ABSTRACT

Global financial markets have changed as a result of high-frequency trading (HFT) and algorithmic trading (AT), which make trading techniques quicker and more effective. HFT entails carrying out massive quantities of orders at incredibly fast rates, whereas AT refers to the automation of trading decisions through the use of computer algorithms. Significant advantages have been brought about by these approaches, including increased market efficiency and liquidity. But as seen by incidents like the 2010 Flash Crash, they have also sparked worries about market manipulation, instability, and the possibility of escalating volatility. This article’s goal is to examine the increasing demand for regulatory frameworks in order to control the dangers related to AT and HFT. It explores the regulatory obstacles that international markets must overcome, such as the difficulties of managing ever-more-advanced technology. With an emphasis on important laws like the Dodd-Frank Act in the US and MiFID II in Europe, the article also examines international attempts to control these activities. The essay also examines recent regulatory changes and pertinent case law, providing insight into how legal frameworks are changing to handle the dangers associated with certain trading activities.

KEYWORDS

Market manipulation, financial regulation, market abuse, algorithmic trading, high-frequency trading (HFT), and legal compliance

INTRODUCTION

The use of computer programs and algorithms to quickly execute financial market trades based on preset parameters including price, volume, and time is known as algorithmic trading. High-frequency trading (HFT) is a subset of algorithmic trading that uses advanced technology and ultra-low-latency data links to execute several orders in a matter of milliseconds in order to profit from even the smallest price fluctuations. Financial markets have undergone tremendous change as a result of the development of computerized trading. Automated technologies that improve market efficiency, liquidity, and price discovery have essentially supplanted traditional manual trading. In order to reduce bid-ask spreads and boost market participation, HFT has been essential. However, additional hazards including market manipulation, flash crashes, and systemic vulnerabilities have also been brought about by the growth of algorithmic trading.

The possible risks of unregulated automated trading are brought to light by notable events like the 2010 Flash Crash. Regulation is necessary in light of these concerns in order to preserve market integrity, stop unfair benefits, and lessen systemic hazards. Global regulators, such as the FCA, ESMA, and SEC, have implemented regulations to keep an eye on and manage algorithmic trading. It is still difficult to strike a balance between innovation and efficient oversight, necessitating ongoing regulatory framework adaption.

LEGAL FRAMEWORK FOR ALGORITHMIC TRADING AND HFT

United states

The Securities and Exchange Commission (SEC) and the Commodity Futures Trading Commission (CFTC) are principally responsible for regulating High-Frequency Trading (HFT) and Algorithmic Trading (AT) in the United States. To address the particular difficulties presented by these trading methods, these organizations have put in place a number of laws and restrictions.
Regulations of the Securities and Exchange Commission (SEC): In order to guarantee that broker-dealers maintain efficient risk management procedures when they enter the market, the SEC has developed regulations. SEC Rule 15c3-5, also referred to as the Market Access Rule, is one of the important rules in this regard.

The Market Access Rule (SEC Rule 15c3-5): SEC Rule 15c3-5, which was adopted in November 2010, mandates that broker-dealers who have access to the market put supervisory and risk management systems in place. The rule’s objectives are to stop incorrect orders, guarantee adherence to legal requirements, and protect the stability of the financial system. In particular, it requires broker-dealers to: Put pre-trade risk controls in place to stop incorrect orders. Verify that all legal criteria are being met. Maintain protocols to control the risks of market access, including financial and regulatory ones. These safeguards are intended to stop actions that might endanger the integrity of the securities markets, other market participants, or the broker-dealer’s financial standing[1].

Regulations of the Commodity Futures Trading Commission (CFTC)

The CFTC has suggested legislation to mitigate the risks associated with automated trading and regulates the futures and derivatives markets. Given the rise and popularity of electronic trading, the CFTC proposed Regulation Automated Trading (Reg AT) in 2015 as a way to modernize its rules. Reg AT sought to improve the automated trading regulatory framework by implementing policies like: requiring some market players who trade using algorithms to register putting pre-trade risk controls and additional risk management strategies into action.
defining guidelines for algorithmic trading system development, testing, and oversight.
It’s crucial to remember that Reg AT is still a proposal and has not yet been finalized.[2]

The Effect of the Dodd-Frank Act on HFT Oversight

Some of the major changes to U.S. financial regulation brought about by the Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 have an effect on HFT. Notably, the Commodity Exchange Act was changed by Section 747 of the Dodd-Frank Act to forbid specific disruptive trading techniques that may be connected to HFT tactics. Among these forbidden behaviors are: breaking offers or bids. displaying willful or careless disregard for the proper closing-period transaction execution. Spoofing is the practice of making an offer or bid with the intention of withdrawing it before it is executed. The purpose of these clauses is to prevent manipulative practices that are frequently connected to high-frequency trading. Comprehensive legislative frameworks and regulatory standards have greatly influenced the regulation of high-frequency trading (HFT) and algorithmic trading (AT) in the UK and the EU.[3]
The European Union

Directive II on Markets in Financial Instruments (MiFID II)

When MiFID II went into effect in January 2018, it imposed stricter requirements on companies that trade using algorithms.

Efficient Systems and Risk Controls: Businesses need strong systems to guarantee that their trading systems are robust, have enough capacity, and are subject to the right thresholds and restrictions to avoid incorrect orders that could cause market disruption.

Notification Requirements: Companies that trade algorithmically must report their activities to the appropriate authorities in both their home Member State and the trading venues in which they conduct business. They have to give specifics about their risk controls, system specifications, and trading techniques.

Record-Keeping: Companies that use high-frequency algorithmic trading methods are required to keep precise, time-sequenced records of every order they place, including cancelled and executed transactions, and provide them to the appropriate authorities upon request.[4]

Market Making duties: Companies that use algorithmic trading to pursue market-making strategies must enter into legally enforceable agreements with trading venues, maintain efficient systems to meet these duties, and supply liquidity on a regular and predictable basis Regulation of Market Abuse (MAR) By addressing any abuses resulting from algorithmic trading, the Market Abuse Regulation enhances MiFID II. It requires businesses to put in place mechanisms and safeguards against market manipulation, including layering and spoofing, which can be made easier by algorithmic tactics. Additionally, businesses must notify the appropriate authorities of any suspicious orders or transactions.

India

The main regulatory body in charge of regulating high-frequency trading (HFT) and algorithmic trading (AT) is the Securities and Exchange Board of India (SEBI). In addition to addressing issues with market integrity and investor protection, SEBI has put in place a thorough structure to guarantee honest and open trading practices.

SEBI’s Algorithmic Trading and HFT Framework

In 2008, SEBI implemented Direct Market Access (DMA), which allowed brokers to provide their customers with direct access to the exchange’s trading system via the broker’s infrastructure. This marked the introduction of algorithmic trading in India.

Risk Management and System Integrity: To avoid incorrect orders and guarantee system resilience, algorithmic trading firms must put in place strong risk management mechanisms.
Order to Trade Ratio (OTR): To regulate the number of orders in relation to completed trades, SEBI has set stringent OTRs. Disincentives are used to discourage high daily OTRs, encouraging effective utilization of the trading infrastructure.[5]
Audit Requirements: To make sure that execution-related rules and risk control procedures are being followed, algorithmic trading companies must submit to audits every six months. Keeping order, trade, and control parameter logs is part of these audits.

Facilities for Co-location and Regulatory Reactions: By putting their servers close to the exchange’s trading servers, trade members can lower latency and possibly gain a competitive edge by using co-location services. Although there is nothing fundamentally unfair about this arrangement, issues with fair access have come up. Allegations that some trading members were given an unfair trading edge and speedier transaction executions by regularly connecting to secondary systems with fewer users resulted in a noteworthy case. As a result, SEBI has put policies in place to provide fair and equal access to co-location facilities, with the goal of avoiding any compromise of market security and integrity.

Key Regulatory Frameworks in Other Jurisdictions: A Comparative Study

Different nations have taken different stances on AT and HFT regulation:
China: To keep an eye on algorithmic trading, the China Securities Regulatory Commission (CSRC) has put policies in place that require traders to disclose their trading tactics and register their algorithms.

Japan: High-frequency traders must register with the Financial Services Agency (FSA) and adhere to its regulations for risk management and system stability.

Singapore: In order to prevent market disruptions, the Monetary Authority of Singapore (MAS) mandates that companies that use algorithmic trading have suitable risk management procedures and make sure their trading systems are sufficiently tested.

Ensuring market integrity, fostering equitable access, and reducing systemic risks related to algorithmic and high-frequency trading are the shared goals of these frameworks.

LEGAL CONCERNS IN ALGORITHMIC AND HIGH FREQUENCY TRADING

High-frequency trading (HFT) and algorithms have created complicated legal issues, especially with regard to market manipulation and abuse. Important issues include:

Abuse and Market Manipulation

Layering and Spoofing: In order to manipulate prices, spoofing entails placing huge orders with the intention of cancelling them before they are executed. A similar strategy is layering, which involves placing several orders at various price points to deceive other traders.

Case Study: The 2010 Flash Crash and Navinder Sarao

Known as the “Flash Crash,” the U.S. stock market had a sharp drop on May 6, 2010, with the Dow Jones Industrial Average plunging over 1,000 points in a matter of minutes. Later, it was discovered that London-based trader Navinder Singh Sarao used spoofing to help cause this incident.

The Effects of Quote Stuffing on Market Stability

The practice of quickly placing and cancelling a lot of orders in attempt to exceed the market’s processing capacity is known as quote stuffing. This strategy may result in higher trading expenses, decreased liquidity, and increased volatility. According to research, markets suffer from reduced liquidity and elevated short-term volatility during times of high quotation activity, which has a negative impact on market quality.

Pump and Dump Plans Using Algorithmic Techniques

In pump and dump operations, manipulators use coordinated buying or false information to artificially raise the price of an asset. They then sell off their holdings at the higher prices, leaving other investors with losses. By conducting quick, high-volume trades to raise prices and inflate the perception of market interest, algorithmic techniques can help implement these plans. To preserve market integrity and safeguard investors, addressing these legal issues calls for strong regulatory frameworks, sophisticated monitoring systems, and strict enforcement measures.

Systemic Risk and Market Stability

High-frequency trading (HFT) and algorithms have drastically changed the financial markets, bringing with them both opportunities and difficulties. The systemic danger that these trading strategies may bring to market stability is one of the main issues.

Flash crashes’ effects

The “Flash Crash” of May 6, 2010, which saw the Dow Jones Industrial Average drop by around 1,000 points in a matter of minutes before quickly rising again, is a noteworthy example. Although HFT was not the main cause of the crisis, investigations showed that it increased market volatility by quickly removing liquidity during the decline.
Concerns about Volatility and Liquidity: Liquidity imbalances may result from algorithmic and HFT tactics. These traders frequently supply liquidity, but they may also quickly take it away, particularly when the market is stressed, which increases volatility. Price fluctuations may be exacerbated by the quick placing and cancellation of orders, which may give the appearance of liquidity but disappear when required most.

Regulators’ Risk Mitigation Techniques

Regulators have taken a number of actions in response to these difficulties:
Circuit breakers are devices that stop trading if prices rise over predetermined levels in a brief period of time, allowing markets to calm down and avoiding rash judgments.

Pre-Trade Risk Controls: Rules such as SEC Rule 15c3-5 require brokers to put controls in place to stop false transactions and make sure orders are within predetermined bounds before they are executed.

Improved Monitoring and Surveillance: To ensure prompt interventions, regulatory agencies have upgraded their monitoring systems to identify and handle possible market abuses in real-time. These steps seek to strike a compromise between the advantages of algorithmic trading and the need for investor protection and market integrity.

LANDMARK CASE AND ENFOVEMENT ACTION

Significant legal cases have focused on algorithms and high-frequency trading (HFT), highlighting the necessity of strong regulatory frameworks to preserve market integrity. The difficulties and enforcement reactions in this area are demonstrated by two seminal cases:

Navinder Singh Sarao v. United States (2010 Flash Crash Case), Known as the “Flash Crash,” the U.S. stock market had a sharp drop on May 6, 2010, with major indices plunging sharply within minutes before rising again. A trader from the UK named Navinder Singh Sarao was charged with aiding this incident by engaging in a tactic called “spoofing.” Spoofing is the practice of placing large orders with the intention of cancelling them before they are executed in order to influence prices by fabricating a sense of supply or demand in the market. Sarao engaged in this dishonest activity by using an automated trading software, which had a big effect on the E-mini S&P 500 futures market. He entered a guilty plea to one count of spoofing and one count of wire fraud in 2016. In addition, Sarao was fined more than $38 million by the U.S. Commodity Futures Trading Commission (CFTC) for price manipulation and spoofing offenses.

SEC v. Knight Capital Group (2012 Algorithm Failure Case)

Due to an issue with its automated routing system, Knight Capital Group, a prominent financial services company, experienced a substantial trading disruption on August 1, 2012. Due to a software glitch, millions of orders were inadvertently placed, resulting in over 4 million executions across 154 stocks and costing the company almost $440 million. After looking into the matter, the U.S. Securities and Exchange Commission (SEC) concluded that Knight Capital did not have sufficient protections in place to control the risks connected to its market access. The company did not carry out enough system assessments or put in place efficient measures to stop incorrect trades. Knight Capital was consequently fined $12 million by the SEC for these shortcomings.[6] These incidents demonstrate how important it is for financial institutions to set up strong risk management procedures and follow legal requirements in order to avoid market disruptions and preserve investor trust.

High-frequency trading (HFT) and algorithms have come under intense regulatory scrutiny, which has resulted in noteworthy enforcement actions meant to preserve market integrity.

Enforcement Actions by the SEC and Citadel Securities

Citadel Securities was fined $7 million by the Securities and Exchange Commission (SEC) in September 2023 for breaking Regulation SHO, which regulates short-selling activities. Due to a coding issue in its automated trading system, the company was discovered to have mismarked sale orders, sending regulators false information. The regulatory issues surrounding algorithmic trading techniques were further highlighted in October 2024 when Citadel Securities consented to pay a $1 million punishment to the Financial Industry Regulatory Authority (FINRA) for purported rule violations.

RECENT DEVELOPMENT IN REGULATION AND COMPLIANCE

Algorithmic and high-frequency trading (HFT) regulations are changing to take advantage of new developments in technology and solve new problems. The following are some recent advancements in compliance and regulation:

Tighter Supervision and Compliance:

Development of Regulatory Sandboxes for Trading Driven by AI

Under regulatory oversight, companies can test new financial services and products in a controlled setting at regulatory sandboxes. These sandboxes enable the testing of novel algorithms and trading tactics in the context of AI-driven trading while guaranteeing adherence to current laws. To encourage innovation across member states, the European Union’s Artificial Intelligence Act, for example, suggests creating coordinated AI regulatory sandboxes.

Increased Requirements for Algorithm Registration and Reporting

By placing more stringent reporting requirements on companies involved in algorithmic trading, regulators are increasing transparency. This covers the need to register trading algorithms and submit thorough documentation of their construction and functionality. These steps are intended to make oversight easier and guarantee that trading activity doesn’t jeopardize the integrity of the market. The U.S. Securities and Exchange Commission (SEC), for instance, has put regulations into place requiring businesses to keep thorough records of their algorithmic trading operations.[7]

Technology-Based Regulatory Mechanisms

The Growth of RegTech in Compliance

Regulatory Technology (RegTech) is the application of new technologies to improve regulatory processes. RegTech solutions are being used in algorithmic trading to automate compliance chores, monitor trading activity, and verify regulatory criteria are met. Artificial intelligence (AI) and machine learning algorithms can evaluate large datasets in real time, detecting anomalies and potential compliance issues. The International Monetary Fund (IMF) observes that AI systems are used for market surveillance to detect collusive activity and price manipulation in the securities market.[8]

AI-Powered Market Surveillance for Real-Time Anomaly Detection

Advanced AI-based surveillance techniques are increasingly being used to track trading operations in real time. These technologies can detect anomalous trading patterns, flag potential instances of market abuse, and give regulators with actionable information. These tools use machine learning to continuously enhance their detection skills, reacting to changing market behaviors. The Financial business Regulatory Authority (FINRA) highlights the spread of artificial intelligence (AI) technologies in the securities business, which are altering numerous roles within broker-dealers.

Future Trends and Global Coordination.

Potential Reforms After Brexit and the Pandemic

The regulatory systems governing algorithmic trading are experiencing significant changes as a result of Brexit and the COVID-19 epidemic. The United Kingdom, having left the European Union, is reviewing its financial laws, especially those governing algorithmic and high-frequency trading. This reappraisal tries to strike a balance between market competitiveness and robust oversight. Similarly, the epidemic has expedited digital development in financial markets, requiring regulators around the world to revise their policies to accommodate new trading tools.

Increased Harmonization of Global Trading Regulations

As financial markets become more interconnected, there is a greater emphasis on aligning trade regulations across jurisdictions. International authorities and regulatory agencies are working together to develop universal rules for algorithmic and high-frequency trading. His global coordination is to eliminate regulatory arbitrage, promote fair competition, and keep the market stable. Efforts include coordinating reporting requirements, establishing definitions of market abuse, and sharing best practices for surveillance and enforcement. These changes reflect a collaborative effort by regulators and industry participants to adapt to the difficulties brought on by algorithmic and high-frequency trading, ensuring that markets remain fair, transparent, and robust.

RECOMMENDATIONS

The fast expansion of algorithmic and high-frequency trading (HFT) has changed financial markets, opening up new opportunities while also posing considerable regulatory and enforcement issues. While these technologies improve efficiency, liquidity, and lower transaction costs, they also raise questions about market fairness, stability, and the possibility of manipulation.

Summary of Major Regulatory Gaps and Enforcement Challenges
Market manipulation and abuse: Despite strict laws, techniques including spoofing, layering, and quotation stuffing continue to jeopardize market integrity. Regulatory systems have struggled to keep pace with the speed and complexity of algorithmic methods, resulting in gaps in detection and enforcement.

openness and Accountability: The lack of openness in algorithmic trading activities remains an issue. While audit trails and reporting requirements evolve, many companies continue to operate without fully revealing the design, function, and impact of their algorithms.
Systemic Risk: Events such as the 2010 Flash Crash demonstrated how algorithmic trading can increase market volatility and liquidity difficulties, highlighting gaps in processes that could help prevent such incidents.

Global Regulatory Coordination: As markets become more integrated, regulatory approaches vary across governments, complicating enforcement. A lack of homogeneity in trading regulations may result in regulatory arbitrage, compromising market stability.

The Need for Balanced Regulation.

To successfully regulate the dangers posed by algorithmic trading, rules must find a balance between promoting innovation and guaranteeing market protection. Overly stringent regulations could hamper technical breakthroughs and economic growth, whereas insufficient regulation could lead to market abuse and systemic instability.

Suggestions for Firms, Policymakers, and Compliance Officer

For those in charge of policy: Boost Enforcement and Surveillance: To identify unusual trading activity and market manipulation, regulators should keep improving real-time surveillance systems and use AI-based technologies.

Harmonize Global Regulations: To minimize regional disparities and avoid regulatory arbitrage, more international cooperation is required to standardize legal frameworks for algorithmic and high-frequency trading.

Revise Reporting and Registration Requirements: To guarantee complete transparency and facilitate regulatory monitoring, policymakers should require algorithmic trading operations to be reported in a clear, thorough, and uniform manner.

For Businesses:

have Strong Risk Controls in Place: Businesses need to have strong pre- and post-trade risk controls in place, including measures to stop market manipulation and incorrect trades.
Keep Thorough Audit Trails: Businesses should make sure that their algorithmic techniques can be thoroughly audited, giving regulators thorough documentation of trading activity and algorithm designs.

Increase Transparency: To encourage a more open trading environment, companies can take the initiative to inform authorities about the purpose and effects of their trading algorithms.

For Compliance Officers:

Keep abreast with regulatory developments: Maintaining complete compliance for their companies requires compliance officers to stay up to date on changing regulatory frameworks, particularly those pertaining to algorithmic trading.

Working together with regulators: Businesses can align their operations with the most recent compliance standards by investing in regulatory sandboxes and cultivating good connections with regulators. Invest in RegTech: To ensure prompt identification of any deviations from regulatory requirements, compliance professionals should make use of technological solutions like RegTech for real-time compliance tracking and ongoing monitoring.

CONCLUSION

In summary, one dynamic and developing field of financial law is the regulation of algorithmic and high-frequency trading (HFT). Regulators worldwide are finding it more and more difficult to preserve market stability and fairness as these trading systems’ speed and complexity continue to increase. The financial sector is clearly changing, as seen by the historic instances of market manipulation like the 2010 Flash Crash and the emergence of cutting-edge regulatory technology like RegTech and AI-based surveillance. More stringent compliance standards, such as heightened reporting requirements and algorithm registration, are becoming the norm. Regulators are using technology to improve oversight in the meanwhile; real-time market surveillance technologies provide a proactive means of identifying irregularities and stopping market abuse. As authorities work to standardize standards and minimize regulatory arbitrage, the growing trend of global cooperation is also crucial.

Future developments in algorithmic trading regulation are probably going to involve more technologically driven solutions and global regulatory convergence, especially in the financial environment following Brexit and the pandemic. The legal frameworks must, however, change along with the market to maintain open, equitable, and responsible commercial practices. In summary, even while algorithmic and HFT provide substantial advantages in terms of efficiency and liquidity, their potential for systemic risk and abuse means that regulators must remain vigilant and adjust as necessary to protect the integrity of financial markets around the world.

REFERENCES

SECURITY AND EXCHANGE COMMISSION, 17 CFR PART 240 [Release No. 34-63241; File No. S7-03-10] , https://www.sec.gov/files/rules/final/2010/34-63241.pdf(last visited Jan. 30, 2025).
COMMODITY FUTURES TRADING COMMISSION 17 CFR Parts 1, 38, 40, and 170 RIN 3038-AD52, https://www.cftc.gov/sites/default/files/idc/groups/public/%40newsroom/documents/file/federalregister112415.pdf (last visited Jan. 30, 2025).
Gary Shorter, Rena S. Miller, High-Frequency Trading: Background, Concerns, and Regulatory Developments, COGRESSIONAL RESEARCH SERVICE,(Jan. 30, 2025, 9:00PM) https://crsreports.congress.gov/product/pdf
ESMA, https://www.esma.europa.eu/publications-and-data/interactive-single-rulebook/mifid-ii/article-17-algorithmic-trading, ( last visited Jan. 30, 2025).
Tambiama Madiega with Anne Louise Van De Pol, Artificial intelligence act and regulatory sandboxes, EPRS, (Feb.2, 2025), https://www.europarl.europa.eu/RegData/etudes/BRIE/2022/733544/EPRS_BRI%282022%29733544_EN.pdf.
Tobias Adrian, AI and RegTech, INTERNATIONAL MONETARY FUND, ( Feb.2, 2025), https://www.imf.org/en/News/Articles/2021/10/29/sp102921-ai-and-regtech.
Anil Nair, Sebi opens algorithmic trading to retail investors: Opportunities, risks, and the future of HFT in India, THE ECONOMIS TIMES, (Feb. 2, 2025), https://economictimes.indiatimes.com/opinion/et-commentary/sebi-opens-algorithmic-trading-to-retail-investors-opportunities-risks-and-the-future-of-hft-in-india/articleshow/116923245.cms.

[1]SECURITY AND EXCHANGE COMMISSION, 17 CFR PART 240 [Release No. 34-63241; File No. S7-03-10] , https://www.sec.gov/files/rules/final/2010/34-63241.pdf

(last visited Jan. 30, 2025).

[2] COMMODITY FUTURES TRADING COMMISSION 17 CFR Parts 1, 38, 40, and 170 RIN 3038-AD52, https://www.cftc.gov/sites/default/files/idc/groups/public/%40newsroom/documents/file/federalregister112415.pdf (last visited Jan. 30, 2025).

[3] Gary Shorter, Rena S. Miller, High-Frequency Trading: Background, Concerns, and Regulatory Developments, COGRESSIONAL RESEARCH SERVICE,(Jan. 30, 2025, 9:00PM) https://crsreports.congress.gov/product/pdf

[4] ESMA, https://www.esma.europa.eu/publications-and-data/interactive-single-rulebook/mifid-ii/article-17-algorithmic-trading, ( last visited Jan. 30, 2025).

[5] Anil Nair, Sebi opens algorithmic trading to retail investors: Opportunities, risks, and the future of HFT in India, THE ECONOMIS TIMES, (Feb. 2, 2025), https://economictimes.indiatimes.com/opinion/et-commentary/sebi-opens-algorithmic-trading-to-retail-investors-opportunities-risks-and-the-future-of-hft-in-india/articleshow/116923245.cms.

[6] Jacob Bunge, SEC: Knight Capital Missed Warnings Before Errant Trades, THE WALL STREET JOURNAL, ( Feb. 2, 2025), https://www.wsj.com/articles/sec-levies-12-million-penalty-over-errant-knight-capital-trades-1381940340.

[7] Tambiama Madiega with Anne Louise Van De Pol, Artificial intelligence act and regulatory sandboxes, EPRS, (Feb.2, 2025), https://www.europarl.europa.eu/RegData/etudes/BRIE/2022/733544/EPRS_BRI%282022%29733544_EN.pdf.

[8] Tobias Adrian, AI and RegTech, INTERNATIONAL MONETARY FUND, ( Feb.2, 2025), https://www.imf.org/en/News/Articles/2021/10/29/sp102921-ai-and-regtech.

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