Sebastian Mallaby., More money than god : hedge funds and the making of a new elite, 2010.
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
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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
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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
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.
“”─“”‘’•“”
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.
“”─“”‘’•“”
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.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.
“”─“”‘’•“”
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.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.
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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(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, )
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