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|General Discussion · The Formula That Killed Wall Street|
|3/23/2009 1:55:51 AM
Recipe for Disaster: The Formula That Killed Wall Street
(Clicking on the link takes you to the original message with a graphic of the formula)
WIRED MAGAZINE: 17.03
Tech Biz : IT
Recipe for Disaster: The Formula That Killed Wall Street
By Felix Salmon 02.23.09
In the mid-'80s, Wall Street turned to the quants—brainy financial engineers—to invent new ways to boost profits. Their methods for minting money worked brilliantly... until one of them devastated the global economy.
Photo: Jim Krantz/Gallery Stock
Road Map for Financial Recovery: Radical Transparency Now! A year ago, it was hardly unthinkable that a math wizard like David X. Li might someday earn a Nobel Prize. After all, financial economists—even Wall Street quants—have received the Nobel in economics before, and Li's work on measuring risk has had more impact, more quickly, than previous Nobel Prize-winning contributions to the field. Today, though, as dazed bankers, politicians, regulators, and investors survey the wreckage of the biggest financial meltdown since the Great Depression, Li is probably thankful he still has a job in finance at all. Not that his achievement should be dismissed. He took a notoriously tough nut—determining correlation, or how seemingly disparate events are related—and cracked it wide open with a simple and elegant mathematical formula, one that would become ubiquitous in finance worldwide.
For five years, Li's formula, known as a Gaussian copula function, looked like an unambiguously positive breakthrough, a piece of financial technology that allowed hugely complex risks to be modeled with more ease and accuracy than ever before. With his brilliant spark of mathematical legerdemain, Li made it possible for traders to sell vast quantities of new securities, expanding financial markets to unimaginable levels.
His method was adopted by everybody from bond investors and Wall Street banks to ratings agencies and regulators. And it became so deeply entrenched—and was making people so much money—that warnings about its limitations were largely ignored.
Then the model fell apart. Cracks started appearing early on, when financial markets began behaving in ways that users of Li's formula hadn't expected. The cracks became full-fledged canyons in 2008—when ruptures in the financial system's foundation swallowed up trillions of dollars and put the survival of the global banking system in serious peril.
David X. Li, it's safe to say, won't be getting that Nobel anytime soon. One result of the collapse has been the end of financial economics as something to be celebrated rather than feared. And Li's Gaussian copula formula will go down in history as instrumental in causing the unfathomable losses that brought the world financial system to its knees.
How could one formula pack such a devastating punch? The answer lies in the bond market, the multitrillion-dollar system that allows pension funds, insurance companies, and hedge funds to lend trillions of dollars to companies, countries, and home buyers.
A bond, of course, is just an IOU, a promise to pay back money with interest by certain dates. If a company—say, IBM—borrows money by issuing a bond, investors will look very closely over its accounts to make sure it has the wherewithal to repay them. The higher the perceived risk—and there's always some risk—the higher the interest rate the bond must carry.
Bond investors are very comfortable with the concept of probability. If there's a 1 percent chance of default but they get an extra two percentage points in interest, they're ahead of the game overall—like a casino, which is happy to lose big sums every so often in return for profits most of the time.
Bond investors also invest in pools of hundreds or even thousands of mortgages. The potential sums involved are staggering: Americans now owe more than $11 trillion on their homes. But mortgage pools are messier than most bonds. There's no guaranteed interest rate, since the amount of money homeowners collectively pay back every month is a function of how many have refinanced and how many have defaulted. There's certainly no fixed maturity date: Money shows up in irregular chunks as people pay down their mortgages at unpredictable times—for instance, when they decide to sell their house. And most problematic, there's no easy way to assign a single probability to the chance of default.
Wall Street solved many of these problems through a process called tranching, which divides a pool and allows for the creation of safe bonds with a risk-free triple-A credit rating. Investors in the first tranche, or slice, are first in line to be paid off. Those next in line might get only a double-A credit rating on their tranche of bonds but will be able to charge a higher interest rate for bearing the slightly higher chance of default. And so on.
"...correlation is charlatanism"
Photo: AP photo/Richard Drew The reason that ratings agencies and investors felt so safe with the triple-A tranches was that they believed there was no way hundreds of homeowners would all default on their loans at the same time. One person might lose his job, another might fall ill. But those are individual calamities that don't affect the mortgage pool much as a whole: Everybody else is still making their payments on time.
But not all calamities are individual, and tranching still hadn't solved all the problems of mortgage-pool risk. Some things, like falling house prices, affect a large number of people at once. If home values in your neighborhood decline and you lose some of your equity, there's a good chance your neighbors will lose theirs as well. If, as a result, you default on your mortgage, there's a higher probability they will default, too. That's called correlation—the degree to which one variable moves in line with another—and measuring it is an important part of determining how risky mortgage bonds are.
Investors like risk, as long as they can price it. What they hate is uncertainty—not knowing how big the risk is. As a result, bond investors and mortgage lenders desperately want to be able to measure, model, and price correlation. Before quantitative models came along, the only time investors were comfortable putting their money in mortgage pools was when there was no risk whatsoever—in other words, when the bonds were guaranteed implicitly by the federal government through Fannie Mae or Freddie Mac.
Yet during the '90s, as global markets expanded, there were trillions of new dollars waiting to be put to use lending to borrowers around the world—not just mortgage seekers but also corporations and car buyers and anybody running a balance on their credit card—if only investors could put a number on the correlations between them. The problem is excruciatingly hard, especially when you're talking about thousands of moving parts. Whoever solved it would earn the eternal gratitude of Wall Street and quite possibly the attention of the Nobel committee as well.
To understand the mathematics of correlation better, consider something simple, like a kid in an elementary school: Let's call her Alice. The probability that her parents will get divorced this year is about 5 percent, the risk of her getting head lice is about 5 percent, the chance of her seeing a teacher slip on a banana peel is about 5 percent, and the likelihood of her winning the class spelling bee is about 5 percent. If investors were trading securities based on the chances of those things happening only to Alice, they would all trade at more or less the same price.
But something important happens when we start looking at two kids rather than one—not just Alice but also the girl she sits next to, Britney. If Britney's parents get divorced, what are the chances that Alice's parents will get divorced, too? Still about 5 percent: The correlation there is close to zero. But if Britney gets head lice, the chance that Alice will get head lice is much higher, about 50 percent—which means the correlation is probably up in the 0.5 range. If Britney sees a teacher slip on a banana peel, what is the chance that Alice will see it, too? Very high indeed, since they sit next to each other: It could be as much as 95 percent, which means the correlation is close to 1. And if Britney wins the class spelling bee, the chance of Alice winning it is zero, which means the correlation is negative: -1.
If investors were trading securities based on the chances of these things happening to both Alice and Britney, the prices would be all over the place, because the correlations vary so much.
But it's a very inexact science. Just measuring those initial 5 percent probabilities involves collecting lots of disparate data points and subjecting them to all manner of statistical and error analysis. Trying to assess the conditional probabilities—the chance that Alice will get head lice if Britney gets head lice—is an order of magnitude harder, since those data points are much rarer. As a result of the scarcity of historical data, the errors there are likely to be much greater.
In the world of mortgages, it's harder still. What is the chance that any given home will decline in value? You can look at the past history of housing prices to give you an idea, but surely the nation's macroeconomic situation also plays an important role. And what is the chance that if a home in one state falls in value, a similar home in another state will fall in value as well?
Here's what killed your 401(k) David X. Li's Gaussian copula function as first published in 2000. Investors exploited it as a quick—and fatally flawed—way to assess risk. A shorter version appears on this month's cover of Wired.
Specifically, this is a joint default probability—the likelihood that any two members of the pool (A and B) will both default. It's what investors are looking for, and the rest of the formula provides the answer. Survival times
The amount of time between now and when A and B can be expected to default. Li took the idea from a concept in actuarial science that charts what happens to someone's life expectancy when their spouse dies.
A dangerously precise concept, since it leaves no room for error. Clean equations help both quants and their managers forget that the real world contains a surprising amount of uncertainty, fuzziness, and precariousness.
This couples (hence the Latinate term copula) the individual probabilities associated with A and B to come up with a single number. Errors here massively increase the risk of the whole equation blowing up.
The probabilities of how long A and B are likely to survive. Since these are not certainties, they can be dangerous: Small miscalculations may leave you facing much more risk than the formula indicates.
The all-powerful correlation parameter, which reduces correlation to a single constant—something that should be highly improbable, if not impossible. This is the magic number that made Li's copula function irresistible.
Enter Li, a star mathematician who grew up in rural China in the 1960s. He excelled in school and eventually got a master's degree in economics from Nankai University before leaving the country to get an MBA from Laval University in Quebec. That was followed by two more degrees: a master's in actuarial science and a PhD in statistics, both from Ontario's University of Waterloo. In 1997 he landed at Canadian Imperial Bank of Commerce, where his financial career began in earnest; he later moved to Barclays Capital and by 2004 was charged with rebuilding its quantitative analytics team.
Li's trajectory is typical of the quant era, which began in the mid-1980s. Academia could never compete with the enormous salaries that banks and hedge funds were offering. At the same time, legions of math and physics PhDs were required to create, price, and arbitrage Wall Street's ever more complex investment structures.
In 2000, while working at JPMorgan Chase, Li published a paper in The Journal of Fixed Income titled "On Default Correlation: A Copula Function Approach." (In statistics, a copula is used to couple the behavior of two or more variables.) Using some relatively simple math—by Wall Street standards, anyway—Li came up with an ingenious way to model default correlation without even looking at historical default data. Instead, he used market data about the prices of instruments known as credit default swaps.
If you're an investor, you have a choice these days: You can either lend directly to borrowers or sell investors credit default swaps, insurance against those same borrowers defaulting. Either way, you get a regular income stream—interest payments or insurance payments—and either way, if the borrower defaults, you lose a lot of money. The returns on both strategies are nearly identical, but because an unlimited number of credit default swaps can be sold against each borrower, the supply of swaps isn't constrained the way the supply of bonds is, so the CDS market managed to grow extremely rapidly. Though credit default swaps were relatively new when Li's paper came out, they soon became a bigger and more liquid market than the bonds on which they were based.
When the price of a credit default swap goes up, that indicates that default risk has risen. Li's breakthrough was that instead of waiting to assemble enough historical data about actual defaults, which are rare in the real world, he used historical prices from the CDS market. It's hard to build a historical model to predict Alice's or Britney's behavior, but anybody could see whether the price of credit default swaps on Britney tended to move in the same direction as that on Alice. If it did, then there was a strong correlation between Alice's and Britney's default risks, as priced by the market. Li wrote a model that used price rather than real-world default data as a shortcut (making an implicit assumption that financial markets in general, and CDS markets in particular, can price default risk correctly).
It was a brilliant simplification of an intractable problem. And Li didn't just radically dumb down the difficulty of working out correlations; he decided not to even bother trying to map and calculate all the nearly infinite relationships between the various loans that made up a pool. What happens when the number of pool members increases or when you mix negative correlations with positive ones? Never mind all that, he said. The only thing that matters is the final correlation number—one clean, simple, all-sufficient figure that sums up everything.
The effect on the securitization market was electric. Armed with Li's formula, Wall Street's quants saw a new world of possibilities. And the first thing they did was start creating a huge number of brand-new triple-A securities. Using Li's copula approach meant that ratings agencies like Moody's—or anybody wanting to model the risk of a tranche—no longer needed to puzzle over the underlying securities. All they needed was that correlation number, and out would come a rating telling them how safe or risky the tranche was.
As a result, just about anything could be bundled and turned into a triple-A bond—corporate bonds, bank loans, mortgage-backed securities, whatever you liked. The consequent pools were often known as collateralized debt obligations, or CDOs. You could tranche that pool and create a triple-A security even if none of the components were themselves triple-A. You could even take lower-rated tranches of other CDOs, put them in a pool, and tranche them—an instrument known as a CDO-squared, which at that point was so far removed from any actual underlying bond or loan or mortgage that no one really had a clue what it included. But it didn't matter. All you needed was Li's copula function.
The CDS and CDO markets grew together, feeding on each other. At the end of 2001, there was $920 billion in credit default swaps outstanding. By the end of 2007, that number had skyrocketed to more than $62 trillion. The CDO market, which stood at $275 billion in 2000, grew to $4.7 trillion by 2006.
At the heart of it all was Li's formula. When you talk to market participants, they use words like beautiful, simple, and, most commonly, tractable. It could be applied anywhere, for anything, and was quickly adopted not only by banks packaging new bonds but also by traders and hedge funds dreaming up complex trades between those bonds.
"The corporate CDO world relied almost exclusively on this copula-based correlation model," says Darrell Duffie, a Stanford University finance professor who served on Moody's Academic Advisory Research Committee. The Gaussian copula soon became such a universally accepted part of the world's financial vocabulary that brokers started quoting prices for bond tranches based on their correlations. "Correlation trading has spread through the psyche of the financial markets like a highly infectious thought virus," wrote derivatives guru Janet Tavakoli in 2006.
The damage was foreseeable and, in fact, foreseen. In 1998, before Li had even invented his copula function, Paul Wilmott wrote that "the correlations between financial quantities are notoriously unstable." Wilmott, a quantitative-finance consultant and lecturer, argued that no theory should be built on such unpredictable parameters. And he wasn't alone. During the boom years, everybody could reel off reasons why the Gaussian copula function wasn't perfect. Li's approach made no allowance for unpredictability: It assumed that correlation was a constant rather than something mercurial. Investment banks would regularly phone Stanford's Duffie and ask him to come in and talk to them about exactly what Li's copula was. Every time, he would warn them that it was not suitable for use in risk management or valuation.
David X. Li
Illustration: David A. Johnson In hindsight, ignoring those warnings looks foolhardy. But at the time, it was easy. Banks dismissed them, partly because the managers empowered to apply the brakes didn't understand the arguments between various arms of the quant universe. Besides, they were making too much money to stop.
In finance, you can never reduce risk outright; you can only try to set up a market in which people who don't want risk sell it to those who do. But in the CDO market, people used the Gaussian copula model to convince themselves they didn't have any risk at all, when in fact they just didn't have any risk 99 percent of the time. The other 1 percent of the time they blew up. Those explosions may have been rare, but they could destroy all previous gains, and then some.
Li's copula function was used to price hundreds of billions of dollars' worth of CDOs filled with mortgages. And because the copula function used CDS prices to calculate correlation, it was forced to confine itself to looking at the period of time when those credit default swaps had been in existence: less than a decade, a period when house prices soared. Naturally, default correlations were very low in those years. But when the mortgage boom ended abruptly and home values started falling across the country, correlations soared.
Bankers securitizing mortgages knew that their models were highly sensitive to house-price appreciation. If it ever turned negative on a national scale, a lot of bonds that had been rated triple-A, or risk-free, by copula-powered computer models would blow up. But no one was willing to stop the creation of CDOs, and the big investment banks happily kept on building more, drawing their correlation data from a period when real estate only went up.
"Everyone was pinning their hopes on house prices continuing to rise," says Kai Gilkes of the credit research firm CreditSights, who spent 10 years working at ratings agencies. "When they stopped rising, pretty much everyone was caught on the wrong side, because the sensitivity to house prices was huge. And there was just no getting around it. Why didn't rating agencies build in some cushion for this sensitivity to a house-price-depreciation scenario? Because if they had, they would have never rated a single mortgage-backed CDO."
Bankers should have noted that very small changes in their underlying assumptions could result in very large changes in the correlation number. They also should have noticed that the results they were seeing were much less volatile than they should have been—which implied that the risk was being moved elsewhere. Where had the risk gone?
They didn't know, or didn't ask. One reason was that the outputs came from "black box" computer models and were hard to subject to a commonsense smell test. Another was that the quants, who should have been more aware of the copula's weaknesses, weren't the ones making the big asset-allocation decisions. Their managers, who made the actual calls, lacked the math skills to understand what the models were doing or how they worked. They could, however, understand something as simple as a single correlation number. That was the problem.
"The relationship between two assets can never be captured by a single scalar quantity," Wilmott says. For instance, consider the share prices of two sneaker manufacturers: When the market for sneakers is growing, both companies do well and the correlation between them is high. But when one company gets a lot of celebrity endorsements and starts stealing market share from the other, the stock prices diverge and the correlation between them turns negative. And when the nation morphs into a land of flip-flop-wearing couch potatoes, both companies decline and the correlation becomes positive again. It's impossible to sum up such a history in one correlation number, but CDOs were invariably sold on the premise that correlation was more of a constant than a variable.
No one knew all of this better than David X. Li: "Very few people understand the essence of the model," he told The Wall Street Journal way back in fall 2005.
"Li can't be blamed," says Gilkes of CreditSights. After all, he just invented the model. Instead, we should blame the bankers who misinterpreted it. And even then, the real danger was created not because any given trader adopted it but because every trader did. In financial markets, everybody doing the same thing is the classic recipe for a bubble and inevitable bust.
Nassim Nicholas Taleb, hedge fund manager and author of The Black Swan, is particularly harsh when it comes to the copula. "People got very excited about the Gaussian copula because of its mathematical elegance, but the thing never worked," he says. "Co-association between securities is not measurable using correlation," because past history can never prepare you for that one day when everything goes south. "Anything that relies on correlation is charlatanism."
Li has been notably absent from the current debate over the causes of the crash. In fact, he is no longer even in the US. Last year, he moved to Beijing to head up the risk-management department of China International Capital Corporation. In a recent conversation, he seemed reluctant to discuss his paper and said he couldn't talk without permission from the PR department. In response to a subsequent request, CICC's press office sent an email saying that Li was no longer doing the kind of work he did in his previous job and, therefore, would not be speaking to the media.
In the world of finance, too many quants see only the numbers before them and forget about the concrete reality the figures are supposed to represent. They think they can model just a few years' worth of data and come up with probabilities for things that may happen only once every 10,000 years. Then people invest on the basis of those probabilities, without stopping to wonder whether the numbers make any sense at all.
As Li himself said of his own model: "The most dangerous part is when people believe everything coming out of it."
— Felix Salmon (email@example.com) writes the Market Movers financial blog at Portfolio.com.
|General Discussion · BestFreeCharts|
|3/23/2009 1:43:32 AM
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|General Discussion · CNBC is the joke|
|3/7/2009 2:36:46 AM
I assumed you already knew this ... cnbc "for entertainment purposes only" ... anyway, we need CBC to scare all the newbie / amateur traders into making mistakes
|Filter Exchange · easiest way to make a fortune-block trade screening for giant spikes|
|2/22/2009 10:02:34 PM
ditto Chetron :D
|General Discussion · Tell Congress to Block the Trader Tax|
|2/19/2009 10:31:28 PM
Tell Congress to Block the Trader Tax
Sign the Petition : 9,461 Letters and Emails Sent So Far
On Friday, February 13, your colleague, U.S. Congressman Peter DeFazio, introduced H.R. 1068: “Let Wall Street Pay for Wall Street's Bailout Act of 2009”, which aims to impose a 0.25% transaction tax on the “sale and purchase of financial instruments such as stock, options, and futures.” Without a doubt, many Americans are appalled at the reckless behavior of large Wall Street companies, and the notion of making those who are responsible for putting the global financial system in jeopardy help repay taxpayers for bailing them out is certainly justifiable.
Unfortunately, I feel that this proposal is the wrong way to do that, as this tax applies to all investors, the vast majority of whom have done no wrong. Effectively, this tax will punish anyone who wants to save their money, whether it be by investing in stocks or options directly, putting their hard earned money in any mutual fund, or by simply placing a portion of their paycheck in a 401K. There’s no doubt that banks and mutual funds will pass along this added cost to their customers, giving this proposed tax a much further reach than was initially imagined.
Moreover, the unintended consequences associated with H.R. 1068 are also hard to ignore.
First, many hard-working Americans make their livings by running small businesses that trade stocks, options and other financial instruments. Many of whom will be put out of business due to the fact that their margins are often quite thin. In addition, those who work for or with these individuals will also lose their jobs.
Second, a transfer tax such as this will lower capital gains dollar for dollar, making the notion that anyone who invests their money will be on the hook for the excesses of Wall Street all that more poignant.
Finally, such a tax will undoubtedly affect the number of shares traded on an absolute basis, thus reducing liquidity – a necessary ingredient in the effective pricing of assets. It’s the complete lack of liquidity, for example, which made collateralized mortgage obligations effectively worthless.
The body of the bill suggests that such a tax would have a negligible impact on the average investor. I beg to differ. For example, a $10,000 trade (or approximately 100 shares of stock in Apple, Inc.) would increase the cost of a round trip transaction by $50. 100 shares is generally considered to be a minimum size for a trade, which would devastate any small business executing even a handful of similar trades each day.
As you can see, while this bill may sound good on the surface, the effects, if it is passed, will reach anyone who wants to invest their money and will ruin many small business people who are not at fault for this distressing situation all Americans are struggling through.
I urge you to vote NO on H.R. 1068
|General Discussion · DecisionBar Trading|
|2/19/2009 4:26:59 PM
I just got this email a few days ago and, after watching the demo video, was wondering what technicals he, Les Schwartz, uses to calculate those breakout and exhaustion bands and the "risk oscillator". Any ideas?
Following, in its entirety, is a letter I received form one of our long time DecisionBar subscribers last night. I believe this says it all. Bold Face was added by me.
Jackie and I are getting settled in our new home in Canada, but man there has been a lot of snow, and much colder than I remembered.
I made $18k on RIMM today thanks to DecisionBar, so the software is still more than paying for itself, and that is probably the understatement of the year. Decision Bar has probably paid for itself for the next 50 years (hopefully I will live that long) LOL.
I can't imagine why you are not signing up hundreds of thousands of new users (and having said that, don't go increasing my subscription rate). Seriously though, this program is worth far more than what you charge for it, and of course you know that it works in any market. I just can't understand why people pay thousands of dollars for software that does not work, when there is a program available that virtually anyone can afford that generates consistent profits overall in virtually any market.
If you read the manual, know that you will not win every trade, follow the guidelines, and stick to your strategy, you will make money!
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FYI - Here is a portion of yesterday's chart of RIMM:
|General Discussion · Please Help Me Understand The Last 10 Days(Strategy Breakdown)|
|2/19/2009 3:10:19 AM
The market is merely voicing its opinion on the stimulus plan
How the economic stimulus plan could affect you
By The Associated Press The Associated Press
Sat Feb 14, 6:14 pm ET
An examination of how the economic stimulus plan will affect Americans.
The recovery package has tax breaks for families that send a child to college, purchase a new car, buy a first home or make the ones they own more energy efficient.
Millions of workers can expect to see about $13 extra in their weekly paychecks, starting around June, from a new $400 tax credit to be doled out through the rest of the year. Couples would get up to $800. In 2010, the credit would be about $7.70 a week, if it is spread over the entire year.
The $1,000 child tax credit would be extended to more low-income families that don't make enough money to pay income taxes, and poor families with three or more children will get an expanded Earned Income Tax Credit.
Middle-income and wealthy taxpayers will be spared from paying the Alternative Minimum Tax, which was designed 40 years ago to make sure wealthy taxpayers pay at least some tax, but was never indexed for inflation. Congress fixes it each year, usually in the fall.
First-time homebuyers who purchase their homes before Dec. 1 would be eligible for an $8,000 tax credit, and people who buy new cars before the end of the year can write off the sales taxes.
Homeowners who add energy-efficient windows, furnaces and air conditioners can get a tax credit to cover 30 percent of the costs, up to a total of $1,500. College students — or their parents — are eligible for tax credits of up to $2,500 to help pay tuition and related expenses in 2009 and 2010.
Those receiving unemployment benefits this year wouldn't pay any federal income taxes on the first $2,400 they receive.
Many workers who lose their health insurance when they lose their jobs will find it cheaper to keep that coverage while they look for work.
Right now, most people working for medium and large employers can continue their coverage for 18 months under the COBRA program when they lose their job. It's expensive, often over $1,000 a month, because they pay the share of premiums once covered by their employer as well as their own share from the old group plan.
Under the stimulus package, the government will pick up 65 percent of the total cost of that premium for the first nine months.
Lawmakers initially proposed to help workers from small companies, too, who don't generally qualify for COBRA coverage. But that fell through. The idea was to have Washington pay to extend Medicaid to them.
COBRA applies to group plans at companies employing at least 20 people. The subsidies will be offered to those who lost their jobs from Sept. 1 to the end of this year.
Those who were put out of work after September but didn't elect to have COBRA coverage at the time will have 60 days to sign up.
The plan offers $87 billion to help states administer Medicaid. That could slow or reverse some of the steps states have taken to cut the program.
Highways repaved for the first time in decades. Century-old waterlines dug up and replaced with new pipes. Aging bridges, stressed under the weight of today's SUVs, reinforced with fresh steel and concrete.
But the $90 billion is a mere down payment on what's needed to repair and improve the country's physical backbone. And not all economists agree it's an effective way to add jobs in the long term, or stimulate the economy.
Homeowners looking to save energy, makers of solar panels and wind turbines and companies hoping to bring the electric grid into the computer age all stand to reap major benefits.
The package contains more than $42 billion in energy-related investments from tax credits to homeowners to loan guarantees for renewable energy projects and direct government grants for makers of wind turbines and next-generation batteries.
There's a 30 percent tax credit of up to $1,500 for the purchase of a highly efficient residential air conditioners, heat pumps or furnaces. The credit also can be used by homeowners to replace leaky windows or put more insulation into the attic. About $300 million would go for rebates to get people to buy efficient appliances.
The package includes $20 billion aimed at "green" jobs to make wind turbines, solar panels and improve energy efficiency in schools and federal buildings. It includes $6 billion in loan guarantees for renewable energy projects as well as tax breaks or direct grants covering 30 percent of wind and solar energy investments. Another $5 billion is marked to help low-income homeowners make energy improvements.
About $11 billion goes to modernize and expand the nation's electric power grid and $2 billion to spur research into batteries for future electric cars.
A main goal of education spending in the stimulus bill is to help keep teachers on the job.
Nearly 600,000 jobs in elementary and secondary schools could be eliminated by state budget cuts over the next three years, according to a study released this past week by the University of Washington. Fewer teachers means higher class sizes, something that districts are scrambling to prevent.
The stimulus sets up a $54 billion fund to help prevent or restore state budget cuts, of which $39 billion must go toward kindergarten through 12th grade and higher education. In addition, about $8 billion of the fund could be used for other priorities, including modernization and renovation of schools and colleges, though how much is unclear, because Congress decided not to specify a dollar figure.
The Education Department will distribute the money as quickly as it can over the next couple of years.
And it adds $25 billion extra to No Child Left Behind and special education programs, which help pay teacher salaries, among other things.
This money may go out much more slowly; states have five years to spend the dollars, and they have a history of spending them slowly. In fact, states don't spend all the money; they return nearly $100 million to the federal treasury every year.
The stimulus bill also includes more than $4 billion for the Head Start and Early Head Start early education programs and for child care programs.
One thing about the president's $790 billion stimulus package is certain: It will jack up the federal debt.
Whether or not it succeeds in producing jobs and taming the recession, tomorrow's taxpayers will end up footing the bill.
Forecasters expect the 2009 deficit — for the budget year that began last Oct 1 — to hit $1.6 trillion including new stimulus and bank-bailout spending. That's about three times last year's shortfall.
The torrents of red ink are being fed by rising federal spending and falling tax revenues from hard-hit businesses and individuals.
The national debt — the sum of all annual budget deficits — stands at $10.7 trillion. Or about $36,000 for every man, woman and child in the U.S.
Interest payments alone on the national debt will near $500 billion this year. It's already the fourth-largest federal expenditure, after Medicare-Medicaid, Social Security and defense.
This will affect us all directly for years, as well as our children and possibly grandchildren, in higher taxes and probably reduced government services. It will also force continued government borrowing, increasingly from China, Japan, Britain, Saudi Arabia and other foreign creditors.
The package includes $9.2 billion for environmental projects at the Interior Department and the Environmental Protection Agency. The money would be used to shutter abandoned mines on public lands, to help local governments protect drinking water supplies, and to erect energy-efficient visitor centers at wildlife refuges and national parks.
The Interior Department estimates that its portion of the work would generate about 100,000 jobs over the next two years.
Yet the plan will only make a dent in the backlog of cleanups facing the EPA and the long list of chores at the country's national parks, refuges and other public lands. It would be more like a down payment.
When it comes to national parks, the plan sets aside $735 million for road repairs and maintenance. But that's a fraction of the $9 billion worth of work waiting for funding.
At EPA, the payout is $7.2 billion. The bulk of the money will help local communities and states repair and improve drinking water systems and fund projects that protect bays, rivers and other waterways used as sources of drinking water.
The rest of EPA's cut — $800 million — will be used to clean up leaky gasoline storage tanks and the nation's hazardous waste sites.
The stimulus bill includes plenty of green for those wearing blue.
The compromise bill doles out more than $3.7 billion for police programs, much of which is set aside for hiring new officers.
The law allocates $2 billion for the Byrne Justice Assistance Grant, a program that has funded drug task forces and things such as prisoner rehabilitation and after-school programs.
An additional $1 billion is set aside to hire local police under the Community Oriented Policing Services program. The program, known as COPS grants, paid the salaries of many local police officers and was a "modest contributor" to the decline in crime in the 1990s, according to a 2005 government oversight report.
Both programs had all been eliminated during the Bush administration.
The bill also includes $225 million for general criminal justice grants for things such as youth mentoring programs, $225 million for Indian tribe law enforcement, $125 million for police in rural areas, $100 million for victims of crimes, $50 million to fight Internet crimes against children and $40 million in grants for law enforcement along the Mexican border.
The maximum Pell Grant, which helps the lowest-income students attend college, would increase from $4,731 currently to $5,350 starting July 1 and $5,550 in 2010-2011. That would cover three-quarters of the average cost of a four-year college. An extra 800,000 students, or about 7 million, would now get Pell funding.
The stimulus also increases the tuition tax credit to $2,500 and makes it 40 percent refundable, so families who don't earn enough to pay income tax could still get up to $1,000 in extra tuition help.
Computer expenses will now be an allowable expense for 529 college savings plans.
The final package cut $6 billion the House wanted to spend to kick-start building projects on college campuses. But parts of the $54 billion state stabilization fund — with $39 billion set aside for education — can be used for modernizing facilities.
There's also an estimated $15 billion for scientific research, much of which will go to universities. Funding for the National Institutes of Health includes $1.5 billion set aside for university research facilities.
Altogether, the package spends an estimated $32 billion on higher education.
More than 37 million Americans live in poverty, and the vast majority of them are in line for extra help under the giant stimulus package. Millions more could be kept from slipping into poverty by the economic lifeline.
People who get food stamps — 30 million and growing — will get more. People drawing unemployment checks — nearly 5 million and growing — would get an extra $25, and keep those checks coming longer. People who get Supplemental Security Income — 7 million poor Americans who are elderly, blind or disabled — would get one-time extra payments of $250.
Many low-income Americans also are likely to benefit from a trifecta of tax credits: expansions to the existing Child Tax Credit and Earned Income Tax Credit, and a new refundable tax credit for workers. Taken together, the three credits are expected to keep more than 2 million Americans from falling into poverty, including more than 800,000 children, according to the private Center on Budget and Policy Priorities.
The package also includes a $3 billion emergency fund to provide temporary assistance to needy families. In addition, cash-strapped states will get an infusion of $87 billion for Medicaid, the government health program for poor people, and that should help them avoid cutting off benefits to the needy.
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|2/19/2009 2:59:07 AM
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