Driving market decisions with data
In 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 programs?
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
enforcement.
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
prognosis".
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 participants.
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 trading.
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 brokers.
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.
Content belongs to FIA Principal Traders Group. For the original please visit http://www.futuresindustry.org/ptg/fia-ptg-blog.asp?i=2431
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