Mallaby, Sebastian.
More money than god : hedge funds and the making of a new elite / Sebastian Mallaby.
1. hedge funds.
2. investment advisors.
HG4530.M249 2010
332.64'524──dc22
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Sebastian Mallaby., More money than god : hedge funds and the making of a new elite, 2010.
(More money than god : hedge funds and the making of a new elite / Sebastian Mallaby., 1. hedge funds., 2. investment advisors., HG4530.M249 2010, 332.64'524──dc22, 2010, )
“”─“”‘’•
p.408
5. Elaine Crocker, who was in charge of identifying and seeding portfolio managers at Commodities Corporation in the 1980s and who became president of Moore Capital in 1994, comments, “Rarely do portfolio managers articulate why they are successful. Sometimes they try to do so but are wrong. I have worked with hundreds of portfolio managers and found that articulating why they are successful is quite difficult for them ── although often they are not aware that it is.” (Elaine Crocker, e-mail communication with the author, September 8 2009.) Similarly, Roy Lennox, a long time macro trader at Caxton, says, “Trading can be intuitive. We are looking at so many factors in the markets [that] a lot of our analysis operates on a subconscious level. All of a sudden you just know this is the right trade. If somebody really quizzed you, you probably couldn't clearly articulate your views and would just say, no no no, I know this is the right trade. It's because all these things have been taken in ── the market action, the technicals, the things that you read in the newspapers or on Bloomberg and the conversations you have with other traders analysts and policy makers. It just comes together.” (Roy Lennox, interview iwth the author, June 24, 2009.)
“”─“”‘’•
leverage, 106, 189-90, 332, 357, 362, 370, 388-90
of Amaranth, 389
of AQR, 341, 342
banks and, 12, 377, 409n
of Bear Stearns, 368, 460n
bonds and, 178, 180, 186, 437n
of Citadal, 363, 365, 368, 370, 389
commodity futures and, 70
crowding and, 182
equities and, 69
hedge funds and, 2, 3, 9, 10, 12, 23, 29, 31,
38, 100, 156, 178, 179, 186, 189, 206, 221,
226-27, 252, 256, 274, 294-95, 341, 342,
344, 345-46, 363, 365, 38, 370, 370-71,
389, 422n, 433n, 445n, 448n
limits on, 412n
of LTCM, 226-27, 247, 252, 274, 388, 390, 445n
macro investing and, 126
regulation of, 245-46, 448n, 456n-57n
risks posed by, 187, 247
of Tiger, 252, 256, 448n
value at risk and, 228
Steyer, Tom, 268-72, 274, 275, 279-83, 372, 387, 450n
crash of 1987 and, 270, 450n
merger arbitrage of, 269-71, 307
Swensen's partnership with, 271-72, 451n
wife of, 270, 450n
carry trade, 81, 125, 137, 446n
central banks, 11, 13, 75, 151-55, 157-64, 168, 182, 186, 331-32, 382, 435n
LTCM compared with, 237
see also Bank of England; Bundesbank; Federal Reserve
Citadel Investment Group, 3, 4, 308-9, 337-40, 363-70, 389-90, 455n, 461n
p.6
Perhaps the most extreme version of this problem is presented by the young Paul Tudor Jones. To this day, Jones maintains that he anticipated the 1987 crash because his red-suspendered, twenty something colleague, Peter Borish, had mapped the 1980s market against the charts leading up to 1929; seeing that the two lines looked the same, Jones realized that the break was coming. But this explanation of Jones's brilliant market timing is inadequate, to say the least. For one thing, Borish admitted to massaging the data to make the two lines fit.6 For another, he predicted that the crash would hit in the spring of 1988; if Jones had really followed Borish's counsel, he would have been wiped out when the crash arrived the previous October. In short, Jones succeeded for reasons that we will explore later, not for the reasons that he cites.
p.6
“Out of all the research that we've done with top players, we haven't found a single player who is consistent in knowing and explaining exactly what he does”, the legendary tennis coach Vic Braden once explained. “They give different answers at different times, or they have answers that simply are not meaningful.”7
Starting in the 1980s, financial academics came around to the view that markets were not so efficient after all.
p.20
1948 a writing assignment for Fortune
which appeared in March 1949 under the titled “Fashions in Forecasting”
which was to examine freight-car loadings, commodity prices, and other economic data to determine how stocks ought to be priced.
p.20
the notion that stock price were drive by predictable patterns in investor psychology.
; it [money] was a barometer of crowd psychology.19
p.20
; and this feedback loop drives stock prices up, creating a trend that can be followed profitably.
The trick was to bail out at the moment when the psychology turns around ── when the feedback loop has driven prices to an unsustainable level, and greed turns to fear, and there is a reversal of the pendulum.
p.21
Others argued that if stock prices were rising but trading volume was falling, the bull market was running out of buyers and the tide would soon reverse. All shared the view that stock charts held the secret to financial success, because the patterns in the charts repeated themselves.
p.21
there was at least one point on which the two saw eye to eye: Both believed that successful market forecasters could not sustain their performance. The very act of forecasting a trend [and making the information public] was likely to destroy it [, if and only if enough people take action on their belief of the trend]. ([ by taking action, you create a real world informational feedback loop into the market ])
p.23
By selling a portion of his fund short as a routine precaution, even when the charts weren't signaling a fall, Jones could insure his portfolio against market risk. That freed him to load up on promising stocks without worrying about a collapse in the Dow Jones index:
p.27
was to distinguish between the money that his fund made through stock picking and the money that it made through its exposure to the market. Years later, this distinction because a commonplace: Investors called skill-driven stock-picking returns “alpha” and passive market exposure “beta”.53
p.28
Jones's calculation were impressive on two levels. In the precomputer age, figuring the volatility of stocks was a laborious business, and Jones and his small staff performed these measurements for about two thousand firms at two-year intervals. But, more than Jones's patience, it was the conceptual sophistication that stood out.
p.28
in 1952
the publication of a short paper titled “Portfolio Selection”
the author was a 25-year-old graduate student named Harry Markowitz,
his chief insights were two fold:
not merely to maximize return but to maximize risk-adjusted return,
the amount of risk that an investor takes depends not just on the stocks he owns but on the correlations among them.
Markowitz's insight that the risk of a portfolio depends on the relationship among its components.36
p.29
working out the correlations among a thousand of stocks
p.29
William Sharpe,
in a paper titled “A Simplified Model for Porfolio Analysis”, Sharpe replaced the hopeless injunction to calculate the multiple relationships among stocks with the simpler idea of calculating a single correlation between each stock and the market index. This was precisely what Jones's velocity calculations were designed to do. By the time Sharpe published his paper in 1963, Jones had been implementing its advice for more than a decade.
p.29
His fund made one judgment about which companies to own and a second about how much risk to take, adjusting the risk as it was fit by using the device of leverage.57
p.30
To begin with, Jones wanted to protect his investment methods from competitors:
p.30
It was Valentine who realized that if managers took a share of a hedge fund's investment profits rather than a flat management fee, they could be taxed at the capital-gains rate:
p.30
Jones duly charged his investors 20 percent of the upside, claiming that he had been inspired to do so by Mediterranean history rather than tax law: He told people that his profit share was modeled after Phoenician merchants, who kept a fifth of the profits from successful voyages, distributing the rest to the investors.
p.30
Jones's performance fee (termed a “performance reallocation” in order to distinguish it from an ordinary bonus that would attract normal income tax)
p.30
Securities Act of 1933
Investment Advisors Act of 1940,
p.31
In 1961 he set up a second partnership rather than allow his first one to cross the permissible threshold of one hundred members.42
p.32
Starting in the early 1950s, he invited brokers to run “model portfolio” for his fund: Each man would select his favorite shorts and longs, and phone in chages as though he were running real money. JOnes used these papers portfolios as a source of stock-picking idea. His statistical methods, which separated the fruits of stock selection from the effect of market moves, allowed him to pinpoint each manager's results precisely. Jones then compensated the brokers according to how well their suggestion worked. It was a marvelous technique for getting brokers to phone in hot ideas before they gave them to others.47
This sytem gave Jones an edge over his competitors.
p.33
Alan Dresher, one of the Jones stock pickers, had the idea of going over to the Securities and Exchange Commission offices to read company filing the moment they came out. The extraordinary thing was that he was all alone. The rest of the Street was waiting for the filing to arrive in a bundle from the post office.
p.38
There was no mechanism for getting out before disaster struck; and in May 1969 the stock market started to fall hard, shedding a quarter of its value over the next year.
p.39
After two decades of eminence, Jones's investment edge was gone. The markets had finally caught up with him.
p.43
accounting shenanigans.
In short, the stock market was trading at levels that reflected broad political and financial delusions.
p.43
So whereas in 1969 the young trioka shorted enough stocks to protect themselves from a downturn, they went further in 1972. They positioned their portfolio so that their short positions greatly outweighed their longs, and they waited for the crash to happen.
At first, it did not happen. The market sailed along for the rest of 1972,
“”─“”‘’•
pp.50-51
p.50
The market is difficult to beat ── except when you come up with an approach that others haven't yet exploited.
p.50
The first example of innovation at Steinhardt, Fine, Berkowitz concerns Tony Cilluffo. His enthusiasms for Kondratiev may have been weird, but he brought another passion to the firm that was evidently sensible.
p.50
Starting in the 1960s, Cilluffo had begun tracking monetary data, hoping it might anticipate shifts in the stock market. A decade or so later, this sort of exercise was common on Wall Street: Everybody recognized that fast monetary growth predicted inflation and therefore would compel the Federal Reserve to force up interest rate; when that happened, investors would move their money into bank deposits or bonds, preferring to collect interest rather than incur the risk of staying in the stock market.
p.50
As money shifted out of stocks, the market inevitably would fall; and stocks in companies that were sensitive to interest rates ── home builders, equipment suppliers ── would fall the hardest.
p.50
But during the 1960s, Wall Street's equity [stock market] investors could not be bothered with this sort of analysis. They had learned their trade during the first half of the decades, a time when inflation rate never exceeded 2 percent.
p.50
Monetary conditions and the Federal Reserve's response were marginal to their thinking.24
pp.50-51
An eccentric autodidact from out of the mainstream, Cilluffo was the exception.
By the time he joined Steinhardt, Fine, Berkowitz in 1970, Cilluffo had already devised a crude monetary model. He tracked the large banks that formed the Federal Reserve System, and the moment they switched from reporting spare lending capacity to reporting that they had hit the limit of what could be supported by their capital reserves, Cilluffo's radar bleeped: Banks had maxed out on their lending, so monetary growth was set to slow, so economic growth would head down and stocks would be in trouble.
p.51
Cilluffo examined historical patterns and found that stocks began falling two months after the crossover point in the bank data. The relationship also worked in the opposite direction. If banks switched from reporting no lending capacity to reporting free reserves, the stock market would turn up imminently.25
p.51
Cilluffo had grasped the rules of investing in the high-inflation, post-gold standard world ── even before that world had emerged fully. His approach gave Steinhardt, Fine, Berkowitz an edge in anticipating the hairpin bends in the stock market.
p.51
Cilluffo anticipated both the collapse of 1973-74 and the sharp recovery that followed; in each case he reinforced the conclusions of colleagues who formed their view of the market using traditional stock analysis.
p.51
If Cilluffo deserve a significant part of the credit for the fund's positioning in 1973-75, it follows that he deserves a significant part of the credit for the whole decade. The firm's performance in those three years accounted for the bulk of its profits during the 1970s.
p.51
Cilluffo's colleagues were only dimly aware of his insights because he was bad at explaining them. They knew, for example, that the wiry guy on the trading desk hated Kaufman & Broad, the nation's biggest home builder, and they knew that the firm was short 100,000 shares; they did not necessarily know that Cilluffo hated Kaufman because home builders are vulnerable to rising interest rates and the monetary data screamed that rates were heading upward.
p.51
The short position on Kaufman earned Steinhartd, Fine, Berkowitz over $2 million. And so, wittingly or otherwise, Cilluffo's colleagues were the beneficiaries of his innovation: the application of monetary analysis to stock market.26
pp.77-78
Financial markets are mechanisms for matching people who want to avoid risk with people who get paid to take it on: There is a transfer from insurance seeker to insurance seller.
In the 1960s not many people sought insurance against commodity price fluctuations. Government set minimum prices for agricultural products, while surpluses prevented prices from rising;
the inflationary 1970s, the new volatility in food prices created rush for insurance: Food companies used the futures market to hedge the risk of high prices; food growers used the futures market to hedge the risk of low prices.40
“”─“”‘’•
p.101
The larger the funds grew, the harder it became to jump in and out of the markets without disrupting prices and damaging themselves in the process.
pp.66-67
Shortly after Commodities Corporation got under way, U.S. corn fields were hit by a fungal disease known as the corn blight. Some plant experts predicted that the blight would reappear the following year, and on a bigger scale; corn futures started to moved up in expectation of impending scarcity.
p.67
Faced with a thicket of semiscientific rumor that was scaring the market, Weymar and his colleagues saw a chance to get an edge.
p.67
They retained a plant pathologist at Rutgers university who advised the state of New Jersey, increasing his research budget and covering his expenses as he journeyed around the country attending scientific conferences.
p.67
After some weeks of investigation, the Rutgers pathologist concluded that the blight fright was overdone: The plethora of scare stories reflected nothing more infectious than the alarmist bias of the media.
p.67
Weymar and his colleagues jumped. The pathologist's conclusion meant that corn prices would be coming down, so the traders started to pile in, building vast short positions in anticipation of the time when the alarmism would prove to be unfounded.
p.67
The one Friday night, alongside its regular coverage of Vietnam, CBS news ran a special report on the corn blight. It featured the Illinois state plant pathologist, a man representing a state with a lot more corn than New Jersey. And the man from the corn state was predicting a catastrophic corn harvest.
p.67
They had built a vast short position in the corn market, betting their firm on the advice of a pathologist who was now being contradicted by a senior colleague.
p.67
When the markets finally opened on Monday, corn futures jumped so steeply that trading was immediately suspended: Commodity exchanges place a limit on allowable daily movements to dampen extreme swings in prices. There was no chance whatever to get out of the market; prices hit their limit after a smattering of contracts had changed hands, and Weymar and his friends were trapped in their positions.
p.67
It wasn't until Tuesday that the Commodities Corporation traders managed to dump their short positions, and by then the damage had been done:
p.67
It was not much consolation that the pathologist from Rutgers eventually turned out to be right. There was no corn blight, and Commodities Corporation had closed out its short positions at the absolute top of the market.
pp.67-68
The corn debacle of 1971 brought Commodities Corporation to within a hair breadth of closure.
pp.68-69
p.68
After the 1971 debacle, Weymar set about rethinking his theory of the market. He had begun with an economist's faith in model building and data: Prices reflected the fundamental forces of supply and demand, so if you could anticipate those things you were on your way to riches. But experience had taught him some humility. An exaggerated faith in data could turn out to be a curse, breeding the sort of hubris that leads you into trading positions too big to be sustainable.
p.69
If Commodities Corporation had bet against the corn blight on a more modest scale, it might not have been scared out of its positions by an item on the evening news. The result would have been a profit rather than a near-death experience.
Weymar's rethink began with a new approach to risk taking.
p.69
p.69 risk-control system
p.70
The new risk-control system was connected to another rethink that followed the corn debacle: Weymar and his colleagues developed fresh respect for trends in prices.
efficient-market theory holds that such trend did not exist:
random-walk consensus was so dominant that, through the 1970s and such of the 1980s, it was hard to get alternative views published in academic journals.
p.70
a get alternative views
p.70
historical commodity price data
gathered and formatted by Dunn & Hargitt.
a firm in Indiana
But Frank Vannerson had gotten his hands on a trove of historical commodity price data that had been gathered and formatted by Dunn & Hargitt, a firm in Indiana.
p.70
that price trends really did exist,
p.70
devised a computer program that could trade on that finding.
p.70
Weymar was initially skeptical of Vannerson's project.27 His trend-following concept seemed disarmingly simple: Buy things that have just gone up on the theory that they will continue to go up; short things that have just gone down on the theory that they will continue to go down. Even though Vannerson's program took a step beyond that ── it tried to distinguish upticks that might signify a lasting trend from upticks that signified nothing ── Weymar still doubted that anyone could make serious money from something so trivial. But by the summer of 1971, Weymar had reversed himself.
p.71
Weymar's cocoa model, which had worked so well at Nabisco, had misjudged the direction of the market expensively during Commodities Corporation's first year.
But Vannerson's trend-following model, which watched patterns in the market rather than the fundamentals of chocolate consumption or rainfall, had made money consistently from the day the firm opened.
p.71
prohibited traders from committing more than a 10th of their capital in betting against a trend, and the trends used in implementing the controls were the ones identified by Vannerson's program.
p.71
even though trend following had little standing within academia and none within his own research.
p.78
The big jump in insurance seeking explains part of the success of Commodities Corporation. But the most important factor by far was the firm's conversion to trend following. By developing his Technical Computer System and demonstrating how wrong the random walkers were, Frank Vannerson gave Commodities Corporation the confidence to hire trend followers such as Michael Marcus and to turn to his combination of fundamental analysis and charts into a sort of company credo.42
p.78
Years later, financial academia caught up with Vannerson's discovery. In 1986 a paper in the prestigious Journal of Finance found that trend following in the currency markets could earn sizable profits, and in 1988 another study found the same for commodities as well as currency futures.43
“”─“”‘’•“”
p.79
Charles Mackay's Extraordinary Popular Delusions and the Madness of Crowds
• The trouble with [Benoit] Mandelbrot's insight was that it was too awkward to live with; it rendered the statistical tools of financial economics useless, since the modeling of abnormal distributions was a problem largely unsolved in mathematics.
pp.104-105
The efficient-market hypothesis had always been based on a precarious assumption: the price changes conformed to a “normal” probability distribution ── the one represented by the familiar bell curve, in which numbers at and near the median crop up frequently while numbers in the tails distribution are rare to the point of vanishing. Even in the early 1960s, a maverick mathematician named Benoit Mandelbrot argued that the tails of the distribution might be fatter than the normal bell curve assumed; and Eugene Fama, the father of efficient-market theory, who got to know Mandelbrot at the time, conducted tests on stock-price changes that confirmed Mandelbrot's assertion. If price changes had been normally distributed, jumps greater than five standard deviations should have shown up in daily price data about once every 7,000 years. Instead, they cropped up about once every three to four years.
Having made this discovery, Fama and his colleagues buried it. The trouble with Mandelbrot's insight was that it was too awkward to live with; it rendered the statistical tools of financial economics useless, since the modeling of abnormal distributions was a problem largely unsolved in mathematics.
p.105
Paul Cootner, complained that “Mandelbrot, like Prime Minister Churchill before him, promises us not utopia but blood, sweat, toil and tears. If he is right, almost all of our statistical tools are obsolete ── least squares, spectral analysis, workable maximum-likelihood solutions, all our established sample theory, closed distribution functions. Almost without exception, past econometric work is meaningless.”66
p.105
To prevent itself from toppling into this intellectual abyss, the economics profession kept its eyes trained the other way, especially since the mathematics of normal distributions was generating stunning breakthroughs.
p.105
In 1973 a trio of economists produced a revolutionary method for valuing options, and a thrilling new financial industry was born. Mandelbrot's objections were brushed off.
p.105
The crash of 1987 forced the economics profession to reexamine that assertion.
p.105
To put that probability into perspective, it meant that an event such as the crash would not be anticipated to occur even if the stock market were to remain open for twenty billion years, the upper end of the expected duration of the universe,
p.106
As well as challenging the statistical foundation of financial economists' thinking, Black Monday forced a reconsideration of their institutional assumptions.
p.106
In the chaos of the market meltdown, brokers' phone lines were jammed with calls from panicking sellers; it was hard to get through and place an order.
p.106
And, most important, the sheer weight of selling made it too risky to go against the trend. When the whole world is selling, it doesn't matter whether sophisticated hedge funds believe that prices have fallen too far. Buying is crazy.
At a minimum, it seemed, the efficient-market hypothesis did not apply to moments of crisis.
pp.106-107
But the crash raised a further question too: If markets were efficient, why had the equity bubble inflated in the first place? Again, the answer seemed to lie partly in the institutional obstacles faced by speculators. In the summer of 1987, investors could see plainly that stocks were selling for higher multiples of corporate earnings than they had historically; but if the market was determined to value them that way, it would cost money to buck it.
p.126
Jim Chanos, the short seller who worked with both Soros and Robertson, vouched for Robertson's superior grasp of stocks; but no macro trader would have said the same about Robertson's grasp of interest rates or currencies.46
p.126
Value investors generally buy stocks using little or no leverage, and they hold them for the long term; if the investment moves against them, they typically buy more,
p.126
But macro investors [traders] take leveraged positions, which make such trend bucking impossibly risky; they have to be ready to jump out of the market if a bet moves against them.
p.126
Similarly, value investors pride themselves on rock-solid convictions. They have torn apart a company balance sheet and figured out what it is worth; they knew they have found value. Macro investors [traders] have no method of generating comparable conviction.
p.126
One time the macro men feared that the markets would turn against their European bond position in the short term, and they advised Robertson to protect Tiger from losses by putting on a temporary hedge.
p.127
After the Plaza accord it was clear that the dollar would be in for a weak spell; Robertson identified the U.S. firms that would benefit from strong exports and rode them to great profits. After U.S. real estate collapsed in 1990, Robertson correctly saw which banks to short; and the moment that the bad property debts were gone, Robertson went long financials.
pp.131-132
Paul Tudor Jones II, the Patton aficionado and Soros friend
p.131
After studying undergraduate economics at the University of Virginia, Jones landed an apprenticeship with a cotton trader in New Orleans, then moved after two years to the New York Cotton Exchange.
p.131
1983
Still not 30, he was young to head out on his own; but he had a helping hand from Commodities Corporation, which invested $35,000 in his fund and put him in touch with a community of veterans who validated his view of the markets.
p.131
Quickly, Jones emergd as a prodigy with a distinctive style. He approached trading as a game of psychology and high-speed bluff, a kind of poker that combined sly subtlety with crazed bravado. It was not enough to look at your own cards and decide what you might bet; you had to sense what other traders were up ── whether they felt greedy or afraid, whether they were poised to go all in or were dangerously extended. You might hear bullish news for sugar, but then you had to ask yourself how others would react. If the big traders had already bought their fill, the news would scarcely budge the price; but if they there waiting to rush in, the market would take off like a rocket.4
pp.131-132
The more you watched your rival traders, the more you knew how they would play; and eventually you could get inside their heads, luring them along when they were in the mood to buy, spooking them out of the market when they were feeling fearful. If you sensed that the big traders were nervous, you could yell that you were selling and knew that they'd sell too. Then you could pivot right around and buy the hell out of the market.5
“”─“”‘’•“”
p.133
chief economist, Peter Borish,
Borish had plotted the stock charts of the two periods one on the top of the other, and ── surprise! ── they both rose in a vertiginous line, convincing Borish and his boss that a spectacular 1929-scale crash was coming. In one remarkable candid interview, Borish admitted to fudging his results; he had juggled with the starting points for the two lines until he got the fit he wanted.9
“”─“”‘’•“”
p.134
Jones was taken with Kondratiev wave theory, which held that the world moves in predictable twenty-four-year cycles. Kondratiev's teaching had helped Cilluffo to anticipate the crash of 1973, which presumably meant that the next cataclysm was not due until 1997; yet in 1987 JOnes nonetheless believed that the theory reinforced the case that “total rock and roll” was imminent.
p.134
Jones was even more enamored of Elliot wave analysis, as expounded by an investment guru named Robert Prechter. The guru asserted wth great confidence that stocks would experience one last upward explosion before plunging at least 90 percent: It would be the greatest crash since the bursting of the South Sea bubble in England in 1720. Jones told one interviewer, apparently in all sincerity, “I attribute a lot of my own success to the Elliot Wave approach”.11 But Prechter's predictions of disaster were wildly overblown, and even Jones agreed that Prechter had no way of pinpointing when the crash would happen.12
pp.134-135
The truth was that Jones's trading profits came from agile short-term moves, not from understanding multidecades supercycles whose existence was dubious. Like the traders at Commodities Corporation, Jones was adept at riding market waves; he would get up on his surfboard when a swell seemed to be coming, ready to jump off quickly if the market turned against him.
p.135
“When you take a an initial position, you have no idea if you are right,” he once confessed, undermining the notion that any long-range analysis could explain his success. Rather, as he explained in his more candid moments, his method was “to write a script for the market”, setting out who it might behave; and then to test the hypothesis repeatedly with low-risk bets, hoping to catch the moment when his script became reality.13
p.135
Years later, Jones decribed the mental gymnastics that went into writing these scripts. “Every evening I would close my eyes in a quiet place in my apartment. I would picture myself in the pit. I would visualize the opening and walk myself through the day and imagine the different emotional states that the market would go through. I used to repeat that exercise every day. Then when you get there, you are ready for it. You have been there before. You are in a mental state to take advantage of emotional extremes because you have already lived through them.”14
p.430
13.
Jones also said, “I consider myself a premier market opportunist. That means I develop an idea on the market and pursue it from a very low risk standpoint until I have repeatedly been proven wrong, or until I change my viewpoint.”
14. Elaborating on how he would write a script for the market, Jones says, “I put myself in the mental position of being short the market, and I think how I would react emotionally to different events and see what it would take to get me to take my position off. And I write that down and that will be the high for the day. Because the high for the day will be the point at which the shorts capitulate. I close my eyes and imagine myself long. I say, ‘Okay, where is the point I get nervous? Where would I say,“Oh my God, I have to get out?”’ And that would be my projected low for the day. That preparation is important to try to determine great entry points to buy and to sell. You know every single high and low is going to be made in the context of these emotional extremes being hit. Execution is fifty percent of the game.” Paul Tudor Jones, interview with the author, April 23, 2009.
“”─“”‘’•“”
p.135
The crash of 1987 demonstrated the power of this sort of preparation. The moment of the S&P 500 started to head down on Friday, October 16, Jones sensed that the expected market break might at last be coming. It didn't matter that Borish's crude comparision with the 1920 had suggested that the crash was several month off; Jones never took that stuff too literally. What did matter was that Jones visualized the possibility of a crash; he understood that once the market started falling, the chances of a really monster fall were significant. Investors had been anticipating a day of reckoning for months; their confidence could crack decisively. Portfolio insurance added to the danger of a downward lurch: Falling stocks would trigger selling by porfolio insurers, which would cause stocks to fall more. Because of the way market was positioned, betting on its decline was irresistible. If the early fall on Friday petered out into nothing, Jones might lose modestly by going short; he would simply close his position and await the next opportunity. But if investor skittishness and portfolio insurance caused the market to crater, the payoff could be enormous.
p.135
The balance of risk and reward was overwhelmingly attractive.15
By Friday evening, Jones had sold armfuls of S&P 500 futures.
pp.135-136
He took off for his hunting lodge in a remote part of Virginia, together with Louis Bacon, the fellow trader and Commodities Corporation seedling, and some friends from Europe.
p.136
When the weekend was over, there were too many guests to fit on the private plane that was returning to New York.
p.136
Jones, ever chivalrous, offered the last seats to his friends. He would stay back in Virginia.
“No”, somebody said. “We know you've got a big position”.16
Jones got on the plane, and on the morning of Black Monday he was at his desk in Manhattan. If his guests had been less generous, he would have missed the largest one-day equity collapse of his lifetime.
p.136
Stocks fell sharply in the morning, then went into a bloodcurdling dive, and JOnes rode the cascade all the way down to the bottom. Frantic investors flooded brokers with phone calls, desperate to sell out of the market, and the only people who weren't panicking were the ones who just turned numb in the face of the destruction.
p.136
“I remember the time I got run over by a boat, and my backside was chewed up by the propeller. My first thought was, ‘Dammit, I just ruined by Sunday afternoon because I have to get stitched up.’ But because I was in shock, I didn't even realize how badly cut up I was until I saw the faces of my friends.”17
p.136
The crash of 1987 paralyzed some people's reactions in a similar fashion. But Jones had written a script for the market. He was mentally prepared for mayhem.
p.136
Even as he rode the market down, Jones seized a second chance to profit. He had been thinking about how the Fed would respond to the collapse, writing a script for the markets as he always did, and he had reasoned that the authorities would seek to calm everybody's nerves by pumping cash into the banks to make borrowing cheaper. Here, Jones figured, might be another asymmetrical bet: If the Fed did as he expected, the bond market would soar; but if the Fed did nothing, there was no reason to expect the market to go downward. When the bond market ticked up late on Black Monday, Jones took that as a signal that his script was coming true. He bought the largest bond position that he had ever owned, and soon it turned out to be his most profitable one.
Jones's double coup on Black Monday reportedly netted his Tudor Investment Corporation between $80 million, contributing to the 200 percent return that he racked up that year.
pp.136-137
He launched a charity, the Robit Hood Foundation, which tapped into the new hedge-fund wealth, channeling millions of dollars to New York's poorest neighborhoods.
p.137
Jones's triumpth on Black Monday was not an isolated fluke. The late 1980s were a good time for others who came out of the Commodities Corporation tradition.
p.137
The big three ── Soros, Steinhardt, and Robertson ── all lost heavily in the 1987 crash, but Bruce Kovner and Louis Bacon both fared well, though they made less money than JOnes did.
p.137
The big three and the junior three shared the expectation of a market reversal; they had discussed the prospect frequently among themselves, and Jones had even tried to persuade Julian Robertson to run a portforlio of stock shorts for him.18
p.137
But it was one thing to expect trouble and another to respond like lighting when it actually arrived: This is where the Commodities Corporation trio proved nimbler than the older group, which had come out of the equity tradition. A stock picker like Julian Robertson was wedded to his stocks: His Tigers had researched each of them exhaustively, and it hurt to unload them. But Jones, Kovner and Bacon had none of that baggage. Their hallmark was flexibility, and they could turn on a dime.19 They didn't care about individual stocks. They traded the whole market.
p.138
He began with the standard stock analyst's observation: The market was trading at huge multiples to its earnings. But as with Wall Street in 1987, he focused with particular passion on the way that market players were positioned.23
p.431
23. In an interview in 2000, Jones emphasized the importance of understanding how other players are positioned. “The secret to being successful from a trading perspective is to have an indefatigable and an undying and unquenched thirst for information and knowledge. Because I think there are certain situations where you can absolutely understand what motivates every buyer and seller and have a pretty good picture of what's going to happen. And it just requires an enormous amount of grunt work and dedication to finding all possible bits of information.” Paul Tudor Jones II, interview by Joel Ramin.
p.138
In the Wall Street case, porfolio insurance had created a mechanism that would exacerbate a fall, creating an asymmetrical bet for speculators. In the Tokyo case, Japan's financial culture created a similar asymmetry: Japanese savers expected their fund managers to show returns of 8 percent
“”─“”‘’•“”
pp.141-142
pp.167-168
In the wake of sterling's fall, speculators mounted an attack on the French franc, but this time Druckenmiller believed that the central bank would win out against the markets. Unlike British homeowners, French families were not exposed to floating mortgage rates, and the French state had myriad of ways of subsidizing its people: As a result, it would be easier for the French to fight off speculators with temporary interest-rate hikes than it had been for the British.
p.168
Acting on this theory, Druckenmiller bought armfuls of French bonds, which soared in 1993, helping to explain why Quantum's extraordinary 69 percent return in the year of the sterling bet was followed by a 63 percent return in the year of the sterling bet was followed by a 63 percent return the year after.
p.168
But Quantum's greatest post-sterling coup was also the most discreet. Thanks to Robert Johnson, who had by now joined the fund full-time, Quantum shorted the Swedish krona before its devaluation in November 1992, again pocketing upward of $1 billion. Having learned a lesson from the publicity following the sterling trade, Soros and Druckenmiller make sure that nobody spoke publicly about their killing in Sweden.51
p.436
51. The Swedish trade was conceived by Robert Johnson. On the secrecy of the Swedish trade, Druckenmiller recalls, “By then at least we learned to keep our mouth shut.”
p.169
In Belgium, foreign affairs minister Willy Claes chimed in that Anglo-Saxon financiers were plotting to divide Europe.53
“”─“”‘’•“”
p.185
the bond bubble of 1993,
The collapse of the bond market in early 1994 came as a rude shock.
“”─“”‘’•“”
palladium
p.194
Noril'sk's mine contained more than half the world's palladium. A breakdown in production would have global consquences.3
p.194
He researched the structure of the palladium market and found that the metal had three principal uses: dentistry, for which demand was more or less stable; catalytic converters for automobiles, for which demand was growing thanks to environmental regulation; and cell phones, a new market that looked to have some promise.
p.194
Aside from Noril'sk, the other major suppliers of palladium were in Africa, which faced its own infrastructure challenges.
p.194
Having sized up the situation, McKenzie concluded that demand for palladium would outgrow the uncertain sources of supply.
p.194
By 1994 Tiger had bought $40 million worth of palladium, and a young Tiger commodities specialist named Dwight Anderson was dispatched to investigate.
p.195
As he built his contacts in Russia, he confirmed that the Russians were selling more palladium than they mined: They were ripping the stuff out of disused Soviet military equipment and selling it. Sooner or later, this plundering would have to stop: SOmewhere around the year 2000, Anderson calculated, there would be no more missiles to be scrapped, and the price of palladium would skyrocket. And so, following Anderson's investigations, Tiger held on to its palladium position, even though the logic for owning it has changed.
“”─“”‘’•“”
currency of Thailand
pp.198-207
pp.197-198
To run these new start-ups, Soros recruited new talent, including a Princeton-trained economist named Arminio Fraga.
When the two men met in early 1993, Fraga, a Brazilian, had just left a position as a deputy governor at his country's central bank.
Within a few days, Soros had offered him a partnership.
p.198
For the next four years at Soros, Fraga performed the benign function of hedge funds: to finance emerging economies that were shunned by traditional investors. He bought the bonds of big Latin countries such as Brazil and Venezuela; he branched out into exotica such as Moroccan loans; he bought shares in Brazilian utilities, which were absurbly cheap by international standards.
p.198
Then in late 1996 Fraga attended a talk by Stan Fischer, the number two at the International Monetary Fund. The mood was mostly upbeat: Mexico's currency had recovered from its crisis, and emerging markets were booming. Still, somebody asked Fischer the question “Who do you think is the next Mexico?”
p.198
“I'm not sure there's another one out there at the moment”, Fischer answered. “But I do see some imbalances in Asia. That might be interesting to look at.”
That comment, Fraga recalled later, “put a little light in my mind”.10 A few weeks afterward, Fraga read a joint IMF-Federal Reserve paper titled “The Twin Crises”, which laid out in terrifying detail how a currency collapse could interact with the collapse of a banking system.11
p.198
Casting his mind back to Fischer's remarks, Fraga approached Druckenmiller.
“Do you mind if I go and take a look at what is going on in Asia?” he asked him.
“Sure”, came the answer. “Go”.12
p.198, p.199
in Jaunary 1997, Fraga landed in Thailand
local officials, company executives, and economists,
the country fitted the double-crisis model laid out in the IMF-Fed paper.
If the foreigners tired of lending to Thailand, the country would have to export enough not only to cover its import bill but also to repay outsiders. To boost exports and cut imports, the Thai baht would hav to fall ── sharply.
p.199
The tipping point for Fraga came during a visit to the Bank of Thailand. Together with David Kowitz, Soros's expert on Asian equities, and Rodney Jones, an economist who worked for Soros in Hong Kong, Fraga was granted an audience with a high-ranking official at the central bank.
p.199
Invoking his own experience as the deputy governor of Brazil's central bank, Fraga offered some thoughts on the dilemma that Thailand confronted:
p.200
Fraga had a mild manner, and his Brazilian background helped; he seemed more like a benign emerging-market peer than a menacing Wall Street predator. So the official looked at Fraga and gave him an answer tht was at once honest and naïve.
p.200
The official repeated his statement, and the Soros team got what it was looking for. Their host had told them that he knew the game was up: He had confessed nd reconfessed his nakedness. Whatever the official pronouncements on Thailand's commitment to its exchange-rate peg, it was only a matter of time before the baht was devalued.
p.200
After a stop in South Korea, Fraga returned to New York and reported back to Druckenmiller.
p.200
The big man listened to Fraga's story and quickly approved a trade, and over the space of a few days in late January, the Soros team sold short about $2 billion worth of the Thai currency.14
The selling was both a prediction of a crisis and a trigger that could bring it on:
p.201
The Soros team had taken out baht loans of six months' duration and had locked in the low interest rates that had existed before the government hiked them. Secure in their positions, Druckenmiller and Fraga could afford to wait until the end of July for the inevitable to happen.16
pp.201-202
the morality of speculation in developing countries: If currencies crashed, millions of innocents would be forced into desperate poverty.19
p.203
The new position still represented only a third of the Soros funds' capital, a fraction of what Druckenmiller could have sold if he had leveraged up aggressively. But now Druckenmiller was no longer the only player in the game; Thai investors were leading the charge out of the baht, and other hedge funds were following. Paul Tudor Jones, who spoke with Druckenmiller several times each day, was quick to put on a trade, as did several of the other macro funds from the tight-knit group around him. The biggest player after Druckenmiller was probably Julian Robertson's Tiger, which built a short position in the baht that eventually came to $2 billion.21
p.204
On May 15, the day after Druckenmiller upped the ante, the Thai authorities forbade all banks from lending baht to anyone outside the country. This put short sellers in a bind: They could no longer borrow baht in order to sell them unless they secured the loans offshore at punitive interest rates. Tiger, for example, had financed some of its positions by borrowing baht on a short-term basis, figuring that it could roll over the loans as they came due; now it was forced to renew them at vastly higher interest rates:
“”─“”‘’•“”
p.205
But by doggedly calling the banks that executed the government's sell orders in the forward markets, Jones had pieced together the alarming rate at which real reserves were dwindling. By his reckoning, the Bank of Thailand had used up $21 billion worth of reserves in May alone, a stunning two thirds of its war chest.26
p.206
Over the next three months, it fell by 32 percent against the dollar. The Soros funds gained about $750 million from the devaluation, and Julian Robertson gained perhaps $300 million;29 meanwhile, Thailand's output collapsed by 17 percent from its peak, destroying businesses and jobs and plunging millions into poverty. By an uncanny coincidence, July 1, 1997, was the day when Britain ceded control over Hong Kong.
pp.249-252
p.249
The original of Tiger's losses went back to the summer, when a confident Julian Robertson had written an upbeat investor letter.
p.249
Robertson had been particularly impressed by the discussion of the yen. Japan was deregulating its financial markets, allowing investors to shift money abroad; and with yen interest rates at just over 1 percent, it seemed obvious that Japanese savers would seize the chance to earn more on their investments.
p.249
As Japanese capital flooded abroad, the yen would head down. Robertson left his investors in no doubt that he would short Japan's currency.
p.249
so Robertson had underestimated the extent to which deleveraging would hit his yen trade.
pp.249-250
Precisely because yet interest rates were low, traders borrowed the Japanese currency to finance their positions around the world; if they dumped those positions and paid their yen back, the currency would be pushed upward ── the opposite of what Robertson was expecting.
p.250
It was the day that Tiger's yen bet started to go wrong. Japan's currency rose 7 percent against the dollar over the next month, and Tiger saw over $1 billion of its capital evaporate.
That was only the start of Tiger's troubles, however. Just as Long-Term was hammered by rivals who knew too much about its positions, so Robertson found himself in a similar predictament.
p.250
The moment that Robertson sent out his July letter, every trader knew he was short Japan's currency; and the more the yen rose, the more they expected him to be forced to staunch his lossses by buying back yen and closing his position.
p.250
On October 7 the yen jumped especially sharply , and traders sensed that Robertson would crack. They drove the yen up still more, calculating that Tiger's compelled exist from its trade would deliver yen holders a handsome profit.
p.250
By around 10:00 A.M. on October 8, 1998, Japan's currency had appreciated by an astonishing 12 percent since the previous morning.
p.250
More than $2 billion of Tiger's equity had gone up in smoke;
p.250
Robertson convened a crisis council of his top lieutenants. They gathered in is splendid corner office, with its panoramic views of Manhattan; but the spectacle that mattered was flashing and blinking in the windowless core of their building, where the trading desk monitored the yen's surge upward.
p.251
By an irony that was no doubt lost on the participants, the man who dominated the crisis council was none other than Michael Bills, the son of the military aviator who had joined Tiger after Tom Wolfe called him and talked about the fighter-pilot culture.
p.251
Bills argued to his colleagues that the market had gone crazy because it thought Tiger was on its knees; if Tiger could show that it still had the right stuff, it could restore Wall Street to its senses.
p.251
Bills proposed that Tiger should attack rather than retreat. Rather than closing out its yen short, as the market expected, it should demonstrate its fearlessness by adding to its bet against Japan's currency. One brave gesture would prove to predatory traders that it was not easy meat. The yen would stop speeding upward.
p.251
Dan Morehead, Tiger's currency trader, hurried to his cockpit to execute the Bills plan. He would add $50 million to Tiger's bet against the yen, gambling that his signal would break the currency's momentum.
p.251
Morehead called a dealer at one of the big banks. He asked for a two-way price on dollar-yen, not wanting to give away whether he was buying or selling. Normally it took a couple of seconds for the bank to quote a price. This time there was a lengthy silence. The expectation that Tiger would soon be fored to buy yen by the billion had scared potential sellers to the sidelines; who wanted to shed yen when Tiger was about to force their price up?
p.251
Because of the dearth of seller, the market had dried up; there were no trades and no prices. Morehead's bank dealer would have to name a price in a vacuum.
p.251
After a fully half a minute, the answer came back. The bank would sell Tiger dollars using an exchange rate of 113.5 yen to the dollar; it would buy dollars back using an exchange rate of 111.5 yen to the dollar. The two-yen gap between the quotes was astronomical ── maybe forty times the spread that Morehead was in a normal market. Like LTCM before it, Tiger was discovering that liquidity can dry up when it's most needed.
p.251
“I buy”, Morehead said.
p.252
In that instant, the bank that took his order knew that Tiger was not going to be squeezed out of its trade. Julian Robertson and his Tigers stll had the will to fight! Only a fool would trade against them! Seconds later the dearth of sellers came to an abrupt end: The bank's proprietary traders began dumping yen, and the dumps communicated the sea change to every currency desk on Wall Street. The yen started falling as quickly as it had risen earlier in the day. The aviator's son had won. Tiger had been in a tailspin, but disaster had been averted.3
p.252
; Tiger's debt-to-equity ratio was around five to one, which gave it the muscle to hold on to its yen short rather than getting squeezed out of the position.4 But this vindication was scant comfort to Tiger's partners. During the course of October, Robertson managed to lose $3.1 billion in currencies, primarily from his bet against the yen; and his excuses were not persuasive.
p.252
Tiger had been short an astonishing $18 billion worth of the currency ── a position almost twice as large as Druckenmiller's famous bet against [British] sterling.6
p.252
In the aftermath of this disaster, Robertson promised his investors that he would scale back his currency trading.
“”─“”‘’•“”
p.254
“The market can stay irrational longer than you can stay solvent”, Keynes famously declared. Being early and right is the same as being wrong, as investors have repeatedly discovered.
As the bubble inflated in 1999, Julian Robertson declined to fight it. He had no doubt that technology stocks were way too high, but he had lost money on technology shorts the previous year and had concluded that there was no safe way to bet against the bubble. He was comfortable shorting individual companies, because he could hedge out the risk of a general rise in the market by going long similar stocks. But when the entire technology sector was overvalued, hedging became hard: Robertson couldn't short all tech stocks while going long an equivalent bucket of assets, since there was no such equivalent. Besides, the momentum in the tech bubble seemed almost unstoppable. Robertson likened the NASDAQ to a locomotive hurtling down the tracks. It was certain to come off the rails, but there was no telling when. Only a foll would stand in front of it.10
pp.255-256
p.255
Recognizing the dangers, Tiger's airline analyst had counseled Robertson to sell part of the position when US Airways organized a share buyback in early 1998, but Robertson had refused: “It's one of my best ideas”, he had countered.12
p.255
Given the finite number of opportunities available to a supersized value investor, Robertson was prepared to hold on to a position long after his original investment thesis had paid off, never mind the fact that it had grown too big to be liquid.
pp.255-256
As the stock continued to head down, there was no way that Robertson could sell his vast position on the open market without sending its price into free fall; he had become an owner rather than an investor.
p.256
Tiger was reduced to rooting around for a strategic buyer of the airline that might take a large block off his hands, and meanwhile, Robertson's other value bets were souring.
“”─“”‘’•“”
p.267
When Swensen completed his doctorate in 1980, Salomon immediately hired him, and he thrived on the competitive culture of Wall Street. He helped make financial history the following year by playing a role in the creation of the first currency swap, a deal between IBM and the World Bank that allowed the technology company to hedge its exposure to Swiss francs and German marks; and in 1982 he was lured away by Lehman Brothers to run the bank's fledgling swaps desk.2 But in 1985, when his former professors lobbied him to take over Yale's troubled endowment, Swensen accepted happily. He gave up investment-banking bonuses for a book-lined office on the university campus, taking a pay cut of 80 percent. Years later, a Wall Street admirer remarked that Swensen could have been a billionaire if he had applied his talents to running a hedge fund. “What's the matter with you?” the admirer asked. “A genetic defect”, Swensen responded.3
p.268
the economist in Swensen was impressed by the design of hedge-fund incentives.
He knew that the larger an investment bund, the harder it was for a fund manager to generate returns,
preferring the performance fees that accounted for most hedge-fund revenues.
sought out hedge-fund managers who had their own savings in their funds and
what really interest Swensen was the scale and source of hedge-fund profits.
Hedge funds promised equity-sized returns that were uncorrelated with the market index, offering the free lunch of diversification.
p.268
Tom Steyer
p.269
Steyer founded Farallon in 1985, the same year that Swensen took over the Yale endowment.
a young analyst at Morgan Stanley,
After a stint at Stanford's business school, Steyer had worked at Goldman Sachs for the merger-arbitrage unit run by Robert Rubin, the future Treasury secretary.
p.269
He had begun at a firm that took no responsibility for bad investment.
He had moved to a firm that took responsibility collectively but that did not always recognize an individual's contribution.
p.269
He would ride the elevator up to his office at 5:30 in the morning, clutching his coffee and doughnut, ready to analyze the merger action
The investment style he practiced was the same one he had learned at Goldman Sachs.
When a takeover bid was announced, the stock in the target company would move most of the way to the bid price:
p.270
Knowing whether to risk $8 to make $2 required a special skill. You had to judge whether antitrust regulators would block the merger, or whether shareholders would revolt. You had to estimate the odds that another suitor might emerge stage left, perhaps pushing the stock above $40.
p.270
By buying target companies in deals that would be consummated, Steyer eked out profits, month by month. And by shorting the acquiring firms, he hedged out the risk from general market movements.
p.270
The junk-bond collapse created an opportunity to apply his analytical skills in a different context.8 The companies at the center of the junk-bond market filed for bankruptcy one by one; and an investor who could figure out which piece of busted debt to buy was more likely to profit handsomely. To make matter even better, pension funds, mutual funds, and other institutional investors were forced sellers of junk: Their rules forbade them to hold the bond of companies in default, so they were compelled to concede bargains to nimble players such as Farallon.9
p.270
When Drexel Burnham Lambert, the kingpin of the junk-bond market, filed for bankruptcyB in 1990, Steyer brought a large slice of its debt at cents on the dollar; and when he sold his stake in 1993, Farallon's portfolio chalked up a 35 percent profit.10
p.450
10. Steyer recalls that the conventional wisdom after Drexel's bankruptcy was that “everything Drexel's ever done was fraudulent, nothing they own is worth anything, these companies are all a joke. Everybody knew that, but it just didn't happen to be true. So if you could bid ── which is what we were doing too ── against that underlying absolutely accepted lie, then you can make a phenomenal amount of money.” Steyer interview.
p.271
Steyer had created what would later be known as an “event-driven” hedge fund. He specialized in events that caused existing prices to be wrong ── moments when a disruption suddenly tendered the market's settled view inoperative. The moment before a takeover bid, a company's share price embodies the verdict of investors who had projected future earnings: The price is eficient in the sense that it has been analyzed to death already. The moment after the takeover bid, the old calculations are scrambled: Now the analysts have to look at the size of the takeover premium, the time until it is likely to be realized, the rate at which it should be discounted, and so on.
p.271
In similar fashion, an event such as a bankruptcy scrambles yesterday's consensus on the value of a company's bonds. Again, the challenge si to look afresh at the cash flows that each busted bond seems likely to generate.
p.271
Steyer was generating profits by focusing on occasions in which settled prices were scrambled;
pp.271-272
He insisted that Farallon employees keep their liquid savings in the fund so that they would feel the pain if they lost money.13
p.273
By 2009 roughly half the capital in hedge funds came not from individuals but from institutions.
The rush of endowment money into hedge funds ensured that there was no need at all to write an epitaph for the industry.
“”─“”‘’•“”
p.274
Kondratiev waves and breakout points: To the average investment committee, this was hocus-pocus. But event-driven funds like Farallon involved no mystery at all. These guys studied legal labyrinths. They understood the odds that a given merger would go through. They could judge how a particular slice of subordinated debt was likely to be treated by a particular bankruptcy judge in a particular court. With this sort of edge, of course they would make money!
p.274
Institutional investors had rules that forced them to sell the bonds of companies in default, so they were required to cede profits to Steyer and his imitators. The more endowments displaced rich individuals as the chief investors in hedge funds, the more it mattered that hedge-fund strategies could be understood. A rich investor can bet his personal fortune on a mysterious genius if he so chooses. Endowment committees must protect their backs with PowerPoint presentations.
p.274
event-driven
They used very little leverage,
“”─“”‘’•“”
p.285
James Simons,
As a code cracker, Simons had worked at the Pentagon's secretive Institute for Defense Analyses, where he learned how to build a research organization that was closed toward outsiders but collaborative on the inside.
p.286
a breakthrough known as the Chern-Simon theory
won the American Mathematical Society's highest honor in geometry.
p.286
Leonard Baum, a cryptographer who had worked with Simons at the Institute for Defense Analyses.
James Ax, a winner of the American Mathematical SOciety's foremost prize in number theory.
Elwyn Berlekamp, a Berkeley mathematician who was yet another veteran of the Institute for Defense Analyses.
p.286
Their experiences in cryptography and other aspects of military communications were relevant to finance. For example, Berlekamp had worked on systems that send signals resembling “ghosts” ── faint traces of code in seas of statistical noise, not unlike the faint patterns that hide in broadly random and efficient markets.
pp.286-287
Soldiers on the battlefield need to send messages to air cover that are so wispy and translucent that they won't betray their positions: Not only must the enemy not decode the messages; it must not even suspect that someone is transmitting.
p.287
To Berlekamp, the battlefield adversaries fooled by such systems bore a striking resemblance to economists who declared markets' movements to be random.
p.287
They had stared at the ghosts. They had seen and suspected nothing.2
p.287
The Simons team took their experience with code-breaking algorithms and used it to look for ghostly patterns in market data. Economists could not compete in the same league, because they lacked the specialized math needed to do so.
p.287
Medallion traded commodity and financial futures on the basis of computer-generated signals; and although the heart of the system was unremarkable ── it was a trend-following model not unlike the one built at Commodities Corporation more than a decade before ── a small portion of the money was deployed according to a different set of rules.
p.287
Henry Laufer,
The kernal was the brain child of Henry Laufer, a member of the mathematics faculty at Stony Brook University.3
pp.287-288
p.287
But Laufer's eccentricity was matched by his talent. In a triumph of ghost hunting in the mid-1980s, he had spotted patterns in the way that markets move right after an event purturbs them.
p.287
In the period after a new data release, a commodity or currency would spike upward and downward as different investors reacted, and although the jiggering appeared random to the naked eye, a scientist with high-resolution statistical goggles could make out patterns in the movements.
p.287
It was not that a commodity would jigger in the same way following every piece of news: That would have been too obvious.
pp.287-288
But if you scrutinized thousands of reactions to thousands of events, certain sequences emerged in slightly more than half of all the observations.
p.288
And by betting enough times and in great enough size, it could be assured of handsome profits.4
The algorithms that describe Medallion's lucrative patterns were and have remained a secret.
p.288
By examining a commodity's behavior over brief periods, Laufer could collect thousands of observations, boosting his chances of finding repetitive patterns that were statistically significant. Moreover, short-term signals were likely to be more valuable as well as easier to find.
p.288
If you can predict which way a commodity will move over the next few days, it takes only that long to place your wager and collect your reward; a Tiger investor aspires to buy a company that will double its value in two years, but a statistical trader who makes a quarter of a percent in 24 hours will end up considerably richer.
p.288
Finally, predictions over the short term tend to inspire more confidence than the long-term sort. There's less time for unforeseen factors to knock the forecast off target.
p.288
Because it was dealing in short-term predictions that were relatively robust, the Simons team could leverage its bets and magnify its profits.
p.288
When Simons and Ax launched the Medallion Fund in 1988, about 15 percent of its capital was driven by the short-term signals, with the rest alloted to traditional trend-following models.5
p.288
The fund began promisingly, then dipped into a terrifying nosedive; by May 1989 it was down almost a quarter from its peak, and Simons decided to suspend trading.
p.288
, but Simons was so convinced that Ax was wrong that he ended the partnership.
p.288
Enlisting the help of Berlekamp and Laufer, he embarked on a “study period” to decide Medallino's future.
p.288
The trouble, Simons and his team decided, was that the trend-following mainstay of Medallion's system had run out of juice. Too many Commodities Corporation wannabes had crowded in; brokers such as Deam Witter were marketing dozens of commodity funds to their clients; trend following had grown trendy.6
pp.288-289
After some months of deliberation, Simons and his colleagues resolved to make Laufer's short-term signals the new heart of the system.
p.289
In 1990, the first full year of trading after the relaunch, Medallion notched up 56 percent after subtracting fees. it was a good beginning.
p.289
Elwyn Berlekamp reacted to this bonanza by cashing out. He sold his share of the management company that ran Medallion and returned to his research interests at Berkeley.
p.289
Having bought Berlekamp's share of the management company, he rolled what was left of it into his operations at Renaissance Technologies.
p.289
he redoubled his efforts to hire mathematicians onto his team,
Long Island High Technology Incubator building near the Stony Brook hospital.
The expanding research team discovered that the patterns that worked in American commodities markets often worked in foreign markets too. And, after some setbacks, the Simons team's ghost-hunting methods discovered patterns in equity markets.
p.289
Simons added computer scientists, physicists, and astronomers to his roster, though he never hired economists.
He wanted people who would approach the markets as a mathematical puzzle,
p.289
On one occasion, a member of the faculty gave a presentation on how Medallion had performed over the past week; he presented Friday's results first, followed by Monday's, Thursday's, Tuesday's, and then Wednesday's, assuming that his colleagues would find this bizarre sequencing natural, since computers sort days alphabetically.
p.290
When the big night arrived, the program seated one of Renaissance's long-time investors next to a woman he may have liked too much. She had sued him for sexual harassment.
p.290
Simons invested heavily in computers, which were fed with every conceivable form of data:
prices from financial markets,
economic releases,
information from newswires,
even time series on weather.
The deeper the team went with its ghost hunting, the more it succeeded in discovering profitable patterns.
“”─“”‘’•“”
p.293
The early options models, created among others by the two LTCM Nobel laureates, RObert Merton and Myron Scholes, assumed that stock-price changes were distributed normally. The 1987 crash had demonstrated that this assumption was not merely shaky; it was dangerously wrong ── the truth was that extreme price moves happen far more frequently than the normal distribution anticipated.
“”─“”‘’•“”
pp.298-301
p.298
in 1993
Peter Brown and Robert Mercer.
They came from IBM's research center,
Before arriving at Renaissance, Brown and Mercer had worked a little on cryptography, but their real achievement lay elsewhere.
They had upended a related field ── that of computerized translation.
p.298
on translation, the subject was dominated by programmers who actually spoke some foreign languages. The approach was to understand the language from the inside, to know its grammer and its syntax, and to teach the computer that “la fille” means “the girl” and “les filles” is the plural form, much as you might reach a middle schooler.
p.299
But Brown and Mercer had a different method. They did not speak French, and they were not about to wade into its syntax or grammer. Instead, they got hold of Canada's parliamentary records, which contain thousands of pages of paired passages in French and English. Then they fed the material into an IBM workstation and told it to figure out the correlations.
p.299
their experiment at IBM was written up and published.21 It began with some scrubbing of data: Just as financial-market price histories must be checked for “bad tics” ── places where a sale is reported at $16 instead of $61 ── so the Canadian Hansard contained misprinted words that might confuse a translation program. Next, the computer began to search the data for patterns.
p.299
For all it knew at the outset, a given English word was equally likely to be translatable into any of the 58,000 French words in the sample, but once the computer had checked through the twinned passages, it found that most English words appeared in only some: Immediately, nearly 99 percent of the uncertainty was eliminated. Then the computer proceeded with a series of more subtle tests; for example, it assumed that an English word was most likely to correspond to a French word that came in the same position in the sentence. By now some word pairs were starting to appear: Couplings such as lait/milk and pourquoil/why shouted from the data. But other correlations spoke in a softer voice.
p.299
To hear them clear, you had to comb the data multiple times, using slightly different algorithm at each turn. “Only in this way can one hope to hear the quiet call of marqué d'un asterisque/starred or the whisper of qui s'est fait bousculer/embattled”, Brown and Mercer reported.
p.299
To the code breaker at the Institute for Defense Analyses, this method would not have seemed surprising.22
p.299
Indeed, Brown and Mercer used a tool called the “expectations maximization algorithm”, and they cited its inventor Leonard Baum ── who had worke for IDA [Institute for Defense Analyses] and then later for Simons.23
p.299
But although the idea of “statistical machine translation” seemed natural to the code breakers, it was greeted with outrage by traditional programmers. A reviewer of the Brown-Mercer paper scolded the “the crude force of computers is not science”,
p.300
and when the paper was presented at a meeting of translation experts, a listener recalled, “We were all flabbergasted .... People where shaking their heads and spurting grunts of disbelief or even of hostility.”
“Where's the linguistic intuition?” the audience wanted to know ── to which the answer seemed to be, “Yes that's the point; there isn't any”.
Fred Jelinek, the IBM manager who oversaw Brown and Mercer, poured salt into the wounds. “Every time I fire a linguist, my system's performance improves”, he told the naysayers.24
p.300
By the time Brown and Mercer joined Renaissance in 1993, the skeptics were capitulating. Once the IBM team's program had figured out the sample passages from the Canadian Hansard, it could translate other material too: If you presented it with an article in a French newspaper, it would zip through its database of parliamentary speeches, matching the article's phrases with the decoded materia. The results outclassed competing translation systems by a wide margin, and within a few years the advent of statistical machine translation was celebrated among computer scientists as something of an intellectual revolution.25
p.300
Canadian political rhetoric had proved more useful than suspected hitherto. And Brown and Mercer had reminded the world of a lesson about artificial intelligence.
The lesson concerned the difference between human beings and computers.
p.300
The early translation programs had tried to teach computers vocabulary and grammar because that's how people learn things.
p.300
But a computers are better suited to a different approach: They can learn to translate between English and French without paying much attention to the rules of either language. Computer don't need to understand verb declensions or adjectival inflections before they approach a pile of political speeches; they prefer to get the speeches first, then penetrate their code by combing through them algorithmically.
p.300
Likewise, computers have no trouble committing millions of sentences to memory; they can learn languages in chunks, without the crutch of grammatical rules that human students use to prompt their memories.
pp.300-301
For example, a computer can remember the English translations for phrases as “la fille est intelligente, les filles sont intelligentes”, and a dozen other variations besides; they do not necessarily need to understand that “fille” is the singular form of “filles”, that “est” and “sont” are different forms of the verb “être”, and so on.26
p.301
Contrary to the harrumphing of the IBM team's critics, the crude force of a computer's memory can actually substitute for human notions of intelligence and science. And computers are likely to work best when they don't attempt to reach results in the way that humans would do.
p.301
Brown and Mercer fed the data into the computer first and let it come up with the answers.
“”─“”‘’•“”
p.453
21. See, for example, Peter F. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer, “The mathematics of statistical machine translation: parameter estimation”, computation linguistics 19, no. 2 (1993). As noted below, the Della Pietra brothers followed Brown and Mercer from IBM to Renaissance Technologies.
p.454
22. As far back as 1949, code breakers had wondered about the application of their technique to translation. But they lacked computing power; statistical translation depended on feeding a vast number of pairs of sentences into a computer, so that the computer had enough data from which to extract meaningful patterns. But by around 1990, statistical translation was possible on a well-equipped workstation.
23.
24. An account of the reaction to the Brown-Mercer work is given in Andy Way “A critique of statistical machine translation”. In W. Daelemans and V. Hoste (eds.), Journal of translation and interpreting studies: special issue on evaluation of translation technology, Linguistica antverpiensia, 2009, pp.17-41.
25. See, for example, Pius Ten Hacken, “Has there been a revolution in machine translation?” Machine Translation 16, no. 1 (March 2001): pp. 1-19.
26. The initial version of the IBM program included no linguistic rules at all. Later versions did use some, but they played a far smaller role than in the traditional translation programs.
p.454
29.
explicitly presented their experience with statistical machine translation as relevant to finding order in other types of data, including financial data. See Adam L. Berger, Stephen A. Della Pietra, and Vincent J. Della Pietra, “A maximum entropy approach to natural language processing”, computational linguistics 22, no. 1 (March 1996): pp.39-71.
“”─“”‘’•“”
p.345
; and Richard Bookstaber, an MIT alumnus who had worked at several major funds, pressed the warnings he had recently published in a pessimistic book on finance.37
p.459
Richard Bookstaber, “What's Going On with Quant Hedge Funds?”
(available at http://rick.bookstaber.com/2007/08/whats-going-on-with-quant-hedge-funds.html).
pp.354-355
p.354
A classic example came from Kazakhstan, a sprawling expanse of Eurasia that most Wall Streeters had only heard of via Borat. Thanks to its enormous oil reserves, Kazakhstan was growing at 8 percent or 9 percent a year, and the country was running an export surplus; it was a pretty sure bet that the currency would appreciate against the dollar. The question was how to cash in on this rise. Because of oil revenues, the government had no need to issue debt, so there was no sovereign bonds for foreigners to purchase. Starting around 2003, hedge funds found a way around this obstacle. Rather than buying sovereign bonds, they lent directly to Kazakh banks, getting exposure to the Kazakh currency plus a higher interest rate on their money.
p.356
Pinned up by a window, a page torn from a yellow legal pad bore a scrawled message from Jones to himself: “Always look for a trending market.”
In late June of that year, Jones got himself convinced that the trend was downward. The S&P 500 index had jumped sharply in April and kept rising in May, but Jones thought this was a sucker's rally. The united states in the late 1920s, Sweden in the 1990s. He pored over the price patterns in these historical analogues, hunting for hints about how the market might behave. Then on Saturday, June 28, at 3:05 A.M., he fired off a eureka e-mail to colleagues.
p.356
in 1987, declines in the bond market had spooked stocks, since higher interest rates meant that less money would slosh into the equity market.
p.356
In the first half of 2008, Jones reckoned, rising oil prices had had the same effect: The inflationary pressure from dear oil was driving the Fed to keep interest rates up, draining liquidity from asset markets.
p.357
For the next two months, Jones continued to play the historical detective. Sometimes he thought that the S&P chart resembled the recession of 2001; sometimes it looked like 1987. But no matter which analogue appealed, Jones remained negative on the market outlook, and in the end he reading of the charts mattered less than the instinct behind it. What really counted was that Jones was looking at an asymmetrical bet, and he understood this intuitively.
p.357
During the dot-com mania of the late 1990s, he had written to Alan Greenspan, the Fed chairman, urging him to raise margin requirements on stock traders so as to slow the flood of cash that was inflating the tech bubble.
p.361
For a trader like Paul Jones, the worst thing was that he was trapped in these positions. When he speculated in futures, he always knew he could turn on a dime; indeed, he never created a position without putting a “stop” that would take him out if he began to suffer losses.
p.361
But the emerging-market loans were utterly illiquid: After Lehman declared bankruptcy, nobody wanted to hold any loans at any price, so there was no way to get rid of them.
p.361
“I used to always think, ‘Holy cow, how'd these guys in the 1929 lose it all? How could anybody be so boneheaded? You'd have to be a complete moron!’ And then that day, I thought ‘Oh my God. I see how these guys in '29 got hurt now. They were not just sitting there long the market. They had things that they couldn't get out of.’”23
“”─“”‘’•“”
p.412
40. The top rate on regular income was 91 percent between 1951 and 1964; the top rate on capital gains was 25 percent during that time. In 1965, the top rate on regular income was lowered to 70 percent, where it stayed until 1968. Valentine of Seward & Kissel also figured out that a departing partner could be paid out with shares that carried with them unrealized investment gains, thereby ridding the hedge fund of tax liabilities; he continued to come up with ingenious tax designs for the successor generation of hedge funds, notably Tiger. John Tavss recalls, “He could take almost any problem and start spouting out potential solutions. HE would come up with five ideas immediately.” (John Tavss interview.) Craig Drill recalls: “Everyone in the hedge fund business who knew about this was very quiet about it for ten, or twenty, or thirty years.” (Craig Dril, interview with the author, March 20, 2007.)
41. “If a partner dropped out or he had a slot, he'd just mention it at dinner and say, ‘Are you happy?’ That's what he said to Pauline Plimpton ── widow of the founder of the law firm Debevoise & Plimpton ── and she said, ‘Yeah, I'm getting terrible results’, and she became a partner.” (Dale Burch interview.)
42. The securities act of 1933 contains an exemption for “transactions by an issuer not involving any public offering.” To avoid being deemed to be making a public offering, an investment partnership had to limit the number of partners. Likewise, the Investment Company Act of 1940, which imposes limits on the use of leverage, short selling, and high fees, contains an exemption for partnerships with fewer than one hundred partners that do not offer themselves publicly. Hedge funds were also anxious to avoid entanglement with the Investment Advisors Act, which prohibits “compensation to the investment advisor on the basis of a share capital gains.” To avoid registration under this act, hedge-fund managers argued that they were advising fewer than 15 clients, an assertion that hinged on the claim that the “client” was the investment partnership rather than the more numerous partners. If the SEC had rejected that assertion and forced hedge funds to register, it would probably have crushed them. Richard Radcliffe, the first fund manager hired by A. W. Jones, recalls: “We always were afraid of getting regulated, ad the way we would have been regulated would have been if we had too many partners there .... We got up close to a hundred, and we decided that we should have another fund. And we even separated out the investment strategies to make it look as if we were not just trying to get around the rules.” (Radcliffe interview, April 16, 2007.) Clark Drasher, another A. W. Jones alumnus, offers a similar account. (Drasher interview.)
“”─“”‘’•“”
p.419
17. Commodities corporation survived because Nabisco was keen to keep it in business in order to retain access to Weymar's cocoa forecasts and Vannerson's wheat forecasts. Nabisco was able to overrule the other shareholders, which wanted to close the firm, because it held a senior claim on the remaining assets. In the case of liquidation, Nabisco would have reclaimed its $50,000, leaving the other investors with only $400,000 of their original $2 million. Having been virtually wiped out, the other shareholders decided that there was little to be lost by allowing Commodities corporation to continue trading. Weymar interview.
p.419
20. “Valuable as market analysis and data generation may be, money management discipline is even more important to successful speculation .... Most successful speculative derivatives traders generate more losing trades than profitable trades. They are successful only because their gains on positive trades are substantially larger than their tightly controlled losses on negative trades.”
Weymar, “Orange Juice, Cocoa, Speculation and Entrepreneurship.”
21. For example, Paul Tudor Jones began his hedge fund, Tudor, with the help of seed capital from Commodities Corporation. An official at Tudor recalled: “When we incubated young traders, when they came close to kickouts he [Paul Jones] would bring them into the office and say, ‘You've got to write an analysis on why this happened and how it's not going to happen again.’ He took that away from Commodities Corporation.”
p.419
23. Elaine Crocker, who rose to become a senior manager at Commodities Corporation and later president of Moore Capital, recalls, “The kickout forced you to liquidate your positions and get out of the market for thirty days. During this period you would plot the history of your trades in the period leading up to your losses and see whether you had violated your own trading philosophy. Most of the time, the answer would be yes. The whole system allowed traders to develop an approach to markets that would work for them, but at the same time held them accountable for sticking to it.” Elaine Crocker, interview with the author, July 30, 2008.
p.421
42.
- really learned that trends always go further than you think
- we found that over the short term trends tended to continue at every level.
- The theory was that unless you had a really good reason, you want to stay with the trend.
Roger Lowenstein, Buffett : the making of an American capitalist, 2001
p.411
Jim Grant, “My Hero, Benjamin Grossbaum”
http://www.grantspub.com/articles/bengraham/
p.411
Adam Smith, “Roy Neuberger : where the money is”
unpublished article dated December 5, 2003
ciculated by Craig Drill of Drill Capital
p.411
Peter L. Bernstein, Capital Ideas Evolving, 2009
Markowitz, 1952
“you have to think about risk as well a return”
p.430
13.
14. Elaborating on how he would write a script for the market, Jones says, “I put myself in the mental position of being short the market, and I think how I would react emotionally to different events and see what it would take to get me to take my position off. And I write that down and that will be the high for the day. Because the high for the day will be the point at which the shorts capitulate. I close my eyes and imagine myself long. I say, ‘Okay, where is the point I get nervous? Where would I say,“Oh my God, I have to get out?”’ And that would be my projected low for the day. That preparation is important to try to determine great entry points to buy and to sell. You know every single high and low is going to be made in the context of these emotional extremes being hit. Execution is fifty percent of the game.” Paul Tudor Jones, interview with the author, April 23, 2009.
p.431
23. In an interview in 2000, Jones emphasized the importance of understanding how other players are positioned. “The secret to being successful from a trading perspective is to have an indefatigable and an undying and unquenched thirst for information and knowledge. Because I think there are certain situations where you can absolutely understand what motivates every buyer and seller and have a pretty good picture of what's going to happen. And it just requires an enormous amount of grunt work and dedication to finding all possible bits of information.” Paul Tudor Jones II, interview by Joel Ramin.
“”─“”‘’•“”
p.436
51. The Swedish trade was conceived by Robert Johnson. On the secrecy of the Swedish trade, Druckenmiller recalls, “By then at least we learned to keep our mouth shut.”
p.440
Those who returned capital to investors almost certainly did boost their performance over the ensuring years, since later research was to find an inverse correlation between size and investment returns.
([ if investment returns are measure as a percentage changed in asset price appreciation from year to year, or, over five years, or, over seven years, then yes, mathematically, as the size grow, the percentage changed will be lower incomparison to a smaller size; because when you are smaller, it does not take much to grow; take a look at a baby, how quickly does she grow to a toddler; or, even more dramatic, a look at the size of a sperm and an egg, it takes about 9 months for that egg to grow into a full-size baby in the mother's womb ])
For example, in 2009 the software firm PerTrac Financial Solutions reported that between 1996 and 2008 hedge funds managing less than $100 million made 13 percent a year, compared with 10 percent for those running more than $500 million. (Stephen Taub, “The Hedge Rows of Wall Street”, p.38.) Likewise from Rock Creek Capital, reported in chapter 16, reinforce the view that size is an impediment.
([ beyond a certain threshold, size becomes an impediment; what is that threshold? ])
p.448
57. Reflecting on the evolution of his thinking, Peter Fisher comments, “I was reluctant to say then, ‘Therefore we should regulate leverage.’ I guess I got myself halfway there. I was saying, ‘The problem was leverage, but how do we regulate that?’ Ten years on the problem is leverage and we just got to regulate it; we got to find a way. So that's the policy change for me in ten years.” (Fisher interview.)
“”─“”‘’•“”
p.448
So they just let him get bigger and bigger without letting him know that he was becoming the market.
p.449
16. Other prominent hedge-fund managers observing Tiger's plight explicitly drew the lesson that secrecy was essential to stability. For example, Louis Bacon of Moore Capital deliverd a speech in London in April 2000 drawing this lesson. See Alexander Ineichen, “The Myth of Hedge Funds”, Journal of Global Financial Markets 2, no. 4 (Winter 2001), pp.34-46.
35. Druckenmiller interview. The role of Celera Genomics as a trigger is suggested in a detailed reconstruction of Quantum's last weeks, which quotes Druckenmiller as saying to a trader. “This is insane. I've never owned a stock that goes from $40 to $250 in a few months.” See Gregory Zuckerman, “Hedged out: how the soros funds lost game of chicken against tech stocks”, wall street journal, may 22, 2000.
p.450
6. The partner was Katie Hall, who had known Steyer at Morgan Stanley and at Stanford.
7. Steyer's colleagues Katie Hall recalls, “Tom is a very, very, very, very focused guy, and if he can't sleep he goes into the office.” (Katie Hall, interview with the author, August 28, 2008.)
Likewise, Meridee Moore recalls, “Sometimes you'd be right there with Tom trying to talk to him and he would pick up the phone. I used to go into a conference room and call him on the phone sometimes because it would be easier to get his attention. He would always take the phone call. I think that's an arbitrage thing. What if the phone call is from somebody saying the deal's about to break?” (Meridee Moore, interview with the author, July 24, 2008.)
11. Swensen explains why event-driven funds have a systematic edge in David Swensen, Pioneering Potfolio Management: an unconventional approach to institutional investment, 2009, p.183.
p.451
12. Swensen, Pioneering Port folio Management, p.252. Reflecting on what motivated Steyer, Meridee Moore says, “You get to research different things every dy. You get to work on whatever you want. You're predicting outcomes. And if you're right, there's nothing more rewarding. It's the ultimate challenge. That's what keeps people going; it's not the money.” Moore interview.
p.451
21.
p.452
43. Swensen himself argued that illiquid markets offered bargains. “Success matters, not liquidity. If private, illiquid investments succeed, liquidity follows as investors clamor for shares of the hot initial public offering. In public markets, as once-illiquid stocks produce strong results, liquidity increases as Wall Street recognizes progress. In contrast, if public, liquid investment fail, illiquidity follows as investor interest wanes. Portfolio managers should fear failure, not illiquidity.” Swensen, Pioneering Portfolio Management, p.89.
“”─“”‘’•“”
p.452
Sandor Straus, a mathematician who was a partner at Renaissance Technologies and its antecedents between 1980 and 1996, recalls that the 5 percent fee was chosen in 1988 because that was what was needed to cover technology expenses.
p.454
33. Robert Frey explains, “Those researchers were sort of like hothouse flowers. They sit there. If they need data, the data are provided. They have no clue of the hoops you have to jump through to make sure that the data are available and clean and ready. There are tens of terabytes of data available at the touch of a button. Someone going out, who left the green house, so to speak, and went out into the cold, cruel world, I think would quickly find out that even if you could produce these simulations and do all this stuff, which isn't trivial, you wouldn't have access to the historical data. You wouldn't really know how to call up somebody and execute a trade. If you said to me, Robert you don't have a noncompete agreement and we want you to recreate Renaissance, it would probably be four or five years before you could get to a point where you could actually trade.” (Frey interview). It should be said, however, that Medallion defectors who join a rival hedge fund that has research and trading infrastructure already in place could damage Medallion in well under five years.
p.454
Richard Teitelbaum, “Simons at Renaissance Cracks Code, Doubling Assets”, Bloomberg.com, November 27, 2007.
p.456
13. Blackstone kept its withdrawl secret, at the time. the official explains that publicity might have caused other investors to flee Amaranth, creating a run on the fund that might have provoked a freeze on withdrawals, trapping Blackstone's money.
14. Amaranth's willingness to pay Morgan Stanley a large fee to get out of certain gas positions confirms the verdict that it had grown too big for the market. If it had been able to trade out of its positions easily, it would have done so. The Morgan Stanley evidence matters because Amaranth representatives have sometimes suggested that the fund was brought down not by its excessive size,
15. Hedge-fund transparency is generally considered a good thing, but there is a risk to it.
p.460
14. “I just thought even though one was a weekly and one was a daily, the chart patterns were so similar and the backdrops were so similar ── two huge credit bubbles with enormous overcommitment to a variety of asset markets, real estate and stock market bubbles happening simultaneously.” Paul Tudor Jones, interview with the author, April 15, 2009.
pp.460-461
16. After the back, policy makers argued that they let Lehman fail because they lacked the legal authority to do otherwise. But policy makers had successfully stretched the legal bounds of their authority in other cases, and they acted aggressively again in the following days with respect to AIG, and then with respect to Goldman Sachs and Morgan Stanley, which were hurriedly granted full access to the Fed's emergency loans. Moreover, the policy makers' claim that the Fed could not lend to Lehman because it lacked adequate collateral is weakened by the fact that in the three days after Lehman's bankruptcy, the Fed did actually lend Lehman's broker-dealer unit $160 billion to tide it over until its sale to the British bank Barclays. It seems overwhelmingly likely that the government would have found a legal way to save Lehman Brothers if it had guessed in advance the consequences of its failure.
p.461
18. Jones interview. Jones adds, “From a trading perspective, fear is a much stronger emotion than greed, which is why things go down twice as fast as they go up. And that's also just the law of nature. How long does it take for a tree to grow, and how quickly can you burn it down? It's much easier to destroy things than to build them up. So from a trading perspective, the short side is always a beautiful place to be because quite often when you get paid, you get paid in vertical no-pain type of moves.”
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