the past we have focused on how increasing transparency,
integrating technology, and identifying new risk controls can
improve markets. Now it's time to take the next step and focus
on improving markets by reviewing existing rules and
simplifying anything that is unnecessarily complex.
But, before we can do that, we must first
increase our understanding of the marketplace's numerous moving
parts otherwise we run the risk of implementing change that
does more harm than good.
The good news is the industry has the
tools to achieve that goal. Or, more accurately, the
industry has the data to achieve that goal.
Our markets are not just driven by data,
they are also incredible aggregators of data. Today's
electronic exchanges are capable of recording every trade, new
order, and cancelled order for future analysis.
So it's logical that any action taken to
improve markets should be data-driven. To ignore that
information and blindly implement change would be misguided.
Why make subjective observations when you
can draw objective conclusions? Why make broad
generalisations about trading practices when you can identify
specific behaviours? Why guess as to what might happen in
reaction to a change in market structure when you can predict
what will happen by analysing the data generated by pilot
FIA PTG isn't alone in advocating for a
data-driven approach to market analysis. National
regulators like the Securities and Exchange Commission (SEC),
the Commodity Futures Trading Commission (CFTC), and the
industry's Self-Regulatory Organisations (SROs) are all moving
towards taking an analytical approach to policy-making and
The SEC now uses MIDAS to better
understand market structure and inform policy—as the
SEC's Associate Director, Office of Analytics and Research,
Gregg Berman put it, "…If we don't diagnose the problems
correctly, we certainly won't arrive at the correct
The CFTC has access to Large Trader
Reports and various reports from Swap Data Repositories and
recently requested comments on how to implement a 21st
century surveillance system.
The SROs are constantly monitoring trading
behaviours to prevent manipulative trading while ensuring their
trading platforms continue to meet the needs of market
Armed with this data, the industry can
weed out those with manipulative intent and base any new
regulations on participant behaviours. Take, for
instance, the conversation regarding automated
Automated execution methodologies are
typically attributed to proprietary firms, hedge funds, and
high-frequency traders (HFTs). But the truth is that today,
most market participants utilise automated execution tools in
one way or another, including banks, fund managers, and
If new regulations are only applied to
"typical" automated traders, they would fail to capture a
significant portion of automated trading activity. What's
even more concerning is that if we regulate based on
participant type, rather than behaviour, we may expose the
market to the worst kind of risk, risk that was wrongly thought
to have been addressed.
That's where the data comes into play.
Regulators should use data made available to them by
exchanges to understand how participants interact with markets,
and the risks associated with that interaction.
With that understanding we can work to
remove unnecessary complexity from the marketplace and
implement regulation based on behaviour rather than participant
type. In doing so we can ensure that nobody falls through
regulatory cracks while also preserving the benefits of the
market's recent evolution.
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