The shotgun wedding between quick-on-the-draw automated traders and the venerable but slow-moving institutions of the FX establishment was a short-lived and tempestuous affair. Plenty of ‘Wham! Bam!’, but ‘Thank you, Ma’am’ was an uncommon courtesy as the new century introduced new trading technologies to the FX markets. The term latency arbitrage was coined to describe the highly-profitable strategies employed by hedge funds to exploit differences between the prices a bank would quote on different market channels. Banks retaliated, first by shutting off liquidity to hedge funds that persistently used high-frequency trading techniques to skim risk-free returns, then by deploying algorithms of their own to react more swiftly to price movements and provide liquidity to multiple electronic platforms.

Rather than precipitating a Cold War-style stand-off, a sense of mutual dependence has nurtured a policy of accommodation and entente cordiale. High-frequency statistical arbitrage still plays a part in buy-side activity in the automated FX market, but it is augmented by a wider range of strategies and time frames. Banks have broadened their perspective too. The investment required to handle large volumes rapid-fired from automated trading systems has opened up new revenue streams, with traditional asset managers eager to use execution algorithms and other advanced trading tools to bring their FX execution capabilities up to the standards already achieved in equities.

The resultant leap in volumes and, therefore, liquidity stands to benefit all. Total FX turnover reached USD 3.2 trillion in April 2007, according to the Bank for International Settlements’ Triennial Survey, a 71 per cent increase on 2004. Much of this is attributed to a huge rise in buy-side participation. Transactions between broker-dealers and buy-side financial institutions (hedge funds, mutual funds, pension funds and insurance companies) have more than doubled in three years. Trade volumes between brokers and buy-side institutions (40 per cent) are now almost equal to inter-bank transactions (43 per cent). The FX market now resembles less a warring marriage than an online speed-dating service, where the needs of a diverse range of parties can be matched.

Understanding FX market dynamics

Although not a newcomer to the space, the Maia Institute is an example of the breadth of firms trading FX on an automated basis. The Monaco-based institute does no actual trading, rather it conducts scientific research aimed at predicting how intelligent agents interact within complex systems, including, but not exclusively, the FX market. The institute’s research shapes the design of automated trading strategies deployed in the FX market by its associate, Rudyerd. “We regard the FX markets as complex systems which are primarily human driven and much of our work has been focused on understanding the market dynamics,” says Douglas Connor, Maia’s Director General, who founded the institute in 1991.

Rudyerd’s trading strategies are informed by observation of the dynamics between buyers and sellers. “We do not try to exploit the instant reaction of the market to external events such as news or figures. The key is a deep understanding of the underlying forces which drive market behaviour during the absence of external influences,” says Connor. The firm deploys multiple trading strategies that predict short-term market movements (anything from a few minutes to a matter of hours) in major currency pairs. Trading is 100 per cent automated from trading decision to execution; humans only have the authority to close a trade in the event of a market interruption. Trading strategies only trigger trades when price movements replicate the complex multidimensional patterns that the strategies are looking for. “If the markets are too unpredictable, the strategies shouldn’t generate trades,” says Connor.

Access to reliable data is critical to Maia’s research-led approach, but the path has been long. Because non-banks’ options were severely restricted in the early 1990s, Maia built its own database by manually collecting and inputting data from three voice brokers between 1995 and 1999 and was among the first non-banks to subscribe to EBS market data in 2004. “The market models that we built on that original 1990s data are still applicable today,” says Connor. These large historical data sets – and the software tools created in-house to analyse them – are used by Maia in both the development of market models and the development and backtesting of trading strategies. “Some people may run strategies only as long as they work, then replace them with new ones fitted to the latest data. We aim to model the underlying mechanisms and consequent dynamic characteristics of the market and build strategies that stand the test of time. For us, a trading strategy is only good if it makes money pretty consistently over the last eight or ten years of data,” says Connor.

Though satisfied that Maia has developed a rare level of understanding of the factors underlying the FX market, Connor still believes the exploitation of that expertise remains a work in progress. For example, the firm is looking to introduce an element of intelligent adaption to individual trading strategies as well as greater levels of collaboration with other financial market participants. “We’re still scratching the surface in trading terms,” he says. ...

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