Tuesday, March 27, 2012

Study: 18,520 Cases Of Black Swan Stock Market Manipulation In Last 5 Years

Researchers planning a system to completely computerize stock market trading uncovered 18,520 instances of stock market manipulation between 2006 and 2011.

If you are walking down the streets of New York City and you suddenly see a crowd rushing your way what to you do?
Most likely you will assume the crowd is in panic because they fear for their lives and join them in running to save your own life and ask questions later.
The same kind of herd mentality exists on Wall Street.
With millions of dollars on the line the shares of the stock you are holding start to plummet into a free fall.
The commotion grabs your attention and only thing you can decipher is a room full of people shouting at the market maker to sell, sell, sell.
When everyone else in the room is dumping unloading their positions as the price tanks, you assume there must be some horrible news and jump in on the frenzy and dump your shares to limit your losses and ask what the bad news was after the fact.
Mix tens of thousands of start of the art high-speed computers into the equation, with the ability to execute tens of thousands of trades per second and running highly complex algorithms designed to prey on this herd mentality, and you suddenly enter the shadowy underworld of Wall Street known as High Frequency Trading (HFT).
These trading bots take advantage small differences in prices on the markets along with flaws in the market exchanges while roaming in a realm were executing a trade a few milliseconds faster than the next guy is equivalent to humans having insider information days in advance.
As much as these automated borgs enjoy snacking on the savory taste of the retail investor’s life savings the real feast comes from cannibalizing their own kind.
By flooding the system with thousands of sell orders, often spoofed or cancelled,  in just mere milliseconds, they trigger other bots to into panic and drive the price of whatever shares they target either up or down.
In less time than a human can even react these bots earn their wage for the day and have changed the trajectory of the the shares they have targeted in a manner invisible to the human eye.
Only by using other computers and examining the sequence of trades in the aftermath can one detect the ulfrafast financial black swan event that has just occurred.
On March 23rd, the real-time data feed company NANAX did just that in catching high frequency traders using the same sophisticated computer trading algorithms to manipulate and crash silver prices.

Traders Caught Using NASDAQ Exploit To Manipulate Silver Prices

Hedge funds caught red-handed using flaws in the NASDAQ market exchange to manipulate the price silver using high-speed computer trading algorithms.

Despite documented flaws in the way market exchanges handles high speed trades at high volumes, which are attributed to the DOW flash crash, electronic trading systems still have not been patched to fix the issue.
The vulnerability stems from bad timestamps assigned to orders when traders flood the system which causes trades to executed on quotes before they even yet exist in the system.
Earlier today high frequency traders were caught in act by NANEX taking advantage of the flaw to exploit the NASDAQ silver ETF by barraging the system with a whopping 75,000 trades per second.
The exploit then triggered other trading robots to execute trades based on the high volume of trades quickly plummeting silver price.
Read The Rest…
While I was under the impression that such things did indeed happen I assumed that they were a rare exception.
I assumed only the greediest or most desperate traders would be foolish enough to risk getting caught engaging in such behavior, take for example the case of JP Morgan whose short position on silver would bankrupt the company if they allowed the price of silver to rise to high.
However, I was completely taken off guard when Financial Sense followed up on March 23 silver crash and reported on an interview with Ted Butler.
In the interview, Butler explains that the practices is not only limited to the silver market but,  quite to contrary, all markets are being manipulated by High Frequency Traders and very frequently.

Silver Manipulation Caught in the Act; HFT Swamps NASDAQ with 75K SLV Sell Orders Per Second

Ironically, just days after noted analyst Ted Butler came on the show to explain how silver and other markets are manipulated through the use of high frequency trading, the real-time data feed company, Nanex, showed how the silver ETF (SLV) was forced downwards by a rapid number of machine-generated quotes exceeding a rate of 75,000 per second. Before you start to think that this was merely a bunch of people hitting the sell button all at once, consider this: They were all launched within the space of 25 milliseconds—ten times faster than you and I can blink!
Here’s a chart of the second by second market activity in SLV where you can see the massive lightning-quick spike occurring at 13:22:33.
slv htf spike
Source: Nanex

Ted Butler Explains the Whole Process

“What’s happening is that these commercials [or large traders], through HFT, can set the price suddenly down. It didn’t go down because there was massive selling from the commercials, they just set the price down. They know how to do it with their computers by putting in actual orders, and faking it, and spoofing, canceling them right away; but what happens is when the price moves down then the selling comes, which is the intended effect and result. Commercials basically put the price down in order to set off stops because everybody seems to be some type of technical trader in the market that reacts to prices.”
(Click here for interview and full transcript)
Read The Rest…
In the process of pointing the widespread and rampant manipulation of Wall Street stocks, the article references a newly released study Financial Black Swans Driven by Ultrafast Machine Ecology which truly provides some stunning insight.
The study “looked at 600 different markets around the world and found that these sort of events happen routinely. Over the most recent five years of market data analyzed, 18,520 crashes and spikes occurred at a speed far exceeding human origin.”
The report documented the 18,520 “ulfrafast financial black swans” during the time period between 2006 and 2011.
Given there are approximately 252 trading days in a year, that equates to an average of  approximately 14.7 instances of stock market manipulation per day.
With the trading day being 6 and half hours these stock manipulations are occurring, on average, 2.26 times per hour.
Almost ironically, the purpose of the paper is to propose a system to control these rogue market manipulations so humans can be eliminated from the stock trading system and the entire platform can be placed under the control of automated computers systems.
From the study:

Financial black swans driven by ultrafast machine ecology

Neil Johnson1, Guannan Zhao1, Eric Hunsader2, Jing Meng1, Amith Ravindar1, Spencer Carran1 and
Brian Tivnan3,4
1 Physics Department, University of Miami, Coral Gables, Florida 33124, U.S.A.
2 Nanex LLC, Evanston, Illinois, U.S.A.
3 The MITRE Corporation, McLean, VA 22102, U.S.A.
4 Complex Systems Center, University of Vermont, Burlington, VT 05405, U.S.A.


Society’s drive toward ever faster socio-technical systems1-3, means that there is an urgent need to understand the threat from ‘black swan’ extreme events that might emerge4-19. On 6 May 2010, it took just five minutes for a spontaneous mix of human and machine interactions in the global trading cyberspace to generate an unprecedented system-wide Flash Crash4. However, little is known about what lies ahead in the crucial sub-second regime where humans become unable to respond or intervene sufficiently quickly20,21. Here we analyze a set of 18,520 ultrafast black swan events that we have uncovered in stock-price movements between 2006 and 2011. We provide empirical evidence for, and an accompanying theory of, an abrupt system-wide transition from a mixed human-machine phase to a new all-machine phase characterized by frequent black swan events with ultrafast durations (<650ms for crashes, <950ms for spikes). Our theory quantifies the systemic fluctuations in these two distinct phases in terms of the diversity of the system’s internal ecology and the amount of global information being processed. Our finding that the ten most susceptible entities are major international banks, hints at a hidden relationship between these ultrafast ‘fractures’ and the slow ‘breaking’ of the global financial system post-2006. More generally, our work provides tools to help predict and mitigate the systemic risk developing in any complex socio-technical system that attempts to operate at, or beyond, the limits of human response times.
From the study:

Figure 1: Traded price during black swan events.

HFT Stock Market Manipulation - Traded price during black swan events
HFT Stock Market Manipulation - Traded price during black swan events

(A) Crash. Stock symbol is ABK. Date is 11/04/2009. Number of sequential down ticks is 20. Price change is -0.22.  Duration is 25ms (i.e. 0.025 seconds). Percentage price change downwards is 14% (i.e. crash magnitude is 14%).
(B) Spike. Stock symbol is SMCI. Date is 10/01/2010. Number of sequential up ticks is 31. Price change is +2.75. Duration is 25ms (i.e. 0.025 seconds). Percentage price change upwards is 26% (i.e. spike magnitude is 26%). Dots in price chart are sized according to size of trade.
(C) Cumulative number of crashes (red) and spikes (blue) compared to overall stock market index (Standard & Poor’s 500) in black, showing daily close data from 3 Jan 2006 until 3 Feb 2011.

Figure 2: Empirical transition in size distribution for black swans with duration above threshold r, as function of r.

HFT Stock Market Manipulation - Empirical transition in size distribution for black swans
HFT Stock Market Manipulation - Empirical transition in size distribution for black swans
Top: Scale of times. 650 ms is the time for chess grandmaster to discern King is in checkmate.
Plots show the results of the best-fit power-law exponent (black) and goodness-of-fit (blue) to the distributions for size of crashes and spikes separately, as shown in the inset schematic.

Figure 3: Theoretical transition.
HFT Stock Market Manipulation - Theoretical transition.
HFT Stock Market Manipulation - Theoretical transition.
Model output for the two regimes of strategy distribution among agents ( n = 2m+1 / N) together with timescales from Fig. 2 (top).
n < 1 implies many agents per strategy, hence large crowding which produces frequent, large and abrupt price-changes, i.e. high number of short-duration (<< 1 second) black swans, as observed empirically.
n> 1 implies very few, if any, agents per strategy, hence small crowding. Therefore large changes are rarer and last longer, i.e. low number of longer-duration black swans, as observed empirically.

Figure 4: Prediction and mitigation of ultrafast black swans.

HFT Stock Market Manipulation - Prediction and mitigation of ultrafast black swans
HFT Stock Market Manipulation - Prediction and mitigation of ultrafast black swans
(A) Theoretical black swan produced by our model, similar to Fig. 1A on an expanded timescale. Below are model’s node weights for m = 3 as a function of time, with blue and red denoting the weight values (see text and SI). The more blue the weight on node 0 (or the more red the weight on node 7) the more likely that a large price-drop (or rise) will occur when the model’s trajectory (green curve) hits that node.
(B) Nodes shown as their binary and decimal equivalents (e.g. 000 is 0 in decimal) for m = 3. Red (blue) arrows represent transitions generating a bit-string update of 0 (1) and hence a price drop (rise).
(C) Proposed mitigation scheme. Blue boxes show histogram of strategy possession for existing population, as an ‘occupancy’ in a two-dimensional space spanned by the possible strategy combinations for s = 2 strategies with m = 2. Red boxes are inserted agents, which can induce a steering effect to avoid the crash29 (red dotted line in Fig. 4A showing the mean of the resulting price movement). See text for discussion.

$270 Billion In Student Loans Are At Least 30 Days Delinquent

The first cracks appear in the massive $1 Trillion of US Student loan debt as a whopping 27% of borrowers are now delinquent on $270 billion in loan repayments.

Back in late 2006 and early 2007 a few (soon to be very rich) people were warning anyone who cared to listen, about what cracks in the subprime facade meant for the housing sector and the credit bubble in general. They were largely ignored as none other than the Fed chairman promised that all is fine (see here). A few months later New Century collapsed and the rest is history: tens of trillions later we are still picking up the pieces and housing continues to collapse. Yet one bubble which the Federal Government managed to blow in the meantime to staggering proportions in virtually no time, for no other reason than to give the impression of consumer releveraging, was the student debt bubble, which at last check just surpassed $1 trillion, and is growing at $40-50 billion each month. However, just like subprime, the first cracks have now appeared. In a report set to convince borrowers that Student Loan ABS are still safe – of course they are – they are backed by all taxpayers after all in the form of the Family Federal Education Program – Fitch discloses something rather troubling, namely that of the $1 trillion + in student debt outstanding, “as many as 27% of all student loan borrowers are more than 30 days past due.” In other words at least $270 billion in student loans are no longer current (extrapolating the delinquency rate into the total loans outstanding). That this is happening with interest rates at record lows is quite stunning and a loud wake up call that it is not rates that determine affordability and sustainability: it is general economic conditions, deplorable as they may be, which have made the popping of the student loan bubble inevitable. It also means that if the rise in interest rate continues, then the student loan bubble will pop that much faster, and bring another $1 trillion in unintended consequences on the shoulders of the US taxpayer who once again will be left footing the bill.
From Fitch:
Fitch believes most student loan asset-backed securities (ABS) transactions remain well protected due to the government guarantee on Family Federal Education Program (FFELP) loans. The Federal Reserve Bank of New York recently reported that as many as 27% of all student loan borrowers are more than 30 days past due. Recent estimates mark outstanding student loans at $900 billion- $1 trillion. Fitch believes that the recent increase in past-due and defaulted student loans presents a risk to investors in private student loan ABS, but not those in ABS trusts backed by FFELP loans.
Why is the bubble starting to pop now?
Several macroeconomic factors are putting pressure on student loan borrowers. The main ones are unemployment and underemployment. The Bureau of Labor Statistics estimates the current unemployment rate for people 20 to 24 years old at nearly 14% and for those 25 to 34 years old, 8.7%. Underemployment is difficult to measure for these demographics, but it is likely having a negative impact.
Actually, no: the unemployment for 18-24 year olds is 46%. Yup: 46%.
A month ago, Zero Hedge readers were stunned to learn that unemployment among Europe’s young adults has exploded as a result of the European financial crisis, and peaking anywhere between 46% in the case of Greece all they way to 51% for Spain. Which makes us wonder what the reaction will be to the discovery that when it comes to young adults 18-24) in the US, the employment rate is just barely above half, or 54%, which just happens to be the lowest in 64 years, and 7% worse than when Obama took office promising a whole lot of change 3 years ago.

Only 46 Percent Of Young Adults Are Employed
Only 46 Percent Of Young Adults Are Employed
And while technically this means 46% are unemployed, or the same percentage as in Greece, the US ratio, which comes from Pew, shows the ratio as a % of the total population: a very sensitive topic now that every month we see another 250,000 drop off mysteriously from the total labor force. However, unlike those on the trailing age end, young adults by definition are the labor force in their age group demographic, so it would be difficult to explain away this horrendous number by claiming that ever more 24 year olds are retiring. Although, yes, we agree that some may be dropping out of the labor force in order to go to college, incidentally the locus of the latest credit bubble, where they meet a fate worse even than secular unemployment: they become debt slaves of the Federal System, with non-dischargable debt at that, which even assuming they can get a job would take ages to pay back!

But wait: there’s more – of all age groups, this is the one that has actually seen its wages drop the most under the Obama administration.

So not only are they unemployed, young adults are at least poor.

Net result: double the change, zero the hope.
But fear not dear banks: taxpayers got your back, as usual.
However, we believe that ABS trusts backed by FFELP loans are unlikely to be affected by employment trends, as they are at least 97% backed by the federal government. In addition, recent securitizations have been structured more robustly and many have backup servicing agreements.
Even so, Fich is covering its bases nonetheless:
While FFELP loans are largely protected from these trends, private student loan ABS trusts, especially those that were structured aggressively and with less stringent credit standards before the recession, are expected to continue experiencing high defaults and ratings pressure. Fitch will continue to monitor these political and macroeconomic factors as they evolve and will determine any impact they may have on ABS trusts.
And as a courtesy reminder to our young up and coming “thinkers”, this is $270 billion in debt that can not be discharged. Go ahead – file for bankruptcy – see what happens.
The question then is – what is the student loan version of the ABX trade. After all if Bernanke is willing to blow another bubble, someone has to be able to profit when this latest soon to be failed attempt at central planning.
Finally, here are some more perspectives on the student loan bubble direct from the New York Fed’s blog.
37 Million Student Loan Borrowers By Age
37 Million Student Loan Borrowers By Age

$870 Billion In Student Loan Debt - Balance by Age
$870 Billion In Student Loan Debt - Balance by Age

The average outstanding student loan balance per borrower is $23,300. Again, there is substantial heterogeneity in balances of individual borrowers. The median balance of $12,800 is roughly half the average level, which indicates that a small fraction of people have balances significantly higher than the median. About one-quarter of borrowers owe more than $28,000; about 10 percent of borrowers owe more than $54,000. The proportion of borrowers who owe more than $100,000 is 3.1 percent, and 0.45 percent of borrowers, or 167,000 people, owe more than $200,000. The distribution also varies by age group: for example, borrowers between the ages of thirty and thirty-nine have the highest average outstanding student loan balance, at $28,500, followed by borrowers between the ages of forty and forty-nine, whose average outstanding balance is $26,000 (see chart below).

Student Loan Borrowers by Level Of Balance
Student Loan Borrowers by Level Of Balance

How much difficulty are borrowers having paying back their debts? Of the 37 million borrowers who have outstanding student loan balances as of third-quarter 2011, 14.4 percent, or about 5.4 million borrowers, have at least one past due student loan account. Together, these past due balances sum to $85 billion, or roughly 10 percent of the total outstanding student loan balance. To put this in perspective, the same 10 percent rate applies on average to other types of household delinquent debt, including mortgages, credit cards, and auto loans. Does this mean that the prospects for student loan delinquencies are similar to those for the household debt in general, and thus no special attention is warranted? (See chart below.)

Past Due Student Loan Balance By Age
Past Due Student Loan Balance By Age

Unfortunately, this is not the case—some special accounting used for student loans, not applicable to other types of consumer debt, makes it likely that the delinquency rates for student loans are understated. In the case of federally backed loans, which represent a majority of total lending, repayment is deferred until the student graduates from school and can then be pushed back by another six-month grace period. How do these student loans in deferment or grace periods show up on credit reports and contribute to the delinquency statistics? Given that no payment is necessary until graduation, these deferred student loans are not included in the past due balance but they are included in the total balance from which the delinquency rate is derived. This may help explain the low proportion (12.6 percent) of borrowers with past due student loans among those under thirty years old, compared with 16.9 percent among those between the ages of thirty and thirty-nine, since many of the younger borrowers are still in school and don’t yet have to make any payments.
To address this potential bias in calculating delinquency statistics, we exclude individuals who appear to be temporarily exempt from making payments because they are in school or newly graduated from school. These are students who, as of third-quarter 2011, owed as much as or more than they did in the previous quarter while maintaining a zero past due balance. We will be able to make our inference more precise when loan-level panel data are available, but this is our first-cut analysis given the available data. We warn that there is room for misclassification in this analysis. For example, there could be borrowers who are subject to the income-based repayment plan whose payment fell short of the accrued interest, resulting in a balance that increased. Recall that this exercise looks at the student loan borrowers who have a balance as of third-quarter 2011; therefore, those who had taken out a loan at one point but paid it off before third-quarter 2011 are not accounted for.
From this exercise, we find that as many as 47 percent of student loan borrowers appear to be in deferral or forbearance periods, and thus did not have to make payments as of third-quarter 2011. Specifically, 17.6 percent of borrowers had exactly the same balance in the third quarter as in the second quarter of this year, and 29.1 percent increased their overall student loan balance by taking on new originations or accruing interest to the balance.
We then recalculate the proportion of borrowers with a past due balance excluding this group of borrowers. We find that 27 percent of the borrowers have past due balances, while the adjusted proportion of outstanding student loan balances that is delinquent is 21 percent—much higher than the unadjusted rates of 14.4 percent and 10 percent, respectively (see charts below).
Student Loan Borrowers by Payment Status
Student Loan Borrowers by Payment Status

Delinquent Student Loan Borrowers in the Repayment Cycle
Delinquent Student Loan Borrowers in the Repayment Cycle

In sum, student loan debt is not just a concern for the young. Parents and the federal government shoulder a substantial part of the postsecondary education bill. Moreover, the student loan delinquency picture is not fully captured in the broad statistics since a significant proportion of borrowers and balances are not yet in the repayment cycle. The implications of this last fact for future changes in the student loan delinquency rate are a very important area of research.
Given that student loans are an indispensable tool for educational advancement, this form of debt will remain a critical policy focus for generations to come. Going forward, we will continue to monitor the student loan market with new data each quarter, and we will try to provide useful information on the landscape of student debt.
This article first appeared on ZeroHedge and is published here with permission.

Reality Check: The Federal Reserve is the real reason for high gas prices

Wells Fargo and Other Megabanks Don't Care About Your Business -- So Move Your Money!

Last month banking goliath Wells Fargo, the big bank with most branches nationwide, announced another round of fees on basic checking accounts. Customers in six states will have to pay $7 a month if they receive paper statements, $5 if they get them online. The fees can be waived if the customer direct-deposits more than $500 a month, or maintains a balance of $1,500.
Wells Fargo customers in Georgia, Delaware, Connecticut, New Jersey, New York, and Pennsylvania will be hit beginning in May, and the bank expects to expand it across the country in the coming months. While this fee is more narrowly targeted than the across-the-board debit card fees Bank of America attempted to enact last fall (until it backed down in the face of a wrathful public), it is part of a nationwide trend wherein Big Banks relentlessly penalize consumers for basic services. The banking industry, as Lynn Parramore noted on AlterNet, has become an oligopoly, with big players colluding to extract fees from customers, whether or not there is any justification for doing so.
Wells Fargo’s new fee, and others like it, shows that the banks were not put off their course by last year’s protests. Ordinary consumers have a strictly limited ability to hurt the big banks, which mostly depend on much larger clients for their business. Bank of America’s retraction last year was not a game-changing victory, just a temporary setback.

"The biggest banks support an infrastructure that it’s not clear if consumers benefit from, or that they necessarily benefit from consumers,” says Mike Konczal, a fellow with the Roosevelt Institute who blogs at Rortybomb. “Facilitating the means of payment is one of the core things we want from a banking sector. And banks are basically saying we want to make this really difficult for you. This is exactly the kind of stuff we bailed them out to prevent them from failing to do.”
If you were to ask a random passerby what she wants from a bank, a checking account easily accessible by debit card would likely top her list. The big banks do not have the same priorities. The average checking account does not make them much, if any, money, and paying all those cheery bank tellers and mailing statements is awfully expensive.
In the bad old days, the banks made up for this by hitting you with sly undercover fees and charging merchants exorbitant interchange fees, where the financiers take a percentage of any purchase made by a plastic card. (These charges are the reason why many small businesses only take cash.).

New regulations passed by the Democratic 111th Congress and signed by President Obama—largely within the Dodd Frank law—make it much harder to hit customers and merchants with hidden fees. But for the big banks, checking accounts, debit cards, and the everyday consumers that rely on them are only worthwhile if they hemorrhage money. So the big banks, which do most of their business in the capital markets and other high financial venues anyway, are finding new ways to bleed us.
As Felix Salmon shows in this chart, the big banking institutions have uniformly decided that free checking isn’t a service they want to offer anymore. In 2009, 96 percent of banks worth over $50 billion offered free checking. Today only 34.6 percent do. (By contrast the number of credit unions proffering free checking dropped a mere 6.4 percentage points, from 85 to 78.6 percent.)
Couldn’t the banks be backed down from this strategy? After all, Bank of America was dissuaded from its universal $5/per month debit card fees by a passionate public outcry, and the awful publicity that came with it. But it is unlikely similar tactics can reverse the overall trend. BofA relented because its greed was too apparent—and it hurt everybody, no matter their income or banking habits. In future, the banks will just target smaller and more vulnerable populations, as Wells Fargo has.

Ron Paul Stop the Fed\'s Covert Bailout of Europe

Athens: Protests at the Military Parade, Mar 25/12

They were all there: the President of the Republic, the Prime Minister, the whole cabinet, the military and religious leadership of Greece. They were all there, except the Greek citizens.  The parades of Independence Day turned into a private party between the politicians and 7,000 policemen assigned with their security. To protect them from the citizens’ anger….
The parade in Athens was short and painless, concluded in less than an hour and everybody went home with a bad taste in the lips.
For short time there was tension in the lower part of Syngtagma Square as a group of people tried to near the parade area. No, they weren’t troublemakers. They are seniors. Riot police pushed them with their shields away.

Protester holds picture of Kolokotronis – 1821 Revolution hero
The protesters threw leaflets reading “They owe us – We don’t – Not a dime to loan sharks – No to German occupation”

President Karolos Papoulias stood visibly uncomfortable when a journalist asked him how does he feel that the Disabled of the WWII were absent from the parade. For the first time. Papoulias was unable to give even a very brief answer.
Video: No answer is the best answer?
embedded by Embedded Video

YouTube Direkt
While Papoulias was   a wrath at the Monument of Unknown Soldier, a group of people was chanting “Thieves” and “Traitors”.

Police detained 25 people before and after the parade.

Protesters with banners against the emergency property tax and the banks
Chanted slogans and banners… if the politicians did not hear or see them during the parade, they can watch the “news” on television.
Video: protesters

Ένταση Σύνταγμα 1 από News247
Video: police – protesters

Ένταση Σύνταγμα 2 από News247
The centre of the Greek capital was turned into a high security area for two consequent days to avoid mass protests against the political leadership of the country. “It is sad to organize the parade with the help of the police” said Development Minister Anna Diamantopoulou.