FlexAdvantage Blog

In volatile markets, asst managers were monitoring portfolios in real-time to determine how factors were behaving and affecting risks in their portfolios.

With attention riveted on volatile stock and bond markets and the economic fallout from the COVID-19 pandemic, brokers and asset managers are focused on keeping up with the high volumes of market data and trades. With so many unknowns, the situation could impact market data reforms to overhaul the securities information processor or SIP.

With the proliferation of algorithms in currency markets and regulatory pressure to prove best execution, buy-side trading desks are adopting algorithms to source liquidity and lower trading costs in FX trading.

With mountains of market data, historical prices, and transactions data stored in disparate systems, securities and investment firms are shifting from a focus on collecting data to extracting value from it. But the problem is that most of these huge and large data sets are siloed in legacy system architectures.

Topics such as market data costs, natural language processing, MiFID II research unbundling, Algo Wheels, exchange startups, market structure, dark pools, and desktop interoperability, resonated with the readers of the FlexAvantage industry blog.

The revamped SEC 606 regulation requires more extensive disclosures by broker dealers about the handling and routing of institutional customer orders including the average rebates the broker received from, and fees the broker paid to, trading venues.

As a sign of the evolving U.S. equity trading landscape, two startups and one options exchange operator plan to launch as many as three new exchange platforms in 2020, and a fourth could be waiting in the wings. Emphasizing the need for competition and innovation in US equity market structure, new entrants have downplayed the increased connectivity costs and additional fragmentation of orders across trading venues.

While the concept of big data has focused on the problem of collecting and processing vast quantities of data i— the trend is moving toward data analytics.

Increasingly banks are turning to the field of natural language processing (NLP) and machine learning to extract valuable information from voice, documents, and audio to boost productivity on trading desks.  It’s all part of a broader push to gain efficiencies by training machines and bots to analyze language, capture insights, and replace manual tasks and drive workflows further downstream.

Algo wheels have become a popular technology in equities trading, but now there are signs that algo wheels are expanding beyond equities into multiple asset classes such as futures.