Algo Trading Controls and Governance

Deliberations of the Armstrong Wolfe 2022 Algo Trading Risk and Governance Working Group

This report, written by GreySpark Partners, summarises the deliberations of 28 global heads of controls, risk and governance during three 2022 Algo Trading Risk and Governance Working Group forums. The Working Group and associated forums were created and chaired by Armstrong Wolfe, a global advisory firm for Chief Operating Officers in financial markets, investment banking and asset management.

Published on: 10 Feb, 2023somdn_product_page

Please login or register to download this report for free

Description

Algorithmic trading has been on the rise since the early 2000s and now accounts for roughly 70% of orders in some markets. This growth is due to technological advancements, such as improved computing power, lower storage costs, and the integration of AI and machine learning. In the capital markets sector, cost considerations, competitiveness, regulatory obligations and profitability are key incentives to trade using algorithms.

Algorithmic trading, however, entails risks stemming from potential failures of algorithms, IT systems and processes. In recent years, several major algorithmic trading failures have resulted in substantial losses, fines and reputational damage for investment firms.

Related content

European Fixed Income Markets and the Transparency Mandates

European Fixed Income Markets and the Transparency Mandates

Post-trade Automation 2025

Post-trade Automation 2025

Market Map: Governance Risk and Compliance Tools

Market Map: Governance Risk and Compliance Tools

Buyer’s Guide: Sellside Risk Management Solutions 2024

Buyer’s Guide: Sellside Risk Management Solutions 2024

Our latest reports

FX Trade Settlement 2026

FX Trade Settlement 2026

AI Engineering Best Practices Guide – A CTO’s Guide to Transforming the R&D Organisation

AI Engineering Best Practices Guide – A CTO’s Guide to Transforming the R&D Organisation

AI Engineering Best Practices Guide – A CEO’s Strategic Guide to Enterprise Value Creation

AI Engineering Best Practices Guide – A CEO’s Strategic Guide to Enterprise Value Creation

Prediction Markets

Prediction Markets

Table of Contents

  • Chapter 1: Trends in Algorithmic Trading
  • Chapter 2: Regulatory Stance on 3LoD
    • 2.1 Regulatory View on 3LoD Model
    • 2.2 Staffing the First and Second Line of Defence
    • 2.3 The Value of Control Work
    • 2.3 A Holistic Future for 3LoD and Data-driven Risk Management
  • Chapter 3: Controls and the Risk & Controls Self-Assessment
    • 3.1 Comprehensive and Risk-Based RCSAs
    • 3.2 The Cost / Benefit Paradigm of the RCSA
    • 3.3 The Road to a ‘Smart’ Comprehensive RCSA
    • 3.4 Finding Value in the RCSA
  • Chapter 4: Governance Models for Algorithm Development and Testing
    • 4.1 Differentiation in Algorithms Development and Testing
    • 4.2 Models of Deployment for Algorithm Development and Testing
    • 4.3 Centralised and Decentralised Algorithm Development and Testing
    • 4.4 The Future of Algorithm Development and Testing
  • Chapter 5: The Algorithmic Trading Outlook for 2023
    • 5.1 Transparency & Clear Division of Roles Between First, Second and Third Lines
    • 5.2 Train from Within versus Acquire Outside Talent
    • 5.3 The Value of Risk Controls
    • 5.4 Horizon Scanning