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Tony Sio, Head of Regulatory Strategy and Innovation, Nasdaq Anti-Financial Crime has worked closely with exchanges and regulators around the globe for over 20 years and advises on surveillance best practices. In this Q&A, Tony shares insights on how detecting and preventing market abuse has evolved over the past few decades, how advances in technology continue to protect the markets, and what compliance professionals can do to combat market abuse.
What is the historical background of protecting markets from manipulation and abuse?
Fraudsters and market manipulators have always sought to cheat financial markets – the first known insider trading scandal in the U.S. was in 1792! As methods of fraud and manipulation have evolved, those whose role it is to protect markets have also had to evolve. Initial approaches to protecting markets tended to be very manual, passive, and reactive, with a high reliance on whistleblowers or investigating the trail of events after they have happened. Like a good beat cop, intimate personal knowledge of the markets and the people that traded was key. However, as markets opened, became more international, and much more electronic, these historical approaches could no longer be relied upon.
When Nasdaq opened the first electronic stock market in the 1970s, it ushered in a new world of electronic trading and huge volumes of data. Automated, data-driven approaches to surveillance were developed, able to trawl huge data sets for abusive behavior in real time. This enabled protectors of markets to keep up the higher speeds and increased complexity that was only going to accelerate.
How has protection of markets evolved more recently?
Markets continued to evolve in the 1990s and 2000s with the emergence of high-frequency and algorithmic trading. This had two major impacts: it introduced large amounts of noise into the trading activity, and it allowed for new forms of manipulative behavior. For example, spoofing, a form of order book-level manipulation, made its initial appearance and has been a focus of regulatory scrutiny since. The algorithms themselves were also being manipulated, with multiple examples of individuals placing orders to trick algorithm flow or multiple algorithms interacting with each other to cause market instability. Surveillance programs needed to monitor for all of this.
The need for a cross-market, cross-asset, order book understanding of market interactions was shown with the ‘Flash Crash’ of 2010. Not long after, we also saw multiple complex market abuse examples in derivative and commodity markets, prompting many jurisdictions to put in place cross-market surveillance programs or requirements. And at the same time, extending surveillance in the derivatives and commodities space.
With these scandals there has been a greater recognition that market abuse can also occur in markets beyond equities, and surveillance practitioners have spent much of the past few years extending their programs into Fixed Income, Derivatives, FX, Commodities, and Power. Each new asset class has its own specific potential abuse scenarios. Crypto and digital assets are the most recent additional to the continually expanding perimeter.
In the 2020s we have seen a surge in retail participation who bring with them a greater role of social media in financial markets. The meme stocks craze has required surveillance practitioners to grapple with a new type of market force and we have already seen several prosecutions of market manipulators who have used social media as a key part of their manipulation techniques.
What are some key takeaways for compliance professionals to consider to best combat market abuse?
Even a high-level history of market abuse shows that we are in a constant state of change, and compliance professionals should always focus on the effectiveness and efficiency of their programs with evolving landscape. Regular reviews are required to ensure that processes and tools in place have adequate coverage of the changing compliance risks. At the same time, new approaches to surveillance should be incorporated if they can improve overall efficiency of the limited team.
How are we seeing emerging technologies like artificial intelligence impacting this and where will it head in the future?
The problem of market surveillance involves finding complex patterns of behavior within large datasets which are constantly growing over time, a problem well suited for many AI techniques. I see great opportunities for AI to make a large impact and I see this occurring in three different ways.
First, there are opportunities for new tools using AI and Nasdaq has a variety of initiatives in this space. Some of these initiatives involve using AI to detect patterns of abuse, and others use AI to reduce the workload of the surveillance analyst allowing them to focus on the investigation.
Second, we are already seeing new types of trading develop which involve AI, and these will need to be handled by the surveillance system. For example, Nasdaq Exchange is releasing the world’s first AI-based order type
Finally, and potentially more immediately, we will see AI as a potential threat and risk. ‘Deep fakes’ and fake news have always existed, but AI tools make them much easier to use and larger in scale. AI tools are right now an unknown risk that will almost certainly impact the financial industry for years to come.
What do you see as the greatest challenge that compliance and surveillance teams will face in the future?
We are approaching a time of multiple converging changes to the who, how, and what people trade. Retail participation is projected to continue to grow and with that social media has an increasing impact on all markets. New technologies such as DLT and AI have shaken up the traditional regimes and introduced greater experimentation of product types and trading styles. These changes both allow for new forms of market abuse and alter how existing techniques are performed. The greatest challenge to compliance, surveillance, and regulators is navigating these changes whilst still allowing firms and markets to innovate.
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