Quantitative Trading, Adversity To Alpha

This article first appeared in Waters Technology High-Frequency Trading special report.
A trading firm’s final goal is to win, to be successful in outpacing the market. Trading firms operate in a fiercely competitive industry where success is measured by profit. They are constantly hunting for talent and technology to achieve that goal. Yet firms are ever threatened by fierce competition, controlling costs, rapidly advancing technology and menacing regulation acting like a dragging anchor on the bottom line. Yet the penetrating lyrics of Kelly Clarkson’s hit song, “What doesn’t kill you makes you stronger” are a testimony that innovation is born out of opportunity.

What Fosters Ingenuity
Quants apply an empirically-tested and rules-based approach to exploit perceived market inefficiencies manifested by human behavior, geo-political events and market structure. With tighter spreads, thinner margins and lower risk appetite, quantitative traders are exploring more cross asset trading models and cross asset hedging. Consequently, the quest for new and revised models is never ending. The side effect of this is increasing demands for deep data over longer time periods across a multiplicity of markets -equities, futures, options and of course cross border currencies. This data dump is the fuel feeding automation technology, quant’s research and strategy modeling tools. That technology plays a critical role in the trade lifecycle. Its fast paced evolution goes hand-in-hand with innovations in trading.

Data accuracy is vital to determining outcomes; asset prices cannot be inaccurate or missing. It means dealing with the vagaries of multiple data sources, mapping ticker symbols across a global universe, tying indices to their constituents, tick-level granularity, ingesting cancelations and corrections, inserting corporation action price and symbol changes and detecting gaps in history. Any and all of these factors are vital to the science of quantitative trade modeling.  With 4.5 billion options contracts traded in 2011, the reliability of the resulting analytics such as implied volatility, delta and gamma for option strategies depend on underlying data accuracy and reliability. Big Data is about linking disparate data sets under some common thread to tease out intelligible answers to drive the creation of smarter trading models.

Reports suggest that 80 percent of hedge funds are looking to join forces with fully automated quant funds and begin trading algorithmically within the next three years. Consequently, as the number of firms deploying algorithms increases, they will be chasing after a diminishing pot. Sophisticated algorithmic strategies have become the hunter and all else the hunted. The days of easy money are over.

Regulators Take Aim
We’re a flood with news that the Securities and Exchange Commission (SEC) is taking aim directly at high frequency trading (HFT), proposing fees on the trade-to-order cancelation ratio. Such actions are born out of fears that the markets are speeding dangerously out of control.

Price movements are determined by a complex global collective of trading agents including both humans and machine algorithms. Like many other complex systems, the mechanics and microstructure of financial markets are dynamic. Yet like a vice grip on reality, regulators want to exert pressure on just one demonized element in this dynamic system.

A common portrayal is one of HFT firms operating “in the shadows”, eliciting images of mathematicians using super secret computer technology to steal from innocent investors. In chasing that elusive goal of market integrity, harsh regulatory controls are misguided. With fees and tax prospects on the horizon, get used to daily headlines announcing exchange volumes dropping. So far this year trading volume on the Toronto Stock Exchange is down  about 16% over the same period last year  as high frequency traders head for greener pastures.

When volume is low, no one can be sure whether the bid/offer prices represent the real market value. Lower numbers of transactions massively affect volatility, since one large transaction can have a disproportionate effect impacting all participants.

HFTs are making on average between $.0005 and $.00075 per share on each trade according to Rosenblatt Securities. An order-to-quote cancelation fee won’t just deter firms from cancelling orders – they will stop trading to limit their risk and exposure. The results will likely have the opposite effect of what regulators intended. Effective regulation will remain elusive without a deeper quantitative understanding of the market’s dynamics.

Managing change
Firms face a battleground on multiple fronts with the challenges of managing rapid-fire trading technology, competition and menacing regulations. This will channel trading firm’s energy, efforts and ingenuity to devise smarter trading models and compliance while looking for ways to manage the controls and still stay in business.

Once again thanks for reading.
Louis Lovas

For an occasional opinion or commentary on technology in Capital Markets you can follow me on  twitter, here.

About Louis Lovas

Director of Solutions, OneMarketData, with over 20 years of experience in developing cutting edge solutions and a leading voice in technology and trends in the Capital Markets industry.
This entry was posted in Algorithmic Trading, Analytics, Big Data, Complex Event Processing, Equities, Foreign Exchange, HFT Regulation, High Frequency Trading, OneTick, Tick database. Bookmark the permalink.

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