Still Believe There Is ANY “Justice” In America?

SEC May Have DESTROYED Documents On Bank Probes

The Securities and Exchange Commission may have destroyed documents related to investigations into major banks and hedge funds, according to Sen. Chuck Grassley (R-Iowa).

“From what I’ve seen, it looks as if the SEC might have sanctioned some level of case-related document destruction,” Grassley said in a statement. “It doesn’t make sense that an agency responsible for investigations would want to get rid of potential evidence. If these charges are true, the agency needs to explain why it destroyed documents, how many documents it destroyed over what time frame, and to what extent its actions were consistent with the law.”

Grassley has asked the SEC to respond to these allegations, which he says were made by an agency whistle-blower decribing “the SEC’s unlawful destruction of the federal records generated in at least 9,000 informal investigations.”

The allegedly destroyed documents are said to pertain to “matters under inquiry,” or MUIs. That’s the official term used by the SEC for the preliminary stages of an investigation. MUIs may or may not turn into official investigations.

After reviewing the whistleblower’s letter and supporting documents, Grassley sent a letter to SEC chairman Mary Schapiro.

“If (the whistleblower’s) allegations are correct, the intentional destruction of at least 9,000 MUIs would appear to greatly handicap the SEC’s ability to create patterns in complex cases and calls into question the SEC’s ability to properly retain and catalog documents,” the letter said.

Grassley is requesting a “full accounting” of the SEC’s document destruction policies, including whether the allegations are correct that the SEC destroyed documents related to Bernard Madoff, Goldman Sachs, (NYSE: gs) Wells Fargo, (NYSE: wfc) Bank of America, (NYSE: bac) Deutsche Bank, (NYSE: db) Lehman Brothers, and SAC Capital.

Original story at Yahoo! Finance.

[Chartsky Note:  A Who's-Who of Wall Street Insiders, these Banksters had their paid-for Government Investigators destroy all the evidence . . . no evidence = no crime.  Now all that's left is for the Whistleblower to have an accident or commit "suicide."]

[UPDATE:  When I posted this I didn't know Matt Taibbi of Rolling Stone had done a GREAT piece on the same subject.  It's a must-read and can be found here.]

This Is What Things Are REALLY Like These Days In the United States . . .

By:  Dave Jamieson (Huffington Post)
August 12, 2011

SPRINGFIELD, Penn. — Among those here in the parking lot of the Masonic Hall, there’s an abiding sense that the economic recovery has stalled, if it was ever there to begin with.

Young and old, well educated and uneducated, they’re all coming here for the same thing: a job at the new area Walmart, which has set up a hiring center inside the hall; and most of them don’t care how many hours the store can give them, what they’ll have to do, or even how much they’ll be paid.

They just want a job.

First requirement: Humility.

“I’ll take anything,” many say.

In less than two weeks, managers have fielded well over 1,000 applications for just 300 jobs. On Wednesday, one of those applications was filled out by Andrea Parella, another by Tee Dempsey. The two women just met here, and outside in the parking lot they give each other words of encouragement after long, brutal spells without work.

“It will get better. It has to,” says Dempsey.

Parella, a graphic designer with an associate’s degree, has been out of work for the two-and-a-half years since her company went under. She long ago exhausted her 99 weeks of unemployment, and she now finds herself filling out up to 25 online applications a day, many of them well outside her chosen field. The search is like a full-time job, punctuated with disappointment, and she says it’s tougher than any paying gig she’s ever had. The biggest challenge is staying positive.

“It’s hard to keep up the smile,” says Parella. “I worry that they can read in my face how I really feel.”

Dempsey, who lives in nearby Havertown, has her own business hanging wallpaper, but the work has ground to a halt during the prolonged downturn.

“They’re afraid to spend the money,” Dempsey says of homeowners. “The painters aren’t getting any work, either.”

Since she is technically self-employed, Dempsey hasn’t had any unemployment benefits to help her make ends meet. Recently she started selling off her jewelry.

“I’m borrowing money from my mother to pay my mortgage,” says Dempsey.

“I just want to work,” says Parella.

Such desperation is evident in the jobs numbers. Although the unemployment rate fell slightly last month to a still-dismal 9.1 percent, the small dip was primarily due to discouraged workers dropping out of the workforce. Meanwhile, many of the job gains we’ve recently seen in health care and retail — these Walmart positions are a good example — have been obliterated by public-sector losses as state and municipal governments pare back.

So aspiring workers have come here to the hall to scrap for a job that’s become shorthand for “low-wage,” despite the fact that most of them realize they won’t get a position. Another troubling sign of the present downturn: many of the workers interviewed by The Huffington Post don’t factor into that 9.1 percent figure. They already have jobs. They just aren’t getting enough hours or a wage high enough to survive. They’re the more nebulous “underemployed,” those who want full-time work but still can’t find it.

“It’s bad — really bad — and I’m blessed to be working at all,” says J, a father of three who’s applying for a job with his teenager in tow.

J didn’t want to give his full name because he already has a job at Home Depot. He’s technically a part-timer there, though he works more or less full-time hours. He receives no health insurance through the job, and the pay isn’t enough to support his family. His goal is to work night shifts at Walmart on top of his hours at Home Depot, for more than 70 hours total. He’s hoping the Walmart gig will even pay a tad more than the Home Depot position.

“It’s very necessary,” says J. “I’ll take anything.”

“It’s hard,” says Tara Durnell, a 37-year-old mother looking for work at Walmart. She gets just one day a week at a Yankee Candle store in the mall, and a few more hours cleaning up a doctor’s office.

“I love to work. I don’t like sitting at home. This isn’t me,” she says.

“It doesn’t really matter what it is: overnight, during the day, anything,” says Robert Lee, another worker in search of more hours. He has a part-time job at an AMC theater, where his schedule has been unpredictable. He worked only 17 hours last week.

One man, when asked about his job situation, starts shaking his head and pacing. He hasn’t found anything in seven months. Even the summer, which he finds ripe for construction work, has been fallow.

“I’m applying for a job at Walmart,” he says, sounding defeated and not wanting to give his name. “I don’t want to work at Walmart.”

A red-headed woman says she’s been out of work since she lost her job earlier this year. “I’ll take anything. I just want to work, and work legally,” she says.

“If everybody feels like things will get better,” she goes on, as if asking a question, “then maybe they will?”

The last applicant to leave for the day is Rob Ernst, 54. Ernst been looking for work for more than a year. He spent a long time inside the hall trying to nail the Walmart application.

“It’s been one hell of a battle,” he says of his unemployment. “I’ve been living off my savings, which is almost gone.”

In 2008, outsourcing to India cost him the quality job he had had for ten years, doing graphics work for telephone books. With help from the federal Trade Adjustment Assistance program, which helps people whose jobs go overseas, Ernst went back to school and earned an associate’s degree in computer-aided design. An honors student, he wound up with his picture in the local paper when he finished last spring. But the jobs weren’t there.

“They were having jobs fairs at school, and some of the companies weren’t even showing up,” he says.

He’s applied for around 50 jobs in his new field, to no avail. “I’ve given up on the specialty work,” he says.

In addition to Walmart, he’s put in applications at Staples, Kohl’s and Target. He says he’s had to get used to not wearing a suit to certain job interviews, lest he feel overdressed and awkward at the more blue-collar workplaces.

Ernst’s hope is that some kind of job will come through, even if it’s part-time, to tide him over until things rebound. He hopes his new degree will be of some use in the future, but for now he isn’t picky.

“Now, I’ll take general work. That’s what brought me here,” he says, just before hopping into his Toyota. “It’s getting to the point where you’re just looking for a life raft.”

[Chartsky Note:   THIS is what it's come to in America, folks.   Desperate people trying to get part-time jobs at Wal-Mart just to feed their kids!   I've been warning for a long time that this growing number (now MILLIONS) of righteously angry people are simmering just beneath the surface.   They see their Government doesn't really care about them . . . just politicians all seeking re-election as their only real concern. I posted this so anyone who reads will know and understand:  You're not alone!]

Thanks to the Huffington Post for the original article which can be found here.

Amen . . . and AMEN!

Finally someone in the corporate-controlled media speaks the truth . . . and, once again, it’s Dylan Ratigan.  I don’t know how long he’ll be around actually telling the truth like this:

The Wall Street Ponzi Scheme In A Single Graph

By: Chartsky

Background

January 28, 2011 — For decades it was possible to measure the increase in the United States dollar money supply — how much the Federal Reserve (Fed) printed and created out of thin air — by comparing week-to-week and month-to-month money supply numbers. These were called M3.

It made it easier to see why prices steadily increased. For example, in January 1959 (the first month reported) there was $288.8 Billion out there. In January 2006 there was $10,242.8 Billion. Yeah, that’s confusing . . . there was over 35-times as much money that had been created (Poof!) . . . and everything the money bought was similarly much more expensive.

In 2006 the Fed abruptly stopped providing the M3 numbers. M3 was M2 plus “large” deposits and large, long-term deposits. M2 is now used to define the U.S. money supply and is much less accurate than M3. Keep in mind those pesky “large” deposits are never accounted for now.

In the last year numbers were given, M3 increased 8.4% versus only 4.8% for M2. This discrepancy was not new. During the last 10 years it was reported, M3 increased at a average annual rate of 8.2% versus 6.3% for M2. These numbers really say everything we need to know about why the Fed criminals stopped telling the truth about our ever-increasing money supply.

Dow 12,000

So why is the Dow climbing steadily up when everyone who has half a brain can tell the U.S. economy is all but dead?

Keep in mind that the true money supply, M3, not reported in almost 5-years rose almost twice as much as M2 and look at this graph:

This reeks of the last gasp efforts of desperate swindlers who are running a giant ponzi scheme otherwise known as Wall Street.

The increase in M2 has been 45-degrees — practically exponential — but now seems poised to go parabolic.

For the week ending 1/17/2011, the Fed increased M2 by a mind-blowing $46.6 Billion! Just one week! And that doesn’t count the “large” deposits they have refused to report for the last 5-years.

This is the Wall Street Ponzi Scheme in a single graph.

And this is also why gold has climbed steadily higher. There is a 92% correlation between M2 and the price of gold.

But I find it criminal that the bought-and-paid-for U.S. politicians (who know all of this) stand idly by while a greedy group of sleazy banksters continue to destroy our economy — and all the Retirement Accounts and Pensions of people who have worked and paid their dues for 25-years or more.

How much is that old Confederate Money worth now?

We’ll soon be able to buy a packet of $1,000 for  . . . 1 Amero.

Chartsky

This is SCARY folks . . .

Algorithms Take Control of Wall Street
By Felix Salmon and Jon Stokes
December 27, 2010
Wired January 2011

January 14, 2011 — Last spring, Dow Jones launched a new service called Lexicon, which sends real-time financial news to professional investors. This in itself is not surprising. The company behind The Wall Street Journal and Dow Jones Newswires made its name by publishing the kind of news that moves the stock market. But many of the professional investors subscribing to Lexicon aren’t human they’re algorithms, the lines of code that govern an increasing amount of global trading activity and they don’t read news the way humans do. They don’t need their information delivered in the form of a story or even in sentences. They just want data:  the hard, actionable information that those words represent.

Lexicon packages the news in a way that its robo-clients can understand.  It scans every Dow Jones story in real time, looking for textual clues that might indicate how investors should feel about a stock.  It then sends that information in machine-readable form to its algorithmic subscribers, which can parse it further, using the resulting data to inform their own investing decisions. Lexicon has helped automate the process of reading the news, drawing insight from it, and using that information to buy or sell a stock. The machines aren’t there just to crunch numbers anymore; they’re now making the decisions.

That increasingly describes the entire financial system. Over the past decade, algorithmic trading has overtaken the industry. From the single desk of a startup hedge fund to the gilded halls of Goldman Sachs, computer code is now responsible for most of the activity on Wall Street.  (By some estimates, computer-aided high-frequency trading now accounts for about 70 percent of total trade volume.)  Increasingly, the market’s ups and downs are determined not by traders competing to see who has the best information or sharpest business mind but by algorithms feverishly scanning for faint signals of potential profit.

[Chartsky Note:  In the futures arena, I don't think we see 70%+ algorithm trading . . . but we now see MORE than enough to spike price past a reasonable protective stop, without any apparent reason, before it moves straight in the original direction.  It has certainly caused my Break-Even trades to DOUBLE, or more, just since last Fall.]

Algorithms have become so ingrained in our financial system that the markets could not operate without them. At the most basic level, computers help prospective buyers and sellers of stocks find one another without the bother of screaming middlemen or their commissions. High-frequency traders, sometimes called flash traders, buy and sell thousands of shares every second, executing deals so quickly, and on such a massive scale, that they can win or lose a fortune if the price of a stock fluctuates by even a few cents.  Other algorithms are slower but more sophisticated, analyzing earning statements, stock performance, and newsfeeds to find attractive investments that others may have missed. The result is a system that is more efficient, faster, and smarter than any human.

It is also harder to understand, predict, and regulate. Algorithms, like most human traders, tend to follow a fairly simple set of rules. But they also respond instantly to ever-shifting market conditions, taking into account thousands or millions of data points every second. And each trade produces new data points, creating a kind of conversation in which machines respond in rapid-fire succession to one another’s actions. At its best, this system represents an efficient and intelligent capital allocation machine, a market ruled by precision and mathematics rather than emotion and fallible judgment.

But at its worst, it is an inscrutable and uncontrollable feedback loop. Individually, these algorithms may be easy to control but when they interact they can create unexpected behaviors a conversation that can overwhelm the system it was built to navigate. On May 6, 2010, the Dow Jones Industrial Average inexplicably experienced a series of drops that came to be known as the flash crash, at one point shedding some 573 points in five minutes. Less than five months later, Progress Energy, a North Carolina utility, watched helplessly as its share price fell 90 percent. Also in late September, Apple shares dropped nearly 4 percent in just 30 seconds, before recovering a few minutes later.

[Chartsky Note:  Does anyone else find this almost impossible to believe?  Why would our bought-and-paid-for politicians allow such potentially devastating behavior?  Of course -- they're bought-and-paid-for by the very same corporations that use these highly dangerous algorithms for program trading.]

These sudden drops are now routine, and it’s often impossible to determine what caused them. But most observers pin the blame on the legions of powerful, superfast trading algorithms simple instructions that interact to create a market that is incomprehensible to the human mind and impossible to predict.

For better or worse, the computers are now in control.

[Chartsky Note:  I don't agree that the computers are now "in control" because they lack the single most important element a successful, professional trader has:  discretion.  But they are making it MUCH harder to trade.]

Ironically enough, the notion of using algorithms as trading tools was born as a way of empowering traders. Before the age of electronic trading, large institutional investors used their size and connections to wrangle better terms from the human middlemen that executed buy and sell orders.  We were not getting the same access to capital, says Harold Bradley, former head of American Century Ventures, a division of a midsize Kansas City investment firm.  So I had to change the rules.

Bradley was among the first traders to explore the power of algorithms in the late 90s, creating approaches to investing that favored brains over access. It took him nearly three years to build his stock-scoring program. First he created a neural network, painstakingly training it to emulate his thinking to recognize the combination of factors that his instincts and experience told him were indicative of a significant move in a stock’s price.

* * * (I cut-out a long description of how Bradley’s original algorithm was programmed.)

Bradley’s effort was just the beginning. Before long, investors and portfolio managers began to tap the world’s premier math, science, and engineering schools for talent. These academics brought to trading desks sophisticated knowledge of AI methods from computer science and statistics.

And they started applying those methods to every aspect of the financial industry. Some built algorithms to perform the familiar function of discovering, buying, and selling individual stocks (a practice known as proprietary, or prop, trading). Others devised algorithms to help brokers execute large traders massive buy or sell orders that take a while to go through and that become vulnerable to price manipulation if other traders sniff them out before they’re completed. These algorithms break up and optimize those orders to conceal them from the rest of the market. (This, confusingly enough, is known as algorithmic trading.) Still others are used to crack those codes, to discover the massive orders that other quants are trying to conceal. (This is called predatory trading.)

The result is a universe of competing lines of code, each of them trying to outsmart and one-up the other.  We often discuss it in terms of The Hunt for Red October, like submarine warfare, says Dan Mathisson, head of Advanced Execution Services at Credit Suisse.  There are predatory traders out there that are constantly probing in the dark, trying to detect the presence of a big submarine coming through. And the job of the algorithmic trader is to make that submarine as stealth as possible.

Meanwhile, these algorithms tend to see the market from a machine’s point of view, which can be very different from a human’s.  Rather than focus on the behavior of individual stocks, for instance, many prop-trading algorithms look at the market as a vast weather system, with trends and movements that can be predicted and capitalized upon.  These patterns may not be visible to humans, but computers, with their ability to analyze massive amounts of data at lightning speed, can sense them.

The partners at Voleon Capital Management, a three-year-old firm in Berkeley, California, take this approach. Voleon engages in statistical arbitrage, which involves sifting through enormous pools of data for patterns that can predict subtle movements across a whole class of related stocks.

Situated on the third floor of a run-down office building, Voleon could be any other Bay Area web startup. Geeks pad around the office in jeans and T-shirts, moving amid half-open boxes and scribbled whiteboards. Cofounder Jon McAuliffe is a stats wonk from Berkeley and Harvard University whose resume includes a stint at Amazon.com working on the company’s recommendation engine. The other cofounder, CEO Michael Kharitonov, is a computer scientist from Berkeley and Stanford who formerly ran a networking startup.

To hear them describe it, their trading strategy bears more resemblance to those data-analysis projects than to classical investing. Indeed, McAuliffe and Kharitonov say that they don’t even know what their bots are looking for or how they reach their conclusions.  What we say is “Here’s a bunch of data. Extract the signal from the noise,” Kharitonov says.  We don’t know what that signal is going to be like.

The kind of trading strategies our system uses are not the kind of strategies that humans use, Kharitonov continues. We’re not competing with humans, because when you’re trading thousands of stocks simultaneously, trying to capture very, very small changes, the human brain is just not good at that. We’re playing on a different field, trying to exploit effects that are too complex for the human brain. They require you to look at hundreds of thousands of things simultaneously and to be trading a little bit of each stock. Humans just can’t do that.

In late September, the Commodity Futures Trading Commission and the Securities and Exchange Commission released a 104-page report on the May 6 flash crash.  The culprit, the report determined, was a large fundamental trader that had used an algorithm to hedge its stock market position. The trade was executed in just 20 minutes an extremely aggressive time frame, which triggered a market plunge as other algorithms reacted, first to the sale and then to one another’s behavior.  The chaos produced seemingly nonsensical trades — shares of Accenture were sold for a penny, for instance, while shares of Apple were purchased for $100,000 each. (Both trades were subsequently canceled.) The activity briefly paralyzed the entire financial system.

The report offered some belated clarity about an event that for months had resisted easy interpretation. Legislators and regulators, spooked by behavior they couldn’t explain, much less predict or prevent, began taking a harder look at computer trading. In the wake of the flash crash, Mary Schapiro, chair of the Securities and Exchange Commission, publicly mused that humans may need to wrest some control back from the machines.  Automated trading systems will follow their coded logic regardless of outcome, she told a congressional subcommittee, while human involvement likely would have prevented these orders from executing at absurd prices.  Delaware senator Ted Kaufman sounded an even louder alarm in September, taking to the Senate floor to declare, “Whenever there is a lot of money surging into a risky area, where change in the market is dramatic, where there is no transparency and therefore no effective regulation, we have a prescription for disaster.”

In the months after the flash crash, the SEC announced a variety of measures to prevent anything like it from occurring again. In June, it imposed circuit breakers, rules that automatically halt trading if a stock’s price fluctuates by more than 10 percent in five minutes. (In September, the SEC’s Schapiro announced that the agency might tweak the circuit breakers to prevent unnecessary freezes.)  The agency is considering requiring trading algorithms to include a governor, which limits the size and speed at which trades can be executed. And it has also proposed the creation of a so-called consolidated audit trail, a single database that would collect information on every trade and execution, and which would in the words of an SEC press release help regulators keep pace with new technology and trading patterns in the markets.  Others have suggested implementing a transaction tax, which would impose a particular burden on massive, lightning-fast trades.

[Chartsky Note:  After the carnage, our paid-for politicians -- or their surrogates -- scramble for a TV camera or microphone to speak, then always seem to conveniently forget about correcting the disaster after our attention has been captured by this week's American Idol episode.  That's how you know the American government has been completely bought and is owned.]

But these are not ways of controlling the algorithms, they are ways of slowing them down or stopping them for a few minutes. That’s a tacit admission that the system has outgrown the humans that created it. Today a single stock can receive 10,000 bids per second; that deluge of data overwhelms any attempt to create a simple cause-and-effect narrative.  Our financial markets have become a largely automated adaptive dynamical system, with feedback, says Michael Kearns, a computer science professor at the University of Pennsylvania who has built algorithms for various Wall Street firms. There’s no science I’m aware of that’s up to the task of understanding its potential implications.

For individual investors, trading with algorithms has been a boon: Today, they can buy and sell stocks much faster, cheaper, and easier than ever before. But from a systemic perspective, the stock market risks spinning out of control. Even if each individual algorithm makes perfect sense, collectively they obey an emergent logical artificial intelligence, but not artificial human intelligence. It is, simply, alien, operating at the natural scale of silicon, not neurons and synapses. We may be able to slow it down, but we can never contain, control, or comprehend it. It’s the machines’ market now; we just trade in it.

See: Wired Magazine Online — January 2011 Edition

U.S. Debt MUCH Worse Than Admitted:

September 20 — The actual figure of the US’ national debt is much higher than the official sum of $13.4 Trillion given by the Congressional Budget Office, according to analysts cited on Sunday by the New York Post.

“The Government is lying about the amount of debt. It is engaging in Enron accounting,” said Laurence Kotlikoff, an economist at Boston University and co-author of The Coming Generational Storm: What You Need to Know about America’s Economic Future.

“The problem is we’re seeing an explosion in spending,” added Andrew Moylan, director of government affairs for the National Taxpayers Union.

In 1980, the debt — the accumulated red ink incurred by the Federal Government — was $909 Billion.

This represented some 33 per cent of gross domestic product, according to the Congressional Budget Office (CBO).

Thirty years later, based on this year’s second-quarter numbers, the CBO said the debt was $13.4 Trillion, or 92 per cent of GDP.  [Chartsky Note:  This is STAGGERING folks!  This is beyond bankruptcy level debt!  And it's worse:  the FED is printing monopoly money to buy the Government's IOUs:  U.S. Treasury Notes -- because no one else wants to get stuck with worthless paper.  Yet you're not being told about that and encouraged to invest in the ponzi-scheme stock market.]

The CBO estimates the debt will be at $16.5 Trillion in two years, or 100.6 per cent of GDP.

But even these numbers are incomplete (errrr . . . you mean more LIES).

Why?  They do not count off-budget obligations such as required spending for Social Security and Medicare, whose programs represent a balloon payment for the Government as more Americans retire and collect benefits.

In the case of Social Security, beginning in 2016, the US Government will be paying out more than it is collecting in taxes.

Without basic measures – such as payment cuts or higher payroll taxes – the system could be on the road to bankruptcy, according to officials.

“Without changes,” wrote Social Security Commissioner Michael Astrue, “by 2037 the Social Security Trust Fund will be exhausted. There will be enough money only to pay about $0.76 for each dollar of benefits.”  

Mr Kotlikoff and Mr Moylan agree US national debt is much more than the official $13.4 Trillion number, but they disagree over how to add up the exact number.

Mr Kotlikoff says the debt is actually $US200 trillion.

You can read the rest of the story here.

Japanese Yen Recovering . . .

September 16 — The yen advanced against the dollar, paring losses from yesterday when Japan’s first intervention in foreign-exchange markets since 2004 sent the currency tumbling the most in 22 months.

The yen gained earlier against the euro on speculation Japanese exporters seized the chance to buy the currency near a two-week low to bring home overseas earnings before the fiscal first half ends. China’s yuan earlier touched 6.7180, the strongest level since the central bank unified official and market exchange rates at the end of 1993, as U.S. lawmakers faulted China’s currency policy as predatory.

“The market is testing the Bank of Japan to see where they are setting the floor,” said Geoffrey Yu, a currency strategist at UBS AG in London. “It’s not clear yet whether they are trying to promote a weaker yen or just prevent it from rising. There’s a seasonal factor here too, but that’s not as strong as the market one.”

You can read the rest of the Bloomberg story here.

Goldman Sach$ Causes U.S. Dollar To Tank

September 14 — Thanks a lot!

I’m sure Goldman Sach$ was nice and short the market when they made their “announcement.”

Their reason was a continuation of the  monopoly money printing scheme by the Federal Reserve . . . which isn’t hard to imagine at all. The FED has been printing money and buying U.S. securities for while to forestall the market “correction” that is long overdue.

“We don’t expect this at the Sept. 21 meeting, but in November or December there’s certainly a possibility that it will be announced,” Jan Hatzius, chief economist at the bank, said Tuesday. He added the Fed is likely to buy U.S. Treasuries worth around $1.0 trillion to kick-start the economy.

To fight the financial crisis in 2008 and 2009, the Fed bought $1.7 trillion in mainly mortgage-backed securities, a move that helped to keep mortgage and other long-term borrowing rates low. That program ended in March. But with the recovery slowing, the Fed said Aug. 10 that it would reinvest the proceeds of mortgage bonds into U.S. Treasuries to prevent its portfolio of securities from shrinking. The question now is whether the central bank will start a new program of asset purchases that would increase the size of its $2.0 trillion balance sheet further.

Goldman Sachs expects this to happen soon given the weakness in the U.S. economy as a result of lower business inventory accumulation and a fading fiscal stimulus.

The upcoming meetings of the Fed’s policy-setting committee this year are Sept. 21, Nov. 2-3 and Dec. 14.

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