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InvestmentHedge Funds
« Previous 1 2 3 4 Next » » Normxxx - Quants must work on shortcomings Quants must work on shortcomings [¹]
Dry though the business of investing using computer models may be, it arouses the basest human instincts. People like it when quants lose money. Human envy of the rich is natural. But quants are not merely rich. They are also incredibly intelligent. Long-Term Capital Management, which melted down nine years ago, famously had two Nobel prizewinners on its boards. Goldman Sachs Asset Management is also staffed by people who are very clever indeed. The advanced mathematics they use puts their models far beyond the comprehension of lay people. Let us move on from this. What are quants trying to do, how did they lose so much money so quickly, and what does this tell us about quantitative investing going forward? There are two reasons for using quantitative models. First, quants aim to find market inefficiencies. Both theory and common sense tell us that the only way to beat the market is to find inefficiencies. Mathematical models can home in on minute inefficiencies and exploit them. But they need computer technology to execute swiftly [[very, very swiftly: normxxx]] while the inefficiencies exist. As very many people try to perform this same trick, inefficiencies are eliminated [[in milliseconds: normxxx]], and the mathematics needed to find those that are left gets ever more complicated. But the historic performance of quant funds suggests that out-performance is possible. Long-short funds made nice profits during the correction in February and March of this year. However, that performance alone may not be terribly exciting. LTCM used to describe the job of exploiting tiny mispricings as "hoovering up nickels". So, leverage is needed to make those returns interesting [[often as much as 20x: normxxx]]. Second, the growing body of research in behavioural finance shows that human judgment when it comes to investment is flawed in predictable ways that lead to predictable mispricings in the market. A quantitative model, that will follow rules set for it by humans without the risk of human judgment subsequently messing things up, is needed to take advantage of those mispricings. Both of these justifications for quant investing remain intact. So how have the quants just lost so much money? In essence, many quant hedge funds were guided by their models to hold the same positions. When some of them started to lose money thanks to the credit sell-off, the need to meet margin calls on their debt forced them to sell their "good" investments. As so many quants crowded in, the result was a stampede downwards for the "good" investments. This happened several days in a row. These were "25 standard deviation" events, according to Goldman-- meaning that in a normal Gaussian bell curve distribution, such a day would happen only once every 100,000 years. Several such days in succession was, therefore, far beyond what the models had anticipated. And leverage magnified the losses. This pins the central problem that quants must address. It lies in the statistical concept of "fat tails". Returns on financial markets do not show a classic "normal" distribution, popularly known as a "bell curve" because of its shape. In this distribution, 95 per cent of data points are within two standard deviations of the mean, and 99 per cent are within 3 standard deviations. In financial markets, the curve appears to be more pointed, with longer, flatter tails at either end. Maybe even more than 95 per cent of returns lie within 2 standard deviations of the mean, but there is at least one "fat tail" of extreme outliers, such as the 25 standard deviations seen this month. When they do deviate, they can deviate extremely. This effect may be exacerbated by the herding habits of quant funds. This is not surprising. Over time, markets tend to go up slowly and steadily, and occasionally drop sharply. It is common sense that quants, charging in where the market is least efficient, and fuelling the mix with leverage, will find their returns magnify these tendencies-- very nice profits most of the time-- but even sharper falls when the models go wrong. So, this need not mean the end to quantitative investing. There is a role for the use of mathematical models to identify and exploit market inefficiencies. There is also a role for a process that avoids persistent behavioural errors made by humans. Plenty of human beings have lost their shirts this month, remember, without the aid of computers. But it does show that the models must improve. Somehow, the quants must model the "herding" effects when their peers pile into the same trades. They must build in the fact that their own actions will move the market-- and may even be more likely to move parts of the market that are relatively inefficient in the first place. It also demonstrates quants' limits. "Hoovering up nickels" is only worth the effort if the returns can be magnified by leverage. If the regular stream of profits must come with occasional blow-out losses, and there is reason to believe that it must, then the combination of rigid quant strategies with leverage looks unappealing. From this, it is fair to guess there will be less leveraged active quant funds going forward-- and, maybe, that they will be restricted to those, like large investment banks, who have large supplies of capital to draw on when the market springs a surprise. But, sadly for some, there will still be a role for clever, and well-paid, mathematicians in the world of fund management. Normxxx The content of any message or post by normxxx anywhere on this site is not to be construed as constituting market or investment advice. Such is intended for educational purposes only. Individuals should always consult with their own advisors for specific investment advice. -- posted by Normxxx » Normxxx - WSt's Mathematicians No Magicians Wall Street's mathematicians are no magicians Quant funds still cannot predict every market-moving event, critics say [¹]
Short for "quantitative equity," a quant fund is a hedge fund that relies on complex and sophisticated mathematical algorithms to search for anomalies and non-obvious patterns in the markets. These glitches, often too small for the human eye, can present opportunities for short-- and long-term trades that yield high-profit returns. The models replace instinct. They try to turn historical trends into predictive science, using elegant mathematics seemingly above the comprehension of your average 401(k) participant or Wall Street fund manager. Instead of veteran, market-savvy traders waving fistfuls of sell slips, the elite quant funds employ Nobel nerds with math PhDs, often divorced from the real world. It's not for nothing that they are called "black-box" funds-- opaque to outsiders, the boxes contain investment magic understood by only the wizards who conjured it up. But the 387-point drop in the Dow Jones industrial average Aug. 9 and the continuing turmoil in the markets, in part attributed to massive sell-offs by the quant funds, have tarnished some of the quants' glimmering intellectual credentials and shown that, when push comes to shove, they can rush toward the exits as fast as a novice investor. Last week, Goldman Sachs said its Global Alpha quant fund had lost 27 percent of its value this year because its computers failed to anticipate what the firm called "25 percent standard deviation moves" or events so rare Goldman had seen them only twice before in the firm's history. On the same day Goldman revealed the bad news, the firm said it would lead a group of investors in pouring $3.6 billion into another Goldman quant fund, aiming to shore up confidence in the quants. Barclays Global Investors, with $450 billion of its $2 trillion in assets under quant management, began applying mathematical tools to its funds in 1978. Last week, Barclays spokesman Lance Berg said the firm was "maintaining its investment process" despite the recent troubles. He would not say how much the Barclays quant funds had fluctuated during the period of turmoil. The acknowledged quant king is James Simons, 69, an M.I.T.-trained mathematician with a groundbreaking theory that physicists are using to plumb the mysteries of superstring study and get at the nature of existence itself. Simons turned his brain on investing after his math career, founding Renaissance Technologies quant shop. The firm pocketed $1.7 billion in investor fees last year, among the highest in the industry. In return, his clients can reap annual returns of more than 30 percent, according to news reports. As elegant as the models are, they cannot predict unpredictable events, or human panic, some say. Further, some say, too many quant funds are full of myopic brainiacs, overly reliant on their tools.
Taleb believes in monkey-wrench events that shatter the models of the quant-jocks. He says their algorithms don't adequately account for huge, rare anomalies, such as the current surprise credit crunch. Or the Russian credit crisis in 1998 that nearly put the superstar quant fund of the time, Long-Term Capital Management, out of business in a matter of days, saved by cash infusion organized by the Federal Reserve. The sentiment is reminiscent of the demise of Enron, a company said to have been designed by geniuses but run by idiots. The oil-and-gas trader used next-generation financial tools designed by brilliant mathematicians. But they couldn't overcome the inept and criminal actions of the management. The allure of a unifying, perfect mathematical formula is powerful; it is an alchemy for the enlightened age. In the irrational financial markets, mathematic models offer the hope of certitude. The quant funds thrive on volatility-- it's how they make their profit margins. But recent weeks have proved too volatile for some of the funds, almost all of them highly leveraged, which seemingly all at once got spooked into seeking liquidity. When they ended up seeking liquidity by selling the same stocks, the Aug. 9 plunge happened, analysts speculate, resulting in the Dow's second-largest one-day slump of the year. Normxxx The content of any message or post by normxxx anywhere on this site is not to be construed as constituting market or investment advice. Such is intended for educational purposes only. Individuals should always consult with their own advisors for specific investment advice. -- posted by Normxxx » Normxxx - Algorithmic, Seeking an Edge Operational Risk-- Algorithmic, Seeking an Edge [¹] Click here for link to complete article: http://www.garp.com/resources/newsfeed.a...
Traders and vendors are now increasingly looking for ways to sharpen their edge, either by making their already fast trading engines interact with the market even more rapidly, or by tailoring their algorithms ever more closely to the specific characteristics of individual futures markets. In fact, there are signs that some traders are beginning to design algorithms that prey on other algorithms, or hide their presence from other algorithms behind a flurry of order messages never meant to be executed. It is a difficult world to track, especially since so many of the algorithms in use in the futures industry were developed by proprietary traders, who naturally are reluctant to reveal any clues to their trading strategies [[or allow peer reviewed evaluation of their success or failure: normxxx]]. The field is getting crowded, however. Several independent software vendors have developed algorithmic trading tools of varying degrees of complexity for use in trading futures, and many of the broker-dealers that dominate algorithmic trading in the U.S. equities markets have adapted their models to the specific characteristics of the futures markets. Both type of vendors see futures trading firms such as commodity trading advisors as potential customers, and they are eager to talk about their offerings. "Three years ago, most of the algorithmic trading systems in the futures industry were self-developed," Jim Johanik, head of U.S. technology for Euronext.liffe, said at the OpTech conference. "Now we see a lot more off-the-shelf products being put to use."
Although many observers assume that algorithmic trading began in equities and then migrated to futures and options, the reality is that algorithmic trading has a long history in the listed derivatives world. More than 15 years ago, traders were writing computer programs to automate their trading on Deutsche Terminbörse, the predecessor exchange to Eurex. Electronic options trading in Europe was another important incubator for automated trading engines, as market makers developed high-speed automated systems to cancel and replace quotes across dozens if not hundreds of instruments. Arbitrage is especially well suited to algorithmic trading, mainly because the machines can operate so much more quickly than human beings. Russell Abramson, executive director at J.P. Morgan Futures, says some black box trading firms can transmit several thousand order messages to an exchange in less than a second, constantly canceling and replacing orders as the market changes, and quickly capturing any price discrepancy as soon as it emerges. "As soon as Nymex moved to Globex, we started getting calls," says Jesper Alfredsson, head of algorithmic trading at Orc Software. "The Globex platform is well known to algorithmic traders, and the arbitrage with ICE is suited very well for that kind of trading." Both exchanges permit trading firms to locate their servers close to their matching engines. This gives electronic traders rapid access to their markets, with order message round-trip times measured in milliseconds. Three of the world's largest futures exchanges-- CME, ICE and Eurex-- offer this type of service, and Euronext.liffe expects to do so by the end of the year. The problem arises when a trading firm wants high-speed access to more than one exchange. If the firm locates its server closer to one exchange, it will be farther from the other, and vice versa. Either way, there will be some latency in the connection to the more distant exchange. Even if that latency is only a few milliseconds, it can make all the difference in the world of high-speed trading.
Co-location is hardly the only way that high-frequency traders gain an edge over the rest of the trading community. The players in this game have constantly to make an overall analysis of latency in the trading infrastructure to see if there is any part of the system where a few milliseconds can be shaved off. This is not a one-time exercise. In the old days, a trader might be watching four screens at the same time, commented one speaker. Now it's two rows of four, with one row displaying the markets and the other row displaying network conditions. "It's all about [ultra] fine-tuning the performance of the system," says Draughon. One of the effects of algorithmic trading is an explosion of market data. As traders use these tools to slice up their orders into smaller pieces to reduce market impact, it increases the number of trades. Average order size in the E-mini futures markets, one of the first U.S. futures markets to go electronic, has fallen to approximately two contracts, and many market participants are expecting the same result sooner or later in other futures markets. At the same time, the development of high frequency automated trading machines has contributed to a sharp increase in the order to fill ratio. Of the thousands of order messages that these machines transmit in a single second, only a few actually result in a trade. What makes this flood of market data especially problematic is that many traders using algorithmic trading systems want every available piece of information. Just getting the bid and the ask is not enough; they need the full market depth with price and quantity, including every order resting in the order book and updated on a real-time basis. In addition, they want the data stored, processed, and available for analysis on a real-time basis. The faster their machines can receive the data, the faster they can identify a price discrepancy and act on it. This in turn creates a feedback effect. Each time a price change is transmitted by an exchange, the automated systems respond by sending a new batch of order messages, and the cycle starts all over again. The bottom line is that exchanges are sending out far more market data than ever before. Euronext.liffe's Johanik estimates that his exchange is now sending out four times as much data as it was two years ago, and in December Eurex quadrupled the capacity of its data feed to 1096k. Database vendors say their systems have to be capable of receiving many hundreds of thousands of order messages per second, and the one million messages-per-second mark is not far around the corner. The Options Price Reporting Authority currently has the capacity to transmit 573,000 market data messages per second and expects to raise that to 700,000 in January. TradingScreen's Buhannic warns that it is very expensive to build systems capable of handling the torrent of market data necessary for algorithmic trading. "Only the top players can spend the money to create their own tick-by-tick database," he says. "It is not enough to have a great model. You have to have the order management, connectivity and market data infrastructure to support that model, and that is becoming more difficult to do in-house." As a result, he expects a handful of large banks and trading firms that have the resources to develop this infrastructure to gain an edge. Andrew Yao, who heads sales to the futures industry for Credit Suisse's AES division, says the firm spent two years building models for the futures markets, and now is focusing on marketing these algorithms. He says they have been designed for use not only with equity and interest rate futures, but also with commodity futures markets such as crude oil and corn. Equity traders that also trade futures naturally are among the first to adopt these tools, he says. For example, a mutual fund that uses S&P 500 futures might use an algorithmic trading tool to minimize the market impact of its trades. But he says Credit Suisse is also having success with commodity trading advisors, who see these algorithms as a way to improve the efficiency of their trading desks. The danger, of course, is that relying on a vendor for algorithmic trading solutions may expose a firm's trading strategies to the vendor. "Information leakage" is a critical concern for hedge funds, commodity trading advisers and institutional investors worried about someone copying their trading strategies or front-running their orders. That is why the investment banks that market algorithmic trading solutions say they take great pains to separate their algorithmic services from the rest of the firm. Rus Newton, co-head of Global Advisors, a commodity hedge fund based in London with about $150 million in assets under management, agrees that information leakage is a concern, but says the investment banks are well aware of the damage this would do to their reputations. In his view, the real issue is more a question of resources.
Newton sees a problem with this approach, however. CTAs live or die by their ability to generate profitable returns on their trading strategies, and typically emphasize the unique qualities of their strategies. "If you are telling investors that your trading system is truly unique, aren't you shooting yourself in the foot by admitting to using someone else's algorithms?" he questions. There are two sides to every story, of course. Just as the proprietary traders worry about predation by the investment banks, the investment banks are hearing complaints from their customers about predatory behavior by algorithmic traders. J.P. Morgan's Abramson says there has been a noticeable increase in complaints about "phantom liquidity" over the last six months, and he sees this as resulting from the impact of algorithmic trading on the futures markets. "What is happening is that customers will see a quote displayed in the order book that they want to hit, but by the time they send in their order, the quote is gone," he explains. The suspicion among some firms, according to several other participants at FIA's OpTech, is that some of the algorithms are programmed to coax liquidity into themarket, a practice called "fishing." A firm will display an order on one side of the market, knowing that this will trigger a response from algorithmic traders, and then cancel the order and hit the other side of the market. This might get a slightly better price or more volume at the same price than was initially displayed. This kind of behavior is hardly new to the futures industry. It may happen a lot faster than before, but industry veterans remember similar gamesmanship when trading took place on the floors. When one of the legendary figures in the business entered the pits, the other traders watched for any clues to their trading intentions. In order to disguise their moves, they would have their real trades executed by other brokers on the other side of the pit. It does illustrate, however, why the development of algorithmic trading tools is often compared to an arms race. As soon as a particular algorithm is widely used, the trading community immediately creates a new algorithm that takes advantage of the predictable patterns of the older algorithm. Some vendors say they are now on their "third generation" of algorithms, and no doubt there will be many more as traders continue their never-ending search for an edge. Normxxx The content of any message or post by normxxx anywhere on this site is not to be construed as constituting market or investment advice. Such is intended for educational purposes only. Individuals should always consult with their own advisors for specific investment advice. -- posted by Normxxx -- posted by Normxxx » Normxxx - Humans versus black boxes Hedge Fund: Humans versus black boxes http://normxxxruminates.blogspot.com/200... -- posted by Normxxx » Normxxx - Financial Engineering Doesn’t Work? Why Financial Engineering Doesn't Workhttp://normxxxruminates.blogspot.com/200... -- posted by Normxxx » Normxxx - The Weakness of Quants The Weakness of Quant Funds http://normxxxruminates.blogspot.com/200... -- posted by Normxxx
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Inya Ivkovic
