- v1.0 Version
- 346 Download
- 3.3mb File Size
- 1 File Count
- 12th July 2019 Create Date
- 17th February 2020 Last Updated
- Liquidity Fragmentation: An Evolving Challenge
- A Current Picture of Smart Order Routing
- The Case for Adaptive Trading Technologies
- Machine Learning & Smart Order Routing
"Liquidity fragmentation, the phenomenon of multi-listing of an instrument on different venues, creates deeper and broader markets and lowers transaction costs. Despite the benefits, it has become a major challenge to market participants from buy-side to sell- side institutions, as it has led to a more complex trading landscape. Some of the tools used to address this problem have been in place for a long period, while others are new to the market.
Smart Order Routing (SOR), which is undeniably the best solution to tackle liquidity fragmentation, has been around for over a decade. SOR is also increasingly the critical element in the building of any trade automation technology. Current SORs - both the simplistic rule-based and the newer generation using a liquidity seeking algorithmic approach - are offered by bulge-brackets and some vendors with a wide range of capabilities and limitations.
This whitepaper provides an update on our previous Smart Order Routing whitepaper and looks at the innovation in this domain. In addition, this paper is written as Artificial Intelligence (AI), and more precisely Machine Learning (ML), are rapidly reshaping the IT landscape. We will naturally look at the areas of application for ML in SOR and more broadly in automated trading."
Quod Financial presents in our latest Smart Order Routing whitepaper the rapidly changing landscape for Automation and Order Routing. How AI/ML (Machine Learning) applies in SOR and more broadly in Automated Trading.