between deception/manipulation and other forms of unfair conduct.

Section 10(b) is directed toward fraudulent or deceptive conduct rather than every unfair corporate action.

Algorithmic relevance

This is important because:

Unprofitable, aggressive, unusual or unfair-looking algorithmic trading is not automatically market manipulation.

A claimant normally needs to identify legally prohibited deceptive or manipulative conduct.

10. Ernst & Ernst v. Hochfelder

Ernst & Ernst v. Hochfelder, 425 U.S. 185 (1976)

Principle

The Supreme Court emphasised the importance of scienter in private Rule 10b-5 securities fraud claims.

Algorithmic relevance

Suppose an algorithm accidentally places a huge number of orders because of a software bug.

That is different from:

Programming the algorithm specifically to place orders that are intended to create a false market signal.

Evidence of:

• intent;
• knowledge;
• recklessness;
• instructions;
• communications;
• algorithm design

can therefore be extremely important.

Key principle

Accidental automation and deliberate algorithmic manipulation must be distinguished.

11. United States v. Coscia

United States v. Coscia, 866 F.3d 782 (7th Cir. 2017)

This is one of the most important cases for algorithmic market manipulation.

Facts

Michael Coscia used computer algorithms in futures markets that employed strategies involving large numbers of orders and rapid cancellations.

The government alleged that the trading was designed to create artificial market conditions.

Legal issue

The case concerned whether the conduct constituted unlawful spoofing and manipulation.

Holding

The Seventh Circuit upheld Coscia's conviction.

Importance

The case demonstrates that:

• algorithmic trading can constitute market manipulation;
• rapid order placement/cancellation can be unlawful;
• algorithmic execution does not prevent criminal responsibility;
• the design and purpose of the algorithm can be evidence of manipulative intent.

Key principle

A computer program is not a legal shield against manipulation liability.

12. United States v. Radley

United States v. Radley, 659 F. Supp. 3d 922 (N.D. Ill. 2023)

Principle

The case involved allegations of commodities-market manipulation and spoofing-related conduct.

Algorithmic relevance

The case illustrates the continuing importance of:

• order patterns;
• cancellation behaviour;
• trading strategy;
• communications;
• economic purpose;
• knowledge of market effects.

Lesson

A regulator or prosecutor may reconstruct an algorithmic strategy from the sequence of orders and cancellations, even where no individual order appears unlawful in isolation.

13. CFTC v. Oystacher and 3 Red Trading LLC

CFTC v. Oystacher and 3 Red Trading LLC, No. 1:15-cv-09196 (N.D. Ill.)

Principle

The case concerned allegations of spoofing and manipulative trading strategies.

The proceedings illustrate the importance of examining:

• order placement;
• cancellations;
• execution patterns;
• intent;
• market impact.

Algorithmic relevance

The case is particularly useful because modern algorithmic manipulation is often detected through patterns rather than individual transactions.

For example:

large order → price moves → genuine order executes → large order disappears.

Repeated patterns can provide evidence of manipulation.

14. CFTC v. Ruggles

CFTC v. Ruggles, 2023 WL 1781610 (N.D. Ill.)

Principle

The case concerned spoofing allegations and illustrates the continuing application of anti-manipulation provisions to sophisticated electronic trading strategies.

Relevance

Algorithmic market-manipulation claims may depend on reconstructing:

• trader instructions;
• algorithm parameters;
• order-book behaviour;
• execution percentages;
• cancellation timing;
• profitability;
• surrounding communications.

15. SEC v. Panuwat

SEC v. Panuwat, 2024 WL 168052 (S.D. Cal.)

This case is not a pure algorithmic manipulation case, but it is important for modern automated-market conduct.

Principle

The case concerned insider-trading theories involving material non-public information and trading in securities affected by that information.

Algorithmic relevance

Automated trading systems can potentially incorporate information that should not lawfully influence trading.

An algorithm therefore does not eliminate the need for:

• information barriers;
• compliance controls;
• restricted lists;
• surveillance;
• pre-trade controls.

Key lesson

Automation does not cleanse an otherwise unlawful trading decision.

16. India — SEBI v. Kanaiyalal Baldev Patel

SEBI v. Kanaiyalal Baldev Patel, (2017) 15 SCC 1

Principle

The Supreme Court considered the scope of the prohibition against fraudulent and unfair trade practices and examined the meaning of manipulative/fraudulent conduct in securities markets.

Algorithmic relevance

The case is important because Indian market-abuse law is concerned not merely with the formal legality of individual trades but with whether conduct produces:

• deception;
• manipulation;
• artificial market conditions;
• unfair advantage.

Application to algorithms

If an algorithm is designed to create artificial demand or supply, its electronic nature does not take it outside the regulatory framework.

17. SEBI v. Rakhi Trading Pvt. Ltd.

SEBI v. Rakhi Trading Pvt. Ltd., (2018) 13 SCC 753

Principle

This is a particularly important Indian market-manipulation authority.

The Supreme Court examined synchronized/reversal transactions and artificial trading activity.

The Court emphasised that the overall trading pattern and surrounding circumstances must be examined rather than viewing each transaction in isolation.

Algorithmic relevance

This approach is highly applicable to algorithmic trading.

An algorithm may generate thousands of transactions.

The legality of the conduct cannot necessarily be determined by asking:

“Was each individual order technically permissible?”

Instead, the court can examine the overall pattern and economic substance.

Key principle

A series of individually executable transactions can collectively constitute manipulative conduct when viewed in context.

18. SEBI v. Kishore R. Ajmera

SEBI v. Kishore R. Ajmera, (2016) 6 SCC 368

Principle

The Supreme Court considered the evidentiary assessment of synchronized and manipulative trades.

It recognised that direct evidence of an agreement to manipulate may not always be available and that circumstances and trading patterns can be significant.

Algorithmic relevance

This is extremely important in algorithmic cases.

Manipulators may not send an email saying:

“Let us manipulate the market.”

Instead, investigators may discover:

• repeated timing patterns;
• unusual cancellations;
• coordinated accounts;
• abnormal order sizes;
• rapid reversals;
• consistent profits;
• trading behaviour inconsistent with genuine investment.

Key principle

Manipulative intent can be inferred from the totality of circumstances.

19. Case-Law Table

20. How an Algorithmic Manipulation Claim Is Proved

Because algorithms operate through enormous numbers of transactions, conventional evidence may be insufficient.

Investigators may analyse:

Order-book data

• order size;
• order price;
• order placement;
• cancellation;
• execution.

Timing

For example:

large order → market response → genuine trade → cancellation

Repeated thousands of times can be significant.

Algorithm instructions

Source code or strategy documentation can be particularly powerful.

Trader communications

Emails, messages and internal documents can demonstrate:

• purpose;
• knowledge;
• intent;
• instructions.

Profitability

Evidence that the strategy consistently profits from the artificial price movement may be relevant.

Account relationships

Related trading accounts can reveal coordinated activity.

21. Intent vs Accident

This is one of the most important issues.

Scenario A — Software malfunction

A programming error causes 20,000 orders to be submitted accidentally.

Possible legal consequences may involve:

• negligence;
• exchange rules;
• risk-control failures;
• regulatory sanctions.

But proving intentional manipulation may be difficult.

Scenario B — Deliberate algorithm design

The algorithm is specifically programmed to:

1. place large orders;
2. cancel them before execution;
3. move the price;
4. execute genuine orders;
5. repeat.

This is much stronger evidence of manipulation.

Scenario C — Reckless deployment

The trader knows that the algorithm repeatedly creates artificial price movements but continues using it.

This can create serious regulatory exposure even where the trader did not write the original software.

22. The Importance of Economic Purpose

Not every cancellation is spoofing.

Legitimate algorithmic traders may cancel orders because:

• market prices change;
• liquidity disappears;
• risk limits change;
• better prices become available;
• portfolio exposure changes;
• market conditions become volatile.

The central question is therefore often:

Was the order genuinely intended to be executed when placed, or was it placed primarily to create a false market signal?

23. Liability of Different Actors

A. Trader

Potentially liable where the trader:

• designed the strategy;
• instructed the algorithm;
• knew its purpose;
• knowingly allowed manipulation.

B. Investment firm

Potentially liable for:

• inadequate controls;
• poor supervision;
• failure to monitor;
• failure to investigate warnings.

C. Algorithm developer

Liability depends on:

• contractual obligations;
• knowledge;
• negligence;
• participation in manipulation;
• design defects.

D. Broker

Potential liability may arise from:

• inadequate surveillance;
• failure to enforce trading restrictions;
• knowing participation;
• regulatory breaches.

E. Exchange

Exchange liability is generally subject to its statutory and regulatory framework and is fact-specific.

24. Algorithmic Trading Controls

A responsible trading institution should maintain:

Pre-trade controls

• maximum order size;
• price collars;
• position limits;
• order-frequency limits;
• duplicate-order detection.

Real-time controls

• unusual cancellation monitoring;
• spoofing indicators;
• abnormal order-to-trade ratios;
• self-trading controls.

Post-trade surveillance

• behavioural analysis;
• account linkage;
• profit analysis;
• pattern recognition;
• suspicious-order investigations.

Governance

• algorithm approval;
• testing;
• documentation;
• version control;
• audit trails;
• human supervision;
• emergency shutdown mechanisms.

25. Algorithmic Market Manipulation and AI

Generative or machine-learning AI creates additional problems because the system may:

• adapt strategies dynamically;
• discover profitable patterns independently;
• change parameters;
• learn from market feedback;
• execute orders faster than human supervision.

Nevertheless, legal responsibility does not ordinarily disappear merely because the strategy was machine-generated.

The relevant questions remain:

• Who deployed the system?
• Who had control?
• What instructions were provided?
• What safeguards existed?
• Was the behaviour foreseeable?
• Did the organisation monitor the system?
• Did it act after receiving warnings?

26. Defences

A defendant may argue:

1. Legitimate trading purpose

Orders were genuinely intended to execute.

2. No manipulative intent

The unusual pattern resulted from market volatility or technical malfunction.

3. Independent algorithm

The defendant did not control the relevant algorithm.

4. No material market effect

The conduct did not meaningfully affect the market.

However, some market-abuse regimes may not require proof of successful manipulation in the same way as a private damages action.

5. No knowledge

The defendant did not know the algorithm behaved improperly.

6. Reasonable compliance controls

The firm may show that it had:

• testing;
• surveillance;
• risk controls;
• monitoring;
• incident response.

27. Remedies and Consequences

Depending on the jurisdiction and legal basis, consequences may include:

Regulatory

• monetary penalties;
• disgorgement;
• trading restrictions;
• suspension;
• prohibition from market participation;
• regulatory directions.

Criminal

Serious intentional spoofing or manipulation may result in:

• prosecution;
• fines;
• imprisonment.

Civil

Possible remedies include:

• damages;
• restitution;
• rescission in appropriate cases;
• injunction;
• declaratory relief.

Institutional

Regulators may also require:

• enhanced surveillance;
• algorithmic controls;
• independent audits;
• compliance programmes;
• employee supervision.

28. Practical Hypothetical

Assume Trader X operates an AI trading system.

The algorithm repeatedly performs:

Step 1: Places 50,000 sell orders.

Step 2: Other traders interpret this as significant selling pressure.

Step 3: Market price decreases.

Step 4: X's algorithm purchases genuine buy orders at the lower price.

Step 5: The 50,000 sell orders are cancelled.

Step 6: The strategy repeats 2,000 times.

Evidence

Investigators discover:

• 98% cancellation rate for the large orders;
• very low execution rate;
• repeated price movements immediately following the orders;
• profitable purchases immediately afterwards;
• source-code instructions designed around the sequence;
• internal messages describing the strategy as a way to “move the book.”

Legal assessment

This would present a strong potential manipulation case because:

artificial orders

→ false market signal

→ price movement

→ genuine execution

→ cancellation

→ profit

→ repetition

The algorithm's automation would not itself provide a defence.

29. Indian Perspective

For India, SEBI v. Rakhi Trading and SEBI v. Kishore R. Ajmera are particularly useful.

They demonstrate that securities-market manipulation can be established by examining the pattern and circumstances of transactions, rather than artificially separating every trade from the overall strategy.

This becomes even more important for algorithmic markets because a manipulative strategy can be distributed across thousands of automated transactions.

The SEBI PFUTP Regulations provide an important framework for examining fraudulent and unfair trading practices.

30. Most Important Legal Principles

Principle 1 — Automation is not a defence

An algorithm can commit the mechanics of manipulation, but legal responsibility remains with relevant human and corporate actors.

Principle 2 — Individual transactions are not always decisive

The overall trading pattern matters.

Rakhi Trading + Kishore R. Ajmera

Principle 3 — Intent matters

A genuine order that is later cancelled is not automatically spoofing.

Principle 4 — Circumstantial evidence is important

Algorithmic manipulation often leaves evidence through patterns rather than explicit admissions.

Principle 5 — System design can prove intent

Source code, parameters and strategy architecture can be powerful evidence.

Principle 6 — Risk controls matter

A firm that discovers suspicious algorithmic activity and nevertheless permits it to continue faces significantly greater exposure.

31. Conclusion

Algorithmic market manipulation claims concern the use of automated trading technology to create artificial or misleading market conditions, manipulate prices, generate false liquidity, distort supply or demand, or obtain an unlawful trading advantage.

The strongest claims generally involve:

Algorithmic strategy

→ artificial orders/activity

→ misleading market signal

→ actual or intended price/market effect

→ knowledge, intent or legally relevant manipulation

→ profit or market harm

→ regulatory/civil/criminal consequences.

The most important authorities include United States v. Coscia, CFTC v. Oystacher, United States v. Radley, ATSI Communications v. Shaar Fund, SEC v. Masri, Santa Fe Industries v. Green, Ernst & Ernst v. Hochfelder, SEBI v. Kanaiyalal Baldev Patel, SEBI v. Rakhi Trading, and SEBI v. Kishore R. Ajmera.

The central rule is simple:

Markets are regulated according to the substance and effect of trading conduct, not merely the technological mechanism by which the orders were generated.

Accordingly, replacing a human trader with an algorithm does not transform manipulative conduct into lawful conduct; instead, it creates an additional evidentiary layer involving code, order-book data, execution patterns, cancellation patterns, system logs, governance records and human instructions.