How Technology Impacts Trading in Foreign Exchange

How Technology Impacts Trading in Foreign Exchange
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Foreign exchange market participants are increasingly concerned about the quality of their execution. Gone are the days of laid-back forex execution – the compensation of today’s forex managers is increasingly tied to their performance. Indeed, how you execute the orders in today’s markets may make or break investment profitability. Changes to the markets over the past several years, place execution quality at the top the priority list for investment managers.

Most of the changes in the markets are directly related to computing power and automation within the financial services sector. Automation is a driving force in the financial markets: today’s computers are scoring mortgage applications, reviewing technicalities in swap legal agreements, process trading orders and even increasingly make investment decisions in a push toward a lower-cost automated portfolio management. Automation drastically reduces costs, improves the customer experience, lowers the expenses of businesses across the industry ensuring competitive survival. Relative to other areas of business, execution expense now really matters in ways not seen before, and nickels and dimes associated with execution decisions impact forex managers.

What are the current market forces that portfolio managers should be aware of in the execution space? One of the issues facing forex managers today is trading venue selection. In the 5×24 (5 days per week, 24 hours per day) forex markets that truly barely sleep, rules are few and execution choices are close to limitless. Each venue may offer variations in rules and options, and managers should be aware of their opportunities and potential pitfalls.

High-frequency trading (HFT) is another execution concern for portfolio managers. HFT likewise capitalizes on plunging costs of technology. When programmed correctly, HFT software has built-in advantages over manual trading. Computers seldom become ill, are hardly emotional, and make, in short, superior cool-headed traders who stick to the script and don’t panic.

Of course, some recent research purports that some people, especially those attuned to their intuitive or biological responses, can outperform machines. Well, good for those few! By and large, however, human traders tend to be a superstitious, irrational lot prone to, well, human behavior, and no match for their steely automated trading brethren.

Perhaps one of the largest advantages of machines is not that they can contain their nonexistent feelings, but in their information processing power. Humans have a finite ability to process data. We may, possibly, be able to stare at 16 screens all at once, but our eyes can still only process 24 distinct visual frames per second. Should the information update faster than that, we simply miss it. Computers, on the other hand, can process unlimited volumes of data at the speed of light.

Even more importantly, following just a few news sources and several price charts in today’s interconnected continuously arbitraged markets is simply not enough. News leaks out into the markets in chaotic and often unforeseeable ways. Processing the entire realm of information, including quotes for all currency pairs, equities, hundreds of thousands of options, interest rates, futures, and social media is what separates today’s successful traders from the not-so-successful ones. And machines simply do it better. No extreme human physiology allows us to simultaneously read in-depth news and perform analyses on even 100 financial instruments.

HFT strategies, on the other hand, are generally well-equipped to process reams of information on the fly. Still, all HFTs do not fit in the same mold. Some are market makers, using predominantly limit orders, passively waiting for the market-order-armed liquidity takers to arrive. Others are aggressively pursuing the best price available at a given point in time. Both categories, passive HFT and aggressive HFT, have their parallels in the world of human traders: passive HFTs are automated versions of their human market-maker predecessors, and aggressive HFTs are modeled on former prop traders and day traders.

Passive HFT are a set of HFT strategies mostly placing limit orders. As such, passive HFT end up buffering market liquidity, and making markets. Prominent passive HFT firms include Virtu and Knight Capital Group. The market makers, whether human or robotic, follow the same basic principles. Market-making strategies consist of often-simultaneous placement of limit orders on both the buy and the sell side of the limit order book. The simple two-sided quotation works great when markets are quiet or range-bound. However, when the markets move rapidly in one direction or another, market makers risk severe losses. The phenomenon is known as adverse selection whereby the uninformed market makers and other traders are “picked off” by better-informed market participants.

Is being better-informed illegal? Of course not. Some superior information costs a pretty penny, and substantially reduces the informed traders’ gains from trading, when netted out at the end of the day. What is a market maker to do? The market maker needs to (1) hedge his exposure, and (2) become better informed. Hedging exposure is always costly. For instance, the market maker may decide to purchase options or other derivatives to hedge his exposure to the underlying. A put option will do the trick, but at an upfront premium.

Another route is to obtain better information. One way of gearing up on the information frontier is segmenting traders into better-informed and worse-informed, and essentially front-running better-informed traders using pre-hedging or anticipatory hedging. The strategy works mostly at a broker-dealer’s market maker, since order flow on exchanges and other venues outside of a broker dealer are largely anonymous. Another, more ethical route comprises investing into premium data that is indicative of the market’s near-term direction, shrinking the information barrier and pre-empting sharp moves. Once again, computerized market makers win the bots-versus-humans debate as information is king, and the ability to process vast amounts of information simultaneously is cash.

Robots, often abbreviated to “Bots”, use market orders that trade using intricate strategies. The professional traders deploying them are collectively known as aggressive HFT, as opposed to passive market makers. Unlike passive HFTs, aggressive HFTs tend to use market orders to capitalize on fleeting information at their fingertips. In the industry, the return advantage from fleeting informational is often referred to as rapidly decaying alpha.

What kinds of inferences do aggressive HFTs deploy? To put it simply, all kinds. The most successful aggressive HFTs, like QuantLab, use a multitude of information sources to create an informational haystack, from which big data-driven inferences are extracted about the prospective market movements. To complicate matters even more for forex markets, many of the HFT strategies observed there are a result of the cross-border equities trading. For example, suppose an aggressive HFT simultaneously trades IBM in New York and in London, pouncing on minute discrepancy. Such a trader is likely to simultaneously trade GBP/USD in a bid to contain his forex exposure and hedge potential losses. In doing so, his trading of GBP/USD may have little to do with news traditionally impacting GBP or USD; instead, the HFT GBP/USD activity may be a direct product of the news affecting the equity markets in both countries.

Why should portfolio managers care about HFT? Aren’t HFTs’ actions too short term to interfere with portfolio managers’ investment horizons? And aren’t latest delay-based exchanges like IEX explicitly designed to stem HFT?

Latest research shows that aggressive HFT tend to rapidly consume available market orders, eroding the liquidity available to portfolio managers. The effect is particularly severe during news announcements and other times when aggressive HFT is imbalanced: when participation of aggressive HFT buyers among all trades significantly dominates participation of aggressive HFT sellers, and vice versa. In the process, aggressive HFTs increase bid-ask spreads and create higher volatility. This affects portfolio managers on two fronts:

  1. financial instruments with higher aggressive HFT participation tend to be more volatile, a condition that can be explicitly incorporated in the portfolio management framework, and,
  2. execution in the wake of imbalanced HFT produces highly substandard results relative to the market due to removed liquidity; the adverse impact, however, dissipates in about 20 minutes when the markets return to their normal state.

To enhance performance, portfolio managers should incorporate aggressive HFT tracking in their own toolkits in order to not only improve their forex exposure management, but also to improve their execution by knowing when the aggressive HFT imbalances occur and, consequently, when to scale down or halt execution to avoid aggressive HFT.

The question of when to execute, as opposed to where to execute, is very topical, and tracking aggressive HFT provides the answer. The tracking follows aggressive HFT in anonymous markets and is looking to pinpoint specific footprints. The footprints, unintelligible to the human eye, become clearly visible following mathematical transformations. Again, robust computer systems come to the rescue processing in-depth market information and delivering straightforward inferences from complex data.

Today’s portfolio managers’ jobs are more data-driven than ever, placing demands of sophisticated data analysis and other techniques on them. Some portfolio managers are instead relying on outside data vendors, opting to purchase quality data inferences rather than spend several years building the data sets. Companies like provide services of tracking and reporting aggressive HFT participation across a wide breadth of financial instruments. Tracking today’s market conditions for forex managers is no longer limited to the daily Noon rate.

Irene Aldridge is a Managing Director, Research, of AbleMarkets, the Big Data for Capital Markets company. She is also portfolio manager and author of High-Frequency Trading: A Practical Guide to Alorithmic Strategies and Trading Systems (Wiley, 2nd edition, translated into Chinese, 2013) and co-author of Real-Time Risk: What Investors Should Know About Fintech, High-frequency Trading and Flash Crashes (Wiley, 2017).

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