Salmon, Stiglitz and the problems of HFT reconsidered (1) – Speed
Building bridges between scientists and the public in communicating research findings is one of the most rewarding activities of bloggers and journalists. Felix Salmon is an outstanding example in this respect. In a recent article, he summarized the key points of a widely noticed speech delivered by Nobel prize-winner Joseph Stiglitz on the problems of financial innovation in general, and high-frequency trading (HFT) in particular.
Stiglitz asked Are Less Active Markets Safer and Better for the Economy? and he came to the conclusion that they are. His arguments revolved around three aspects: speed, costs and social value.
In the first part of this article the speed aspect is discussed. The second part will deal with the costs and social value of HFT. A short glossary of terms is provided at the end.
Definitions of high-frequency trading differ but, as the following examples show, all emphasize the role of speed.
The CNBC Glossary Of Terms, for instance, defines HFT as a “fast type of algorithmic trading using a supercomputer that can make trades in less than a second. The strategy is meant to capitalize on small differences in the bid-ask spread through a high volume of shares.”
In The Flash Boys delusion: Why high frequency trading critics are wrong Johannah Ladd emphasized that “HFT is a technology, not a type of company. HFT describes the use of low latency connections to exchanges, such as co-location, which allows traders to send high volumes of messages to post orders and to manage their risk. These technologies can be used in the execution of any trading strategy and are now an integral part of the market, employed by many types of market participant (including proprietary firms, buy-side, and investment banks).”
The latter point was also stressed by Christopher Whalen who in “Flash Boys,” Michael Lewis Misses the Point — Deliberately wrote that “the top three HFT firms – Goldman Sachs, Morgan Stanley and Credit Suisse – have been very visibly investing in trading technology for decades.”
In his book Inside the Black Box Rishi Narang specified:
“HFT is no well-defined, homogenous activity … But a definition that probably contains most of these kinds of traders is as follows: High-frequency traders (a) require a high-speed trading infrastructure, (b) have investment time horizons less than one day, and (c) generally try to end the day with no positions whatsoever.”
He further added:
“The fastest HFTs (sometimes referred to as ultra-high frequency traders, or UHFTs) will no doubt scoff at the notion that someone who holds positions for as much as six and a half hours should be considered high frequency. But there is an important distinction between overnight risk and intraday risk, as most news comes out when markets are closed. Any further attempt to narrow down the holding period of an HFT strategy would seem arbitrary: What makes one second “HFT”, while one minute is not? Furthermore, our definition specifies that the strategy should require a high-speed infrastructure.”
In his comments on Michael Lewis’ book Flash Boys Zachary David defined high-frequency trading as “the ability to quickly execute trades and manage order activity.”
“This is a broad definition because the field is broad. Loosely, the three main categories of automated trading strategies are (1) latency arbitrage, (2) market making, and (3) statistical arbitrage. There are many algorithmic firms employing many different strategies that fall into some combination of the above. However, it is important to note that none of these strategies are risk free and thus arbitrage, by the economic definition, is a misnomer.”
These examples may give you an idea of the phenomenon. The speed of HFT reactions which became possible with the technological developments in recent years is indeed breathtaking. As Jacob Loveless wrote in October 2013 in Barbarians at the Gateways:
“Today it is possible … to parse market data and send executions in 740 nanoseconds (or 0.00074 milliseconds). (Human reaction time to a visual stimulus is around 190 million nanoseconds.)”
The latter is one reason why HFT is done by computers performing operations which are determined by algorithms rather than by human traders – a fact which many observers are seeing with unease. In his book, Rishi Narang mused about these feelings and about the difference between discretionary traders and quant traders (as high-frequency traders and other algorithmic traders are also called). Comparing their activities with those in other industries he argued that, to a large extent, quant traders are simply systematizing processes exploiting the advances of technology:
“Car building is still car building, whether it’s human hands turning ratchets or machines doing it. Flying a plane is not viewed differently when a human pilot does the work than when an autopilot does the work. …
If I say, “I’d like to own cheap stocks,” I could, theoretically, hand-compute every company’s price-to-earnings ratio, manually search for the cheapest ones, and manually go to the market place to buy them. Or I could write a computer program that scans a database that has all of those price-to-earnings ratios loaded into it, finds all the ones that I defined up-front as being cheap, and then goes out and buys those stocks at the market using trading algorithms. …
… what we’re talking about here is a completely rational evolution in how we’re doing a specific kind of work”.
Now they are trading with each other all alone
High-frequency trading is exploiting the fact that technology allows a machine to react faster than the human brain, just like a scanning electron microscope is seeing more than the human eye, or ultrasound is overcoming the limit of the human hearing range.
However, as Maureen O’Hara stressed in High Frequency Market Microstructure, these days relying on speed also describes non-HFT trading:
“All trading is now fast, with technological improvements originally attaching to HFTs permeating throughout the market place. Latencies at broker/dealer firms, the main pathway for “everyone else’s” trading, are now sub one mili-second ranging down to 500 micro-seconds for a market order sent via DMA (direct market access). Such speeds were unheard of for even HFTs a few years ago, let alone for our “EE” [Everybody Else] trading! Of course, the speeds at the pure ultra-latency shops are even faster, with some claiming round trip latencies of sub 10 micro-seconds. The bottom line is that trading is now very fast for everyone in the market.”
With reference to the U.S. markets she further explained:
“Algorithms are simply computer-based strategies for trading, and they are used to minimize transactions costs for both institutions and retail traders. Even without the complications introduced by HFT, trading is a challenging task in the fragmented market structure of current U.S. equity markets. Finding, and accessing, liquidity generally requires routing orders to multiple locations, all the while being cognizant of differing trading fees, rebates, and access charges in each venue. Moreover, because trading patterns differ across the day, so, too, do spreads and the price impact of trades, requiring traders to optimize trading temporally as well. Add in opportunistic HFTs who spot deterministic trading patterns of unsophisticated traders and take advantage of them, it is little wonder that EE trading now relies on increasingly sophisticated trading algorithms.”
Speed has always been an advantage – in every financial business. From the early beginnings of trading great efforts were made to overcome the limits of time and space in communication. Winning an information advantage was a major driver of innovation and technical progress as the following excerpt of an article may illustrate which I already included in an earlier blog post and wish to include here again:
“Prior to the invention of telegraphy in the 19th century, information was bound to move at the same speed, and over the same distance, as the prevailing transport system would allow (Dicken,1998, p. 151). Financial activities were largely determined by personal knowledge of people and circumstances (Favier, 1992, pp. 24 ff.). As one author put it:
‘Success in money and banking operating in a number of countries … required having a large number of brothers or cousins, with a single combined interest and thinking more or less alike, to solve the agency problem.’ (Kindleberger, 1993, p. 258)
The most common method to communicate over long distances was to hire a person to deliver a message as fast as possible, either a human runner or a rider on horseback. … Other means of transmission were homing pigeons, mirrors, flags, fire beacons and semaphores. For example, it was a pigeon that brought Nathan Rothschild the news of Napoleon’s defeat. Mail was delivered by stage coach, caravans and merchant vessels. Travellers were routinely asked to take messages with them. …
The arrival of the telegraph made all the difference allowing messages to be sent with great speed over very large distances. The first optical telegraph line started operating in France between Paris and Lille in May 1794. Soon other European countries followed and in 1830 “lines of telegraph towers stretched across much of western Europe, forming a sort of mechanical Internet of whirling arms and blinking shutters” (Standage, 2000, p. 18). But the system had also its drawbacks. It was expensive to run requiring shifts of skilled operators at each station and involving to build towers all over the place. Beside, optical telegraphs would not work in the dark or in fog and mist.
Eventually, it were electric inventions that changed the world. The discovery of the electric telegraph in the 1830s, and the telephone in the 1870s, marked a distinct new era. …
Financial market participants have been among the early users of telegraph facilities, but to them the real watershed became the submarine cable. London became linked to Paris in 1851, to New York in 1866 and Melbourne in 1872 (Inwood, 1998, p. 480). The transatlantic cable was used to arbitrage the London and New York securities markets immediately after its opening. Some days after the event the New York Evening Post started publishing price quotations from the London market (Garbade and Silber, 1994). In those years, English investors held a substantial volume of US Treasury debt which traded in London as well as New York. Before the establishment of the submarine link, information travelled with a time delay equal to the duration of an ocean crossing, or about three weeks. And, since purchase and sale orders directed to the foreign market had to cross the Atlantic, too, execution took the same time once again. After the opening of the transatlantic cable those delays were reduced to one day. By the 1890s, telegrams between the London and the New York Stock Exchanges took three minutes from sender to receiver (Headrick, 1988, p. 104).”
A remarkable investment in information technology
Then and now besides anticipating market movements traders tried to exploit price differences for the same product in different places. But, in contrast to former times, with the advent of electronic trading markets became much more transparent, reaction times shortened considerably and arbitrage opportunities almost vanished. This changed again in recent years when investments in computers and fast network connections allowed making money out of smallest price discrepancies in different markets by trading large volumes (albeit in Exposing the Falsehood of a Prominent HFT Critic’s Arguments Rishi Narang stressed that these opportunities of “latency arbitrage” are still extremely rare).
In A Much-Needed HFT Primer for ‘Flash Boys’ Author Michael Lewis Manoj Narang (who contributed to the part on High-Speed and High-Frequency Trading of his brother Rishi’s book) argued that as a result of HFT, “the speed differentials between fast and slow players are now measured in microseconds or nanoseconds, whereas ten or 20 years ago they were measured in seconds or minutes. It ought to be clear to any logical person that if zero difference between players represents total fairness, then the closer such differences are to zero, the closer you are to total fairness.”
In Take the Time to Understand the Complexities of the Markets Larry Tabb confirmed that
“for individual investors, the market is deeper, cheaper, more liquid, and more fair than ever before.”
But, this does not hold for all market participants. He added: “Institutions looking to execute large positions … can’t wander into the market naively and not expect to be played.”
In Flash Boys for the People Philip Delves Broughton reported of an experience that provided further evidence for this phenomenon. He wrote:
“High-frequency trading may be annoying to large institutional investors like mutual funds, but it is actually a boon for small retail investors.
These advantages were demonstrated in a recent natural experiment set off by Canada’s stock market regulators. In April 2012 they limited the activity of high-frequency traders by increasing the fees on market messages sent by all broker-dealers, such as trades, order submissions and cancellations. This affected high-frequency traders the most, since they issue many more messages than other traders.
The effect, as measured by a group of Canadian academics, was swift and startling. The number of messages sent to the Toronto Stock Exchange dropped by 30 percent, and the bid-ask spread rose by 9 percent, an indicator of lower liquidity and higher transaction costs.
But the effects were not evenly distributed among investors. Retail investors, who tend to place more limit orders — i.e., orders to buy or sell stocks at fixed prices — experienced lower intraday returns. Institutional investors, who placed more market orders, buying and selling at whatever the market price happened to be, did better. In other words, the less high-frequency trading, the worse the small investors did.”
“… the conclusion is obvious: If you are an institutional investor trying to buy and sell large blocks of securities without moving the price as you trade, then high-frequency traders are a pest. They swarm all over your trade, messing with the price, and driving up your cost as you build up your position from multiple exchanges. Ultimately this affects the return on your trade. But if you’re a small investor, trading for yourself, high-frequency trading can help lower transaction costs and create a more efficient, dynamic market.”
However, as Zachary David dryly indicated the other day on Twitter, adverse market movements of trading large positions are neither a new phenomenon and nor a particular problem of HFT presence:
In A Fervent Defense of Front-running HFTs Chris Stucchio picked up the problem of using multiple exchanges focusing on cases ”when a market participant wants to move an amount of stock larger than the current set of open orders on a single public matching engine (e.g. NYSE/ARCA/BATS)”. He argued that, traditionally, traders looking to move a large position have an information advantage over everyone else:
“They know that the price of a security is about to move, whereas their counterparties do not. When the informed trader trades, they reap most of the benefits and their counterparty loses.
Predatory traders break this information advantage. Instead of Goldman, David Einhorn or other informed traders gaining the full benefit of their information, predatory traders detect that information and split the profits with the counterparties (taking a cut in the process). Predatory traders improve market efficiency by turning information about market demand into price movements. They are simply criticized because the price movement happens before the whale actually wanted it to.
It’s pretty clear why Goldman, David Einhorn and Charles Schwab are opposed to this. Their information advantage is being reduced and they make less money …”
The latter echoes the argument of Harry Bruinus (The Christian Science Monitor) citing Charles M. Jones, director of the Program for Financial Studies at Columbia University in Manhattan, that in this debate about high-frequency trading “a lot of the old winners suddenly became losers” and the old guard is crying foul.
Charles M. Jones is the author of an academic study on What do we know about high-frequency trading where he presented a comprehensive overview of recent theoretical and empirical research on HFT. In the abstract of the paper he summarized the findings:
“Economic theory identifies several ways that HFT could affect liquidity. The main positive is that HFT can intermediate trades at lower cost. However, HFT speed could disadvantage other investors, and the resulting adverse selection could reduce market quality.
Over the past decade, HFT has increased sharply, and liquidity has steadily improved. But correlation is not necessarily causation. Empirically, the challenge is to measure the incremental effect of HFT beyond other changes in equity markets. The best papers for this purpose isolate market structure changes that facilitate HFT. Virtually every time a market structure change results in more HFT, liquidity and market quality have improved because liquidity suppliers are better able to adjust their quotes in response to new information.
Does HFT make markets more fragile? In the May 6, 2010 Flash Crash, for example, HFT initially stabilized prices but were eventually overwhelmed, and in liquidating their positions, HFT exacerbated the downturn. This appears to be a generic feature of equity markets: similar events have occurred in manual markets, even with affirmative market-maker obligations. Well-crafted individual stock price limits and trading halts have been introduced since. Similarly, kill switches are a sensible response to the Knight trading episode.”
There are many variants of high-frequency trading as Rishi Narang, Zachary David and others emphasized. As Maureen O’Hara wrote in High Frequency Market Microstructure: “High frequency trading is a misnomer, a seemingly precise term used to describe a large and diverse set of activities and behaviors.”
In The Lewis Effect Felix Salmon cited an example from Michael Lewis’ highly popular book, which apparently also influenced the thinking of Joseph Stiglitz:
“ … Lewis tells the story of Rich Gates, a mutual fund manager being front-run by HFTs. Gates “devised a test,” writes Lewis, to see whether he was “getting ripped off by some unseen predator.” The test involved placing two orders, a few seconds apart: The first would be an order to buy 1,000 shares of a certain thinly traded stock at $100.05, and then the second would be an order to sell 1,000 shares of exactly the same stock at $100.01. Gates “was dutifully shocked” when he discovered the results of his test: He ended up buying the stock at $100.05, selling it at $100.01, and losing 4 cents per share. “This,” he thought, “obviously is not right.””
“Lewis does have a point here: It’s not right. (Whether it’s obviously not right rather depends on how familiar you are with stock-market protocols.) If the stock market works the way it’s meant to work, then the stock exchange’s order-matching algorithms should have seen that the best bid, at $100.05, was higher than the best offer, at $100.01. They then should have matched the two, so that Gates would have traded with himself, and lost no money. Instead, a fast algorithm managed to insert itself between the two orders, buying at $100.01, selling at $100.05, and making a 4-cent profit for itself, in a fraction of a second. In terms of the official ticker tape, rather than one trade taking place at $100.03, there were two trades: one at $100.01, and another at $100.05.”
The question is whether the situation really differs fundamentally from earlier times when “specialists had ‘zero’ latency by virtue of being right there at the booth” as Cliff Asness, Aaron Brown, Michael Mendelson and Hitesh Mittal wrote in High Frequency Hyperbole, Part Deux. In those days an investor outside the exchange presumably would have abstained from an experiment like the one described in Lewis’ book well aware of others’ speed advantage and of the low chance of getting both trades executed at the same time without someone coming in between. The argument that stock markets are no longer working “the way they’re meant to” can be no excuse for ignoring how they changed and not adjusting accordingly.
Critics are quick to call the practice in Lewis’ example front running, implying a highly unethical and illegal activity. But it is not front running. One anonymous comment to Noahpinion’s review of Flash Boys pointed to the Wikipedia definition which says that front running
“is the illegal practice of a stockbroker executing orders on a security for its own account while taking advantage of advance knowledge of pending orders from its customers. When orders previously submitted by its customers will predictably affect the price of the security, purchasing first for its own account gives the broker an unfair advantage, since it can expect to close out its position at a profit based on the new price level. The front running broker either buys for his own account (before filling customer buy orders that drive up the price), or sells (where the broker sells for its own account, before filling customer sell orders that drive down the price).”
The comment then rightly asked: “HFTs don’t have customers typically. How can they front run customers they do not have?“ The same point is made by Rishi Narang in greater detail in High-frequency traders can’t front-run anyone:
“HFTs can, by virtue of having invested in superior infrastructure, react faster to the information embedded in a new order, but let us not confuse speed with front-running. This information is public and available to anyone willing to overcome the challenges of acquiring and processing it very quickly.”
However, the Flash Boys example demonstrates still another important aspect. As Christopher Whalen argued in his review of the book:
“The abusive aspect of HFT which Lewis rightly identifies is not so much about the speed of the trading but rather always being first in line. If you think of the current market price of a stock, a couple of years ago, the trader using HFT used to sit just above and below the current market price, and sought to execute quickly when the market price either went up or down. The fact of computers and fast network connections enables this HFT activity, but it is not really the key part of the strategy. Instead the key is to always be first in line.
Let’s take an example. The BATS order type known as “display-price sliding” allows an investor to essentially position themselves in the center of the equity market for a given stock. This means that when the market price changes, instead of the HFT “market order” being canceled as per the National Best Bid and Offer (NBBO) rule, it simply “slides” to follow the market. Most investors and advisors don’t even know that such an order type exists.
For example, when Lewis talks about the fact that Virtu Financial had made money almost every day for five years, the reader is given the impression that the speed of the trading gave Virtu and other HFT shops the advantage. But the reality is that the high frequency trader not only executes before the retail customer, as Lewis describes, but is always first in line. This structural duplicity is programed into the system …”
Maureen O’Hara confirmed: “All HFT is strategic because its goal is generally to be the “first in line” to trade”. Traders are exploiting the opportunities offered by the exchanges. She added:
“This is where microstructure comes to the fore because how to achieve this goal depends on the rules and structure of the market (i.e. its microstructure). At a minimum, this requires maximizing your trading strategy against a particular market’s matching engine. The matching engine determines how orders are processed, and thus how trades and prices emerge. The matching engine also processes messages regarding the arrival, execution, and cancellation of orders.”
Whalen’s complaint echoes Haim Bodek’s concerns about the role of order types as they were summarized by Jaffray Woodriff in The Complexity of High Frequency Trading. Woodriff also stressed that there are three, as he called it, “unfortunate properties” of the US stock market:
(1) Order type proliferation and confusion. As Scott Patterson and Jenny Strasburg emphasized “hundreds of order-type options are available, which translate to thousands of variations because they behave differently depending on how an investor’s trading programs are coded.” In order to illustrate the point Jaffray Woodriff hinted to the “exhaustive, complex, and extremely opaque list of order types provided by the exchange Direct Edge”.
(2) Destination proliferation. This refers to the large numbers of lit exchanges, dark pools and the practice of internalization.
The latter aspect was also stressed by Dennis Dick in In the Dark. He argued that the latest hype about high-frequency trading is overlooking deeper problems :
“…while speed and order types can help HFTs to stay at the top of the order queue, some significant advantages aren’t focused around speed. Manoj Narang … said, “Speed matters less in today’s market than it has ever mattered.” After that comment, critics were quick to attack his statement, challenging his claim, but I completely agree with him. While speed helps to avoid getting picked off, the real advantages aren’t reliant on speed at all. The real advantages are built on relationships. And this is where the market starts to get “shady.””
Dennis Dick further explained:
“OTC (over the counter) market-making HFT firms, known as internalizers, actually have built relationships with online brokers in which they buy order flow from retail brokerages to trade directly against incoming marketable orders. They pay the retail broker a fee for the privilege of getting “first dibs” on retail orders.
… assume stock XYZ is bid at $20.00 for 20,000 shares and offered at $20.01 for 20,000 shares. Any participant placing an order to buy the stock at $20.00 on that exchange would be behind the other 20,000 shares that were there first (first-time priority). But the HFT internalizer can trade directly against the retail market orders that they have purchased access to (first dibs), executing against these orders off exchange. The only regulatory requirement is that they match or beat the displayed national best bid or offer (NBBO). This arrangement gives them a significant advantage.”
(3) Rebate distortions and complexity. Woodriff argued that US stock exchanges employ complicated systems of fees and rebates which are not public or transparent meaning that “some exchanges are affording unfair advantages to certain traders, either because they pay for them, because they are confidentially informed about how to get their orders higher in the queue, or because they have collusive relationships with the exchanges themselves.”
Exchanges have a strong interest to attract high-frequency traders (and, as Maureen O’Hara emphasized, in some cases to deter them). Woodriff quoted Themis Trading arguing that there are “perks sold to them by the exchanges – including colocation, data feeds, and dubious order types – to make sure that they maximize the number of free shots at a risk free rebate. This means they want to bid and be first in line, and only when they are assured that there are other buyers deep behind them, and collect rebates. If they are successful and buy stock and collect a rebate, they can turn around and instantly sell that stock at the same price to the person behind them, still making a profit. This is not liquidity provision. They game ways to be at the top of the book in stocks that already have the liquidity that serves as a backstop.”
But again, views on these practices differ. Scott Patterson and Jenny Strasburg mentioned in a similar context that “exchange officials don’t deny making available certain advantages, like data feeds with detailed information about trades, that the high-frequency traders can use.” But the exchanges’ position is “that these are fully disclosed; they can be used by anyone with the right hardware and technical savvy; and they ultimately benefit all investors because by pulling in a higher volume of orders, they make it possible to buy and sell more easily and at better prices. “
This is not the place to weigh and sort out the various arguments. Coming back to Joseph Stiglitz, the important point here is that speed does not seem to be the real problem – and if it is a problem it is not limited to high-frequency trading. But certain market practices and abuses are. However, Stiglitz appeared not concerned about these practices, the overall changing market microstructure or the advantages and disadvantages of individual investors small and large.
In his speech Stiglitz focused on the macroeconomic effects of high-frequency trading on price volatility. He argued that markets in general can be — and usually are — too active, and too volatile. Quoting Felix Salmon:
“… faster price discovery is generally associated with higher volatility, and higher volatility is in general a bad thing, from the point of view of the total benefit that an economy gets from markets.”
Stiglitz named the example of cross border capital flows. Felix Salmon quoted the following passage:
“When countries do not impose capital controls and allow exchange rates to vary freely, this can give rise to high levels of exchange rate volatility. The consequence can be high levels of economic volatility, imposing great costs on workers and firms throughout the economy. Even if they can lay off some of the risk, there is a cost to doing so. The very existence of this volatility affects the structure of the economy and overall economic performance.”
Traders seeking profit can ultimately cause more harm than good and their activities must be contained – this is the message, and Stiglitz wants to see the same logic applied to high-frequency trading, with similar consequences.
Volatility caused by financial activity which is unrelated to business transactions in the “real” economy is a long and widely debated topic. Leaving aside the fact that the highest market turbulences and biggest price jumps in history happened under collapsing fixed or “managed” currency regimes with capital controls – whether financial speculation, proprietary trading, or whatever it is called, is increasing volatility or, on the contrary, containing it through the liquidity and price signals it provides is a fundamental issue on which opinions are divided. The mentioned research results for the Flash Crash indicate that the picture is not clear, and that in this respect high-frequency trading does not differ fundamentally from other market activities.
Proponents of capital controls and other restrictions assume that it is possible to separate the “bad guys” who are seeking short-term profits from financial trading from the “good guys” who turn to the markets for financing or hedging “real” activities. I pointed to the impossibility of this task elsewhere. However, in this context the question is not about motives but about whether HFT is able to add to market dynamics beyond the millisecond or the day thereby reinforcing overall volatility in a way that matters for “real” businesses or whether, in providing additional liquidity and taking pressure off the market, it would have a moderating influence in a volatile environment.
Considering the preceding quotes the latter appears more evident. Unfortunately, economics is not well- equipped to analyze the interplay of dynamics of market activities on different time scales in a way which could provide a definite answer. But, following Stiglitz’s arguments, there may be other reasons to condemn high-frequency trading for making the economy “worse”. These will be discussed in the second part.
Algorithmic trading (AT) Automated trading. The use of computer programmes to enter trading orders where the computer algorithm decides on aspects of execution of the order such as the timing, quantity and price of the order. (Foresight)
Arbitrage The riskless (!) exploitation of price differences for the same product in different markets.
Co-location Traders locating their computers adjacent to the exchange’s price matching engines to minimise latency and gain information and speed advantages over competitors in exchange for fees. (Satyajit Das)
Latency The time it takes to execute a trade from the moment it is placed, typically measured in round-trip times (including order and confirmation).
Latency arbitrage The practice of buying or selling an instrument slightly ahead of other market participants, by taking advantage of small delays in price dissemination. (The Trading Mesh)
Statistical Arbitrage The exploitation of deviations of securities prices from historical trends.