Monday, October 23, 2017

Ex-HSBC Currency Trader Convicted Of Fraud In Massive Front-Running Scandal

Ex-HSBC currency trader Mark Johnson, who was unwittingly captured on an audio recording saying "I think we got away with it," has just been convicted by a jury in New York of fraud for front-running a $3.5 billion transaction that netted his firm some $8 million in illicit profits.  Per Bloomberg:
Former HSBC Holdings Plc currency trader Mark Johnson was found guilty of fraud for front-running a $3.5 billion client order, a victory for U.S. prosecutors as they seek to root out misconduct in global financial markets.

He was convicted on Monday after a month-long trial in Brooklyn, New York.

Johnson was the first person to be tried since the global currency-rigging scandal that resulted in global banks paying more the $10 billion in penalties. The charges stemmed from HSBC’s execution of a trading order from Cairn Energy Plc in 2011 to convert the proceeds of a unit sale from dollars into pounds.

"This sends a signal to traders and banks that this type of behavior is absolutely inappropriate and will be pursued by the government," Michael Weinstein, a former Justice Department trial attorney, said. "That’s a big hammer over the banks -- it may force them to monitor and self-regulate their people."
Johnson
For those who haven't followed this particular story, Mark Johnson was arrested at New York’s Kennedy Airport in 2016 before he could return to the U.K. following a nearly 3-year investigation into efforts on the part of several large investment banks to rig FX markets but Stuart Scott has remained free at his home in the London suburbs pending the outcome of the extradition proceedings.  Per Bloomberg:
Mark Johnson, HSBC’s global head of foreign exchange cash trading in London, was taken into custody at John F. Kennedy International Airport Tuesday and is scheduled to appear before a judge in federal court in Brooklyn Wednesday morning, said the people, who asked not to be named because the case hasn’t been made public. He’s charged with conspiracy to commit wire fraud, the people said.

According to Bloomberg, Johnson’s arrest comes more than a year after five global banks pleaded guilty to charges related to the rigging of currency benchmarks. HSBC, which wasn’t part of those criminal cases, in November 2014 agreed to pay $618 million in penalties to U.S. and British regulators to resolve currency manipulation allegations. HSBC, which still faces investigations by the Justice Department and other authorities for the conduct, has set aside $1.3 billion for possible settlements, according to an August filing.

Rob Sherman, an HSBC spokesman, and Peter Carr, a Justice Department spokesman, declined to comment.
According to the original DOJ complaint, HSBC was selected by Cairn Energy Plc to execute a foreign exchange transaction – which was going to require converting approximately $3.5 billion in sales proceeds into British Pound Sterling – in October 2011.  But, before executing that trade, he tipped off a bunch of HSBC traders who loaded up their proprietary accounts with Pounds just before the massive trade sent the currency higher.
“As alleged, the defendants placed personal and company profits ahead of their duties of trust and confidentiality owed to their client, and in doing so, defrauded their client of millions of dollars,” stated United States Attorney Capers.  “When questioned by their client about the higher price paid for their significant transaction, the defendants wove a web of lies designed to conceal the truth and divert attention away from their fraudulent trades.  The charges and arrest announced today reflect our steadfast commitment to hold accountable corporate executives and licensed professionals who use their positions to fraudulently enrich themselves.”

“The defendants allegedly betrayed their client’s confidence, and corruptly manipulated the foreign exchange market to benefit themselves and their bank,” said Assistant Attorney General Caldwell.  “This case demonstrates the Criminal Division’s commitment to hold corporate executives, including at the world’s largest and most sophisticated institutions, responsible for their crimes.”
As we've noted over the past couple of weeks, tidbits of the prosecution's case has made it's way into the media recently, including reports last week that Johnson used the code phrase "my watch is off" to trigger trading by HSBC traders all around the globe.  Meanwhile, as Law360 recently pointed out, jurors also had the opportunity to hear some rather damning recordings of Johnson's phone conversations with traders, including the one below in which he says "I think we got away with it."
Prosecutors played a recording of a call between Johnson and Stuart after the 3 p.m. fix as they debrief, with Johnson telling Stuart, “I think we got away with it,” but Stuart replies that HSBC executive Dipak Khot — who acted as the go between with Cairn and HSBC — thinks otherwise and suspects that Cairn will protest.

Johnson in turn argued that Cairn is still in a better position than it would have been if it had taken any other offers to execute the deal in alternate methods as opposed to the fix. “They don’t really have a lot of room to complain,” he said on the call.

But as Cahill was trading ahead of the 3 p.m. fix on the day of the transaction, Johnson sounded more concerned about “ramping it up” too much. Jurors heard another recording of a call between Johnson and Scott, with Scott talking to Cahill in the background as he trades, in which Johnson cautions against spiking the price of sterling too high out of concern that Cairn will "squeal."

“Frank, Frank if it rates above 30 at the fix, I think they’ll start to ah ... if you need to buy them, obviously, but ideally don’t ramp it above 30,” Scott tells Cahill. “Do what you need to do, but ... sorry I know I’m probably not helping much...I’ll leave you alone.”

“Is he getting a bit tetchy?” Johnson asks.

“No, he’s not,” Scott replies.

“He can’t, fucking moaning bastard,” Johnson said. “I do all the work and he gets all the glory.”

Jurors heard that days later in a call with HSBC forex trader Ed Carmichael in Hong Kong, Johnson told him that HSBC’s London forex trading desk, “just had a bonanza” on the Cairn deal, and described his response when Cairn sought an explanation on the less than stellar result for the oil and gas developer.
Of course, when HSBC's client complained about their less than stellar execution price, Johnson admits that he blamed all the usual suspects: "Russians, other central banks, all that sort of stuff."
“And they said, well you know it jumped up a bit, who else was buying? And we said the usual Russian names, other central banks, all that sort of stuff,” Johnson said on the call.
As we noted last week, nearly a dozen HSBC traders around the globe netted over $8 million in profits by allegedly front-running their own client.
Trading Gains
Of course, while the DOJ will undoubtedly celebrate their conviction in the media, there is little doubt that Mark Johnson's "pre-hedging" scandal is hardly unique for an industry that has been built on front-running clients.

Tuesday, October 17, 2017

Where Trump is failing to fix the economy with monetary policy

(Elite E Services) 10/17/2017 — Dover, DE — It’s no secret that the US economy has diverged into a massive 2 world system where there is also a massive gap in between; the employed and the wealthy have increasingly good lives while the poor and unemployed have a deteriorating quality of life.  As we have explained in Splitting Pennies, it is monetary policy that ‘Trumps’ all else.  Using The Gini Coefficient we can visualize what this means:
In economics, the Gini coefficient (sometimes expressed as a Gini ratio or a normalized Gini index) (/d?ini/ jee-nee) is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation’s residents, and is the most commonly used measure of inequality. It was developed by the Italian statistician and sociologist Corrado Gini and published in his 1912 paper Variability and Mutability (ItalianVariabilità e mutabilità).
Taking a look at basic 2013 data (it’s worse since then) we get a picture of reality vs. what is said in the media.  Using Russia as a good example to stay with the Trump theme, USA (41) has roughly the same Gini as Russia (40.9):
forex
Remember that Russia is the unfair system that is a top-down Byzantine oligarchy, right?  Then why Russia and USA share almost identical Gini, being surpassed only by the Banana Republics in South America?
Everyone in finance knows there are several things that Trump can do to fix the economic problems of USA in about 5 minutes:
  • Surprise increase interest rates to 5% or 10%
  • Import tariffs on Chinese crap
  • Unwind USA’s Petro Dollar system (drink it!)
  • Repeal the Dodd-Frank Consumer Rip-Off Fraud Act that has caused billions to flow out of USA.  Make America the world’s banker once again.  (This is our industry – FX – we know that this alone would create thousands and thousands of jobs and be a huge boost for the economy – billions would flow into USA and we’d again be the world’s banker.  But this same approach likely applies to many industries.  Dodd-Frank regulations killed FX and have cost millions of jobs.)  We’ve outlined this in previous articles.
It’s not really practical to bring factories back to USA, however we have robot technology and the financial sector is a great example of how we can create high skilled high paid white collar jobs.  But Trump seems to be more obsessed with form not essence (and his form is not good).
Oh – you’re thinking that a President can’t do that, right?  President is a figurehead, only Congress can pass laws.  That’s true.  But Nixon did it.  Somehow, Nixon was able to accomplish all these things in 1 day and created the floating FX market as it exists today.  It was not Nixon’s only genius move, there were many.  Was Nixon’s real genius just listening to his advisors like Henry?  Either way, in practice, it fixed the problem – only to be unwound by future administrations.  Was it a temporary fix?  Of course – but that’s what we need.  We need to unwind QE which is practically impossible, so jacking up rates is a first good start.  Wall St. and the stock market will suffer.  But they’ve had a long bull run.
Winter is coming – it’s bear time.

Monday, October 16, 2017

Black Monday 2.0: The Next Machine-Driven Meltdown

Brian Stauffer
Black Monday. Although the event to which those two words refer occurred 30 years ago, they still carry the weight of that day—Oct. 19, 1987—when the Dow Jones Industrial Average shed nearly a quarter of its value in wave after wave of selling.
No one in living memory had seen anything like it, at least not in the U.S., and in the postmortems conducted to understand just how the Dow managed to drop 508 points in one day, experts found a culprit: so-called portfolio insurance, a quantitative tool designed to use futures contracts to protect against market losses. Instead, it created a poisonous feedback loop, as automated selling begat more of the same.
Since that day, markets have rallied and markets have tumbled, and still we marvel at the unintended consequences of what, in hindsight, was an obviously misguided strategy. Yet in the ensuing years, market participants have come to rely increasingly on computers to run quantitative, rules-based systems known as algorithms to pick stocks, mitigate risk, place trades, bet on volatility, and much more—and they bear a resemblance to those blamed for Black Monday.
The proliferation of computer-driven investing has created an illusion that risk can be measured and managed. But several anomalous episodes in recent years involving sudden, severe, and seemingly inexplicable price swings suggest that the next market selloff could be exacerbated by the fact that machines are at the controls. “The system is more fragile than people suspect,” says Michael Shaoul, CEO of Marketfield Asset Management.
THE RISE OF COMPUTER-DRIVEN, rules-based trading mirrors what has happened across nearly every facet of society. As computers have grown more powerful, they have been able to do what humans were already doing, only better and faster. That’s why Google has replaced encyclopedias in the search for information, why mobile banking is slowly replacing bank branches, and why—someday—our cars will be able to drive us to work. And it is also why Wall Street has embraced computers to help with everything from structuring portfolios and trading securities to making long-term investment decisions.
In the years since 1987, huge strides have been made in understanding what drives stock performance and how to apply it to portfolio construction. At first, researchers focused on “factors,” such as a stock’s volatility relative to the market—known as beta; whether a stock is large-cap or small—the size factor; and whether it is cheap or expensive—the value factor. More recently, the use of factors has proliferated to include many others, such as quality and momentum. (The latter involves buying the best-performing stocks and shunning the worst performers.)
Quantitative investors understood early on that betting on stocks based on their characteristics—and not the underlying business fundamentals of a particular company—was a good way to outperform the market. So good, in fact, that many fundamental, or “active,” money managers now use quantitative tools to help construct their portfolios and ensure that they don’t place unintended bets. Nomura Instinet quantitative strategist Joseph Mezrich says that 70% of an active manager’s performance can be explained by quantitative factors. “Factors drive a lot of the returns,” Mezrich says. “Over time, this has dawned on people.”
Has it ever. One result has been the rise of indexing and exchange-traded funds. The ability to buy an index fund based on the Standard & Poor’s 500—effectively a bet that large companies will outperform small ones—made the need for traditional fundamental research and stock-picking unnecessary. Since then, indexes and ETFs have been created to reflect just about any factor imaginable—low volatility and momentum among them. Some funds even combine multiple factors in a quest for better performance.
As a result, an increasing amount of money is being devoted to rules-based investing. Quantitative strategies now account for $933 billion in hedge funds, according to HFR, up from $499 billion in 2007. And there’s some $3 trillion in index ETFs, which are, by definition, rules-based. The upshot: Trillions of dollars are now being invested by computers. “We’ve never seen so many investment decisions driven by quantitative systems,” says Morningstar analyst Tayfun Icten.
That’s quite a change from the 1980s. If you wanted to place a trade 30 years ago, you picked up the phone and called your broker; your broker called the firm’s trader; the trader would ring up a specialist, the person in charge of running trading in a given stock; and the trade would be executed. The process was slow, cumbersome, and inefficient. As computer technology advanced, machines gradually took most of these steps out of the hands of humans. Today, nearly every trade is handled by an algorithm of some sort; it is placed by a computer and executed by computers interacting with one another.
The entity handling trades isn’t the only thing that has changed in the past 30 years. Trading now occurs in penny intervals, not fractions such as eighths and 16ths. While that has made it cheaper for investors to buy and sell a stock, pennies made trading far less lucrative for market makers, who historically profited by playing the “spread” between the highest bid to buy and the lowest offer to sell. Consequently, market makers have been replaced by algorithms programmed to instantaneously recognize changes in liquidity, news flow, and other developments, and respond accordingly. At the same time, the proliferation of exchanges helped to lower trading costs but also created a fragmented market that can make shares hard to find during dislocations.
Most of the time, none of this matters. If you want to buy a stock, you boot up your computer, log in to your brokerage account, and place an order that gets filled almost immediately. The fee you pay is so low that it would have been unimaginable 30 years ago. The system has worked well for individual investors, and will continue to do so—as long as nothing goes wrong.
BUT MISTAKES HAPPEN. In 1998, the “quants” at Long-Term Capital Management, led by Nobel Prize winners Myron Scholes and Robert Merton, nearly caused a massive market selloff when the hedge fund’s highly leveraged trades, based on quantitative models of expected market behavior, suddenly lost money after Russia unexpectedly defaulted on its debt. The damage was magnified by the borrowing that LTCM had used to supersize its bets. Only a bailout organized by the Federal Reserve prevented the broad market from plummeting.
In August 2007, a selloff occurred in quantitative funds that would become known as the “quant quake.” To this day, no one knows what sparked the selling, but once it began, computer models kicked in, causing further selling. Humans added to the mess as risk managers looking at losses dumped shares. Funds specializing in quantitative investment strategies reportedly suffered massive losses: The Renaissance Institutional Equities fund was thought to have lost nearly 9% early in that month, while Goldman Sachs ’ Global Alpha suffered a double-digit decline.
The impact on the market wasn’t huge—the S&P 500 dropped just 3.3% during the first two weeks of August—but the event demonstrated what happens when a trade sours and too many funds are forced by their models to sell at the same time. It was a wake-up call for quants, who have since created more-sophisticated systems to reduce the kind of crowding that led to the selloff.
More recently, problems have been caused by algorithms that are supposed to provide stock for investors to buy, or buy when investors sell, creating liquidity. On May 6, 2010, the S&P 500 dropped 7% in just 30 minutes, as bids and offers for stocks moved far away from where stocks had been trading, in some cases leaving bids down as low as a penny and offers as high as $100,000.
Again, no one knows what caused the sudden decline. Investors had been on edge because of an unfolding European debt crisis, but that alone seemed unlikely to have triggered the flight of automated market makers. The U.S. Commodity Futures Trading Commission blamed the swoon on fake orders placed by a futures trader, while the Securities and Exchange Commission fingered a massive sell order in the futures market allegedly placed by a mutual fund company seeking to protect itself from a potential downturn. That order, it argued, had been handled by a poorly designed algorithm—yet another reminder that an algorithm is only as good as the inputs used by the people designing it.
While the rout was over quickly, and the S&P 500 finished the session down a more modest 3.2%, the episode raised concerns about the potential for computerized trading to exacerbate selloffs.
REGULATORS AND EXCHANGES have made changes since then, but so-called flash crashes continue to happen, even if they are no longer quite as disruptive as the 1987 selloff. On Aug. 24, 2015, for instance, the Dow dropped almost 1,100 points during the first five minutes of trading. The selloff was spurred by a plunge in China’s stock market, which led to a drop in Europe. All of this happened when U.S. markets were closed, which meant that investors turned to the futures and options markets to place their trades.
Chaos prevailed when the stock market opened: Only about half of the stocks in the S&P 500 had started trading by 9:35 a.m.; a quarter of the Russell 3000 index was down 10% or more intraday, and many large ETFs traded far below the value of their underlying assets. Algorithms, sensing something amiss, simply stepped back from the market. Once again, the S&P 500 recovered much of its sudden loss, but savvy market observers detected eerie echoes of an earlier era. In a much-read note at the time,JPMorgan strategist Marko Kolanovic cited the feedback loop of selling and compared it to the Black Monday selloff of 1987.
Flash crashes have not been limited to stocks—or even crashes. On Oct. 15, 2014, the price of the 10-year Treasury note soared, causing yields to tumble 0.35 of a percentage point in mere minutes before quickly reversing. The SEC blamed the increasing role of automated high-frequency algorithms for the sudden move.
The most recent scare occurred on May 18, when the iShares MSCI Brazil Capped ETF(ticker: EWZ) dropped as much as 19% in a single trading session before closing the day down 16%. To put that move in perspective, the Brazil ETF’s worst single-day decline at the height of the financial crisis in 2008 had been 19%. While there was bad news in May—reports that Brazilian President Michel Temer had been ensnared in a corruption scandal—that seemed insufficient cause for such a precipitous decline.
Shaoul, of Marketfield, attributes the Brazil ETF’s plunge to a combination of factors, including the growth of passive investing, which has made it easy to buy and sell an entire country’s market with the press of a button, combined with computer-driven trading. “There was no way of knowing what was a human being pressing a button, or a computer pressing a button,” he says. “But it generates the potential for sudden spikes in volatility that come out of nowhere.”
The Brazil ETF recovered its losses fairly quickly. By the end of August, it was trading above its May 17 close.
U.S. markets haven’t suffered declines like that, but have experienced numerous “fragility events”—sudden one-day declines—during the current rally, says Chintan Kotecha, an equity derivatives strategist at Bank of America Merrill Lynch. But because stocks have been in a bull market, there has been little follow-through after the initial selloff. As a result, some quantitative strategies reposition for more volatility, but none arrives. Kotecha attributes the lack of follow-through, in part, to central bankers’ continued bond-buying, which has provided much-needed support for the markets.
Follow-through was all the market had in 1987, as selling automatically triggered more selling. To some observers, the risks of a similar scenario are growing. One particular area of concern: volatility-targeting strategies, which try to hold a portfolio’s volatility constant, and risk-parity strategies, which attempt to equalize the risk in a portfolio among bonds, stocks, and other assets—and sometimes use leverage to do it. When volatility is low, these portfolios can hold more-risky assets than when volatility is high. But as soon as volatility rises—and stays high—these types of funds will need to start selling stocks and other assets to keep the risk of their portfolios at the same level. If they sell enough, volatility could spike higher, leading to even more selling.
The PROLIFERATION of COMPUTER-DRIVEN INVESTING has created an illusion that RISK can be measured and managed. But several anomalous episodes in recent years involving sudden, severe, and seemingly INEXPLICABLE PRICE SWINGS suggest the next MARKET SELLOFF could be exacerbated by the fact that the MACHINES are at the controls.
In a market selloff, commodity-trading advisors similarly could exit their long positions quickly and look to short stocks, creating further selling pressure as they head for the exits. “Action leads to more action,” says Richard Bookstaber, chief risk officer at the University of California and author of The End of Theory, a book about financial crises caused by positive feedback loops.
PERHAPS THE BIG QUESTION is who might be left to buy. Warren Buffett once quipped that investors should be fearful when others are greedy and greedy when others are fearful, but the current market structure has turned that maxim on its head. Algorithms provide less liquidity in a downturn than a human market maker, who might be thinking about how to profit from a dislocation.
The rise of momentum and passive strategies has caused some $2 trillion to shift away from active money managers, who could be counted on to look for bargains as stocks sold off, says Kolanovic, the JPMorgan strategist. “We think the main attribute of the next crisis will be severe liquidity disruptions resulting from market developments since the last crisis,” he says.
But most strategists acknowledge that such an occurrence isn’t a high-probability event. Much will depend on the cause of any disruption, as well as seasonal factors—stocks are more thinly traded in summer, for example. Also, computers aren’t the only cause of selling cycles; bear markets, after all, long predate machine-driven trading.
Quantitative investors argue that they have learned from past mistakes and are less likely to be leveraged or crowded into the same trades. Moreover, regulators and exchanges have instituted rules that could help arrest a bout of unchecked selling, with trading halts imposed when the S&P 500 falls 7%, 13%, and 20%.
Maybe these precautions will work to stem a tidal wave of selling. One of these days—possibly soon, given stocks’ lofty valuation and the Fed’s plan to shrink its balance sheet—we’ll find out. 
http://www.barrons.com/articles/black-monday-2-the-next-machine-driven-meltdown-1507956435?shareToken=sta34fcb09ee6a423fa9e0acc127844d01&utm_source=newsletter&utm_medium=email&utm_campaign=sendto_newslettertest&stream=top-stories

Sunday, October 15, 2017

The New Bit Currency Crypto FX paradigm

(GLOBALINTELHUB.COM) — 10/15/2017 Dover, DE — The Bit Paradigm has arrived; with billions being thrown into projects that no one knows who are the founders, or if the profiles they use for their ‘team’ pages are guys working from home or have day-jobs at the local grocery store.  It is transforming the landscape so rapidly, we compiled a sequel to Splitting Pennies entitled Splitting Bits – Understanding Bitcoin and the Blockchain – available on Amazon Kindle for $2.99 and Paperback $9.99.
As Currency experts, we found nothing unusual in the Bit World, it’s just FX 2.0 and hopefully a catalyst for real global financial reform beyond the scope of the myopic Dodd-Frank Consumer Rip Off and Exploitation Regulation that have plagued the US consumer going on 5 years now.  As we’ve explained in our previous work, Splitting Pennies – FX is the basis for the global financial system.  Don’t forget that Bitcoin is denominated in US Dollars.  While FX is the least understood market in the world it is also the most important.
Just remember one thing – customers (business) need currency, they don’t need stocks or Crypto.  Take any business as an example, McDonalds (MCD) is always a great FX example – they need foreign currency as they accept it in more than 110 countries worldwide.
forex
And being based in Chicago, they need to repatriate those currencies into US Dollars, making them one of the biggest FX traders in the world.  So where does Bitcoin fit into all this?  At the moment, it doesn’t.  Of course that’s all changing – and changing quickly.  The news changes by the day – as the Bit Paradigm goes mainstream.  The current market cap of the entire CryptoCurrency Market is $170 Billion according to Coinmarketcap.com.
While that is still far away from traditional markets, the growth rate is beyond parabolic.  Skeptical traders should remember the late 90’s when fears about the Euro kept investors away.  Just take a look at this Monthly EUR/USD chart showing the Euro’s rise against the dollar from lows of .83 to highs of 1.58 before settling into the range that it’s been in recently:
Euro Historical
The Red line from .83 to 1.58 is about 190% or double – and traders should also bear in mind in FX there is a lot of leverage, so the 100% return in 6 years could have been 1000% or greater (many funds did profit from this simple trade).
Of course, the real money in FX is in algorithmic trading, what the banks learned the hard way.. But the Euro is a great example of a synthetic currency that was created artificially, and finally succeeded to be an alternative to the US Dollar as a world reserve.  Although the technicalities of Bitcoin are far different, the gestalt is the same – Bitcoin is a currency created artificially, backed by nothing, and is increasing in value because people believe that it will be used in the future and that the price will go up.  Just like there’s nothing behind Bitcoin, there’s really nothing behind the Euro – with one key difference.  It’s possible for the ECB to print (mint) as many Euros as they want, but it’s not possible to do this with Bitcoin because of the design (there is a limited number of Bitcoin) and because there’s no central bank behind it.
The big story of currency trading Crypto is of course, new alt-coins other than Bitcoin, which are being issued so rapidly it’s impossible to even keep track of them.  Coinmarketcap.com lists 1170 different Cryptocurrencies, you can see the full list here.
For a detailed breakdown of how you can profit from trading Bitcoin, checkout our new book Splitting Bits.