Open Access Research Article
White Black Legal – International Law Journal · ISSN 2581-8503
MARKET MANIPULATION THROUGH ALGORITHMIC AND HIGH FREQUENCY TRADING (HFT) WITH SPECIAL REFERENCE TO THE JANE STREET GROUP CASE
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Abstract
MARKET
MANIPULATION THROUGH ALGORITHMIC AND HIGH FREQUENCY TRADING (HFT) WITH SPECIAL
REFERENCE TO THE JANE STREET GROUP CASE
AUTHORED BY - ANURAG MISHRA, GUNJEETA
& MADHAV YADAV
Abstract
The migration of securities trading
from human-scale execution to automated, high-speed decision-making has changed
both the form of market manipulation and the evidentiary problem faced by
securities regulators. High-frequency trading (HFT) is not itself manipulative:
many HFT strategies improve liquidity, reduce transaction costs, and contribute
to price discovery. The regulatory problem arises when speed, automation, and
cross-market positioning are used to create artificial price or trading
signals. This article examines whether India’s existing market-manipulation
framework is capable of addressing that problem. It argues that the Securities
and Exchange Board of India (SEBI) Act, 1992 and the SEBI (Prohibition of
Fraudulent and Unfair Trade Practices relating to Securities Market)
Regulations, 2003 (PFUTP Regulations) are sufficiently technology-neutral to
reach algorithmic manipulation, but that the principal weakness lies elsewhere:
fragmented surveillance, inadequate visibility into strategy-level behaviour,
and uncertainty about how knowledge and manipulative purpose should be inferred
from automated trading patterns. The article uses the July 2025 interim order
against Jane Street Group as a case study, not as a final adjudication of
liability, because the proceedings remained pending before the Securities
Appellate Tribunal as of 8 October 2026. It then compares the Indian framework
with United States and European Union approaches and proposes a strategy-level
model built around cross-segment surveillance, auditable algorithmic
provenance, calibrated intent inference, pre-trade risk controls, and
procedural safeguards. The article concludes that India does not presently
require a wholly new offence of “algorithmic manipulation”; instead, it needs clearer
regulatory guidance and an enforcement architecture designed for machine-speed,
multi-instrument trading.
Keywords
Algorithmic
trading; high-frequency trading; market manipulation; PFUTP Regulations; SEBI;
Jane Street; spoofing; layering; marking the close; index manipulation; market
surveillance; financial regulation.
The rapid evolution of technology has
fundamentally transformed securities markets worldwide, enabling high-frequency
trading and algorithmic trading to play a dominant role in modern trading
ecosystems. While these technological advancements improve market efficiency,
liquidity, and price discovery, they also create significant risks of market
manipulation. Manipulative practices executed through sophisticated algorithms
can create false impressions of market activity, distort prices, and undermine
investor confidence. Recognizing these threats, regulators such as the
Securities and Exchange Board of India have developed a comprehensive legal and
regulatory framework to prevent fraudulent or unfair trading practices,
including those executed through automated trading systems.
In India, this disparity is stark. A
recent study by Securities Exchange Board of India (SEBI), highlighted by the
All-India Brokers’ Association (AIBI), revealed that 93% of individual traders
incurred losses in equity and derivatives markets between FY 2022 and FY 2024.[1] Such data underscores the challenges
faced by retail investors in a market increasingly dominated by institutional
algorithmic strategies. The asymmetry has prompted regulators to scrutinize
practices that may distort fair price formation or create artificial market
movements, particularly in derivatives and index-based securities.
The rapid advancement of algorithmic
trading, fuelled by developments in AI and sophisticated coding, has created
significant regulatory challenges worldwide. A recent SEBI study highlights the
growing dominance of such strategies, revealing that 97% of profits for foreign
portfolio investors and 96% of proprietary trading profits in F&O markets
for FY24 were generated through algorithmic trading.
Market manipulation in the context of
algorithmic trading presents unique challenges because the complexity and speed
of trades make detection and enforcement difficult. SEBI’s statutory framework,
which includes the SEBI Act, 1992 and the Prohibition of Fraudulent and Unfair
Trade Practices Regulations, 2003, addresses both traditional and algorithmic
forms of manipulation. This framework distinguishes between fraudulent acts
that require proof of intent and manipulative practices where the creation of
misleading market activity is sufficient to invoke regulatory action. This dual
standard allows SEBI to adapt enforcement to the rapidly evolving technological
landscape, as reflected in cases such as the NSE co-location matter, the Ajay
Shah and Infotech algorithmic software case, and the Jane Street high-frequency
trading enforcement action.
The
gap can therefore be stated precisely. Existing research tends either to ask
whether HFT should be regulated more strictly or whether a new offence is
necessary. Less attention is paid to the middle layer between statutory
prohibition and technological supervision: the evidentiary architecture through
which a regulator converts a high-speed sequence of trades into a legally
intelligible allegation of manipulation. The present article addresses that
layer by proposing a strategy-level test that connects economic exposure,
timing, price impact, reversal or offsetting behaviour, and the rationality of
the trading pattern with the legal elements of knowledge and manipulation.
The paper is structured as follows: Firstly,
it discusses the legal and regulatory framework governing market manipulation
in India, outlining SEBI’s statutory provisions and their interpretation by
courts and tribunals. Secondly, it examines notable Indian cases
involving algorithmic and high-frequency trading, analysing the facts,
contentions, and rulings in each instance. Thirdly, it explores SEBI’s
regulatory responses and circulars aimed at curbing unfair practices through
algorithmic trading. Lastly, it considers international perspectives and
jurisprudence, highlighting enforcement actions by the SEC, CFTC, ESMA, and
Japan’s Financial Services Agency against manipulative algorithmic trading.
The
following provisions govern this subject matter in the Indian law enforcement,
wherein, Section 12A of the SEBI Act,
1992 lists down the prohibited
activities in the Securities market.[2]
It provides that no
person shall, directly or indirectly:
a) Use
deceptive or manipulative practices when issuing, buying, or selling securities
listed or proposed to be listed on a recognized stock exchange, in violation of
SEBI laws and regulations.
b) Resort
to any device, scheme, or artifice to defraud or mislead others in connection
with securities trading or issuance.
c) Carry
out any action, practice, or business method that results in fraud or deception
in dealing with listed or proposed securities.
d) Engage
in insider trading, including transactions based on material, non-public
information.
e) Disclose
or share material non-public information with others in a way that breaches
SEBI rules.
f) Acquire
excessive control over a company or its listed/proposed securities, exceeding
the limits set under SEBI regulations.
Further, SEBI (Prohibition of Fraudulent and Unfair
Trade Practices relating to Securities Market) Regulations, 2003[3]:
Regulation
2(1)(c): The meaning of "fraud"
-
The term
“fraud” is defined widely. It covers acts, statements, omissions or concealment
done to induce another person to deal in securities, "whether in a
deceitful manner or not", and "whether or not there is any wrongful
gain or avoidance of any loss".
Regulation 3: Prohibition of certain
dealings in securities
-
It forbids any person, directly or
indirectly, from:
a) buying,
selling or otherwise dealing in securities in a fraudulent manner;
b) using
any "manipulative or deceptive device or contrivance" in connection
with the issue, purchase or sale of listed or proposed-to-be-listed securities,
contrary to SEBI's laws and rules;
c) using
any device, scheme or artifice to defraud in connection with dealing in or
issuing such securities;
d) engaging
in any act, practice or course of business that "operates or would operate
as fraud or deceit" in dealings in such securities.
Regulation 4: Prohibition of
manipulative, fraudulent, and unfair trade practices
i.
Regulation
4(1):
Apart from Regulation 3, no person is allowed to engage in fraudulent or unfair
trading activities in the securities market.
ii.
Regulation
4(2): Dealings are treated as such a practice "if it
involves fraud", and the provision lists examples, including:
a.
4(2)(a): an act that creates a "false or misleading
appearance of trading in the securities market", and
b. 4(2)(e): any act or omission that amounts to
"manipulation of the price of a security".
III.
Judicial And
Regulatory Treatment of Algorithmic Manipulation in India
SEBI’s
framework governing market manipulation under the Prohibition of Fraudulent and
Unfair Trade Practices Relating to Securities Market Regulations (FUTP
Regulations), 2003[4]
reflects a nuanced and evolving approach towards the requirement of intention.
Under Regulation 2(1)(c), “fraud” includes acts or omissions “whether deceitful
or not” that induce another person to deal in securities. This represents a
significant departure from the 1995 formulation, which required that acts be
committed “with the intent to deceive.” The removal of the express intent
requirement indicates that mens rea is not an essential element to
establish “fraud” under the current framework. This interpretation was affirmed
by the Supreme Court in SEBI v Kanaiyalal Baldev Patel (2017),[5]
which held that intention is not necessary for establishing fraudulent conduct
under Regulation 2(1)(c).[6]
However,
Regulation 4(2)(a) of the FUTP Regulations which prohibits creating a “false or
misleading appearance of trading” expressly requires knowledge.[7]
This element was introduced by the 2018 amendment following recommendations of
the Committee on Fair Market Conduct, intended to exclude inadvertent trades
from liability. The concurring opinion in SEBI v Rakhi Trading[8]
further clarified that the provision involves “a deliberate attempt to
interfere with the free and fair operation of the market,” thus implying a
requirement of intention. SEBI itself relied on this interpretation in its
interim order against Jane Street.[9]
Accordingly,
SEBI’s framework presents a dual standard: while Kanaiyalal confirms
that “fraud” under Regulation 2(1)(c) does not necessitate proof of intention,
a violation of Regulation 4(2)(a) requires some degree of deliberate or knowing
conduct. Therefore, in enforcement actions, SEBI can strategically invoke
provisions that do not require proof of intention, depending on the factual
matrix of the case.
There
have been various instances where SEBI has ruled on cases involving usage of
algorithmic and high frequency trading for manipulation of market. One of the
most notorious cases is the matter of NSE and Others (Co-location),
often referred to as NSE
Co-location scam.[10]
This is closely linked to high-frequency and algorithmic trading. While SEBI’s
initial order took a strict stance on governance lapses, the SAT ruling
highlighted that negligence without proven intent does not amount to fraud. The
case remains a critical precedent in shaping India’s regulatory approach to HFT
and co-location practices, balancing technological complexity with principles
of market fairness.
The
author shall now discuss the co-location matter which illustrates the
evidentiary difficulty of distinguishing technological advantage from conduct
amounting to fraud or manipulation.
From
around 2010, the National Stock Exchange (NSE) introduced a co-location
facility that allowed brokers to place their trading servers within or near the
exchange’s premises. This provided faster access to market data and reduced
latency in order execution. In 2015, a whistleblower alleged that certain
trading members involved in algorithmic trading, particularly OPG Securities,
were consistently accessing a secondary server, which was less congested, and
therefore received Tick-By-Tick (TBT) data ahead of others. This created a
microsecond advantage in placing orders and executing high-frequency trades.
Allegations also included preferential treatment, misuse of infrastructure, and
lack of an adequate monitoring mechanism.
SEBI’s investigation consequently focused on whether
the exchange’s governance failures compromised fair and equitable access to the
co-location facility.
SEBI
found that NSE had not ensured fair and equitable access to the co-location
facility and lacked effective internal controls governing server access. In
2019, it held the exchange responsible for governance and system lapses under
the applicable securities-law framework, imposed a penalty of approximately
₹625 crore and barred NSE from introducing new products for six months, while
also directing former CEOs Ravi Narain and Chitra Ramkrishna to return portions
of their salaries. SEBI’s later review, however, found insufficient evidence of
collusion between NSE officials and OPG Securities and continued to treat the
principal failing as one of procedural negligence.
The position was substantially reconsidered by the
Securities Appellate Tribunal in January 2023, which set aside much of SEBI’s
2019 order and treated the conduct as negligence and lack of due diligence
rather than fraudulent intent. SAT also questioned the evidentiary basis of the
disgorgement calculation, while maintaining some findings against OPG
Securities and reducing NSE’s penalty to ₹100 crore before remanding certain
issues for reconsideration.
In
January 2023, the Securities Appellate Tribunal (SAT) set aside much of SEBI’s
2019 order. SAT held that the actions of NSE reflected negligence and lack of
due diligence rather than fraudulent intent. It also noted that SEBI’s
disgorgement calculation lacked a clear evidentiary basis. While maintaining
some findings against OPG Securities, SAT reduced NSE’s penalty to ₹100 crore
and remanded certain issues for reconsideration.
Subsequently,
a different problem arises where
algorithmic execution itself can generate repeated self-trades without
necessarily reflecting a deliberate manipulative strategy.
In
the case concerning Crosseas Capital Services Pvt. Ltd. (Noticee), SEBI's
Adjudicating Officer, issued an order on November 27, 2013. The proceedings
were initiated due to allegations that the Noticee had executed 5,201
self-trades totaling 981,186 shares of Servalakshmi Papers Limited (SPL) on the
listing day of its IPO, thereby creating artificial volumes in the market.
The Noticee therefore argued that the trades
resulted from the operation of its ultra-high-frequency algorithm rather than
from an intention to create artificial volume. They
contended that these trades were a result of its algorithmic trading system,
"Crossfire," operating at ultra-high frequencies, leading to internal
matching of buy and sell orders. They argued that such occurrences were
incidental and not intentional.
The Adjudicating Officer ultimately accepted that
the existence of the trades, by itself, did not establish manipulation. The
self-trades formed only a minimal percentage of total traded volume, and the
record did not provide substantial evidence of an intention to deceive. The
officer also recognised that internal matching could occur because of the
automated system and the latency inherent in high-frequency trading.
The
Adjudicating Officer similarly found that, although the self-trades had
occurred, they represented only a minimal proportion of total traded volume and
the record did not establish substantial evidence of manipulation or an
intention to deceive. The possibility of internal matching caused by the
automated system and the latency inherent in high-frequency trading therefore
remained a credible alternative explanation.
The
charges under the SEBI Act and PFUTP Regulations were accordingly not
substantiated and the matter was disposed of without penalty.
The
author states that over the passage of time, SEBI’s investigation then moved beyond execution patterns to the use of
confidential exchange data in developing and commercially exploiting
algorithmic trading software.
Between
2009 and 2016, SEBI investigated the misuse of confidential NSE data to develop
algorithmic trading software that was commercially exploited.
The
probe focused on Dr. Ajay Shah, his wife Susan Thomas, sister-in-law
Sunita Thomas, NSE officials including Suprabhat Lala, and Infotech Financial
Services Pvt. Ltd. SEBI found that confidential trading data was shared
with Infotech and used to create software products sold to market participants,
providing them with unfair trading advantages and violating SEBI’s Prevention
of Fraud and Unfair Trade (PFUTP) regulations.
In the related orders concerning NSE officials and
Infotech Financial Services, SEBI also identified failures to disclose
conflicts of interest and to supervise the handling of sensitive information.
The LIX project was criticised because its award to Infotech allegedly allowed
the commercial exploitation of proprietary NSE data without adequate
disclosure. The proceedings resulted in two-year restrictions on Dr. Shah and
his associates and underscored that the risks of algorithmic trading extend
beyond the orders placed in the market to the governance, information and
institutional arrangements that make those orders possible.[14]
SEBI’s
orders highlighted failures by NSE officials to disclose conflicts of interest
and to supervise the handling of sensitive data. The NSE was criticized for
awarding the LIX project to Infotech without adequate disclosure, allowing
commercial exploitation of proprietary data. Dr. Shah and his associates
were barred from associating with market infrastructure institutions, listed
companies, or registered intermediaries for a period of two years.
The
orders underscored the regulatory risks of misusing algorithmic software and
sensitive market information, emphasizing the importance of governance,
compliance, and conflict-of-interest disclosures in algorithmic trading.
In
July 2025, SEBI issued an interim order against Jane Street Group, a prominent
global trading firm, alleging market manipulation in the Indian securities
market.
“Jane
Street’s business model revolves around spotting price differences across
financial instruments or markets and exploiting them. The firm employs
high-frequency trading technology to identify and act on these inefficiencies,
often holding positions for very brief periods, sometimes only a few
milliseconds.”
SEBI
accused Jane Street of employing manipulative strategies including: intra-day
index manipulation and extended marking-the-close, on the NIFTY and BANKNIFTY
indices.
The
regulator directed the impounding of Jane Street's alleged unlawful gains,
amounting to approximately ₹4,843 crore and barred the firm from accessing the
securities market until the amount was impounded. Jane Street deposited the
specified amount into an escrow account on July 14, 2025, and resumed trading
in the Indian securities market on July 21, 2025.
SEBI’s case was therefore
framed around two connected forms of alleged conduct.
i.
First, on
15 BANKNIFTY option-expiry days, Jane Street allegedly bought large quantities
of constituent stocks in the cash market during the earlier part of the day,
temporarily pushing the index upward, and later reversed those positions.
ii.
SEBI
alleged that the resulting movement benefited positions held in the related
options market. The order also alleged that an Indian entity was used to
undertake intraday cash-market activity in circumstances relevant to Regulation
20(4) of the SEBI (Foreign Portfolio Investors) Regulations, 2019.
The events are explained
herein below:
i.
Intra-day Index
Manipulation:
SEBI alleged that on 15 BANKNIFTY option expiry days, Jane Street aggressively
bought significant quantities of BANKNIFTY constituent stocks in the cash
market, temporarily inflating the index.” Later in the day, the firm sold off
these securities, exerting downward pressure on the index. The regulator
contended that such trading activities were designed to influence the options
market, benefiting Jane Street's positions.
As
per Regulation 20(4) of the Securities
Exchange Board of India (Foreign Portfolio Investors) Regulations, 2019,[15]
the FPIs are prohibited from conducting intra-day trade and cannot buy and sell
within the same day. The Group incorporated an entity in India to execute
intra-day trade and to circumvent this regulation.
ii. Second,
SEBI
alleged an extended marking-the-close strategy in which aggressive selling
during the final hour reduced the index closing level and generated an alleged
profit of approximately ₹560 crore. The significance of these allegations lies
not simply in the volume or speed of the trades, but in the relationship
between the underlying cash-market activity, the resulting index movement and
the derivative positions capable of monetising that movement.
Against this factual
pattern, SEBI applied the existing PFUTP framework rather than treating
algorithmic trading as a separate offence.
SEBI
observed that:
a) “Regulation
4(2)(a) considers any action manipulative or fraudulent if it creates a false
or misleading impression of trading activity in the securities market.”
b) “Regulation
4(2)(e) treats any act as manipulative or fraudulent if it distorts the price
of a security or affects the benchmark price of securities. Since indices represent a basket of
securities, any price movement in the underlying stocks automatically impacts
the benchmark index.”
The order examined the sequencing of the trading
across the day: aggressive buying between 9:15 AM and 11:46 AM was followed by
substantial reversal through selling from approximately 11:49 AM to the end of
the trading day.
-
During
the first period, aggressive quantity buying allegedly pushed the prices of the
relevant constituent securities upward.
-
During
the second period, that position was substantially reversed through aggressive
selling.
SEBI
considered the size, aggression, manner of trading and transaction costs
together, observing that such activity, viewed on a standalone basis, could
more often than not result in net trading losses.
On
a preponderance of probabilities, SEBI therefore concluded that the pattern was
consistent with an attempt to artificially move the index upward and later
exploit the resulting movement through opposite-side positions.
The
order further reasoned that the trading systems were capable of anticipating
the later reversal and treated that sequencing as evidence supporting an
inference of manipulative purpose.
The
interim order consequently barred Jane Street and affiliated entities from
participating in the Indian securities market and directed the impounding of
alleged unlawful gains of approximately ₹4,843 crore. Jane Street deposited the
specified amount into an escrow account on 14 July 2025 and resumed trading on
21 July 2025, while contesting SEBI’s characterization of the conduct and
pursuing the matter before the Securities Appellate Tribunal.
The
order also considered the interaction with the takeover disclosure framework
because the positions were exited intraday and the relevant derivatives were
cash-settled.
Under the SEBI (Substantial Acquisition of Shares
and Takeovers) Regulations, 2011, shareholding is determined with reference to
the relevant holding position, the 5% threshold applies to voting rights or
shares, and cash-settled instruments are excluded from the relevant
calculation.[16]
-
These features meant that the intraday and
cash-settled structure did not itself trigger the disclosure requirement on the
facts considered in the paper.
-
The Jane Street matter therefore
illustrates a broader regulatory difficulty: where positions are short-lived
and distributed across related instruments, a legally relevant strategy may be
visible only when the regulator reconstructs the complete economic sequence
rather than examining any single trade or instrument in isolation.
-
Excludes cash settled instruments.
Therefore,
the author states that the Jane Street Group closely escaped the disclosure
requirements due to their intra-day activities and cash settled instruments
instead of other derivatives.
V.
Comparative
Regulatory Approaches On Manipulative Trading Using Algorithms And High
Frequency Trading
The
regulation of high-frequency and algorithmic trading is not a challenge only to
India. Regulators around the world continue to address issues arising from
increasingly electronic and fragmented markets dominated by sophisticated
algorithmic traders.
The
United States
In
the United States, the Securities and Exchange Commission (SEC) actively
regulates and enforces rules against fraud by high-frequency traders. The SEC
has set clear limits on practices such as quote stuffing,[17]
layering, and spoofing, prosecuting firms engaged in these activities because
they create false or misleading impressions of trading and disrupt the fair
functioning of markets.
The
SEC can bring civil actions against spoofing under the Commodity Exchange Act
(CEA), 1936[18]
and the Securities Act, 1933.[19]
The Securities Act, 1933 prohibits deceptive sales of securities and forbids
anyone from trading or offering securities or security-based swap agreements
through any communication method using fraudulent schemes, tricks, or by
omitting important information. Traders are also prohibited from using any
facility of a national securities exchange to engage in deceptive or
manipulative practices.
The
SEC also monitors latency arbitrage strategies, where traders exploit tiny
delays in the transmission of market data. High-frequency traders may use
advanced technology or locate servers close to exchanges to gain faster access
to price information and execute trades more quickly, profiting from these
small-time differences. To counteract this, the SEC has approved the use of
“speed bumps,” which intentionally delay information to ensure a more level
playing field. The Dodd-Frank Act, 2010[20]
amended the CEA, granting the Department of Justice (DOJ) the authority to
prosecute spoofing offenses.
SEC’s first enforcement
action against a high-frequency trading firm for market manipulation:
The
SEC’s action against Athena Capital Research illustrates the same principle in
the United States: the use of sophisticated technology is not itself
prohibited, but an algorithmic strategy becomes unlawful where it is used
deliberately to manufacture an artificial price signal.
Between June and December 2009,
Athena Capital Research employed an algorithm known as “Gravy” in a
marking-the-close strategy that concentrated aggressive, rapid-fire trades in
the final two seconds of trading.
The volume of these
last-second transactions was sufficient to overwhelm available liquidity and
push the market price, and consequently the closing price, in Athena’s favour
across thousands of NASDAQ-listed stocks.
The regulator’s case was
reinforced by internal communications describing the strategy as “owning the
game”, which supported the inference that the trading pattern was not simply a
by-product of automated execution.
-
The volume of Athena’s last-second trades
allowed the firm to overwhelm the market’s available liquidity and artificially
push the market price, and therefore the closing price in its favor.
The conduct was
prosecuted under Section 10(b) of the Securities Exchange Act of 1934 and Rule
10b-5 because the strategy artificially influenced closing prices and therefore
interfered with market integrity.
The matter ended with an SEC order finding a
violation. Although Athena neither admitted nor denied the findings, it agreed
to pay a $1 million civil monetary penalty and to cease and desist from future
violations, making the action an important early example of enforcement against
an HFT firm for manipulative trading.
The
SEC charged Athena Capital Research with violating Section 10(b) of the
Securities Exchange Act of 1934 and Rule 10b-5, which prohibit fraudulent
practices and market manipulation. The firm’s actions were found to have
artificially influenced the closing prices of securities, undermining the
integrity of the securities markets.
The
SEC’s order reprimands Athena and concludes that the firm breached Section
10(b) of the Securities Exchange Act of 1934 and Rule 10b-5. While neither
admitting nor denying the SEC’s findings, Athena Capital Research agreed to pay
a $1 million civil monetary penalty and cease and desist from committing or
causing any future violations of the securities laws.
In March 2007, Optiver traders executed a series of
trades in U.S. oil futures, including crude oil, heating oil and gasoline
contracts, in a strategy directed at influencing settlement prices. The firm
accumulated substantial positions before the market close and then offset those
positions before the close in what became known as “banging the close”.
-
The CFTC alleged that the strategy
produced artificial price movements and generated approximately $1 million in
profit. Its complaint described 19 attempted instances across 11 trading days,
including successful attempts to move futures prices both upward and downward,
and also alleged false statements to NYMEX officials to conceal the conduct.
-
The enforcement response demonstrates the
seriousness with which regulators treat repeated price-setting strategies
around benchmark periods.
-
The matter was ultimately resolved by a
federal-court-approved settlement in April 2012 under which Optiver paid $14
million in civil monetary penalties and disgorgement and accepted a two-year
prohibition on trading U.S. oil futures during the final three minutes before
the close. Individual traders also received trading bans of varying duration.
The
CFTC's complaint detailed 19 instances of attempted manipulation on 11
different days in March 2007, with at least five instances where manipulation
was successful. In three cases, Optiver forced futures prices lower, and in two
cases, they forced prices higher. The CFTC also charged Optiver and van Kempen
with making false statements to NYMEX officials to conceal their manipulative
scheme.
-
In April 2012, a federal court approved a
settlement requiring Optiver to pay a total of $14 million in civil monetary
penalties and disgorgement.
-
The settlement also imposed a two-year ban
on Optiver from trading U.S. oil futures in the three minutes prior to market
close. Individual traders involved were barred from trading commodities for
varying periods: eight years for Christopher Dowson, four years for Randal
Meijer, and two years for Bastiaan van Kempen.
Moving further, Michael Coscia’s case provides a
more direct example of algorithmically generated spoofing. He was convicted on
counts of commodities fraud and spoofing after using algorithms to place large
orders that he intended to cancel before execution, thereby creating a
misleading impression of market depth and influencing prices to his advantage.
Michael
Coscia, a high-frequency trader, was convicted by a jury on six counts of
commodities fraud under 18 U.S.C. § 1348 and six counts of spoofing under 7
U.S.C. §§ 6c(a)(5)(C) and 13(a)(2). Spoofing involves placing orders with the
intent to cancel them before execution, creating a false impression of market
activity. This practice was made illegal in 2010 under the Dodd-Frank Act.
Coscia's trading strategy involved using algorithms to place large orders that
he intended to cancel before execution, influencing market prices to his
advantage.
After
conviction, Coscia relied on additional evidence and arguments concerning the
trial record, including alleged errors in the data presented to the jury and
later cases involving other traders. The Seventh Circuit nevertheless affirmed
the district court’s denial of his post-conviction motions.
The court’s reasoning is important for HFT
regulation because it distinguished speed from illegality: high-frequency and
algorithmic trading are not unlawful per se, but the deliberate use of large,
misleading orders with an intention to cancel them can constitute criminal
spoofing. The case therefore reinforces the importance of identifying the
purpose and structure of the algorithmic strategy rather than treating
automation itself as the prohibited conduct.
The
Seventh Circuit affirmed the district court's denial of both motions.
The
court acknowledged that Coscia’s strategy involved high-frequency and
algorithmic trading. It confirmed that spoofing is illegal under the Dodd-Frank
Act, which forbids placing orders with the intention of cancelling them to give
a false impression of trading activity. Coscia’s conviction on six counts of
spoofing was upheld because the evidence showed he deliberately used large,
misleading orders to influence market prices. It clarified that using
algorithms to trade at high speed is not illegal per se, but when combined with
intentional spoofing, it constitutes a criminal offense.
Lastly,
the 2010 Flash Crash provides another illustration of how algorithmic order
placement can create artificial signals. On 6 May 2010, the Dow Jones
Industrial Average fell by nearly 1,000 points within minutes before recovering
rapidly. Navinder Singh Sarao was subsequently identified as a significant
contributor to the episode after using a modified trading platform to place
large sell orders for E-mini S&P 500 futures that he intended to cancel
before execution.
-
The 2010 Flash Crash, occurring on May 6,
2010, was a sudden and severe stock market downturn in the United States, where
the Dow Jones Industrial Average plunged nearly 1,000 points within minutes
before recovering just as quickly. This event wiped out approximately $1
trillion in market value, though most of the losses were reversed within the
same trading day.
-
These orders created a false impression of
available market depth and encouraged other traders to react to the apparent
supply. The subsequent investigation by the CFTC and DOJ resulted in criminal
charges in 2015, including wire fraud, commodities fraud and spoofing. Sarao
ultimately pleaded guilty in November 2016 to wire fraud and spoofing and was
ordered to pay more than $38 million in monetary sanctions.
The Sarao matter thus
complements Coscia:
i.
Both cases
demonstrate that the legal concern is not the use of an algorithm or trading
speed in isolation,
ii.
but the
deliberate deployment of automated orders to create a market signal that the
trader does not genuinely intend to stand behind.
Between
2009 and 2015, Sarao's trading activities were investigated by the U.S.
Commodity Futures Trading Commission (CFTC) and the Department of Justice
(DOJ).
-
In April 2015, the DOJ filed 22 criminal
charges against him, including wire fraud, commodities fraud, and spoofing.
Sarao was accused of placing orders amounting to about $200 million worth of
bets that the market would fall, which were replaced or modified 19,000 times
before being cancelled.
-
In November 2016, Sarao pleaded guilty to
one count of wire fraud and one count of spoofing. He was ordered to pay over
$38 million in monetary sanctions for price manipulation and spoofing. In
January 2020, he was sentenced to one year of home confinement, with no jail
time, partly due to his cooperation with authorities and his diagnosis of
Asperger syndrome.
The
European Union
Similarly,
the European Securities and Markets
Authority (ESMA) has introduced rules under MiFID II to regulate
algorithmic and high-frequency trading.[25]
Like SEBI, both European and US regulators aim to balance the efficiency
benefits of technological advancements with the need to maintain market
integrity and fairness.[26]
Japan
Japan's Financial Services Agency (FSA)
is intensifying efforts to combat manipulative high-frequency trading (HFT)
practices. The FSA plans to increase fines for firms engaging in illicit HFT
activities, such as quote stuffing and layering, which can distort market
prices and undermine investor confidence.[27]
An advisory council is expected to begin discussions by the end of this year,
aiming to revise the Financial Instruments and Exchange Act by 2026.
Currently,
Japan imposes surcharges based on illicit profits from such violations. The FSA
is moving to close a loophole that allows high-frequency traders (HFTs) to
escape penalties for small but repeated illicit trade.[28]
This
initiative is part of Japan's broader efforts to strengthen its financial
regulatory framework and ensure fair trading practices. While the move has been
welcomed by those advocating for market fairness, some industry stakeholders’
express concerns about potential impacts on liquidity and trading costs.
The
regulatory framework for algorithmic trading in India has evolved through a
series of SEBI measures, beginning with the 2012 guidelines and extending to
the recent SEBI circular dated 4 February 2025.[29]
The 2025 circular seeks to facilitate safer participation in algorithmic
trading by retail investors while strengthening accountability and risk
controls for algorithmic trading activities.
The following changes have been introduced through
the circular:
1. SEBI
has mandated the use of a unique client identifier for all trades, ensuring
every order can be traced to a specific user. This enhances transparency and
deters market manipulators or insiders from exploiting algorithmic anonymity
for fraudulent activities.
2. The
circular also requires that algorithms developed by investors for personal use
be registered if they exceed a SEBI-specified order-per-second threshold,
limiting excessive or manipulative trading activity.
3. To
improve security and traceability, SEBI has prohibited open APIs and introduced
a key-based API system with OAuth authentication, reducing the risk of
unauthorized trades.
4. Additionally,
algorithms are now classified into two types. “White-box” algorithms are fully
transparent and available to any registered trader, while “black-box”
algorithms retain hidden logic and require providers to register as Research
Analysts and maintain detailed research documentation.
While
these measures democratize algorithmic trading and allow broader participation,
they also highlight the risks of automation. Minor technical glitches or coding
errors can trigger unintended trades and potentially destabilize the market,
making robust risk management and fail-safe mechanisms essential for
maintaining market integrity.
VII.
Strategy
Level Regulatory Framework for India
A. Why a New Offence Is Not the First
Solution
There is a legitimate argument for
a dedicated statutory offence dealing with algorithmic manipulation. Yet
creating a new offence before clarifying how the existing provisions are to be
applied risks duplicating broad prohibitions already found in Sections 12A and
Regulation 4. The immediate problem revealed by Jane Street is not a simple
textual gap. It is the difficulty of translating complex machine-speed
behaviour into legally reliable proof.
A new offence may ultimately become
necessary if future technologies produce manipulative conduct outside the
effective reach of existing rules. At present, however, a more proportionate
response would be a dedicated SEBI guidance framework under the existing PFUTP
Regulations, supported by surveillance standards and record-keeping
requirements. This approach preserves statutory flexibility while reducing
uncertainty for market participants.
B. Proposed Algorithmic Manipulation
Assessment Framework
SEBI should issue interpretive
guidance identifying non-exclusive factors relevant to algorithmic
manipulation. The following factors can be grouped into a four-stage analytical
sequence: exposure, capacity, conduct and benefit.
|
Stage
|
Regulatory
question
|
|
1. Economic
exposure
|
Identify
positions across cash, futures, options and other linked instruments;
determine whether the participant benefits from a particular direction of
price movement.
|
|
2. Price-impact
capacity
|
Measure
the participant’s share of depth, executed volume and short-window price
movement relative to prevailing liquidity and other market activity.
|
|
3. Conduct
and sequencing
|
Examine
order timing, aggression, repetition, clustering around expiry or benchmark
windows, cancellations, reversals and cross-segment coordination.
|
|
4. Benefit
and alternative explanation
|
Compare
the realised or expected benefit with the cost of the allegedly price-moving
leg and test whether a credible non-manipulative explanation survives the
complete record.
|
The framework should not operate as
a mechanical checklist. Large trades should not trigger liability merely
because they cause price movement, and high cancellation ratios should not be
treated as proof of spoofing without context. Each factor should instead
operate as cumulative evidence. The stronger the convergence between economic
exposure, measurable price impact, suspicious sequencing and derivative
benefit, the stronger the inference of manipulation.
C. Strategy-Level Auditability and
Signal Provenance
Current algorithmic controls
increasingly focus on tagging the order and identifying the broker or algo
provider. For enforcement against sophisticated institutional strategies, that
is necessary but not sufficient. SEBI should move toward strategy-level
auditability: regulated entities using material algorithmic strategies should
maintain records of strategy identifiers, relevant model versions, parameter
changes, material risk-limit changes, human overrides, and the systems
responsible for order generation. SEBI need not demand source code or
commercially sensitive mathematical models in routine supervision. The
objective is traceability of decision-making, not disclosure of proprietary
intellectual property.
This recommendation is consistent
with the direction of recent scholarship on “signal provenance”, which argues
that an order trail can show what was sent to the market without necessarily
showing what information or signal caused the order to be generated. The
distinction is important for regulatory design. A regulator investigating
manipulation needs to reconstruct not only what the algorithm did, but which
strategy or decision-rule produced the sequence and whether that sequence was
materially altered during the relevant period.[30]
D. Cross-Market and Cross-Entity
Surveillance
Jane Street demonstrates why
surveillance cannot remain instrument-specific. A manipulation strategy can
operate in the cash market, influence an index, and monetise the movement
through options. SEBI and market infrastructure institutions should therefore
develop consolidated surveillance views that connect related positions across
segments and affiliated entities, subject to appropriate legal and privacy
controls. Surveillance alerts should be capable of detecting combinations such
as concentrated constituent purchases plus directional index-option exposure
plus subsequent reversal during a reference-price window.
The objective is not to prohibit
cross-market strategies. It is to ensure that the regulator can see them as a
single economic strategy when the evidence indicates that they operate as one.
This is also an area where data science can assist without deciding liability.
Machine-learning or rule-based models can flag abnormal sequences, but a legal
finding should remain grounded in explainable evidence and an opportunity to
respond.
E. Calibrated Pre-Trade Controls
India should extend proportionate
pre-trade controls for participants whose algorithmic activity can materially
influence market benchmarks. These controls can include hard limits on order
size relative to displayed depth, concentration alerts, dynamic participation
caps around sensitive benchmark windows, automatic escalation when the same
entity simultaneously builds substantial exposure in a related derivative, and
kill-switch mechanisms where risk limits are breached. Such tools should be
calibrated by market impact and strategy type, not by a blanket distinction
between “manual” and “algorithmic” trading.
F. A Safe Harbour for Bona Fide
Market-Making and Arbitrage
A regulatory framework must avoid
chilling beneficial liquidity. SEBI should clarify that a strategy is not
presumptively manipulative merely because it is algorithmic, high-frequency,
cross-market, profitable, or capable of moving prices. Firms that can
demonstrate objective market-making or arbitrage criteria, documented risk
limits, tested algorithms, and consistent hedging behaviour should benefit from
a rebuttable presumption that reduces enforcement uncertainty. The presumption
should disappear where the trading pattern materially diverges from the stated
strategy, uses artificial orders, or is concentrated around a benchmark in a
way that generates a disproportionate directional benefit.
G. Due Process and Evidentiary
Disclosure
Finally, stronger surveillance
should be matched by stronger evidentiary discipline. When SEBI relies on
high-frequency order data to allege manipulation, an enforcement notice should
identify, to the extent legally permissible, the relevant trading windows, the
reference price, the positions that allegedly benefited, the price-impact
methodology, and the principal data categories used to reach the inference. A
respondent cannot meaningfully explain a strategy if the alleged price effect,
data inputs or counterfactual assumptions remain opaque. The ongoing Jane
Street document-access dispute demonstrates why disclosure is part of the
substantive regulatory architecture, not an afterthought.
VIII.
Policy
Implications for SEBI and Market Infrastructure Institutions
A. The regulatory objective should be
market integrity, not equalisation of technology
The policy objective should not be
to eliminate technological advantages. Securities markets compete partly
through infrastructure, execution quality and information processing. A rule
that attempts to make all participants equally slow could impair liquidity and
price discovery. The appropriate objective is narrower: prevent technological
capability from being used to manufacture false prices or artificial market
signals.
B. Retail-protection and
institutional-market-conduct rules should remain distinct
The full implementation of the 2025
retail algorithmic-trading framework on 1 April 2026 is a significant
regulatory milestone, but it should not be described as a complete answer to
institutional HFT manipulation. Retail algorithmic access raises questions of
broker responsibility, algo-provider registration and client protection.
Institutional manipulation raises questions of strategy governance,
cross-market exposure, benchmark influence and evidentiary attribution.
Conflating the two risks either over-regulating retail access or
under-regulating institutional strategies.[31]
C. OTR should remain a risk signal,
not a proxy for manipulation
The order-to-trade ratio is useful
for identifying excessive messaging, but it cannot become a proxy for
manipulation. Legitimate market-making can generate large numbers of
cancellations, while some manipulative strategies may have modest cancellation
rates. SEBI’s 2026 OTR revision, which excludes designated market-maker orders
and creates specific exemptions for certain equity-option orders, reflects this
principle of calibration. OTR should therefore function as one surveillance
indicator among several, not as a standalone legal test.[32]
IX.
Conclusion
And Suggestions
The
Algorithmic and high-frequency trading have changed the speed, scale and
architecture of securities markets, but they have not displaced the basic legal
concept of market manipulation. The central regulatory task remains identifying
conduct that creates artificial conditions in the market or distorts prices for
an unlawful advantage. What has changed is the evidentiary environment in which
that task must be performed.
India
already possesses a broad and technologically neutral foundation in Section 12A
of the SEBI Act and the PFUTP Regulations. The Supreme Court’s jurisprudence on
artificial and non-genuine trading provides a foundation for inference from
trading patterns, while the 2018 amendment to Regulation 4(2)(a) requires
post-amendment enforcement to address knowledge explicitly. The result is not a
legal vacuum. It is a system in transition from order-level supervision to
strategy-level oversight.
The
Jane Street proceedings illustrate the transition. The alleged conduct, as
framed by SEBI, is not a conventional spoofing scheme in which false orders
alone create a misleading order book. It is an alleged cross-market strategy in
which trading in underlying securities influences an index and the resulting
movement is monetised through related options positions. The legal challenge is
therefore to distinguish legitimate arbitrage from self-created price movement,
and to do so using evidence that remains intelligible and contestable.
The best regulatory
response is consequently neither technological prohibition nor immediate
statutory over-expansion. SEBI should strengthen strategy-level auditability,
cross-segment surveillance, benchmark-sensitive risk controls, interpretive
guidance on evidentiary factors, and procedural disclosure. At the same time,
it should preserve room for legitimate market-making and arbitrage through a
calibrated safe harbour. Such a framework would better align Indian securities
regulation with the realities of machine-speed markets while retaining the
central principle that markets must reflect genuine supply, demand and
information rather than manufactured signals.
[1] Department of Economic and Policy
Analysis, 'Analysis of Profits& Losses in the Equity Derivatives Segment
(FY22-FY24)' (SEBI, 23 September 2024)
<https://www.sebi.gov.in/reports-and-statistics/research/sep-2024/study-analysis-of-profits-and-losses-in-the-equity-derivatives-segment-fy22-fy24-_86905.html>
accessed 13 October 2025.
[2] Securities and Exchange Board of
India Act, 1992 (15 of 1992) s 12A.
[3] SEBI (Prohibition of Fraudulent
and Unfair Trade Practices relating to Securities Market) Regulations, 2003.
[4] SEBI (Prohibition of Fraudulent
and Unfair Trade Practices relating to Securities Market) Regulations, 2003.
[5] Securities and Exchange Board
of India v Kanaiyalal Baldevbhai Patel (2017) 15 SCC 1.
[6] SEBI (Prohibition of Fraudulent
and Unfair Trade Practices relating to Securities Market) Regulations, 2003 reg
2(1)(c).
[7] SEBI (Prohibition of Fraudulent
and Unfair Trade Practices relating to Securities Market) Regulations, 2003 reg
4(2)(a).
[8] Securities and Exchange Board
of India v Rakhi Trading Private Ltd (2018) 13 SCC 753.
[9] Interim Order in respect of
Jane Street Group WTM/AN/MRD/MRD-SEC-3/31516/2025-26.
[10] In the matter of NSE and Others
(Co-location) WTM/KV/MRD/MRD- PoD-3/30754/2024-25.
[11] ibid.
[12] In the matter of Servalakshmi
Papers Limited Adjudication Order No. EAD-2/DSR/RG/07/2013.
[14] ibid.
[15] Interim Order in respect of
Jane Street Group WTM/AN/MRD/MRD-SEC-3/31516/2025-26.
[16] SEBI (Substantial Acquisition of
Shares and Takeovers) Regulations, 2011.
[17] Anand Rathi v SEBI (2001)
SCC OnLine Bom 381.
[18] The Commodity Exchange Act 1936, 7
USC §§ 1–27f.
[19] The Securities Act, 1933, 15 USC
§§ 77a–77mm.
[20] Dodd-Frank Wall Street Reform and
Consumer Protection Act, Pub L No 111-203, 124 Stat 1376 (2010) s 747; CEA (n
A) 7 USC § 6c(a)(5)(C).
[21] US Securities and Exchange
Commission, ’Press Release on SEC Charges New York-Based High Frequency
Trading Firm with Fraudulent Trading to Manipulate Closing Prices’
(Securities and Exchange Commission, 16 October 2024)
<https://www.sec.gov/newsroom/press-releases/2014-229> accessed
3 October 2025.
[22] Commodity Futures Trading
Commission, Press Release: CFTC Charges Optiver Holding BV, Two
Subsidiaries, and High-Ranking Employees with Manipulation of NYMEX Crude Oil,
Heating Oil, and Gasoline Futures Contracts (Commodity Futures Trading
Commission, 24 July 2008) <https://www.cftc.gov/PressRoom/PressReleases/5521-08>
accessed 1 October 2025.
[23] Coscia v United States
No. 20-1032 (7th Cir. 2021).
[24] U.S. Commodity Futures Trading
Commission v Nav Sarao Futures Ltd. PLC and Navinder Singh Sarao
(2015) Civil Action No. 1:15-cv-03398, U.S. District Court for the
Northern District of Illinois.
[25] Directive 2014/65/EU of the
European Parliament and of the Council of 15 May 2014 on markets in financial
instruments [2014] OJ L173/349, arts 17 and 48.; Regulation (EU) No 600/2014 of
the European Parliament and of the Council of 15 May 2014 on markets in
financial instruments [2014] OJ L173/84.
[26] European Securities and Markets
Authority, ‘Algorithmic trading’ (ESMA)
<https://www.esma.europa.eu/esmas-activities/markets-and-infrastructure/algorithmic-trading>
accessed 7 October 2026.
[27] MT Newswires Live, ‘Market
Chatter: Japan to Toughen Penalties for High-Frequency Trading Abuse’ (Tiger
Trade, 14 August 2025) <https://www.itiger.com/news/2559935816 >
accessed 7 October 2026.
[28] ibid.
[29] SEBI Circular
SEBI/HO/MIRSD/MIRSD-PoD/P/CIR/2025/0000013 of February 2025 on Safer
participation of retail investors in Algorithmic trading
<https://www.sebi.gov.in/legal/circulars/feb-2025/safer-participation-of-retail-investors-in-algorithmic-trading_91614.html>
accessed 7 October 2026.
[30]Lokesh Gupta, ‘Signal Provenance in
Retail Algorithmic Trading: An Architectural Gap in SEBI’s 2026 Framework and
its General Form’ (SSRN, July 2026).
[31] SEBI, ‘Safer participation of
retail investors in Algorithmic trading’, Circular No
SEBI/HO/MIRSD/MIRSD-PoD/P/CIR/2025/0000013 (4 February 2025), read with the
September 2025 implementation extension; the framework became applicable to all
stock brokers from 1 April 2026.
[32] SEBI, ‘Revision of Order-to-Trade
Ratio (OTR) framework’, Circular No HO/47/11/16(2)2025-MRD-POD2/I/4113/2026 (4
February 2026).
How to Cite This Article
ANURAG MISHRA, GUNJEETA, MADHAV YADAV, MARKET MANIPULATION THROUGH ALGORITHMIC AND HIGH FREQUENCY TRADING (HFT) WITH SPECIAL REFERENCE TO THE JANE STREET GROUP CASE., White Black Legal – International Law Journal, ISSN: 2581-8503, Vol. Volume 4, Issue VOLUME 4 ISSUE 2, October 2026, pp. 703-727. Available at: https://www.whiteblacklegal.co.in/details/market-manipulation-through-algorithmic-and-high-frequency-trading-hft-with-special-reference-to-the-jane-street-group-case
Author & Publication Record
Authors: ANURAG MISHRA, GUNJEETA & MADHAV YADAV
Registration ID: 107173 | Published Paper ID: WBL7173, WBL7174 & WBL7175
Year: Oct- 2026 | Volume: 4 | Issue: 2
Approved ISSN: 2581-8503 | Country: Delhi, India
Page No.: 703-727
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