Tuesday, December 14, 2010

Today's Currents

Copied from Seeking Alpha
Tuesday, December 14, 4:00 PM At the close: Dow +0.43% to 11478. S&P +0.09% to 1242. Nasdaq +0.11% to 2628.
Treasurys: 30-year -1.72%. 10-yr -1.07%. 5-yr -0.53%.
Commodities: Crude -0.38% to $88.27. Gold -0.61% to $1395.80.
Currencies: Euro -0.07% vs. dollar. Yen -0.47%. Pound -0.44%.

The Fed leaves the Target rate and QE2 unchanged

Please click here to read the article on the moneywatch.com

Monday, December 13, 2010

Excerpt: 'The Quants'

Excerpted from The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It by Scott Patterson. Copyright 2010 by Crown Business. Reprinted by Permission of Crown Business, an imprint of the Crown Publishing Group, a division of Random House, Inc.
Chapter 2: The Godfather: Ed Thorp
Just past 5 a.m. on a spring Saturday in 1961, the sun was about to dawn on a small, ratty casino in Reno, Nevada. But inside there was perpetual darkness punctuated by the glow of neon lights. A blackjack player sat at an otherwise empty table, down $100 and exhausted. Ed Thorp was running on fumes, but unwilling to quit.
"Can you deal me two hands at once?" he asked, wanting to speed up play.
"No can do," she said. "House policy."
Thorp stiffened. "I've been playing two hands all night with other dealers," he shot back.
"Two hands would crowd out other players," she snapped, shuffling the deck.
Thorp looked around at the empty casino. She'll do whatever it takes to keep me from winning. The dealer started rapidly shooting out cards, trying to rattle him. At last, Thorp spied the edge he'd been waiting for. Finally — maybe — he'd have a chance to prove the merits of his blackjack system in the real-world crucible of a casino. Twenty-eight, with dark hair and a tendency to talk out of the corner of his mouth, Thorp resembled hordes of young men who passed through Nevada's casinos hoping to line their pockets with stacks of chips. But Thorp was different. He was a full-blown genius, holder of a Ph.D. in physics from UCLA, a professor at the Massachusetts Institute of Technology, and an expert in devising ingenious strategies to beat all kinds of games, from baccarat to blackjack.
As night stretched into morning, Thorp had kept his bets small, wagering $1 or $2 chips at a time, as he fished for flaws in his system. None were apparent, yet his pile of chips kept shrinking. Lady Luck was running against him. But that was about to change. It had nothing to do with luck and everything to do with math.
Thorp's system, based on complex mathematics and hundreds of hours of computer time, relied primarily on counting the number of Ten cards that had been dealt. In blackjack, all face cards — kings, queens, and jacks — count as Tens along with the four natural Tens in every deck of fifty-two cards. Thorp had calculated that when the ratio of Tens left in the deck relative to other cards increased, the odds turned in his favor. For one thing, it increased the odds that the dealer would bust, since dealers always had to "hit," or take another card, when their hand totaled 16 or less. In other words, the more heavily a deck was stacked with Ten cards, the better Thorp's chances of beating the dealer's hand and winning his bet. Thorp's Tens strategy, otherwise known as the Hi-Lo strategy, was a revolutionary breakthrough in card counting.
While he could never be certain about what card would come next, he did know that statistically he had an edge according to one of the most fundamental rules in probability theory: the Law of Large Numbers. The rule states that as a sample of random events, such as coin flips — or the hands in a game of blackjack — increases, the expected average also increases. Ten flips of a coin could produce seven heads and three tails, 70 percent heads, 30 percent tails. But 10,000 flips of a coin will always produce a ratio much closer to 50-50. For Thorp's strategy, it meant that because he had a statistical edge in blackjack, he might lose some hands, but if he played enough hands he would always come out on top — as long as he didn't lose all of his chips.
As the cards shot from the dealer's hands, Thorp saw through his exhaustion that the game was tipping his way. The deck was packed full of face cards. Time to roll. He upped his bet to $4 and won. He let the winnings ride and won again. His odds, he could tell, were improving. Go for it. He won again and had $16, which turned into $32 with the next hand. Thorp backed off, taking a $12 profit. He bet $20—and won. He kept betting $20, and kept winning. He quickly recovered his $100 in losses and then some. Time to call it a night.
Thorp snatched up his winning and turned to go. As he glanced back at the dealer, he noticed an odd mixture of anger and awe on her face, as if she'd caught a glimpse of something strange and impossible that she could never explain.
Thorp, of course, was proving it wasn't impossible. It was all too real. The system worked. He grinned as he stepped out of the casino into a warm Nevada sunrise. He'd just beaten the dealer.
Thorp's victory that morning was just the beginning. Soon, he would move on to much bigger game, taking on the fat cats on Wall Street, where he would deploy his formidable mathematical skills to earn hundreds of millions of dollars. Thorp was the original quant, the trailblazer who would pave the way for a new breed of mathematical traders who decades later would come to dominate Wall Street — and nearly destroy it.
Indeed, many of the most important breakthroughs in quant history derived from this obscure, puckish mathematician, one of the first to learn how to use pure math to make money — first at the blackjack tables of Las Vegas, and then the global casino known as Wall Street. Without Thorp's example, future financial titans such as Griffin, Muller, Asness, and Weinstein may never have converged on the St. Regis Hotel that night in March 2006.
Edward Oakley Thorp was always a bit of a troublemaker. The son of an Army officer who'd fought on the Western Front in World War I, he was born in Chicago on August 14, 1932. He showed early signs of math prowess, like mentally calculating the number of seconds in a year by the time he was seven. His family eventually moved to Lomita, California, near Los Angeles, and Thorp turned to classic whiz-kid mischief. Left alone much of the time — during World War II, his mother worked the swing shift at Douglas Aircraft and his father worked the graveyard shift at the San Pedro shipyard — he had the freedom to let his imagination roam wild. Blowing things up was one diversion. He tinkered with small homemade explosive devices in a laboratory in his garage. With nitroglycerine obtained from a friend's sister who worked at a chemical factory, he made pipe bombs to blow holes in the Palos Verdes wilderness. In his more sedate moments, he operated a Ham radio and played chess with distant opponents over the airwaves.
He and a friend once dropped red dye into the Plunge at Long Beach, then California's largest indoor pool. Screaming swimmers fled the red blob, and the incident made the local paper. Another time, he attached an automobile headlight to a telescope and plugged it into a car battery. He hauled the contraption to a lover's lane about a half mile from his home and waited for cars to line up. As car windows began to fog, he hit a button and lit up the parked assemblage like a cop's spotlight, laughing as frantic teens panicked and pulled themselves together.
During high school, Thorp started thinking about gambling. One of his favorite teachers returned from a trip to Las Vegas full of cautionary tales about how one player after another got taken to the cleaners at the roulette table. "You just can't beat these guys," the teacher said. Thorp wasn't so sure. Around town, there were a number of illegal slot machines that would spit out a stream of coins if the handle was jiggled in just the right way. Roulette might have a similar hidden weakness, he thought, a statistical weakness.
Thorp was still thinking about roulette in his second year of graduate school physics at UCLA, in the spring of 1955. He wondered if he could discover a mathematical system to consistently win at roulette. Already, he was thinking about how to use mathematics to describe the hidden architecture of seemingly random systems — an approach he would one day wield on the stock market and develop into a theory that lies at the heart of quant investing.
One possibility was to find a roulette wheel with some kind of defect. In 1949, two roommates at the University of Chicago, Albert Hibbs and Roy Walford, found defects in a number of roulette wheels in Las Vegas and Reno and made several thousand dollars. Their exploits had been written up in Life magazine. Hibbs and Walford had been undergraduate students at the California Institute of Technology in Pasadena, and their accomplishments were well known to astute denizens of CIT's neighbor, UCLA.
Thorp believed it was possible to beat roulette even without help from flaws in the wheel. Indeed, the absence of defects made it easier, since the ball would be traveling along a predictable path, like a planet in orbit. The key: Because croupiers take bets after the ball is set in motion, it is theoretically possible to determine the position and velocity of the ball and rotor, and to predict approximately which pocket the ball will fall into.
The human eye, of course, can't accomplish such a feat. Thorp dreamed of a wearable computer that could track the motion of ball and wheel and spit out a prediction of where it would land. He believed he could create a machine that would statistically forecast the seemingly random motion of a roulette wheel: An observer would don the computer and feed in information about the speed of the wheel; a bettor, some distance away, would receive information via a radio link.
Thorp purchased a cheap half-scale wheel and filmed it in action, timing the motion with a stopwatch that measured in splits of one-hundredths of a second. Thorp soon realized that his cheap wheel was too riddled with flaws to develop a predictive system. Disappointed, he tabled the idea as he worked to finish graduate school. But it gnawed at him, and he continued to fiddle with experiments.
One evening, his in-laws visited him and his wife Vivian for dinner. They were surprised when Thorp didn't greet them at the door and wondered what he was up to. They found him in the kitchen rolling marbles down a V-shaped trough and marking how far the marbles spun across the kitchen floor before stopping. Thorp explained that he was simulating the path of an orbiting roulette ball. Surprisingly, they didn't think their daughter had married a lunatic.
The Thorps made their first visit to Las Vegas in 1958, after Thorp had finished his degree and begun teaching. The frugal professor had heard that the rooms were cheap, and he was still toying with the idea of beating roulette with a wearable computer. The smoothness of the wheels in Las Vegas convinced Thorp that he could predict the outcome. Now, he just needed a solid, regulation-size wheel and suitable laboratory equipment.
In the meantime, during this trip Thorp decided to try out a blackjack strategy he'd recently come across. The strategy was from a ten-page article in the Journal of the American Statistical Association by U.S. Army mathematician Roger Baldwin and three of his colleagues — James McDermott, Herbert Maisel, and Wilbert Cantey — who'd been working at the Aberdeen Proving Ground. Among blackjack aficionados, Baldwin's group came to be known as the "Four Horsemen," although no one in the group actually tested the strategy in Las Vegas. Over the course of eighteen months, the Four Horsemen punched a massive amount of data into desktop calculators plotting the probabilities involved in thousands of different hands of blackjack.
Ever the scientist, Thorp decided to give Baldwin's strategy a whirl during a Christmas vacation to Las Vegas with his wife. While the test proved inconclusive (he lost a grand total of $8.50), he remained convinced the strategy could be improved. He contacted Baldwin and requested the data behind the strategy. It arrived in the spring of 1959, just before Thorp moved from UCLA to MIT.
At MIT, Thorp found a hotbed of intellectual creativity that was quietly revolutionizing modern society. The job he stepped into, the coveted position of C.L.E. Moore Instructor, had previously been held by John Nash, the math prodigy who'd won the Nobel Prize in economics in 1994 for his work on game theory, a mathematical approach to how people compete and cooperate. (He later became known as the subject of "A Beautiful Mind," the book and movie about the competing forces of his genius and mental illness.)
That first summer in Cambridge, Thorp crunched the numbers on blackjack, slowly evolving what would become an historic breakthrough in the game. Thorp fed reams of unwieldy data into a computer, seeking hidden patterns that he could exploit for a profit. By the fall, he'd discovered the rudimentary elements of a blackjack system that could beat the dealer.
Eager to publish his results, he decided on the prestigious industry journal, The Proceedings of the National Academy of Sciences. The trouble: The journal only accepted papers from members of the academy. So he sought out the only mathematics member of the academy at MIT, Dr. Claude Elwood Shannon, one of the most brilliant, and eccentric, minds on the planet.
Excerpted from The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It by Scott Patterson. Copyright 2010 by Crown Business. Reprinted by Permission of Crown Business, an imprint of the Crown Publishing Group, a division of Random House, Inc.

Quants

Quants is short for "Quantitative Analysts". Quants are mathematicians, statisticians, and computer scientists who try to use mathematical formulas and computer models to predict the direction of the stock market. The finance investment industry uses quants to try to gain an edge in the market. After all, if you are able to predict the direction of the market with a bit of certainty and are able to detect whether the market is overvalued or undervalued, then you are able to make investment decisions to your advantage.
Among the first quants is Ed Thorp. Thorp was profiled in the Scott Patterson's book "The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It". Scott Patterson is staff reporter for Wall Street Journal. Ed Thorp and Scott Patterson were interviewed by Terry Gross in the NPR program "Fresh Air". Thorp taught at MIT and developed a system of counting blackjack cards that would often beat the house. This was back then when card counting was still new. Back in 1962, he explained how he count cards in his book "Beat the Dealer: A Winning Strategy for the Game of Twenty-One"After seeing that his mathematical models worked successfully in the casinos, he used mathematics and computers to see if he can beat the stock market.  Thorp says "the biggest casino in the world appear to be Wall Street" [quote from Fresh Air program]. So that is how he became a quant trader and a hedge fund manager.


Work of the Quants

Other people considered quants that made contributions to quantitative finance are Harry Markowitz's published the paper "Portfolio Selection" in 1952 and Robert Merton who used stochastic calculus in 1969 in the finance field.
The work of the quants gave rise to the famous Black-Scholes formula for options pricing. The formula was named after Fischer Black and Myron Scholes who wrote the paper The Pricing of Options and Corporate Liabilities in 1973.  This work won Black and Scholes the Nobel Prize in Economics in 1977 for "a new method to determine the value of derivatives".

Quant News

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Yield Curve


            Economists often use complex mathematical models to forecast the path of the US economy and the likelihood of recession. But simpler indicators such as interest rates, stock price indexes and monetary aggregates also contain information about future economic activity.

Currently, the yield curve is thought offer-valued information on expectations.  By developing the basis of such a belief, an understanding of the yield curve can emerge.  Evaluating the expectations content of the yield curve will show the positive and negative aspects of following the yield curve.  Some economists think the yield curve offers constructive information on expectations.

            The reason why economists believe the yield curve can offer valuable information on expectations is because before the last six recessions short-term interest rates rose above long-term rates producing a yield curve inversion (Mishkin).  This statistical anomaly has given rise to the notion that the yield curve can predict recessions.  Since the 1980s, research on the yield curve, mostly empirical, documents correlations rather than building theories to explain such correlations.  Because of this lack of theories, economists have not been able to come to a consensus on the yield curve. 

            By evaluating the expectations of the yield curve, positives and negatives of following the yield curve are established.  Normally, the most common yield curve is derived from the spread of the short-term rates less long-term yields.  Because this is what derives the yield curve, investor expectations can change the yield curve.  Future short-term interest rates depend on future demand; an increase in short-term interest rates by the Federal Reserve might lead to an economic slowdown. The expectations of future short-term interest rates are related to future real demand and to future inflation. This likely slowdown will start pushing future real rates downward, and flatten the yield curve.  Since investor expectations change the yield curve, the yield curve may be more forward-looking than other leading indicators. The yield curve should be included in policy deliberations. Due to the statistical nature of the yield curve, other measures of the economy must be used as well.  Discounting the yield curve completely would not indicate sound judgment.  By understanding what the yield curve is indicating, the policymakers can produce preemptive action to reduce or eliminate downturns in the economy. 

            As with other tools the Fed uses to maneuver the economy, the yield curve has positives and negatives. The Federal Reserve officials who think the yield curve offers valuable information are correct in their assumption.

My Blogs

My blogs mainly based on exam question from the course I am taking or I took. English is my second language and before I went to Graduate School, I never wrote essays in English. There will be a grammar errors, or misspells. But I am doing my best to deliver the content of the essays. Thank you again for reading. You comments are appreciated.

Will QE2 work?

Will QE2 succeed or fail? Why? It is hard to answer these questions, because there are many highly educated, brilliant minds on both sides of the argument. Studying both side arguments, I will try to answer the question. In my opinion, the QE2 was necessary in order to save the economy from going into deflationary spiral.
One of the risks of QE2 is inflation. I think that would be a good problem to have. If we have a little bit of inflation, hopefully that means the economy is growing again. People are making money. Businesses are expanding. The economy starts overheating a little bit. I would much rather has that problem to deal with than deflation. As I wrote on my Term Paper deflation is worse than little inflation. People stop spending during the Great Depression, because they were uncertain.
Let’s say you had some disease that you were trying to avoid. So you took an extra dose of chemotherapy if you had cancer. You are creating another risk, but you are insuring that you have dealt with the previous problem. Same, QE2 is insurance policy. In the history you can see the biggest mistakes of politicians in Washington. They don’t deal with the problems of future. They try to solve the current problems, leaving future problems to future politicians. The current problem is there is money everywhere, but no one is doing anything. Bernanke is trying to shake people out, so they don’t just sit there forever getting no return on their money. Let’s have a little insurance policy to prevent a deflationary spiral, which happened in Japan. That’s turned into a disaster for the Japanese people. Nobody wants the deflation to happen. We would like to create asset price inflation. We would like the home values move up again. And then the debt write-downs to the banks will be less. There are lots of risks dealing with QE2. But QE2 is worth to try it. After the recent financial crises, people stopped spending. The QE2 was necessary, because there is uncertainty in the economy. Also I would like to write about the politics. Big government, huge spending, tax-cut expirations, the “ObamaCare” (the Healthcare Bill) and some other aspects are the reasons in slowing the economy down. I think the President Obama is failing to stabilize the economy.
When the interest rates are close to zero, we get into liquidity trap. The banks will make loans because who wants to loan at such low rates? If you know rates are going up, you wait to loan money until they do. You don’t want to have money loaned our at 3 or 4 percent interest when you think next year you might get 7 or 8 percent interest. This is exactly right thing to do. The solution is higher interest rates. How you get higher interest rates is inflation. Printing money will eventually create inflation. It is slippery slope. Once you get over the hump, you have the potential for a rapid increase in money supply. Then they’ll have to pull the money back out of the economy to keep it from becoming rampant inflation. The QE2’s $600 billion is cautionary step. They can always do more later if they need to. It will hurt some people, mostly those on fixed-income pensions. But the message is a little bit of inflation cures a lot of recession. And if we’ve ever needed cure, we need it right now.

Quantitative Easing 1

   The term “Quantitative Easing” became a popular jargon recently. Following the dramatic worsening of the global financial crisis in the fall of 2008, many central banks quickly moved to the policy to repurchase the agreements and ease the credit for liquidity-hungry banks. The FOMC announced on March 18, 2009 that it would “purchase an additional $750 billion of agency mortgage-backed securities, bringing its total purchases of these securities to up to $1.25 trillion this year, and increase its purchases of agency debt this year by up to $100 billion to a total of up to $200 billion. Moreover, to help improve conditions in private credit markets, the Committee decided to purchase up to $300 billion of longer term Treasury securities over the next six month.”
Most central banks referred to this policy as “quantitative easing” noting that it simply shifted the instrument of monetary policy from the policy rate, which is the price of money, to the quantity of money provided. The Federal Reserve continued to use the term “credit easing”.
   During the financial disruptions, credit easing is important. The Fed chairman, Ben Bernanke said on August 27 that the Fed ready to boost the US economy growth, and had the tools to do so, including increasing holdings of long-term assets such as Treasury Bonds and other securities. I think this will help the boost the economy, giving access to the liquidity and credit. 

Quantitative Easing 2

Quantitative easing (QE) is when a central bank shifts its focus to expanding its balance sheet through the purchase of longer-term securities rather than targeting short-term interest rates. Under QE, central banks inject the banking system with cash, increasing the quantity of reserves held by commercial banks. We can say it’s a liquidity game. Such an increase can occur either directly or indirectly.
How is the QE implemented? Let’s say, a central bank buys assets like mortgage securities, government debt from the commercial banks and credit their accounts with reserves by way of payment. Alternatively, a central bank buys assets from non-banks, and these non-banks then deposit the proceeds at commercial banks. This result increase in reserves provides commercial banks with an opportunity to lend more. Basically it is all about increasing liquidity. By increasing liquidity, the aim of QE is to foster an environment of growth by increasing inflation expectations, reducing real rates, and creating asset inflation.
Let’s talk about the risks. Giving the investors incentives to seek higher yields in riskier assets raises the likelihood of creating asset-price bubbles. Inflation is also one of the risks. Investors say that the right before the economy has completely recovered, the Fed shall need to withdraw all the money that it is printing now in order to avoid a surge in inflation down the road. Most investors and economists don’t believe that the Fed will be able to do that quickly enough, and fear inflation will result.
Another problem is the value of the dollar. If I announce today that I found the way to create the gold from any piece of metal, what will happen to the gold price? I think by announcing that, the price of the gold will go down by couple of hundred dollars. With QE it is the same. Printing more money tends to push down the value of the dollar. While this is a help to the U.S. exports, it also risks pushing up the price of oil and other commodities, threatening an inflation surge that could be difficult to stop if the economy picks up. As we discussed at the class, the weak dollar will result the flood of money to emerging markets with higher interest rates and more strong growth is pushing up their currencies more than some of their governments wants. This already has led some countries to intervene to resist the rise in their currencies, sparking tensions between the U.S. and emerging markets and talk of “currency war.”