Now experts are saying that ‘inaccessible liquidity’ is costing institutional investors three times as much in transaction costs, and it’s affecting the calculations made by their algorithms such as volume weighted average price (VWAP) and percentage of volume (POV), which rely on overall volumes.
With everyone seeking up-to-the minute data, analysts and investment managers utilized foot traffic from geolocation data to quantify retail sales and disruptions to the global supply chain. The rise of new marketplaces should remove some of the onerous work of sourcing, collecting, and normalizing data feeds from multiple vendors.
The rise in off-exchange market share fueled by retail trading, liquidity aggregation on fixed-income trading desks, and how options traders adjusted to all-electronic trading during the pandemic were among the most popular topics. On the technology front, readers were drawn to the adoption of AI and machine learning by algo trading desks, ATSs and Algo Wheels.
Another trend has been the mergers and acquisitions of exchanges and bond trading platforms, and joint ventures between fintech companies and fixed-income venues. Exchanges have been acquiring fixed-income venues over the past few years, which is also driving the consolidation of liquidity pools and e-trading protocols.
Equity market participants are learning about new exchanges, order types, and periodic auctions bringing innovation and competition to the equity trading landscape. Citing the concept of “inaccessible liquidity,” asset managers pointed to the surge of retail trading by mom and pop investors whose orders are executed off-exchange on private platforms.
While adhering to the CAT reporting timeline, sell-side firms are responsible for monitoring the CAT Reporter Portal and for fixing errors and rejects that come back from FINRA. So far, experts say the industry is doing very well with their data submissions. But error rates could escalate as the industry tackles more thorny order events and submits higher volumes of data.
Aggregation tools that pull in direct pricing streams and RFQs into an execution management system (EMS) or order management system (OMS) are becoming a critical part of creating a composite view of the fixed-income market.
With the hiring of data scientists, advances in cloud computing, and access to open source frameworks for training machine learning models, AI is transforming the trading desk. Already the largest banks have rolled out self-learning algorithms for equities trading.
Banks and asset managers relied on global teams to backup and transfer workloads during the pandemic. But, in moments of crisis, exposure to operational risks, model risks, cybersecurity attacks, and fraud can increase exponentially.
In volatile markets, asst managers were monitoring portfolios in real-time to determine how factors were behaving and affecting risks in their portfolios.