In addition to the glitchapalooza going on in U.S. equity and other highly developed listed markets these days, there is this doozy to consider: Central bank intervention is poisoning the data. From direct impacts on market-related data sets to indirect impacts on economic and other fundamental data sets, the Age of Intervention will serve to confuse and disorient a broad spectrum of automated trading methods today and for years to come.
For starters, an unprecedented transformation fueled by a (well-intended) regulatory onslaught is currently underway in the global OTC derivative markets for the purpose of fostering greater transparency. One pillar of these transparency goals includes pre-trade price discovery for interest rate swaps and credit derivatives that have been privately negotiated (i.e., traded bilaterally) since they were first conceived in the early 1980s and that, as of the end of 2011, represent more than $700 trillion of notional outstanding exposures (before trade compression activities). As the logic goes, new multilateral market mechanisms -- modeled largely after those found in equities and listed derivatives -- will yield better, truer prices for swaps.
The funny thing is, there is no true price discovery in U.S. Treasury markets, equity markets, real estate markets or any other market when the Fed and other central banks are using infinite money-creation capabilities and unrestrained access to private information to influence asset prices all over the world. One could argue that there simply cannot be true price discovery in any market where intervention occurs -- which is to say, in most of them.
Moreover, with hints of such activity now becoming downright conspicuous, market demographics are being tragically altered. Though obfuscated under the guises of euro instability, an impending fiscal cliff and all other manner of global economic abnormalities, ongoing central bank intervention is the actual underlying force that spooks traditional segments to the sidelines or into non-traditional investments or crowds them into perceived safe havens. Increasingly, the demographics that remain include only those that are stuck there by internal mandate, such as an indexed fund, or by limitation of design, such as an automated market-making or latency arbitrage program. As a result of this exodus, the machinations of central banks are becoming a proportionately greater force in capital markets.
This is where the quant angle and disorientation come back into the picture: Consider that algorithmic trading is little more than pattern recognition. Patterns are simply manifestations of consistent behaviors. The behaviors of market actors left behind in the data represent the patterns that automated methods are designed to home in on. Furthermore, overlaying logic or intuition, if not actual confirming evidence, about the nature of investment, trading or execution strategies improves the efficiency of algo development by minimizing the prevalence of false positives (or positive identification of patterns that don't actually exist), a major drawback of pattern-recognition studies.
We understand tons of market behaviors today. We know, in advance, how a VWAP or TWAP algo will work -- and thus, faster algos can pick these off. We know that mutual funds perform window dressing at the end of each quarter. We know that certain economic releases and corporate earnings have a high probability to elevate volatility. We know that biasing an order book with excessive quotes can cause other, slower and less-sophisticated algos to flinch. We know that order-handling rules in one liquidity pool versus those in another can cause temporary price discrepancies. We know that resting stops left behind by day traders are like sitting ducks to be goaded into wrong-way, momentum-inducing reactions. We know that most futures markets on Sunday nights and during holiday lulls are easy to push around. And on and on and on.
Literally, hundreds of persistent patterns theoretically can be harvested, given a lot of speed and a little intelligence. The name of the game is converting theoretical, yet hopefully persistent, patterns (or "theoretical alpha") into harvested or actual alpha.
What we don't know (yet) is the behavior patterns of central banks -- other than the fact that they seem to refuse to allow markets to fall very far or very fast (since 2008 and with few exceptions), they enter and exit markets asymmetrically, and they have no logical or intuitive mandate other than the aforementioned hypotheses. We don't know how forceful (or desperate) they will behave going forward, how coordinated their efforts globally, which markets they will seek to influence at any given time, nor when they will stop. One thing, however, is for sure: The record of their activities is preserved in the data; increasingly, their fingerprints are on more and more of the data.
Of course, it is an occupational hazard of quantitative researchers to figure out new patterns. Perhaps there are patterns to be detected that can be pinned confidently on interventionist activities, thereby forging an acceptable level of predictability and sustainable profits.
[Ask a Broker: Should I Trust Your Algos?]
Meanwhile, we are in uncharted territory in terms of market demographics and global interconnectedness. While I am still an advocate for applying automated methods to lower turnover strategies, it is clear that the signals embedded in the data -- both market and economic data -- are potentially polluted by these alien influences. What's worse, when we use data from this period in history to provide guidance for the future, these alien influences will still be lurking in the numbers. They are inextricably intertwined in the record for all of eternity.
Going forward, keen awareness of changing demographics, methods of strategy deployment and cross-market forces are among the skill requirements for ongoing success with automated methods. And, above all, never forget that the Fed uses algos, too.
Paul Rowady is a senior analyst with Tabb Group. He has 20 years of capital markets experience, with a background in research, risk management, trading technology, software development, hedge fund operations, derivatives and enterprise data management. firstname.lastname@example.org