Should certain algorithmic trading strategies be banned? The question have been raised after the so-called “flash crash” in the U.S. stock market on May 6. Two strategies in particular – momentum ignition and order anticipation – were explicitly mentioned as potentially destabilizing forces in the SEC’s January Concept Release on Equity Market Structure. Professor Rajiv Sethi at Columbia University, however, don’t think it’s a good idea.
“If too great a proportion of total volume is driven by strategies that try to extract information from market data, the data itself becomes less informative over time and severe disruptions can arise.”
In an earlier post I noted that according to the SEC’s preliminary report on the flash crash of May 6, the vast majority of executions against stub quotes of five cents or less were short sales. This, together with the fact that there was also significant “aberrant behavior” on the upside (with Sotheby’s trading for almost a hundred thousand dollars a share, for instance) led me to believe that most of this activity was caused by algorithmic trading strategies placing directional bets based on rapid responses to incoming market data.
Two strategies in particular — momentum ignition and order anticipation — were explicitly mentioned as potentially destabilizing forces in the SEC’s January Concept Release on Equity Market Structure.
The SEC invited comments on the release, and dozens of these have been posted to date.
There is one in particular, submitted by R.T. Leuchtkafer about three weeks before the crash, that I think is especially informative and analytically compelling. (h/t Dr. Duru).
Leuchtkafer traces the history of recent changes in market microstructure and examines the resulting implications for the timing of liquidity demand and supply.
The comment is worth reading in full, but here are a few highlights. First, a brief history of the rise of the new market makers:
The last 15 years have seen a radical transformation of the equities markets from highly concentrated, semi-automated and intermediate marketplaces to highly distributed, fully automated and nominally disintermediated marketplaces. Along with or because of these changes, we have seen the rise of new classes of very profitable, aggressive, and technologically savvy participants previously unknown in the U.S. markets.
When markets are in equilibrium these new participants increase available liquidity and tighten spreads. When markets face liquidity demands these new participants increase spreads and price volatility and savage investor confidence.
These participants can be more destructive to the interests of long-term investors than most have yet imagined…
What is legal in today’s market includes an exchange that sells real-time data to high frequency trading (“HFT”) firms telling those firms exactly where hidden interest rests and in what direction.
What is legal is the replacement of formal and regulated intermediaries with informal and unregulated intermediaries.
What is legal is the proliferation of high-speed predatory momentum and order anticipation algorithms unrestrained by the anti-manipulation provisions of the Exchange Act.
What is legal is a market structure that dismantled the investing public’s order priorities and gave priority to speed and speed alone and then began charging for speed.
What is legal is the widespread lack of supervision of the most aggressive and profitable groups of traders in American history. What is legal is exactly what the Release says it is worried about, “a substantial transfer of wealth from the individuals represented by institutional investors to proprietary firms…” A HFT market making firm does not need to register as a market maker on any exchange.
To the regulatory world it can present itself as just another retail customer and make markets with no more oversight than any other retail customer… Some HFT firms do register as market makers. By doing so they get access to more capital through higher leverage, they might get certain trading priority preferences depending on the market center, and they get certain regulatory preferences. They are usually required to post active quotes but quote quality is up to the market center itself to specify and some market centers have de minimis standards… Formal and informal market makers in the equities markets today have few or none of the responsibilities of the old dealers. That was the trade-off as markets transformed themselves during the last decade.
In exchange for losing control of the order book and giving up a first look at customer order flow, firms shed responsibility for price continuity, quote size, meaningful quote continuity or quote depth. The result is that firms are free to trade as aggressively or passively as they like or to disappear from the market altogether.
The main problem, as Leuchtkafer sees it, is access to data feeds that make it possible to predict and profit from short term price movements if the information is processed and responded to with sufficient speed:
A classic short-term trading strategy is to sniff out an elephant and trade ahead of it. That is front-running if you are a fiduciary to the elephant but just good trading if you are not, or so we suppose.
Nasdaq sells a proprietary data feed called TotalView-ITCH that specifies exactly where hidden interest lies and whether it is buying or selling interest… Market making, statistical arbitrage, order anticipation, momentum and other kinds of HFT firms are an obvious customer base for this product… The complete details of limit order books can be used to predict short term stock price movements.
An order book feed like TotalView-ITCH gives you much more information than just price and size such as you get with the consolidated quote. You get order and trade counts and order arrival rates, individual order volumes, and cancellation and replacement activity. You build models to predict whether individual orders contain hidden size.
You reverse engineer the precise behavior and outputs of market center matching engines by submitting your own orders, and you vary order type and pore over the details you get back. If you take in order books from several market centers, you compare activity among them and build models around consolidated order book flows. With all of this raw and computed data and the capital to invest in technology, you can predict short term price movements very well, much better and faster than dealers could 10 years ago.
Order book data feeds like TotalView-ITCH are the life’s blood of the HFT industry because of it and the information advantages of the old dealer market structure are for sale to anyone.
But this raises a puzzling question: if the information advantages are truly “for sale to anyone” then free entry should drive down profitability until the return on investment is comparable to other uses of capital. In fact, entry has been substantial:
“In 2000 as the HFT revolution started, dealer participation rates at the NYSE were approximately 25%. In 2008, the year NYSE specialists phased out, HFT participation rates in the equity markets overall were over 60%.”
How, then, can one explain the fact that by Leuchtkafer’s own estimates, “HFT market making was 10 to 20 times more profitable in 2008 than traditional dealer firms were in 2000, before the HFT revolution?”
One intriguing possibility that Leuchtkafer does not consider is that entry generates increasing tail risk, so while ex ante expected profitability is reduced, this does not show up as declining realized profitability until a major market event (such as the flash crash) materializes.
If this interpretation is correct, then some HFT firms must have made significant losses on May 6 that were reversed upon cancellation of trades.
This implicit subsidy encourages excessive entry of destabilizing strategies. The standard argument against increased regulation of the new market makers is that it would interfere with their ability to supply liquidity.
Leuchtkafer argues, instead, that the strategies used by these firms cause them to demand liquidity at precisely those moments when liquidity is shortest supply:
HFT firms claim they add liquidity and they do when it suits them… At any moment when they are in the market with non-marketable orders by definition they add liquidity. When they spot opportunities or need to re-balance, they remove liquidity by pulling their quotes and fire off marketable orders and become liquidity demanders.
With no restraint on their behavior they have a significant effect on prices and volatility. For the vast majority of firms whose models require them to be flat on the day their day-to-day contribution to liquidity is nothing because they buy as much as they sell. They add liquidity from moment to moment but only when they want to, and they cartwheel from being liquidity suppliers to liquidity demanders as their models re-balance.
This sometimes rapid re-balancing sent volatility to unprecedented highs during the financial crisis and contributed to the chaos of the last two years.
By definition this kind of trading causes volatility when markets are under stress.
Imagine a stock under stress from sellers such was the case in the fall of 2008. There is a sell imbalance unfolding over some period of time. Any HFT market making firm is being hit repeatedly and ends up long the stock and wants to readjust its position. The firm times its entrance into the market as an aggressive seller and then cancels its bid and starts selling its inventory, exacerbating the stock’s decline. Unrestrained by affirmative responsibilities, the firm adjusts its risk model to re-balance as often as it wants and can easily dump its inventory into an already declining market.
A HFT market making firm can easily demand as much or more liquidity throughout the day than it supplies. Crucially, its liquidity supply is generally spread over time during the trading day but its liquidity demands are highly concentrated to when its risk models tell it to rebalance.
Unfortunately regulators do not know what these risk models are. So in exchange for the short-term liquidity HFT firms provide, and provide only when they are in equilibrium (however they define it), the public pays the price of the volatility they create and the illiquidity they cause while they re-balance.
For these firms to say they add liquidity and beg to be left alone because of the good they do is chutzpah… The HFT firms insist they add liquidity and narrow effective spreads and they do at many instants in time during the day. They also take liquidity and widen realized spreads as they re-balance in narrow time slices and in the aggregate they can easily be as disruptive as supportive.
Paul Kedrosky made the same point immediately following the flash crash, and it is also mentioned in the SEC report.
As part of the solution, Leuchtkafer proposes that certain trading strategies be prohibited outright:
The SEC should define both “momentum ignition” and “order anticipation” strategies as manipulation since they are both manipulative under any plain meaning of the Exchange Act. These strategies identify and take advantage of natural interest for a trader’s own profit or stimulate artificial professional interest, also for the trader’s own profit. They do so by bidding in front of (raising the price) or offering in front of (depressing the price) slower participants they believe are already in the market or that they can induce into the market. They both depend on causing short term price volatility either to prey on lagging natural interest or on induced professional interest. Any reasonable definition of “manipulation” in the equity markets should explicitly ban them by name.
I don’t have any quarrel with this analysis and recommendation, but it’s also useful to look at the problem from a somewhat broader perspective. Generally speaking, stability in financial markets depends on the extent to which trading is based on fundamental information about the securities that are changing hands.
If too great a proportion of total volume is driven by strategies that try to extract information from market data, the data itself becomes less informative over time and severe disruptions can arise.
Banning specific classes of algorithms is unlikely to provide a lasting solution to the problem unless the advantage is shifted decisively and persistently in favor of strategies that feed information to the market instead of extracting it from technical data.
Professor of Economics, Barnard College, Columbia University & External Professor, Santa Fe Institute.
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