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The Road Ahead for AI in Sell-Side Equity Algorithms

DUBLIN, June 12, 2020 /PRNewswire/ -- The "AI in Sell-Side Equity Algorithms: Survival of the Fittest" report has been added to ResearchAndMarkets.com's offering.

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Sell-side institutions are in a survival of the fittest race for the top spot in clients' algo wheels. After interviewing 50 AI algorithm experts from the buy-side, sell-side, and fintech vendors - as well as producing AI algo ecosystem case studies on U.S., European, and Asian banks - researchers gained depth and breadth of insight leading to the view that only a few banks will dominate the global algorithmic trading space in the next five years.

AI development work in sell-side algorithms started about five years ago, but it's only within the last two years that banks' AI algo ecosystem expansion reached a level where statistically significant AI attributable excess returns are being observed in A/B testing. As automated performance measurement applications, like algo wheels, increasingly drive broker selection decisions, competition to build better, faster, smarter algorithms has become a war of attrition, since the level of investment required to stay ahead of the curve continues to rise. Now more than ever, not keeping up means you're going backwards as researchers believe that consolidation in the algorithmic trading space will continue, just as the sell-side overall continues to consolidate.

The most significant challenge is the changing science of AI and growing investment needed to transition from traditional algos to AI. Some of these AI-based techniques, like t-SNE, were not even in existence 10 years ago. There is, however, an opportunity for sell-side firms that can stay ahead of the rapidly evolving AI algo data science, as only a handful of banks will have the order flow economies of scale and AI development budget needed to compete on performance.

Key Topics Covered:

Introduction

Understanding AI Algorithms

  • Supervised and Unsupervised Learning
  • Artificial Neural Networks
  • Deep Reinforcement Learning
  • The Road Ahead

Applying AI in Sell-Side Algos

Measuring AI Performance

The Challenges

  • Out-of-Sample Predictability
  • Explaining AI Decisions to Clients
  • Transporting AI Models
  • Building vs. Buying vs. Leasing

Governance Concerns

Case Studies

  • Large American Bank
  • Large European Bank
  • Large Asian Bank
  • Mid-Size American Bank
  • Mid-Size European Bank
  • Mid-Size Asian Bank

Conclusion

Methodology

For more information about this report visit https://www.researchandmarkets.com/r/rzhmrk

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SOURCE Research and Markets