• You buy a stock, its value keeps going up, you don’t sell it and you don’t pay taxes.

• If you need cash, you go to your broker and take out a loan, secured by the value of the stock.

• If the stock keeps going up, you never pay back the loan, and you can borrow more money if you want.

• Eventually you die, your heirs get the stock, and they don’t pay taxes on your gains. (This is called the “basis step-up”: When you inherit stock, the IRS pretends that you paid market value for it, so you don’t have to pay taxes on the previous gains.)

This is sometimes called the “buy, borrow, die” tax strategy.

{ Bloomberg/Matt Levine | Continue reading }

Nomura and Credit Suisse are facing billions of dollars in losses after a U.S. hedge fund, named by sources as Archegos Capital, defaulted on margin calls. […]

A margin call is when a bank asks a client to put up more collateral if a trade partly funded with borrowed money has fallen sharply in value. If the client cannot afford to do that, the lender will sell the securities to try to recoup what it is owed.

Margin calls on Archegos Capital prompted a massive unwinding of leveraged equity bets. Shares in ViacomCBS and Discovery each tumbled around 27% on Friday, while U.S.-listed shares of China-based Baidu and Tencent Music plunged during the week, dropping as much as 33.5% and 48.5%, respectively, from Tuesday’s closing levels. […]

Morgan Stanley sold $4 billion worth of shares early on Friday, followed by another $4 billion in the afternoon. […] Goldman liquidated more than $10 billion worth of stocks in the block trades [and] sold $6.6 billion worth of shares of Baidu Inc, Tencent Music Entertainment Group and Vipshop Holdings Ltd, before the U.S. market opened on Friday […] Following this, Goldman sold $3.9 billion worth of shares inViacomCBS Inc, Discovery Inc, Farfetch Ltd, iQIYI Inc and GSX Techedu Inc […]

Hwang, who founded Archegos and ran Tiger Asia from 2001 to 2012, renamed it Archegos Capital and made it a family office.

{ Reuters | Continue reading }

Archegos borrowed a mere five times its capital. Closely regulated Goldman Sachs is at nearly seven times on a risk-weighted basis. […]

Hwang himself was a walking risk factor. He admitted to wire fraud in 2012 and in 2014 was banned from trading in Hong Kong for four years.

{ Reuters | Continue reading }

Data analytics company Palantir Technologies and workplace software maker Asana Inc are set to debut on the U.S. stock market on Wednesday bypassing an initial public offering (IPO). […]

[S]ome investors and corporate executives have been pushing to shed investment banks as their middlemen. For years, they have criticized IPOs as chummy deals that allowed bankers to allocate the most shares to their top clients. […] “If Palantir and Asana are successful, which they should be, more and more companies will return to looking seriously at direct listings,” Narasin added. […]

In 2020, the price of a newly listed company’s shares has risen by an average of 38% on the first day of trading, according to IPOScoop data and Reuters calculations.
This has fueled renewed criticism among investors snubbed by the investment banks underwriting the IPOs, as well as suspicion among some companies that bankers are leaving money on the table in their IPO to help create a first-day trading “pop”. […]

In a direct listing, no shares are sold in advance, as is the case with IPOs. The company’s share price in its market debut is determined by orders coming into the stock exchange.

The downside is that the companies involved cannot raise money, though both NYSE and Nasdaq have requested U.S. regulators allow them to change their rules to allow companies to sell new stock in a direct listing.

{ Reuters | Continue reading }

“Financial machine learning creates a number of challenges for the 6.14 million people employed in the finance and insurance industry, many of whom will lose their jobs — not necessarily because they are replaced by machines, but because they are not trained to work alongside algorithms,” said Marcos Lopez de Prado, a Cornell University professor. […]

Nasdaq runs more than 40 different algorithms, using about 35,000 parameters, to look for market abuse and manipulation in real time.

{ Bloomberg | Continue reading }

related { 90% of high-tech job growth concentrated in just 5 cities: Boston, San Francisco, San Jose, Seattle and San Diego }

photo { Matthew Reamer }

Software from Amenity Analytics promises to automate this process by spotting when chief executive officers try to duck tough questions. The software, its makers say, can even pick up on the signs of potential deception that CIA and FBI interrogators look for—including stalling and the use of qualifiers—and can gauge the sentiment of what is said on calls and reported in public filings, issuing a positive or negative numeric score. The goal is to make it easier for investors to wade through information and quickly make trading decisions.

{ Bloomberg Businessweek | Continue reading }

previously { Former CIA Officer Will Teach You How to Spot a Lie }

photo { Laurie Simmons, Blonde/Pink Dress/Standing Corner, 2014 }

{ while ordinary people are struggling, those at the top are doing just fine. Income and wealth inequality have shot up. The top 1% of Americans command nearly twice the amount of income as the bottom 50%. The situation is more equitable in Europe, though the top 1% have had a good few decades. | The Economist | full story }

{ Netflix performance burns hedge fund short sellers }

Twelve years ago, my now-Bloomberg colleague Joe Weisenthal proposed that startups that planned to disrupt an established industry should short the stock of the incumbents in that industry. That way, if they were right — if they were able to undercut big established public companies — then they’d get rich as those public companies declined. […] Their profits would come from the incumbents’ shrinking.

Weisenthal’s proposal was for disruptors offering a free product; the idea was that the entire business model would consist of (1) offering a free service that public companies offer for money and (2) paying for the service by shorting the public companies. But there’s a more boring and more widely generalizable — yet still vanishingly rare — version of this approach in which it just augments the disruptors’ business model: You sell better widgets cheaper and make a profit that way, while doubling down by also shorting your competitors. It’s a more leveraged way to do the business you were going to do anyway, an extra vote of confidence in yourself.

{ Bloomberg | Continue reading }

You know, someone invented the XIV ETN. And someone invented the VIX, and VIX futures. And when you read the technical specifications for all of those things, it is clear that they are not trivial feats of engineering. Teams of marketers and traders and quants and technologists and lawyers put many hours into getting them just right, so that they would work as intended. They are technologies, highly engineered tools designed to help customers do things that they couldn’t have done before. They are financial technologies, built not out of screens and circuit boards but out of formulas and hedging strategies and legal documents, but that is what you’d expect: Financial firms ought to innovate in financial technology.

Yesterday Goldman Sachs Group Inc. Chief Executive Officer Lloyd Blankfein presented at the Credit Suisse Financial Services Conference, and his presentation is kind of a weird read. The running theme is that Goldman is doing technology stuff to win business. “Engineering underpins our growth initiatives,” says a summary page, and it doesn’t mean financial engineering. In fixed income, currencies and commodities, engineers are 25 percent of headcount, and the presentation touts growth in Marquee (its client-facing software platform) and “systematic market making.” In equities, Goldman touts its quant relationships. In consumer banking (now a thing!), the centerpiece is Marcus, Goldman’s online savings and lending platform. And in investment banking, “Engineering enhances client engagement through apps, machine learning and big data analytics.” […]

Instead of developing new financial technologies, Goldman is developing new computer technologies for its financial clients.

{ Bloomberg | Continue reading }

related { Hedge-fund mediocrity is the best magic trick. Never have so many investors paid so much for such uninspiring returns. }

lithograph { Ellsworth Kelly, Camellia III, 1964–65 }

There are 1,036 virtual currencies out there, from Bitcoin to — no joke — BigBoobsCoin. The price of almost every single one was down Friday morning.

{ Bloomberg | Continue reading }

Stock trading strategies: competition is so stiff that there are only two ways to succeed: (1) insider trading, e.g. you try to obtain job interviews with small publicly traded companies, then based on information glanned during the interview, perform trades and (2) use trading strategies that professional traders will never use, e.g. stay “all cash” for several years on your trading account, and when the right event occurs, massively trade major indexes for a couple of days, then go dormant for another few years. You need sophisticated statistical models to succeed in this, with good back testing, walk-forward and robustness based on state-of-the-art cross-validation.

{ analyticbridge | Continue reading }

art { Rochelle Goldberg, The Cannibal Actif, 2015 }

Two hedge fund “quants” have come up with an algorithm that diagnoses heart disease from MRI images, beating nearly 1,000 other teams in one of the most ambitious competitions in artificial intelligence.

{ Financial Times | Continue reading }

Qi Liu and Tencia Lee, hedge fund analysts and self-described “quants,” didn’t know each other before they won the competition, beating out more than 1,390 algorithms. They met each other in a forum on the Kaggle site, where the competition was hosted over a three-month period.

{ WSJ | Continue reading }

The mantra of Wall St hedge funds was once “only the strongest will survive.” It may now have to change to “the geeks will inherit the earth.”

Hedge fund “quants” who use computer systems to trade financial markets earned more money than some of the industry’s most famous stockpickers, who posted large losses in 2015.

The most prominent among the quants was string theory expert and former code breaker James Simons of Renaissance Technologies, who earned $1.7bn, putting him in joint first place.

He was joined in the top 10 earners by former Columbia University computer science professor David Shaw of DE Shaw who made $750m and John Overdeck and David Siegel of Two Sigma who made $500m each.

Their success came in stark contrast to some of the biggest names on Wall Street who rely on human investment judgment rather than lines of computer code.

{ FT | Continue reading }

quote { Cabinet magazine | full story }

It was an invitation-only party (crabs, cocktails and a D.J. on a moonlit dock) thrown by Jane Street, a secretive E.T.F. trading firm that, after years of minting money in the shadows of Wall Street, is now pitching itself to some of the largest institutional investors in the world.

And the message was clear: Jane Street, which barely existed 15 years ago and now trades more than $1 trillion a year, was ready to take on the big boys.

Much of what Jane Street, which occupies two floors of an office building at the southern tip of Manhattan, does is not known. That is by design, as the firm deploys specialized trading strategies to capture arbitrage profits by buying and selling (using its own capital) large amounts of E.T.F. shares.

It’s a risky business.

As the popularity of E.T.F.s has soared — exchange-traded funds now account for a third of all publicly traded equities — the spreads, or margins, have narrowed substantially, making it harder to profit from the difference.
And in many cases, some of the most popular E.T.F.s track hard-to-trade securities like junk bonds, emerging-market stocks and a variety of derivative products, adding an extra layer of risk. […]

While traders at large investment banks watched their screens in horror, at Jane Street, a bunch of Harvard Ph.D.s wearing flip-flops, shorts and hoodies, swung into action with a wave of buy orders. By the end of the day, the E.T.F. shares had retraced their sharp falls.

It is not only Jane Street, of course. Cantor Fitzgerald, the Knight Capital Group and the Susquehanna International Group have all capitalized on the E.T.F. explosion.

And as these firms have grown, so has the demand for a new breed of Wall Street trader — one who can build financial models and write computer code but who also has the guts to spot a market anomaly and bet big with the firm’s capital. […]

Here is a small sample of Jane Street’s main traders: Tao Wang (doctorate in philosophy and finance from the National University of Singapore), Min Zhu (master’s in chemistry, Columbia), Brett Harrison (master’s in computer science with a focus in artificial intelligence, Harvard) and Srihari Seshadri (bachelor’s in computer science, Carnegie Mellon).

{ NY Times | Continue reading }

oil on Masonite { Grant Wood, Birthplace of Herbert Hoover, 1931 }

A recent Wall Street Journal/NBC News poll found that 49% of Americans still believe the U.S. economy is in recession, even though we are now in the sixth year of the recovery. […] If investing when others are skeptical has historically been a successful strategy, why don’t more investors do so? […] Taking advantage of the findings discussed earlier requires investing when the economy and market seem to be at their worst, and rebalancing when conditions appear to be the best. This is counterintuitive for many investors, who tend to wait for confirming evidence before acting. This is related to herd behavior, the tendency to follow the crowd with portfolio decisions. Investing when others are skeptical is emotionally difficult but, as we’ve shown, tends to be when rewards are the greatest.

{ JP Morgan Funds | PDF }

related { It is not possible for a human to know whether Bank of America made money or lost money last quarter. }

art { Jim Campbell, Ambiguous Icon #1 Running Falling, 2000 }

Since The Fed’s extension of Operation Twist (and subsequent unveiling of QE3) in 2012, the stocks of “weak balance sheet” companies are up over 100%. In that same period, the stock prices of “strong balance sheet” companies are up a mere 43%.

{ ZeroHedge | Continue reading }

The last 5 days saw “strong” companies outperform “weak” companies by the most in 3 years - something appears to be changing.

{ ZeroHedge | Continue reading }

A quarter of all public company deals may involve some kind of insider trading. […] The study [PDF], perhaps the most detailed and exhaustive of its kind, examined hundreds of transactions from 1996 through the end of 2012.

{ NY Times | Continue reading }

Long ago, stock trades were reported over ticker tape, and one type of manipulation was called “painting the tape.” Traders would enter orders to give the appearance of activity in a stock to entice others to buy shares, thus pushing the price higher.

Today, a slightly more sophisticated scheme is called “banging the close,” in which transactions are made in one market at the end of the day to benefit a trader’s positions in another market, say derivatives. Same scheme, different means. […]

The growth of high-frequency trading firms and transactions executed on alternative trading systems, called dark pools, have made it more difficult to police potential manipulative conduct. High-frequency traders buy and sell millions of financial instruments but rarely hold a position for more than a day. While such trading provides greater liquidity to the markets, helping to lower costs for all investors, it can also offer new opportunities for manipulating prices. […]

Manipulation can also involve benchmark indexes, which are incorporated into a wide variety of transactions, including mortgage interest rates. When an index relies on reports provided by rival market participants, the temptation to furnish false information to affect its value can be powerful because a small shift in value can affect billions of dollars. Several large banks have already paid billions in penalties for manipulation of the London interbank offered rate, or Libor, and investigations are gaining steam into how currency prices were reported in the foreign exchange markets.

{ NY Times | Continue reading }

High frequency trading. I won’t go into the details, but basically it has become an arms race of being a millionth of a second faster than the other guy. The exchanges (Nasdaq and NYSE) started offering co-location within their facilities and traders started fighting for the best physical real estate within the co-location center (ie. literally trying to be a few feet closer to the exchanges’ computers).  Some of the high frequency traders complained about how ‘unfair’ it was to be a few feet farther away.  The exchanges conceded and added ‘latency’, basically a few feet of cable, so everyone within the co-location center is equidistant. It baffles me financial progress is moving in this direction.

Prediction by ‘experts’/pundits. Why do people still believe in pundits and ‘experts’ on TV? If ‘experts’ could predict the future with any accuracy, they would be doing something else. They are not always wrong, they are simply not right consistently enough to provide meaningful value. I’m always surprised how confident and certain people sound on CNBC (I rarely feel sure of anything). Keynes got it right when he said, “If you must forecast, forecast often.”

{ Quora | Continue reading }

On average, ATD made less than a penny on every share it traded, but it was trading hundreds of millions of shares a day. Eventually the firm moved out of Hawkes’s garage and into a $36 million modernist campus on the swampy outskirts of Charleston, S.C., some 650 miles from Wall Street.

By 2006 the firm traded between 700 million and 800 million shares a day, accounting for upwards of 9 percent of all stock market volume in the U.S. And it wasn’t alone anymore. A handful of other big electronic trading firms such as Getco, Knight Capital Group, and Citadel were on the scene, having grown out of the trading floors of the mercantile and futures exchanges in Chicago and the stock exchanges in New York. High-frequency trading was becoming more pervasive.

The definition of HFT varies, depending on whom you ask. Essentially, it’s the use of automated strategies to churn through large volumes of orders in fractions of seconds. Some firms can trade in microseconds. (Usually, these shops are trading for themselves rather than clients.) And HFT isn’t just for stocks: Speed traders have made inroads in futures, fixed income, and foreign currencies. Options, not so much. […]

By 2010, HFT accounted for more than 60 percent of all U.S. equity volume and seemed positioned to swallow the rest. […] For the first time since its inception, high-frequency trading, the bogey machine of the markets, is in retreat. According to estimates from Rosenblatt Securities, as much as two-thirds of all stock trades in the U.S. from 2008 to 2011 were executed by high-frequency firms; today it’s about half. In 2009, high-frequency traders moved about 3.25 billion shares a day. In 2012, it was 1.6 billion a day. Speed traders aren’t just trading fewer shares, they’re making less money on each trade.

{ Bloomberg | Continue reading }

related { Today’s Comforting Stat: Hedge funds are a trillion dollars in debt }