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Electronic trading, once the preserve of specialist firms, is now available to the individual, retail investors on their cell phones. This more recent development is a testament to the progress that has been made within the industry over the last decade and how emerging technologies, including artificial intelligence (AI) and machine learning (ML), have become one of the biggest drivers of change in the trading and market infrastructure sector. The adoption of these new tools was a result of the increase in complexity of systems and volumes of data – which humans struggle to keep pace with.
Though the pandemic may have further accelerated the shift toward automation and digitization, this trend was already taking place at exchanges and trading ﬁrms as a way to reduce costs, increase efficiency and oversight, and ultimately meet regulatory requirements. If anything, Covid-19 was a sustained real-world test rather than the driver of this change. With most systems deployed in exchange colocation and other data centers, exchanges and their customers were able to enable their employees to keep the markets open and meet regulatory requirements.
Over recent decades electronic trading has matured across the globe. The United States saw electronic communication networks (ECN) develop and with Reg NMS and SIP’s they have now matured into competing exchanges, and in Europe, the multilateral trading facilities (MTF) which developed to compete with national stock exchanges have also become exchanges. Along with these new venues, there has been an exponential rise in the amount of market data and a renewed focus on reduced latency, including increased exchange colocation, microwave networks, and FPGA. On the post-trade side, we are witnessing a push towards shortening the settlement cycle to T+1, or real-time net settlement, as highlighted by the most recent joint announcement by the Securities Industry and Financial Markets Association (SIFMA), the Investment Company Institute (ICI), and The Depository Trust & Clearing Corporation (DTCC), who are collaborating on efforts to accelerate the U.S. securities settlement cycle from T+2 to T+1.
The volume of both trading and market data has increased with the evolution of automated and algorithmic trading, the use of AI and ML in both trading and compliance functions is a natural evolution. On the latter, not deploying this new technology would make some compliance-related tasks in the required timeframes impossible if it were still done manually. From a regulatory and compliance standpoint, automation helps a firm’s ability to meet regulatory requirements.
With this ever-increasing reliance on and complexity of technology, the need to ensure operational resiliency has never been greater. The software deployed to test such systems must also constantly evolve and become more sophisticated to help build the solid foundations required for the wider adoption of this emerging technology within the electronic trading ecosystem. Although not always at the forefront of minds, adequate testing will be key to the efficient functioning of the global financial system.
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