AI and Algorithmic Trading: Regulatory Considerations in Hong Kong

Introduction

Artificial intelligence and algorithmic trading have become central to modern securities and derivatives markets. In Hong Kong, algorithmic trading — the use of computer programmes to execute orders based on pre-defined criteria — is extensively used by banks, brokers, and hedge funds. The increasing deployment of AI and machine learning models in trading raises both regulatory compliance questions and systemic risk concerns that the SFC and HKMA have begun to address directly.

What Is Algorithmic Trading?

Algorithmic trading encompasses a spectrum of automated approaches:

• Execution algorithms: Systems that optimise the execution of large orders (e.g., VWAP, TWAP strategies) to minimise market impact
• Statistical arbitrage and quantitative strategies: Models that exploit pricing inefficiencies across related securities based on statistical relationships
• High-frequency trading (HFT): Strategies that generate large numbers of orders at very high speed, exploiting micro-structural market features
• AI-driven signal generation: Machine learning models that generate trading signals based on structured and unstructured data inputs, including sentiment analysis, news, and alternative data

SFC Regulatory Framework for Algorithmic Trading

The SFC requires SFC-licensed corporations that engage in algorithmic trading to comply with the Guideline on the Use of Electronic Trading Systems, which sets out requirements for:

Pre-Trade Controls

• Order size and price limits to prevent erroneous orders
• Position and exposure limits
• Automated kill switches to halt trading in the event of erroneous or anomalous behaviour

System Testing and Validation

Algorithms must be tested in a controlled environment before deployment and revalidated following material changes. The SFC expects firms to document their testing methodology and maintain records of test results.

Market Conduct

Algorithms must be designed to avoid market manipulation (such as layering, spoofing, or momentum ignition), regardless of whether the manipulation is intentional. Firms are responsible for the conduct of their algorithms even where the specific manipulative outcome was not intended by the traders who designed the strategy.

Monitoring and Surveillance

Real-time monitoring of algorithmic trading activity is required. Firms must have procedures for identifying and responding to unusual patterns that may indicate algorithm malfunction or unintended market impact.

AI-Specific Regulatory Considerations

AI and machine learning introduce additional regulatory challenges beyond traditional algorithmic trading:

Explainability and Auditability

Regulators expect firms to be able to explain the decisions generated by their trading systems. Black-box AI models that cannot be audited or explained create governance risks. The SFC expects senior management to understand and be responsible for the firm's trading systems, which becomes challenging where complex ML models are used.

Model Risk Management

AI trading models may behave unexpectedly in market conditions that were not represented in their training data — a phenomenon known as model drift or out-of-sample failure. Robust model risk management frameworks (comparable to those required of banks for credit risk models) are expected for material AI-driven trading strategies.

Data Governance

AI models are only as good as the data they are trained on. Data sourcing, cleaning, and licensing require careful attention. The use of non-public information in training AI models raises insider trading and market abuse concerns.

HKMA and Banking Sector AI Guidance

The HKMA has issued a series of circulars and guidance on the use of AI in banking, including in trading and risk management contexts. Key themes include model governance, explainability, fairness, and cybersecurity resilience of AI systems.

How Alan Wong LLP Can Assist

Alan Wong LLP advises financial institutions, fund managers, and fintech companies on the regulatory framework for algorithmic and AI-driven trading in Hong Kong. We assist with SFC compliance frameworks, algorithm governance documentation, regulatory engagement, and the structuring of technology agreements with trading system vendors. For firms developing AI-driven financial products or strategies, we provide integrated advice on SFC licensing, trading system requirements, and data governance.