I. INTRODUCTION
AI-supported algorithms have been instrumental in augmenting trading volumes, deepening market liquidity, and accelerating price formation to the millisecond scale within financial markets. While the capacity of algorithmic trading systems to transmit thousands of orders within seconds improves market efficiency, it simultaneously creates novel vulnerabilities to actions driven by manipulative intent.
Although Article 107 of the SPKn defines market manipulation as “any action that artificially affects the price, supply-demand, or trading volume in the market,” this definition is traditionally predicated on human agency. Consequently, the ability of current legislation to effectively sanction manipulative outcomes caused by autonomous learning AI systems is critically questioned.
The objective of this brief is to conduct an in-depth juridical examination of market manipulation in stock transactions executed via AI algorithms, identify existing lacunae within Turkish Law, and propose concrete regulatory and supervisory recommendations based on a comparative analysis of international best practices.
II. LEGAL FRAMEWORK AND TECHNICAL MANIPULATIONS
AI-supported trading is typically categorized under HFT. These systems generate automated orders well below human reaction time, leveraging machine learning and big data analytics.
1. Types of Manipulation
The most frequently employed algorithmic manipulation tactics under regulatory scrutiny include:
(a) Spoofing (Deception/Misrepresentation): Placing large orders (buy or sell) without the genuine intent of execution, followed by their rapid cancellation shortly after they have influenced the market price. The purpose is to lure other investors in a direction dictated by false supply or demand signals.
(b) Layering: A subset of Spoofing, involving the placement of multiple layers of large, bogus orders across various price levels in the order book, thereby coercing the price to move in the desired manipulative direction.
(c) Wash Trading (Fictitious Trading): Transactions executed between accounts controlled by the same entity, where no genuine change in beneficial ownership occurs, solely to artificially inflate trading volume. This is prevalent in crypto asset markets.
(d) Quote Stuffing: Sending and rapidly cancelling an excessive volume of orders to overwhelm and deliberately slow down the market infrastructure.
2. Legal Regimes
The growing prevalence of artificial intelligence and algorithmic trading systems in financial markets has both rendered traditional forms of market manipulation more sophisticated and given rise to entirely new types of manipulative behavior. Accordingly, assessing the legal implications of these manipulation types requires both technical analysis and normative interpretation.
Under Turkish law, manipulative transactions are generally regulated by Article 107 of the SPKn and the Communiqué on Market Disruptive Actions. However, AI-assisted manipulation techniques differ from traditional market abuses in terms of speed, volume, and complexity; as a result, the existing legal framework is inadequate for defining and sanctioning these acts. Below, the most critical forms of technical manipulation are examined together with their legal counterparts and implementation challenges.
2.1. Spoofing
Spoofing may be characterized as a manipulative act within the meaning of Article 107/1 of the SPKn, which prohibits the “artificial direction of the market.” Although the Communiqué on Market Disruptive Actions contains broad provisions regarding the placement of orders that may create a misleading impression, the term spoofing itself is not explicitly defined.
Determining intent is particularly difficult when orders are generated by artificial intelligence or high-frequency algorithms and cancelled within milliseconds; from a technical standpoint, it is nearly impossible to ascertain the purpose behind the algorithm’s orders or whether they were genuinely intended to be executed. The extremely high speed at which these operations occur also makes detection by traditional supervisory mechanisms more challenging.
The absence of an explicit regulation on spoofing within the Communiqué necessitates broad interpretive approaches to determine liability; this creates issues regarding legal certainty and foreseeability. Unlike in the United States, where spoofing is expressly criminalized under §747 of the Dodd-Frank Act, the lack of a definitional counterpart in Turkish law leads to misalignment with international standards.
2.2. Layering
Layering, a more sophisticated variant of spoofing, is not directly regulated under Turkish law; instead, it is construed as manipulation through interpretive application of Article 107 of the SKn. Due to its structure—placing deceptive layers of orders at different levels of the order book—layering is inherently difficult to detect.
Analyzing its impact on the order book requires advanced data-processing capabilities, and the fact that artificial intelligence may autonomously generate this strategy renders assessments of intent and culpability even more problematic. Turkish law does not impose any obligation to conduct order-book-level layered manipulation analyses, nor does Türkiye implement algorithmic behavior tests comparable to those required under MiFID II in the European Union.
Moreover, due to the absence of systematic requirements to record timestamps, algorithm identifiers, or order-cancellation reasons for each order, the retrospective detection of layering and the identification of the perpetrator become significantly more difficult.
2.3. Wash Trading
Wash trading is considered unlawful under the Communiqué on Market Disruptive Actions, which prohibits “matched orders.” In practice, however, the detection of this conduct is challenging, particularly due to artificial intelligence and high-frequency trading systems. Complex account structures, the simultaneous order-submission capacities of trading bots, and the possibility that certain algorithms may develop such strategies through their own learning processes all render wash trading more intricate than traditional human-driven manipulation.
When an AI system conducts transactions across multiple accounts simultaneously, the question of whether the perpetrator is the human operator or the algorithm itself becomes relevant, further complicating the determination of liability.
Wash trading is particularly common in crypto-asset markets, and the fact that Turkish capital markets legislation does not fully apply to crypto-asset spot trading exacerbates the regulatory gap in this domain.
2.4. Quote Stuffing
Quote stuffing, although not explicitly defined under Turkish law, can be evaluated as a form of manipulation within the meaning of “misleading conduct” under Article 107 of the SPKn. This technique involves submitting a very high number of orders in rapid succession and cancelling them immediately in order to slow down the order book or underlying market infrastructure.
However, Turkish markets lack the data-analysis infrastructure capable of monitoring order flow at millisecond resolution and identifying manipulations intended to cause system congestion. If such conduct were autonomously produced by an AI system, determining fault would pose an additional challenge. The absence of a clear legal distinction between legitimate high-speed order submission and manipulation-driven “order bombardment” creates a risk of violating the principle of legality and results in significant uncertainty regarding the boundaries of liability, especially in the context of autonomous systems.
III. TÜRKİYE AND INTERNATIONAL REGULATORY COMPARISON
1. The Current Situation and Deficiencies in Türkiye
The Turkish Capital Markets Board (“SPK”), pursuant to the Communiqué on the Establishment and Operating Principles of Investment Firms, requires institutions engaged in algorithmic trading to establish risk management frameworks. Yet, these regulations lack specificity regarding the complexities introduced by AI. A dedicated “AI Trading Regulation” defining systems with autonomous learning capabilities does not exist. Furthermore, the SPK’s oversight capacity is constrained by the absence of clear and binding standards for the auditing of algorithm source codes and the retrospective analysis of high-frequency order flows. Most critically, the SPK Communiqué on Market Disruptive Actions does not explicitly define HFT manipulations such as spoofing and layering with corresponding criminal consequences, resulting in legal ambiguity in combating these technical forms of manipulation.
2. International Standards and the Imperative of Compliance
International regulatory frameworks exhibit significantly greater advancement than the Turkish approach to algorithmic trading:
2.1. European Union (EU)
Under the EU’s MiFID II (Markets in Financial Instruments Directive), algorithmic trading is compulsorily subjected to stringent testing, recording, and monitoring regimes, imposing obligations on investment firms to conduct periodic risk assessments. Concurrently, the MAR (Market Abuse Regulation - EU No. 596/2014) explicitly broadened the definition of market abuse to definitively encompass algorithmic and AI transactions. MAR defines methods like spoofing and layering as direct manipulative practices, requiring that the origin and parameters of every order placed by an algorithm be meticulously recorded. This robust approach facilitates the retrospective examination of AI-driven actions.
2.2. United States (US)
The US, particularly through Section §747 of the Dodd-Frank Act, explicitly criminalized the act of spoofing at the federal level, subjecting it to criminal sanctions. This legislative measure provided the foundation for pivotal criminal precedents, such as the U.S. v. Coscia (2015) case, and fortified the SEC’s authority over algorithmic transactions.
These international models unequivocally demonstrate that Türkiye must construct a comprehensive framework for “Supervisory Liability” specific to algorithmic transactions and urgently revise its existing communiqués to address technical manipulations.
IV. NEW PARADIGMS IN LEGAL LIABILITY: THE PROBLEM OF FAULT AND INTENT
1. Determination of Intent (Mens Rea) and the Coscia Precedent
The U.S. v. Michael Coscia (2015) case is a crucial benchmark in the jurisprudence of algorithmic manipulation. The court established that Coscia utilized a deterministic algorithm, concluding that the program did not make an “error,” but merely executed the programmer’s manipulative intent. According to this ruling, algorithms function as tools, and liability rests with the human agent who programmed or operated them.
However, the core challenge presented by modern AI systems (e.g., deep learning models) is profound; in the context of autonomous AI, if an AI system was not explicitly programmed for a manipulative purpose but autonomously develops a manipulative strategy during its self-learning process to maximize profit, how can the “intent” of the perpetrator be established? Traditional criminal law concepts such as direct intent or oblique/contingent intent are fundamentally inadequate for attributing culpability to autonomous AI actions.
2. Supervisory Liability and Explainable AI (XAI)
Two principal solutions are proposed to navigate these complex issues:
(a) Expansion of Supervisory Liability: It is imperative to broaden the supervisory obligation of Investment Firms and AI system developers within Turkish law. Firms must assume the burden of proof to demonstrate that their algorithms will not lead to manipulative outcomes (Reversal of the Burden of Proof).
(b) Explainable AI (XAI): Regulators must mandate the use of systems that are auditable and capable of explaining the rationale behind an algorithm’s transactional decisions. This principle, advocated by regulators such as the UK’s FCA, aims to demystify the AI “black box” structure, thereby facilitating the determination of fault.
V. REGULATION AND OVERSIGHT RECOMMENDATIONS
The following actions are incumbent upon Türkiye to effectively combat AI-based market manipulation:
(a) Development of SPK Systems: In order for the SPK to effectively supervise algorithmic and HFT activities, it is necessary to establish capabilities in big data analytics, machine-learning-based surveillance tools, and real-time order-flow monitoring systems.
(b) Algorithm Certification and Registration: All algorithmic trading systems must be certified following rigorous manipulation simulations prescribed by the SPK before their deployment, and their source codes/parameters must be systematically registered and recorded.
(c) Regulatory Reform:
The Communiqué on Market Disruptive Actions must be updated to explicitly define technical manipulations such as spoofing, layering, and wash trading and clarify their corresponding punitive measures.
The concept of “Autonomous Algorithm Liability” must be integrated into the legal framework, broadening the scope of supervisory liability to comprehensively cover investment firms and developers.
(d) Risk-Based Oversight: In alignment with the MiFID II model, the systemic risks generated by algorithms must be periodically evaluated, and more rigorous oversight and capital requirements must be imposed on systems identified as high-risk.
VI. CONCLUSION
The velocity and complexity of transactions executed by AI algorithms have rendered the Turkish Capital Markets Law insufficient, particularly in establishing the elements of perpetrator and intent. Given that current regulations are grounded in the assumption of a human perpetrator, legal proceedings face significant challenges when confronting manipulative acts perpetrated by autonomous AI.
To safeguard market integrity and sustain international investor confidence, Türkiye must urgently initiate legislative reform by adopting the MiFID II and MAR regimes as templates, mandating algorithm certification, and enforcing the principles of Explainable AI (XAI). The extension of supervisory liability and the burden of proof will be instrumental in mitigating the risks posed by regulatory frameworks lagging behind technological advancements.
B. KEY TAKEAWAYS
(1)While artificial intelligence and algorithmic trading technologies enhance market liquidity and transaction speed, they also facilitate the emergence of technically complex and high-frequency manipulation schemes—such as spoofing, layering, wash trading, and quote stuffing—thereby creating new risk areas for market integrity.
(2)The broad framework of market manipulation under Article 107 of the SPKn is grounded in the assumption of a human perpetrator. Consequently, when autonomous AI algorithms develop manipulative strategies through their own learning processes, significant legal gaps arise regarding the identification of the perpetrator and the determination of intent.
(3)The absence of explicit, technique-specific definitions for spoofing, layering, wash trading, and quote stuffing in Turkish capital markets legislation weakens the principle of legal certainty and complicates the detection and sanctioning of such acts.
(4)The insufficiency of data-processing infrastructure capable of conducting detailed order-book analysis, the absence of mandatory time-stamped order records, and the lack of algorithm identification mechanisms collectively create significant supervisory deficiencies in detecting AI-driven market manipulation.
(5)The explicit classification of technical manipulation practices and the imposition of testing, record-keeping, and monitoring obligations on algorithmic trading systems under regulatory frameworks such as the EU’s MiFID II and MAR, as well as the U.S. Dodd-Frank Act, demonstrate that Türkiye lags behind international regulatory standards.
(6)Although the U.S. v. Coscia decision provides guidance regarding the attribution of intent to the programmer in deterministic algorithms, it remains insufficient to address situations in which autonomous, self-learning AI systems independently generate manipulative strategies. Thus, Turkish law must either reinterpret the concept of intent or establish liability based on an alternative doctrinal foundation.
(7)For investment institutions and algorithm developers, the adoption of an “Expanded Supervisory Responsibility” approach is essential. In this context, imposing an obligation on relevant actors to demonstrate that their algorithms do not produce manipulative behavior—effectively reversing the burden of proof—would mitigate uncertainties arising from autonomous systems.
(8)Mandating explainable artificial intelligence (Explainable AI) mechanisms, supported by source-code recording systems, algorithm-update notifications, and post-trade audit tools, would substantially alleviate evidentiary challenges arising from the “black-box” nature of AI systems.
(9)For the SPK to effectively supervise algorithmic and HFT activities, it is imperative to establish big-data analytics capabilities, machine-learning-based surveillance tools, and real-time order-flow monitoring systems; however, the current institutional framework remains insufficient to meet these requirements.
(10)Revising the Capital Markets Board’s Communiqué on Market Disruptive Actions to provide separate definitions for technical manipulation types and to explicitly incorporate autonomous AI systems has become indispensable for enabling Türkiye to establish a market-supervision regime aligned with MAR and MiFID II standards.
