Trading to Win Inside the Perfect Storm

I once had a colleague that worked in the standards-based software industry. Like a lot of vendors, he was actively engaged in creating commercial software implementations based on the fully sanctioned Java standards. I once asked him, "How do you compete against other vendors when you're all reading the same specification and creating the same thing?" His response, while simple and understandable, has always stuck with me. His top three goals were performance, performance and performance.

Just Win Baby

Ask any fund manager or quant firm what their top three goals are and you will likely get a similar response - win, win and win. First and foremost is to turn a profit, but how to accomplish that lofty goal is a complex one with the devil hiding in the details. As on the field of competitive sports, the strategies to win are numerous. Increasing competition, tighter spreads, thinner margins and a lower risk appetite are resulting in firms researching deeper into big data to create smarter algos, squeezing harder on costs and reaching out beyond single-asset safe harbors for alpha.

A Perfect Storm

"If you can keep your head when all about you...", and so goes the intro of the poem "If" by Rudyard Kipling. The swirl of constant commotion creates a perfect storm for trading firms. There is relentless pressure to come up with the next big quant model. Whether it be squeezing the last drop of profitability though transaction cost analysis or keeping the overblown headlines of the politicalized regulatory debate at bay, weathering the stormy convergence of these forces is a test of mental character.

Quants deal with this issue by applying an empirically-tested and rules-based approach to exploit perceived market inefficiencies manifested by human behavior, geo-political events and market structure. Nonetheless, it's a documented fact that quant models have a short shelf life. In fact, according to Aite Group research, it is only about three to four months. In that short time span market conditions can change considerably, invalidating models or their parameters. Consequently, the quest for new and revised models is never ending. The side effect of this is increasing demands for deep historical data over longer time periods across a multiplicity of markets; Equities, Futures, Options and FX, the fuel feeding quant's research and strategy modeling tools. And with reports of 80 percent of hedge funds looking to join forces with these fully automated quant funds and begin trading algorithmically in the next three years, storm track one is only going to continue to gain steam.

Transaction Cost Analysis (TCA), the idea of monitoring and reporting on trade performance, is a vital aspect in today's competitive trading landscape. The question is how quickly and accurately can transaction costs be delivered and what do traders do with the information once they have it? TCA is typically viewed as either a post-trade or real-time activity, but the whole is greater than the sum of post-trade + real-time for improving alpha.

The continuous analysis of executing orders showing realized and unrealized profit and loss can indicate implicit opportunity costs and the impact of market forces, an effective competitive weapon to outpace the market. When viewing time as a single continuum, TCA can also monitor market prices against benchmarks that include sector and index movements, both intra-day and historic, to gauge volatility and smooth outliers, a vital aspect of participation strategies. Accurately measuring the costs incurred by trading strategies can show another side of their profitability. Storm track two.

Then there is the regulatory debate. Individually, firms don't pay that close attention other than to ensure compliance with their practiced ears recognizing a New York Post-style audacity.

Market volatility is largely driven by opinions and speculation as people bet on matters including how the Eurozone crisis will affect local markets. Yet this is the fuel that ignites strong opinions against algorithmic trading and high frequency in particular. Headlines noting "... make markets go haywire" or "trading clampdown" are overblown and designed to attract eyeballs and raise the ire of the general populace. Such politicization is ludicrous and motivated by attention grabbing media and re-election campaigns.

Potentially dire consequences lie in the wake of regulatory actions from the financial transaction taxes that France and possibly the whole of the European Union are on the verge of enacting or Dodd Frank's Volcker Rule and the possibility of Order Cancellation charges to the CFTC's cheetah chasing high-frequency trading committee. Such actions are like driving a car with the hand brake on.

Price stability would be adversely affected as these actions will lower trading volumes, and it's volatility that regulators hope to quell. A thin market can whipsaw wildly since one transaction can have a disproportionate effect, making it difficult to determine whether the bid/offer represent the real market value.

As the number of firms deploying algorithms increases they will be chasing after a diminishing pot under the eye of regulators. The days of easy money are over as reported by IBISWorld. Storm track three.

May cooler heads prevail

Firms are caught in the cross winds of the perfect storm as they push forward with smarter trading models, analyzing costs and evading the regulatory debate. As David Brooks writes of the science of human nature, such is a test of both mental strength (the processing power of the brain) and mental character (virtues of practical wisdom). It is strength of character that will define winners in a highly regulated industry.

Louis Lovas is director of solutions for OneMarketData.