CNBC Covfefe: Bitcoin Price Could Hit $100,000 in 10 Years

https://cointelegraph.com/news/cnbc-covfefe-bitcoin-price-could-hit-100000-in-10-years

Kay Van-Petersen, an analyst at Saxo Bank, told CNBC that Bitcoin price could hit $100,000 in the next 10 years.

Cointelegraph previously reported that the daily trading volume of the cryptocurrency market was nearing that of major stock markets. However, the cryptocurrency market’s trading volume is still only a fraction of leading stock markets such as NASDAQ.

In order for the cryptocurrency market to surpass the trading volumes of major stock markets, it has to surpass a trillion dollar market cap. In an interview with CNBC, Van-Petersen stated that Bitcoin alone will most likely achieve a trillion dollar market cap in the next 10 years and a price of $100,000.

More to that, Van-Petersen predicted the daily trading volume of the cryptocurrency market to account for at least 10 percent of the average daily volumes of fiat currency trades which is a $500 bln trading volume.

Milestone

Currently, the daily trading volume of the cryptocurrency market is around $4.3 bln and for it to surpass 10 percent of the daily volumes of fiat currency trades, it needs to increase by a factor of 100. Considering Bitcoin and Ethereum’s exponential growth over the past few years, it is not entirely impossible for the cryptocurrency market to achieve such a milestone in the next 10 years.

Van-Petersen, who is also a Bitcoin investor, told CNBC:

"This is not a fad, cryptocurrencies are here to stay. There will emerge two to three main ones. Bitcoin will be one of those. And the reason is the first-mover advantage, the scale and the pioneering."

Van-Petersen also emphasized that Bitcoin is being used as a digital currency in remote parts of the world in countries which lack a stable monetary system and financial networks. In countries such as Venezuela, Bitcoin is a lifeline for citizens who are struggling to finance day-to-day operations.

Volatility

Leading economies such as Japan are also rapidly adopting Bitcoin as a digital currency, with some of its largest conglomerates accepting Bitcoin as payment. Most recently, Japan’s most popular budget airline Peach and largest electronics retailer Bic Camera began to accept Bitcoin as an official payment method.

Van-Petersen says:

"Volumes are going up, volatility is going down. A lot of people talk about the volatility, but if you are in Zimbabwe or Venezuela, this volatility is nothing. This is the interesting thing to me. I think in the West, a lot of people view it is as speculative, but emerging markets will get it, their needs will be different.”

Ultimately, Van-Petersen sees the market cap of Bitcoin rising to $1.75 trillion in the next 10 years. For this to happen, Bitcoin needs to mature as a technology, solve all of its scaling issues and become adopted as a digital currency and digital cash system, instead of a safe haven asset and long-term investment.

President Neel Kashkari breaks down the relationship between monetary policy and bubbles

https://www.minneapolisfed.org/news-and-events/messages/monetary-policy-and-bubbles

I have been asked many times about whether and how the Federal Reserve considers asset prices (such as stock prices and house prices) in its determination of the appropriate level of interest rates. Specifically, some people suggest that the Fed should raise interest rates when asset values appear high relative to historical norms to stop asset bubbles from forming, such as the tech bubble in the late 1990s and the housing bubble in the mid-2000s. After all, when the housing bubble burst, it was devastating for the economy, causing the financial crisis and the Great Recession. Wouldn’t the economy have been better off if the Fed had simply raised rates when the bubble first started forming and thus avoided all that harm? The purpose of this essay is to explain how I think about Federal Reserve policies to address potential bubbles. This topic seems simple, but I will argue it is highly complex, with large potential consequences for Main Street. Let me remind readers that my comments are my own, and do not necessarily represent the views of the Federal Reserve System.
In summary, I will explain five points: (1) It is really hard to spot bubbles with any confidence before they burst. (2) The Fed has limited policy tools to stop a bubble from growing, even if we thought we spotted one. (3) The costs of making policy mistakes can be very high, so we must proceed with caution. (4) What we can and must do is ensure that the financial system is strong enough to withstand the inevitable bursting of a bubble. And finally (5) monetary policy should be used only as a last resort to address asset prices, because the costs to the economy of such a policy response are potentially so large.

The Federal Reserve’s mandates

In 1977, Congress gave the Fed its dual mandate: stable prices and maximum employment. However, we can’t ignore the implicit role the Fed also has to try to achieve financial stability. After all, when Congress first created the Fed in 1913, it did so in response to financial crises that repeatedly hammered the U.S. economy in the late 1800s and in the panic of 1907. The Board of Governors and 12 regional Federal Reserve Banks were specifically created with the goal of promoting financial stability. Price stability and maximum employment came almost 70 years later.
Achieving financial stability is hard—really hard. Human societies are prone to mass delusion and to bubbles; history has numerous examples, from the tulip bubble in Holland in the 1600s to the stock market bubble in the 1920s to the housing bubble in the 2000s. Future generations are exceptionally good at repeating past mistakes. Even if we focus just on the Fed’s official dual mandate, financial crises can cause very high unemployment and low inflation or even deflation. My perspective is that whether it is officially acknowledged or not, whether we want the responsibility or not, the Fed has an important role to try to ensure financial stability. So where does monetary policy fit in?

Spotting bubbles is hard

Everyone can recognize a bubble after it bursts, and then many people convince themselves that they saw it on the way up. Michael Lewis’ highly entertaining book, The Big Short, is a perfect example. With the benefit of hindsight, Lewis picked four guys who happened to be right this time. It would have been far more impressive if Lewis had identified and written about them when the housing bubble was forming. Why didn’t he? Because on any given day, there are lots of people predicting various doomsday scenarios (spend a little time on finance Twitter to see for yourself). How do you know which one is right among all the cranks? And maybe today’s crank will look brilliant tomorrow. As they say about broken clocks…
I will offer three examples of well-intentioned government officials trying unsuccessfully to accurately identify bubbles and potential crises.

1. In 1996, then-Fed Chairman Alan Greenspan gave his famous “irrational exuberance” speech, where he said the stock market was overvalued. Essentially, Greenspan was warning that a bubble was forming and that investors needed to be careful because a correction was coming. At the time, the S&P 500 had a price-to-earnings ratio of 17.8. The stock market did end up correcting in the early 2000s, after the P/E ratio reached 26.9 by the end of 1999. The P/E ratio fell below 16 by mid-2002. Was Greenspan right when he called a bubble in 1996? Should the Fed have raised interest rates in response? Of course, it is impossible to know what would have happened if Greenspan had used monetary policy to act on his irrational exuberance call. But given how the stock market has climbed in the following 20 years, I would say that this was a “false positive”—identifying a bubble far too early, or seeing one where it didn’t exist.
2. When I went to Treasury in July 2006, then-Treasury Secretary Henry Paulson declared to his staff that the U.S. economy was due for some form of crisis. He didn’t know where it would come from but, because markets had been stable for some time, history suggested something would happen. So he tasked his staff (including me) to work with the Federal Reserve and Securities and Exchange Commission to look for signs of trouble. We looked at a variety of scenarios, from an individual large bank running into trouble to a hedge fund blowing up. Sadly (and embarrassingly), we never considered a nationwide housing downturn. We missed it, and we were looking. It seems obvious now. This was clearly a “false negative.”
3. Finally, in the wake of the 2008 financial crisis, all regulators were on alert for potential economic shocks. We had all learned our lessons and weren’t going to make the same mistake again. Yet I don’t know anyone who predicted oil prices climbing to over $100 per barrel and then falling to $26. That is an enormous price decline, and we all missed it. Was oil at $100 a bubble? Should the Fed have raised interest rates when oil started its climb? I’m not sure if oil was a bubble or not.

The Fed’s policy tools to slow down asset price increases are limited

The Fed’s primary policy tool is setting short-term interest rates. When inflation is lower than our 2 percent target and unemployment is high, we lower interest rates to try to stimulate economic activity by reducing borrowing costs. When inflation is high and unemployment low, we raise rates to try to prevent the economy from overheating. Monetary policy is a blunt instrument: We set the overnight interest rate, and it then affects rates all across the country, across different asset classes. That’s one of the biggest challenges in trying to use monetary policy to change asset prices. For example, if we see a bubble forming in commercial real estate, raising interest rates won’t affect just the commercial real estate market, but also housing, automobiles, consumer borrowing and capital-intensive industries, among others. We may want consumers to keep spending, but condo prices to stop rising. Raising interest rates would slow them both down.
The Fed also has regulatory and supervisory tools that it can use to change the behavior of the financial institutions it supervises. For example, the Fed regularly issues and explains its expectations for banks in the form of letters. The letters become guideposts that our examiners use to ensure that firms are safe. But the letters can also influence asset markets by changing bank actions. In 2013, regulatory agencies, including the Fed, observed that lending to companies that were leveraged was rising rapidly and that interest rates on leveraged loans were falling. In response, we issued guidance on leveraged financing to remind the industry of particular risk management expectations and to warn banks holding large amounts of these loans that they would be subject to additional regulatory scrutiny. This letter was followed up by supervisory actions by bank regulators that had the effect of penalizing banks that did not follow the regulations. By ensuring that banks lend appropriately in this market, this supervisory activity could affect the price of high-yield bonds and related investments.
However, the Fed does not have all of the targeted tools to address individual asset markets that some other countries have. While the Fed can limit the amount of debt used to buy stocks, some countries can also adjust the loan-to-value (LTV) requirements of mortgages. By increasing the down payment requirement (lowering LTVs), those countries could directly target the housing market if it were showing signs of overheating. This is an example of a highly targeted tool that, in theory, should be effective in slowing down the housing market without slowing down the entire economy the way raising rates would.

Even with additional tools, bubbles can be exceptionally difficult to slow down

Looking at other countries’ experiences in trying to deal with potential asset bubbles shows how difficult it can be to slow down rapid increases in asset prices. Regulators in Sweden and Canada have tried to use more powerful and targeted tools than the Federal Reserve has to cool their housing markets. As powerful as those tools appear to be in theory, they have not been very effective in slowing down price appreciation.

• Given fast increases in housing prices in Sweden, in October 2010, the Finansinspektionen (FSA) applied an LTV limit of 85 percent to any new mortgage or extensions to existing mortgages that used a home as collateral. Prior to this policy, the average LTV ratio on new loans was over 70 percent, with more than 33 percent of these loans having an LTV ratio above 85 percent. In November 2014, the FSA introduced a mandatory amortization of mortgages for those with an LTV ratio higher than 50 percent. Home prices in Sweden have continued to increase despite these policy actions.
• Authorities in Vancouver have been concerned about increasing home prices for a number of years. The price appreciation seems to be driven by foreign buyers who are looking for safe, offshore investments, which has made buying homes unaffordable for many Vancouver residents. In August 2016, local authorities passed a 15 percent tax on home purchases by foreign buyers. This was a very targeted and, in theory, very powerful policy tool to curb further price appreciation. Price growth in Vancouver did slow for a time, but appears to be climbing again. And prices in Toronto, which does not have such a tax, now seem to be climbing even faster.

My takeaway from these countries’ experiences is that when asset prices are climbing rapidly, they can be very difficult to slow down, even with policy tools that are targeted squarely at the asset class. That suggests to me that if central bankers were to try to use monetary policy to slow those bubbles down, the rate increases necessary to be effective would likely be large, resulting in high economic cost to the rest of the economy.

The costs of false positives can be very high

Imagine if the Greenspan Fed had decided to use monetary policy beginning in 1996 to stop stock prices from climbing further. How high would interest rates have had to go? What would the economic costs have been? I don’t know for sure, but it seems possible that the Fed would have had to push the economy into a recession to stop stock prices from rising further.
Similarly, imagine if the Fed had identified oil as a potential bubble when it started its climb after the Great Recession. How high would the Fed have had to raise rates to stop oil prices from rising further? What would the costs have been?
Given how hard it is to slow down price increases when a bubble is forming (as discussed earlier), I assume the monetary policy response would need to be large enough to risk putting the economy into recession to stop a bubble. So I ask myself: If we think we might see a bubble, are we confident enough that it is worth putting the economy into recession to stop it? Hence, the bar must be high before we should consider using monetary policy to address asset prices.

The costs of false negatives are sometimes very high—but not always

The housing bust, financial crisis and resulting Great Recession were devastating for the American people. Millions lost their jobs, their homes and their savings. It has been a frustratingly slow recovery, and I believe it has directly led to the deep political divisions in the country that we are still experiencing.
But not all asset busts are so costly. When the tech bubble burst in 2000, equity investors lost money, but it led to only a mild, fairly short recession. It seems to me that the Fed was right to not try to slow down equity markets in the late 1990s. The cost of prevention by raising interest rates may have exceeded the cost of the correction. Similarly, I mentioned oil price spikes and falls in the last decade. No doubt these swings were painful for the oil sector, including North Dakota, which is in my Federal Reserve District, but the costs to the economy overall have been small. As with the tech bubble, had the Fed tried to use interest rates to prevent oil prices from rising, the cost of prevention would likely have exceeded the cost of the correction.
My takeaway from the varied costs of false negatives is that we must first try to assess the cost of a correction before we determine whether to try to address asset prices that appear elevated.

Debt seems to be the key risk in bubbles

What determines if an asset price correction will trigger a crisis or just a milder downturn? Debt seems to be a key factor. Housing is a huge, highly leveraged market. The mortgage market is roughly a $10 trillion market. Today, people often buy homes with 20 percent as a down payment. Going into the financial crisis, people were putting little to nothing down with those infamous no-doc loans. Those loans were bundled into mortgage-backed securities, which were then bundled into collateralized debt obligations, and then banks bought them with yet more borrowed money. It was leverage on top of leverage with little equity supporting it all.
Contrast that with the stock market, where individual investors are limited to a 50 percent margin. In other words, those investors have to put at least 50 percent down on their equity investments. Most put down much more. Similarly, mutual funds tend to have much less leverage than mortgage lenders, for example. Direct comparisons between the leverage underlying the housing market and the stock market is complex, but I believe it is safe to say that as an asset class, housing is far, far more leveraged than the stock market.
When the stock market corrects, investors lose money. Technology companies with no profits go bankrupt. But the economy as a whole does not seem as vulnerable as when a large, highly leveraged asset class such as housing corrects. The Fed’s job is not to protect investors. It is to promote financial stability. Sometimes those overlap. Not always.

Public awareness matters when considering potential policy responses

The Fed’s job is “to take away the punch bowl just as the party gets going,” as former Fed Chairman William McChesney Martin famously quipped in 1955. In other words, it’s the Fed’s job to put the brakes on the economy before it overheats, which almost by definition will be unpopular with many people. So why should public awareness matter to the Fed?
Regulators don’t exist in a vacuum. The Fed ultimately gets its power from the American people, through authorities granted to it by their elected representatives. Yes, the Fed must make tough, sometimes unpopular choices, but the ability of the Fed to impose and sustain steep costs on the economy and Main Street is limited by the willingness of the people to accept those costs.
The most famous example of the Fed imposing steep costs on Main Street for the long-term health of the economy is the Volcker Fed dramatically raising interest rates to crush inflation in the early 1980s. The costs were large—a deep recession and unemployment reaching 10 percent, the same as the peak unemployment rate in the Great Recession. But a key factor that enabled the Fed to impose such painful medicine was that the American people hated inflation. In public opinion surveys, inflation was ranked as the number one economic issue on voters’ minds. So when Chairman Volcker explained that high rates and the resulting economic costs were necessary to control inflation, while the public was still angry with the Fed, they at least agreed on the problem.
Now imagine a potential bubble that only the Fed sees. Imagine if Chairman Greenspan declared war on housing prices in 2004, when many Americans were enjoying homeownership for the first time. I suspect many Americans, members of Congress, homebuilders, realtors, banks (and many others) would have been outraged that the Fed was depriving people of participating in the American dream for a problem that they didn’t believe was real. Imagine if the Fed decided to raise rates to prevent housing prices from climbing. Given how painful the Great Recession was, this may have in fact been a better choice than what the Fed actually did, which was basically nothing. But I have doubts about whether the Fed could have maintained the policy long enough for it to have the desired effect before the American people rejected it.¹ At a minimum, the Fed would have had to work very hard to try to convince the public that the housing bubble was real and a danger. It can be very hard for a sole regulator to stand up against a national belief that home prices only go up and say: “We know better than all of you.” But that doesn’t mean we shouldn’t try.

The Fed has stronger tools to mitigate the damage from bubbles than to prevent them

This essay has been pretty skeptical about the powers of the Fed to identify and slow down bubbles. But there is something we can do that does not require us to identify bubbles in the first place. We can make sure our financial institutions are sound and can withstand the shock of asset price corrections. Without question, one of the key factors that magnified the intensity and costs of the 2008 financial crisis was the undercapitalization of the nation’s largest banks. They amplified the shock rather than dampened it. If we make sure the largest banks are highly capitalized (the Minneapolis Fed’s estimate is that they need roughly double the equity capital they currently have), the financial system will be much more resilient against asset price corrections in the future.
In addition, if we identified an asset class that appeared richly valued to which banks had a lot of exposure, we could use existing tools to respond. This is the essence of the current stress test. The Fed tests how a decline in asset values in a weak economy would affect the solvency of a bank. And banks that have insufficient capital to withstand such losses can face reductions to their dividends and share buybacks to ensure that they build adequate capital to withstand a correction. The Fed could potentially use other existing tools to accomplish the same goals or ask for new tools as necessary.

Conclusion: What does all this mean? A strategy for the Fed

OK, so how do I put this all together? First, I hope I have convinced you that identifying bubbles on the way up is extremely difficult, and it will be rare indeed when we make such an identification with any confidence. What we should do now is make sure the financial system is resilient and can withstand a future correction. That means we should force the large banks to raise a lot more capital now, when markets are strong.
Second, given how costly some asset bubbles can be when they burst, even though it is difficult, we must remain on alert, always looking for signs of a new bubble forming. If, however unlikely, we do spot a potential bubble, then we must try to assess how damaging a correction would be.
If we think it’s not likely to be very damaging, then we shouldn’t do anything, because the cost of false positives is high. However, in those cases where debt is fueling the asset value increase, a correction could trigger financial instability, because banks might take huge losses and potentially fail. The worry is not the high asset values themselves, but the exposure of market participants to those assets. In those cases, we should consider a number of options: (1) Speak out to raise awareness of the potential bubble, but not just one mention of “irrational exuberance.” If we really think we see an iceberg ahead, we should be speaking out until people take the risk seriously. (2) Use what nonmonetary tools we have to try to make sure the financial system is positioned to withstand the coming correction (by limiting bank dividends, for example). (3) Ask Congress for new authorities to make the financial system more resilient, which admittedly might take too long to be useful. (4) I would say as a last resort, if we are confident that the potential bubble poses grave danger, consider raising interest rates to try to slow it down.
Keep in mind, by the time we are confident that a dangerous bubble is indeed forming, it may be too late. Raising rates aggressively at that point might just burst the bubble, causing the very harm we hope to avoid.
Given the challenges of identifying bubbles with any confidence and the costs of making a policy mistake, I believe the odds of circumstances ever making sense to use monetary policy to try to slow asset prices down are very low. I won’t say never—but a whole lot of evidence would have to line up just right for it to be the prudent course of action.

Addendum: What might be wrong with my analysis

I always ask myself what my analysis might be missing. One argument not yet captured in this piece is that low rates, which might be appropriate given where inflation and unemployment are, could make bubbles more likely to form in the first place. Much has been written about the low neutral real rate environment we are currently in and expect to be in for the foreseeable future. Basically, due to a range of macroeconomic factors (such as demographic trends and low productivity growth), the interest rate that is neutral, i.e., that neither stimulates nor restrains the economy, is lower than it has been in recent decades. Current estimates are that the neutral real rate (net of inflation) is currently around zero or perhaps slightly negative. Could it be that such low rates make bubbles more likely to form and, if so, what should we do about it?
The truth is we don’t have a good answer to this question. If inflation is low and there is slack in the labor market, how high should we raise rates to reduce the chances of bubbles forming? We don’t have a good economic theory to analyze this scenario and offer policy guidance. It is a question that needs more research. Until we have such a theory that we have confidence in, I believe we should continue to focus on our dual mandate goals to set monetary policy and then keep our eyes open for potential bubbles and respond as best we can. The cost of keeping rates high to reduce the chances for future bubbles would be higher unemployment and a risk of unanchoring inflation expectations to the downside. Those are large economic costs.

Cryptocurrencies Show Just How Nuts Things Have Gotten

http://wolfstreet.com/2017/05/22/cryptocurrencies-alt-coins-ethereum-bitcoin-hedge-funds/http://wolfstreet.com/2017/05/22/cryptocurrencies-alt-coins-ethereum-bitcoin-hedge-funds/

Up 7,000% in two months, then it crashes.

The market capitalization of Ethereum, the second largest “cryptocurrency,” has soared 88% in a week, from $8.4 billion, when I pooh-poohed it on May 15, to $15.8 billion at the moment. Today, the price of the token jumped 15% to $172.26, as I’m writing this. It has skyrocketed 2,000% from January 1 when it was $8.15, and 25,000% since September 2015, when it was $0.68. A veritable miracle of creating wealth out of nothing (chart via WorldCoinIndex):

Bitcoin, the largest of the cryptocurrencies, is up 7% today, after soaring all weekend. At $2,211.56 at the moment, Bitcoin has a market capitalization of $36 billion, up from $30 billion a week ago. It has doubled since April 1 and “edged up” – compared to some of the others – a mere 122% year-to-date (chart via WorldCoinIndex):

Every one of the 800+ “cryptocurrencies” other than Bitcoin – from the ones that are already dead to the new ones that showed up out of nowhere – are the alt-coins. And there, like with Ethereum, the fun to be had is even greater.




Look at Ripple, the third largest. It’s already crashing. It had gone from $0.006 on March 15 to $0.42 on May 16, an increase of nearly 7,000% in two months. When I ridiculed it on May 15, it was at $0.215. By the next day it had doubled to $0.42. Now at $0.29, it has lost about 30% of its value since its peak, including today’s 10% swoon. Its market cap, now at $9.9 billion, has plunged $4 billion in a week (chart via WorldCoinIndex): .

Next largest alt-coin is Gnosis, which came out of nowhere on May 1, after its “initial coin offering” (ICO) – which is like an IPO for stocks but without the legal hoops, disclosures, and registration to jump through – at a price of $30 per token. The ICO raised $12.5 million for someone. Since its ICO, the price jumped 663% in three weeks, to $229 (chart via WorldCoinIndex):

If trading these tokens isn’t quite wild enough, you can also trade derivatives of these tokens. For example, according to BitMex, you might want to trade Gnosis futures. You can even use leverage to put some spice into it:

Traders who think that the price of GNO will rise will buy the futures contract. Conversely, traders who believe the price will drop will sell the futures contract.
All margin is posted in Bitcoin, that means traders can go long or short this contract using only Bitcoin. The GNO futures contracts feature a leverage of up to 2x.
For example, to buy 10 Bitcoin worth of contracts, you will only require 5 Bitcoin of Initial Margin.

Is this the embodiment of the new way of wealth creation? Anyone creates a “cryptocurrency,” gets exchanges on board, hypes it, gets other players to pump in actual currency, and participate in this miracle of a digital number backed by nothing and representing nothing, with no coupon payment and no ownership of anything other than the digital token, hoping fervently to sell it back and forth among each other with the agreement to not ever try to convert it back into real currency because that would cause it to collapse – see Ripple.
After these blistering surges of thousands of percent in the shortest time, no one is even trying to pretend that these are usable currencies. That notion has totally fallen by the wayside. They’re not even called “cryptocurrency” anymore. They’re cryptocoins or alt-coins or bitcoins or just tokens.
Of these 800+ schemes, the top ten alone have now ballooned to a combined market cap of $70 billion. That’s a serious amount. Next week it might be $100 billion or $40 billion, whatever the case may be.
They have become the favorite playground of some hedge funds that can with – for them – relatively small amounts of money drive up the price by thousands of percent.
But how do hedge funds get the real currency out of it – the money that they need to distribute to their clients? They’d have to sell their stake for real currency, and then the game is over, because just like their entry causes a huge surge, their exit causes the opposite – the type of event that Ripple might already be experiencing.
The funniest thing is to listen to all the logical sounding pseudo-reasons why this surge has happened, ranging from the impending collapse of the fiat currency, such as the dollar, to getting in on the ground floor of the future, or something.
On a more philosophical basis, these manias make you wonder about the state of the mind of today’s “investors” including hedge funds that play these games with increasingly hefty amounts of money. It’s symptomatic for something larger. But then people also play the lottery, which someone once called a special tax for those who can’t do math. So looking at these cryptocurrencies, I’m not yet predicting that mankind is totally doomed. But it does make me marvel at how so much liquidity for so many years has inflated asset prices all around and to such an extent that the craziness in these cryptocurrencies barely makes a, well, ripple.
After surging for years, quant hedge funds – where trading is done by machines, not humans – now dominate stock market trading. Read… Can Quant Funds Trigger a Stock Market Crash?

The Quants Run Wall Street Now

By Gregory Zuckerman and Bradley Hope

Alexey Poyarkov, a former gold-medal winner of the International Mathematical Olympiad for high-school students, spent most of his early career honing algorithms

at technology companies such as Microsoft Corp. , where he helped make the Bing search engine smarter at ferreting out pornography.
Last year, a bidding war for Mr. Poyarkov broke out among hedge-fund heavyweights Renaissance Technologies LLC, Citadel LLC and TGS Management Co. When it was over, he went to work at TGS in Irvine, Calif., and could earn as much as $700,000 in his first year, say people familiar with the contract.

The Russian-born software engineer, who declined to comment, as did the hedge funds, had almost no financial experience. What TGS wanted was his wizardry at designing algorithms, sets of rules used to power calculations and problem-solving, which in the investment world can quickly parse data and decide what to buy and sell, often with little human involvement.

WSJ Podcast

• The Quants: Today’s Kings of Wall Street?

Up and down Wall Street, algorithmic-driven trading and the quants who use sophisticated statistical models to find attractive trades are taking over the investment world.
On many trading floors, quants are gaining respect, clout and money as investment firms scramble to hire mathematicians and scientists. Traditional trading strategies, such as sifting through balance sheets and talking to companies’ customers, are falling down the pecking order.

Quantifiable

Amount of Data We Collect Every Day

2 5 00000000000000000 bytes , , , , , ,

(2.5 billion gigabytes)

Source: IBM

“A decade ago, the brightest graduates all wanted to be traders at Wall Street investment banks, but now they’re climbing over each other to get into quant funds,” says Anthony Lawler, who helps run quantitative investing at GAM Holding AG . The Swiss money manager last year bought British quant firm
Cantab Capital Partners for at least $217 million to help it expand into computer-powered funds.
Guggenheim Partners LLC built what it calls a “supercomputing cluster” for $1 million at the Lawrence Berkeley National Laboratory in California to help crunch numbers for Guggenheim’s quant investment funds, says Marcos Lopez de Prado, a Guggenheim senior managing director. Electricity for the computers costs another $1 million a year.
Algorithmic trading has been around for a long time but was tiny. An article in The Wall Street Journal in 1974 featured quant pioneer Ed Thorp. In 1988, the Journal profiled a little-known Chicago options-trading firm that had a secret computer system. Journal reporter Scott Patterson wrote a best-selling book in 2010 about the rise of quants.
Prognosticators imagined a time when data-driven traders who live by algorithms rather than instincts would become the kings of Wall Street.

That day has arrived. In just one sign of their power, quantitative hedge funds are now responsible for 27% of all U.S. stock trades by investors, up from 14% in 2013, according to the Tabb Group, a research and consulting firm in New York.
Quants have almost caught up to individual investors, which outnumber quants and collectively have 29% of all stock-trading volume.
At the end of the first quarter, quant-focused hedge funds held $932 billion of investments, or more than 30% of all hedge-fund assets, estimates HFR Inc. In 2009, quant funds held $408 billion, or 25% of all hedge-fund assets.
Quants got $4.6 billion of net new investments in the first quarter, while the overall hedge-fund business saw withdrawals of $5.5 billion.

Quants nearly doubled their share of stock trades since 2013

2016 27%

2013 14%

That's more than other hedge funds and investment firms

Source: Tabb Group

The computers are outperforming humans at picking investments. In the past five years, quant-focused hedge funds gained about 5.1% a year on average. The average hedge fund rose 4.3% a year in the same period.
In the first quarter, quant funds rose about 3%, compared with 2.5% for the average hedge fund.
Quants have been helped by two transformative forces. Regulatory scrutiny has made it hard for investors to obtain an edge through methods such as prodding company executives for information or tapping expert networks that included employees of public companies.
Even more importantly, investors now have at their fingertips an expanding ocean of data about the global economy and financial data, such as changes in earnings estimates and accounts receivable.

Marc Henrard, head of quantitative research at risk-management software developer OpenGamma, gives a presentation in April to the Thalesians, a social group for quants in London. He has a Ph.D. in mathematics. Photo: Immo Klink for The Wall Street Journal

The next frontier: tapping data from drones and other cutting-edge sources to help understand companies and the economy in real time.
Quants are different from high-frequency traders, who tend to focus on very short-term trades that might last just milliseconds. High-frequency traders have been under pressure as market volatility dips and competition grows.
Exchange-traded funds also use algorithms but are geared more to investors who want exposure to certain industries or sectors.
Quantitative-driven trades can last anywhere from a few minutes to a few months. The biggest quant firms, including Renaissance, Two Sigma Investments LLC, D.E. Shaw Group, PDT Partners and TGS, make thousands of trades and manage tens of billions of dollars in investor assets.
Some analysts worry that firms and investors stampeding into the quant business might be disappointed. The most successful quants have been operating for years. And hiring Ph.D.s doesn’t guarantee profits.
More competition could hurt returns and give a false sense of security about the market’s stability. In 2007, what became known as the “quant meltdown” was caused largely by the similarity of strategies among quants, who simultaneously rushed to sell, causing losses at other firms and more selling.

Mathematician William Byers, who wrote the 2010 book “How Mathematicians Think,” warns that rendering the world in numbers can give investors a deceptive belief that predictions churned out of computers are more reliable than they truly are. The more investors flock to complicated algorithmic models, the more likely it is some algorithms will be similar to one another, possibly fueling larger market disruptions, some analysts say.
So far, though, nothing has stopped the quant arms race, which is creating new jobs previously unheard of in the finance industry.
Citadel, of Chicago, has a chief scientist to run its analytics and quantitative strategies. Balyasny Asset Management LP hired in August data scientist Gilbert Haddad, formerly of Schlumberger Ltd. and General Electric Co. , to overhaul data and analytics at the New York hedge-fund firm. He studied nanoparticles at the University of Wisconsin and has a Ph.D. in engineering.

April’s meeting of the Thalesians, named after ancient Greek geometer Thales of Miletus. Photo: Immo Klink for The Wall Street Journal

“You take tours of offices, and everyone is always pointing out some guy off in a corner, working on his own,” says Alexandru Agachi, chief operating officer at Empiric Capital Ltd., a startup quant hedge fund in London. “They say with pride: ‘Over there is our quant. He’s building signals.’ ”
It’s common for hedge funds to retool themselves to fit the latest popular strategy. Many funds dove into mortgages after the financial crisis ebbed. Some turned into “macro” investors in anticipation of global economic shifts.

Steven Cohen The billionaire investor is using a “man plus machine” approach at his $12 billion family office, Point72 Asset Management.

Hedge-fund billionaire Steven A. Cohen’s investment firm, Point72 Asset Management, with $12 billion in assets, is shifting about half of its portfolio managers to what it calls a “man plus machine” approach.
Teams that use old-school research methods are working alongside data scientists. Financial analysts are taking evening classes to learn data-science basics. Point72 is plowing tens of millions of dollars into a group that analyzes reams of data, including credit- card receipts and foot traffic captured by apps on smartphones. The results are passed on to traders at the Stamford, Conn., investment firm.
Point72 lost money in most of its traditional trading strategies last year, say people familiar with the results. The firm’s quant investors made about $500 million.
Matthew Granade, Point72’s chief market-intelligence officer, recently encouraged London School of Economics students to learn basic programming languages, like R
and Python, to become more competitive when they graduate. Investors are shifting their preference from “artisan to engineer,” he said.

Paul Tudor Jones A legendary trader, Mr. Jones is incorporating quantitative trading methods at his hedge fund, Tudor Investment Corp.

Billionaire Paul Tudor Jones is one of the best-known investors in history. The former cotton trader anticipated the 1987 stock-market crash and made gigantic profits with quick bursts of trading, averaging annual gains of more than 17% since then. His hedge-fund firm, Tudor Investment Corp., barely made any money in 2014 and 2015, though.
By last year, Mr. Jones was feeling pressure from more successful quant traders, according to people close to the firm. In October, Mr. Jones chose Dario Villani, an Italian with a doctorate in theoretical physics who was hired in 2015, to help rejuvenate Tudor.
Hunkered down with a team of quants and other Tudor employees in a small house on an estate in Greenwich, Conn., Mr. Villani began developing computer programs to replicate trading positions of Tudor’s portfolio managers using instruments that better allow the firm to increase risk to improve returns without endangering the hedge fund or Tudor, people familiar with the matter say.

Thalesians mingle and swap business cards at last month's meeting in London. Photos: Immo Klink for The Wall Street Journal

Despite the changes, Tudor’s two key funds were flat in 2016 as well as so far this year, even as markets have climbed.
Humans have long searched relentlessly for ways to gain an information edge. Legend has it that financier Baron Rothschild built a network of field agents and carrier pigeons in 1815 to get a jump on the Battle of Waterloo outcome. Today’s quants hope to digest—and act on—economic and corporate information faster than traditional investors.

Investments held by quant hedge funds more than doubled in three years

2016 $918B

2013 $408B

Source: HFR

Hedge funds with quant-focused strategies have been poring over private Chinese and Russian consumer surveys, illicit pharmaceutical sales on the dark web—a network of websites used by hackers and others to anonymously share information—and hotel bookings by U.S. travelers, according to Quandl Inc., a platform for such data.
In the late 1990s, an algorithm might have simply tried to ride the momentum of a stock’s price rise, buying at a certain price level and selling at a predetermined moment. Today’s algorithms can make continuous predictions based on analysis of past and present data while hundreds of real-time inputs bombard the computers with various signals.
Some investment firms are pushing into machine learning,
which allows computers to analyze data and come up with their own predictive algorithms. Those machines no longer rely on humans to write the formulas.
Algorithms and quants eventually could sharply reduce the need for large investment staffs. A machine-driven algorithm might help quantitative researchers discover dozens of new algorithms in the time it used to take to create one.
In the battle for talent, quant-focused firms often are reluctant to call themselves hedge funds or even investment firms. Quant firms would rather emphasize their similarities to cutting-edge tech companies in Silicon Valley.
Two Sigma, based in New York, has in-house hacker labs, robotics competitions and game rooms. Empiric calls itself a “technology company operating in financial markets.”

What’s an Algorithm, and How Do Quants Use Them?

Algorithms are ​sets of rules used to help drive active decision-making. And they're lurking behind nearly every aspect of financial life. Illustration: Heather Seidel/The Wall Street Journal

Saeed Amen, a quantitative researcher in London, says his investment strategies were considered “very niche” for most of his 14-year career.
He organized social events for quants, including occasional gatherings of a group called the Thalesians after ancient Greek geometer Thales of Miletus. The beer and conversation sometimes attracted fewer than a dozen people.
Mr. Amen’s phone has started ringing with calls from hedge-fund managers in the U.S. and Europe. They don’t all want automated investing algorithms, but they are trying to figure out how to make better predictions, he says.
Much of that push is coming from investors such as Pepperdine University in Malibu, Calif. Last year, the college placed about 10% of its $750 million portfolio in big quant funds, including those run by Man Group PLC of London and AQR Capital Management LLC, Greenwich, Conn.
Until then, Pepperdine had “essentially zero” quant investments, says Michael Nicks, its director of investments. “The narrative of fundamental investing is much more comfortable to digest,” he says. “Finding a company with good prospects makes sense, since we look for undervalued things in our daily lives, but quant strategies have nothing to do with our lives.”
After “years and years of self-education” and dozens of meetings with quant managers, says Mr. Nicks, Pepperdine decided it was ready to make the leap.

—Rob Copeland contributed to this article. https://www.wsj.com/articles/the-quants-run-wall-street-now-1495389108