March of the machines

The stockmarket is now run by computers, algorithms and passive managers

Such a development raises questions about the function of markets, how companies are governed and financial stability

FIFTY YEARS ago investing was a distinctly human affair. “People would have to take each other out, and dealers would entertain fund managers, and no one would know what the prices were,” says Ray Dalio, who worked on the trading floor of the New York Stock Exchange (NYSE) in the early 1970s before founding Bridgewater Associates, now the world’s largest hedge fund. Technology was basic.

Kenneth Jacobs, the boss of Lazard, an investment bank, remembers using a pocket calculator to analyse figures gleaned from company reports. His older colleagues used slide rules. Even by the 1980s “reading the Wall Street Journal on your way into work, a television on the trading floor and a ticker tape” offered a significant information advantage, recalls one investor.

Since then the role humans play in trading has diminished rapidly. In their place have come computers, algorithms and passive managers—institutions which offer an index fund that holds a basket of shares to match the return of the stockmarket, or sectors of it, rather than trying to beat it (see chart 1). On September 13th a widely watched barometer published by Morningstar, a research firm, reported that last month, for the first time, the pot of passive equity assets it measures, at $4.3trn, exceeded that run by humans.

The rise of financial robotisation is not only changing the speed and makeup of the stockmarket. It also raises questions about the function of markets, the impact of markets on the wider economy, how companies are governed and financial stability.

America is automating

Investors have always used different kinds of technology to glean market-moving information before their competitors. Early investors in the Dutch East India Company sought out newsletters about the fortunes of ships around the Cape of Good Hope before they arrived in the Netherlands. The Rothschilds supposedly owe much of their fortune to a carrier pigeon that brought news of the French defeat at the Battle of Waterloo faster than ships.

During the era of red braces and slide rules, today’s technological advances started to creep in. Machines took the easier (and loudest) jobs first. In the 1970s floor traders bellowing to each other in an exchange started to be replaced by electronic execution, which made it easier for everyone to gather data on prices and volume. That, in turn, improved execution by creating greater certainty about price.

In portfolio management, algorithms have also been around for decades. In 1975 Jack Bogle founded Vanguard, which created the first index fund, thus automating the simplest possible portfolio allocation. In the 1980s and 1990s fancier automated products emerged, such as quantitative hedge funds, known as “quant” funds, and exchange-traded funds (ETFs), respectively. Some ETFs track indices, but others obey more sophisticated investment rules by automating decisions long championed by humans, such as buying so-called value stocks; which look cheap compared with the company’s assets. Since their inception many of the quant funds have designed algorithms that can scour market data, hunting for stocks with other appealing, human-chosen traits, known in the jargon as “factors”.

The idea of factors came from two economists, Eugene Fama and Kenneth French, and was put into practice by Cliff Asness, a student of Mr Fama, who in 1998 founded AQR Capital Management, now one of the world’s largest hedge funds. Quant funds like AQR program algorithms to choose stocks based on factors that were arrived at by economic theory and borne out by data analysis, such as momentum (recent price rises) or yield (paying high dividends). Initially only a few money-managers had the technology to crunch the numbers. Now everybody does.

Increasingly, the strategies of “rules-based” machine-run investors—those using algorithms to execute portfolio decisions—are changing. Some quant funds, like Bridgewater, use algorithms to perform data analysis, but call on humans to select trades. However, many quant funds, such as Two Sigma and Renaissance Technologies, are pushing automation even further, by using machine learning and artificial intelligence (AI) to enable the machines to pick which stocks to buy and sell.

This raises the prospect of the computers taking over human investors’ final task: analysing information in order to design investment strategies. If so, that could lead to a better understanding of how markets work, and what companies are worth.

The execution of orders on the stockmarket is now dominated by algorithmic traders. Fewer trades are conducted on the rowdy floor of the NYSE and more on quietly purring computer servers in New Jersey. According to Deutsche Bank, 90% of equity-futures trades and 80% of cash-equity trades are executed by algorithms without any human input. Equity-derivative markets are also dominated by electronic execution according to Larry Tabb of the Tabb Group, a research firm.

This must be the place

Each day around 7bn shares worth $320bn change hands on America’s stockmarket. Much of that volume is high-frequency trading, in which stocks are flipped at speed in order to capture fleeting gains. High-frequency traders, acting as middlemen, are involved in half of the daily trading volumes. Even excluding traders, though, and looking just at investors, rules-based investors now make the majority of trades.

Three years ago quant funds became the largest source of institutional trading volume in the American stockmarket (see chart 2). They account for 36% of institutional volume so far this year, up from just 18% in 2010, according to the Tabb Group. Just 10% of institutional trading is done by traditional equity fund managers, says Dubravko Lakos-Bujas of JPMorgan Chase.

Machines are increasingly buying to hold, too. The total value of American public equities is $31tn, as measured by the Russell 3000, an index. The three types of computer-managed funds—index funds, ETFs and quant funds—run around 35% of this (see chart 3). Human managers, such as traditional hedge funds and other mutual funds, manage just 24%. (The rest, some 40%, is harder to measure and consists of other kinds of owners, such as companies which hold lots of their own shares.)

Of the $18trn to $19trn of managed assets accounted for, most are looked after by machines.

Index funds manage half of that pot, around $9trn. Bernstein, a research firm, says other quantitative equity managers look after another 10-15%, roughly $2trn. The remaining 35-40%, worth $7 to $8trn, is overseen by humans.

A prism by which to see the progress of algorithmic investing is hedge funds. Four of the world’s five largest—Bridgewater, AQR, Two Sigma and Renaissance—were founded specifically to use quantitative methods. The sole exception, Man Group, a British hedge fund, bought Numeric, a quantitative equity manager based in Boston, in 2014.

More than half of Man Group’s assets under management are now run quantitatively. A decade ago a quarter of total hedge-fund assets under management were in quant funds; now it is 30%, according to HFR, a research group. This figure probably understates the shift given that traditional funds, like Point72, have adopted a partly quantitative approach.

The result is that the stockmarket is now extremely efficient. The new robo-markets bring much lower costs. Passive funds charge 0.03-0.09% of assets under management each year. Active managers often charge 20 times as much. Hedge funds, which use leverage and derivatives to try to boost returns further, take 20% of returns on top as a performance fee.

The lower cost of executing a trade means that new information about a company is instantly reflected in its price. According to Mr Dalio “order execution is phenomenally better.” Commissions for trading shares at exchanges are tiny: $0.0001 per share for both buyer and seller, according to academics at Chicago University. Rock-bottom fees are being passed on, too. On October 1st Charles Schwab, a leading consumer brokerage site, and TD Ameritrade, a rival, both announced that they will cut trading fees to zero.

Cheaper fees have added to liquidity—which determines how much a trader can buy or sell before he moves the price of a share. More liquidity means a lower spread between the price a trader can buy a share and the price he can sell one.

But many critics argue that this is misleading, as the liquidity provided by high-frequency traders is unreliable compared with that provided by banks. It disappears in crises, the argument goes. A recent paper published by Citadel, a hedge fund, refutes this view. It shows that the spread for executing a small trade—of, say $10,000—in a single company’s stock has fallen dramatically over the past decade and is consistently low. Those for larger trades, of up to $10m, have, at worst, remained the same and in most cases improved.

Grandmaster flash

The machines’ market dominance is sure to extend further. The strategy of factors that humans devised when technology was more basic is now widely available through ETFs. Some ETFs seek out stocks with more than one factor. Others follow a “risk parity strategy”, an approach pioneered by Mr Dalio which balances the volatility of assets in different classes. Each added level of complexity leaves less for human stockpickers to do. “Thirty years ago the best fund manager was the one with the best intuition,” says David Siegel, co-chairman of Two Sigma. Now those who take a “scientific approach”, using machines, data and AI, can have an edge.

To understand the coming developments in the market, chess offers an instructive example. In 1997 Deep Blue, an IBM supercomputer, beat Garry Kasparov, the reigning world champion. It was a triumph of machine over man—up to a point. Deep Blue had been programmed using rules written by human players. It played in a human style, but better and more quickly than any human could.

Jump to 2017, when Google unveiled AlphaZero, a computer that had been given the rules of chess and then taught itself how to play. It took four hours of training to be able to beat Stockfish, the best chess machine programmed with human tactics. Intriguingly, AlphaZero made what looked like blunders to human eyes. For example, in the middlegame it sacrificed a bishop for a strategic advantage that became clear only much later.

Quant funds can be divided into two groups: those like Stockfish, which use machines to mimic human strategies; and those like AlphaZero, which create strategies themselves. For 30 years quantitative investing started with a hypothesis, says a quant investor. Investors would test it against historical data and make a judgment as to whether it would continue to be useful. Now the order has been reversed. “We start with the data and look for a hypothesis,” he says.

Humans are not out of the picture entirely. Their role is to pick and choose which data to feed into the machine. “You have to tell the algorithm what data to look at,” says the same investor. “If you apply a machine-learning algorithm to too large a dataset often it tends to revert to a very simple strategy, like momentum.”

But just as AlphaZero found strategies that looked distinctly inhuman, Mr Jacobs of Lazard says AI-driven algorithmic investing often identifies factors that humans have not. The human minders may seek to understand what the machine has spotted to find new “explainable” factors. Such new factors will eventually join the current ones. But for a time they will give an advantage to those who hold them.

Many are cautious. Bryan Kelly of Yale University, who is AQR’s head of machine learning, says its fund has found purely machine-derived factors that appeared to outperform for a while. “But in the end they turned out to be spurious.” He says combining machine learning with economic theory works better.

Others are outright sceptics—among them Mr Dalio. In chess, he points out, the rules stay the same. Markets, by contrast, evolve, not least because people learn, and what they learn becomes incorporated in prices. “If somebody discovers what you’ve discovered, not only is it worthless, but it becomes over-discounted, and it will produce losses. There is no guarantee that strategies that worked before will work again,” he says. A machine-learning strategy that does not employ human logic is “bound to blow up eventually if it’s not accompanied by deep understanding.”

Nor are the available data as useful as might initially be thought. Traditional hedge-fund managers now analyse all sorts of data to inform their stockpicking decisions: from credit-card records to satellite images of inventories to flight charters for private jets. But this proliferation of data does not necessarily allow machines to take over the central job of discovering new investment factors.

The reason is that by the standards of AI applications the relevant datasets are tiny. “What determines the amount of data that you really have to work from is the size of the thing that you’re trying to forecast,” says Mr Kelly. For investors in the stockmarket that might be monthly returns, for which there are several decades’ worth of data—just a few hundred data-points. That is nothing compared with the gigabytes of data used to train algorithms to recognise faces or drive cars.

An oft-heard complaint about machine-driven investing takes quite the opposite tack. It is not a swizz, say these critics—far from it. It is terrifying. One fear is that these algorithms might prompt more frequent and sudden shocks to share prices. Of particular concern are “flash crashes”. In 2010 more than 5% was wiped off the value of the S&P 500 in a matter of minutes.

In 2014 bond prices rallied sharply by more than 5%, again in a matter of minutes. In both cases markets had mostly normalised by the end of the day, but the shallowness of liquidity provided by high-frequency traders was blamed by the regulators as possibly exacerbating the moves. Anxieties that the machine takeover has made markets unmanageably volatile reached a frenzy last December, as prices plummeted on little news, and during the summer as they gyrated wildly.

In 1987 so-called program trading, which sold stocks during a market dip, contributed to the Black Monday rout, when the Dow Jones index fell by 22% in a single day. But the problem then was “herding”—money managers clustering around a single strategy. Today greater variety exists, with different investment funds using varying data sources, time horizons and strategies. Algorithmic trading has been made a scapegoat, argues Michael Mendelson of AQR. “When markets fall, investors have to explain that loss. And when they don’t understand, they blame a computer.” Machines might even calm markets, he thinks. “Computers do not panic.”

Money never sleeps

Another gripe is that traditional asset managers can no longer compete. “Public markets are becoming winner-takes-all,” complains one of the world’s largest asset managers. “I don’t think we can even come close to competing in this game,” he says. Philippe Jabre, who launched his hotly anticipated eponymous fund, Jabre Capital, in 2007, said that computerised models had “imperceptibly replaced” traditional actors in his final letter to clients as he closed some funds last December.

And there remains a genuine fear: what happens if quant funds fulfil the promises of their wildest boosters? Stockmarkets are central to modern economies. They match companies in need of cash with investors, and signal how well companies are doing. How they operate has big implications for financial stability and corporate governance. It is therefore significant that algorithms untethered from human decision-making are starting to call the shots.

The prospect of gaining an edge from machine-derived factors will entice other money managers to pile in. It is natural to be fearful of the consequences, for it is a leap into the unknown. But the more accurate and efficient markets are, the better both investors and companies are served. If history is a guide, any new trading advantage will first benefit just a few. But the market is relentless. The source of that advantage will become public, and copied. And something new will be understood, not just about the stockmarket, but about the world that it reflects.

How China dodged a trade war recession

Tariff shock was offset by devaluation and a judicious easing in domestic policy

Gavyn Davies

Pedestrians and shoppers walk past a Furla SpA store on Wangfujing Street in Beijing, China, on Wednesday, June 27, 2018. Consumer sentiment in China remains positive despite concern over the country's economy, yuan weakness and a trade feud with the U.S. Photographer: Giulia Marchi/Bloomberg
Activity growth in China has remained fairly robust despite the trade dispute due in part to timely policy stimulus © Bloomberg

The escalation of the trade war between the US and China in the past 18 months has cast a pall over business sentiment and growth in the advanced economies.

By contrast, activity growth in China has remained fairly robust at around 7 per cent, and inflation is close to the People’s Bank of China’s 3 per cent target ceiling. This buoyancy is surprising, since China was expected by many analysts (and probably by Donald Trump’s administration) to be the main casualty from the trade wars.

How has China managed to survive the trade shock so far? The first issue is to quantify the size of the exogenous shock to the Chinese economy from the trade wars. [1]

Goldman Sachs economist Andrew Tilton, who has published excellent empirical research on the trade war, estimates that the initial rounds of tariffs that took effect before August 1 2019 have reduced Chinese gross domestic product by 0.4 per cent, via the direct trade effects alone.  This is broadly consistent with the worsening of net trade’s contribution to expenditure in GDP from 2017-19, which suggests that the estimate is in the right ballpark.

There have also been potentially much larger negative effects on business investment from the huge rise in uncertainty about trade policy. These are very hard to estimate with any confidence. However, staff economists at the US Federal Reserve Board have recently published a study that estimates trade policy uncertainty will have reduced real GDP in emerging economies, including China, by about 0.9 per cent by the end of 2019. This is very similar to global results published last week by IMF economists, and also close to the recorded decline in investment expenditure by foreigners in China, and by Chinese exporters since the trade war started.

If accurate, this suggests that the impact of trade uncertainty on Chinese GDP has been about three times larger than the direct trade impact, making a total contractionary shock to GDP equal to 1.3 per cent. [2]

This shock has been offset by timely Chinese policy stimulus and an exchange rate depreciation.

Several elements of domestic economic policy have been eased since the trade war started, though this has been done judiciously, with continued efforts to reduce the growth of debt in the economy, especially in the shadow banking sector.

There has been an emphasis on fiscal stimulus, which (according to the IMF) will increase the overall budget deficit by 1.5 per cent of GDP in 2019 alone. In addition, monetary policy has been eased, but only “prudently”. A key policy indicator, the 12-month medium-term lending facility interest rate, has remained unchanged, while the reserve requirement ratio has been cut by 4 percentage points. Credit policy has been loosened, and total credit is expected by JPMorgan to rise by 12 per cent in 2019. Housing policy has been an exception, with no relaxation of regulations that may be constraining construction so far.

The Goldman Sachs index of the overall stance of macro policy in China contains all these elements, and on this measure (see box) the easing seems large enough to have offset, almost exactly, the tariff and uncertainty shocks during the trade wars.

In addition, the 6 per cent decline in the exchange rate since the second quarter of 2018 could account for a further boost to GDP of about 0.7 per cent over a three-year period, calculated from the latest IMF study of currency effects on global economic activity.

In summary, the entire contractionary shock from the trade wars up to August 2019 has been comfortably offset by policy interventions in China.

As a result, China seems less concerned about the additional tariff measures promised by President Trump on August 1 and 23. Some of these new measures have been postponed, but in full they would constitute an additional shock almost as large as the total of all the announcements up to July 2019. China’s State Council has responded by announcing new fiscal and monetary measures that would once again be expected to offset part or all of this new shock.

In recent days, both sides — especially the White House — seem to be tiptoeing away from the brink. With the US economy now slowing more than China, Mr Trump has a large incentive to reach a truce before the start of his re-election campaign.


 [1] All the estimates in this column are imprecise.

[2] The Fed model counts references in press reports related to “trade policy uncertainty” as an independent measure of the uncertainty shock. This measure is plugged into an econometric (VAR) model with other economic variables that together explain the behaviour of GDP, and the effect of trade uncertainty, in the global economy.


China’s policy stance has kept the economy growing at the targeted rate

According to Goldman Sachs, the overall easing in Chinese macro policy has been significant (about one standard deviation as measured by their composite indicator of the macro policy stance). So far, this is about half as large as that in 2015-16, when the policy easing was followed by a rise of around 2 percentage points in the activity growth rate within 12 months. A rule of thumb estimate therefore suggests that the boost to GDP this time will be at least one percentage point. The devaluation effect will constitute a further boost of some 0.7 per cent.

Economic activity in China has continued, on balance, to expand at or above the authorities’ target of 6.0-6.5 per cent growth. This suggests that the expansionary effects of stimulative domestic policy and devaluation have more than offset the damaging effects of the trade war in the past 18 months.

Who’s in Charge in Peru? Peruvians Can’t Agree

By Anatoly Kurmanaev and Andrea Zarate

Police near Peru’s Congress, in Lima, on Tuesday.

Peru is facing its deepest political crisis in at least three decades, with the president dissolving Congress, Congress then moving to suspend the president — and the vice president, who had stepped up to lead the country, renouncing her position as well.

The vice president, Mercedes Aráoz, said late on Tuesday that she gave up her post because “the constitutional order in Peru is broken.”

The head of the dissolved Congress, Pedro Olaechea, who learned of her resignation in the middle of an interview with CNN en Español, said he would also not assume power — a tactical win for the president, Martín Vizcarra, but not the end of turbulence for Peru.

Peru’s dysfunctional and corruption-ridden political system has courted crisis for years, with three former presidents under investigation and another dead after shooting himself during his arrest. But matters came to a head when Mr. Vizcarra confronted the conservative forces controlling Congress and accused them of stonewalling his efforts to fight corruption and pass political reform.

On Monday afternoon, Mr. Vizcarra invoked a constitutional provision that allows him to dissolve Congress and to call new parliamentary elections. Congress responded by suspending him and setting off a constitutional standoff.

Protests in support of the president broke out in cities across the country on Monday night, but by Wednesday a tense calm had set in as the population tried to pick up their lives despite the political disarray in the capital, Lima.

While some Peruvians had celebrated Mr. Vizcarra’s decision as a much overdue purge of the corrupt elites, others saw in the drastic move a reminder of Peru’s despotic past.

Here’s what you need to know to understand this deep-rooted crisis that will shape the future of South America’s fastest-growing economy.

Why did the president suspend Congress?

Mr. Vizcarra claims the opposition, which controls Congress, has repeatedly blocked his attempts to clean up Peruvian politics and pass much-needed reforms.

The last straw for Mr. Vizcarra came Monday, when he asked Congress for a vote of confidence to change the system for appointing judges to the country’s highest court, the Constitutional Tribunal. This is the court that, among other things, arbitrates disputes between the president and Congress.

President Martín Vizcarra announcing on Monday the dissolution of Peru’s Congress.
Juan Pablo Azabache/Agence France-Presse — Getty Images

Lawmakers gave him the vote of confidence, but also went ahead and picked a constitutional judge of their choosing: the cousin of the head of Congress.

But this showdown with Congress was likely only a matter of time, said Carlos Meléndez, a Peruvian expert at the Diego Portales University in Santiago, Chile.

Mr. Vizcarra, a regional politician turned vice president, is a relative outsider in Lima’s power circles. He took power last year when, facing corruption charges, the president, Pedro Pablo Kuczynski, stepped down.

Although his anticorruption platform has been popular with Peruvians, and Congress is widely reviled as venal, Mr. Vizcarra lacks an electoral mandate and a strong party. His party occupies only five seats among the country’s 130 lawmakers.

His conservative opponents, led by the 54 lawmakers from the party of Mr. Kuczynski’s presidential rival, Keiko Fujimori, hold the majority of the seats.

Can he do that?

The Peruvian Constitution says the president can dissolve Congress if it twice denies his cabinet a vote of confidence.

But whether Mr. Vizcarra’s actions met that standard depends on whose interpretation of the law you accept.

Over the past year, Mr. Vizcarra asked for that vote three times, using a constitutional mechanism to package reform proposals as a vote of confidence in his cabinet. In all instances, Congress approved his cabinet but ignored his proposals.

On Monday, Mr. Vizcarra argued that Congress’s sleight of hand constituted a de facto vote of no confidence, giving him the right to shut down the legislature and call new elections.

“In spirit, the Congress had very definitely denied confidence to two cabinets; by the exact letter of the law, probably it had not,” said Cynthia McClintock, a professor of political science at the George Washington University.

Supporters of Mr. Vizcarra outside Congress after he dissolved the legislature in Lima, on Monday. Rodrigo Abd/Associated Press

Because the legality of Mr. Vizcarra’s move is uncertain, leaving open the question of whether Congress has been dissolved, it is also unclear whether it had the power to suspend Mr. Vizcarra and swear in his vice president, Ms. Aráoz. Peru now has a constitutional chicken and egg problem, said Michael Baney, a risk analyst at consultancy WorldAware.

“If the dissolution of Congress was legal, then it voting to strip Vizcarra of power was illegal, since it was no longer even in session,” he said. “Of course, the inverse is also true: If Vizcarra’s dissolution of Congress was illegal, then Congress was indeed in session and thus had the power to strip Vizcarra of his power.”

So what comes next?

The vice president, Ms. Aráoz, said on Twitter that she hoped her renunciation would lead to “general elections as soon as possible for the good of the country.”

Peru’s federal register on Tuesday published the date for new parliamentary elections: They are scheduled for Jan. 26.

Mr. Vizcarra has support from key stakeholders: He published photos Monday night of himself surrounded by Peru’s top generals, to show he has the army’s support. The police obeyed his order and surrounded Congress with riot shields to prevent most lawmakers from entering on Tuesday.

And manifestations of support for him erupted in various cities across the country on Monday, with smiling protesters jumping and shouting, “Yes we could.”

On Wednesday, Mr. Olaechea, the president of Congress, planned to meet with the caretaker commission of 27 lawmakers who by law manage the body while it is dissolved.

When asked during a televised interview if he would take the presidency, he said, “No, at this moment there is a de facto situation,” he said, referring to Mr. Vizcarra effectively remaining in control, with the army’s backing.

Holding up a copy of Peru’s Constitution, he said, “I can’t act any more than with this little book, which is the Constitution.”

Analysts say the resolution of the crisis is likely to fall to Peru’s courts, but even that scenario is uncertain. The main question is, which court? The struggle over appointments to Peru’s Constitutional Tribunal is precisely what set off the current crisis.

Riot police surrounding Congress and blocking traffic in downtown Lima on Tuesday. Many businesses along the main avenues remained closed.
Martin Mejia/Associated Press

How are Peruvians taking this?

After a day in which riot police blocked traffic and many businesses along the main avenues stayed closed, downtown Lima was bustling as usual by Wednesday morning.

The markets have so far largely shrugged off the crisis. Peruvian currency, bonds and stock market recovered most of their initial losses Tuesday, as investors banked on both political camps continuing the business-friendly policies that have fueled Peru’s remarkable economic growth for the past decade.

The general mood in Lima’s middle-class districts Tuesday was a combination of joy at the prospect of breaking the political stalemate and uncertainty. Monday’s demonstrations by mostly young Peruvians in support of Mr. Vizcarra had been replaced by a tense calm.

To many Peruvians, particularly the young and the left-leaning, Mr. Vizcarra’s move is a chance to wipe the slate clean and finally reform the corrupt political system, which allowed the country’s traditional political parties to divvy up power and economic patronage at the expense of the country’s development for decades.

A less vocal section of Peruvians, however, has expressed concern about repeating the mistakes of 1992, when the country last faced a constitutional crisis. Back then, another political outsider, an agronomist descendant of Japanese immigrants, Alberto Fujimori — the father of the current opposition leader, Keiko Fujimori — dissolved the Congress with a similar discourse of national rebirth.

Mr. Fujimori then proceeded to rule with an iron fist, dismantling courts, staffing institutions with loyalists and committing gross human rights violations in his quest to stamp out dissent.

His daughter’s party is still the largest force in Congress, epitomizing in its opponents’ eyes the political decay that her father ostensibly came to power to combat.

O.K., so what are the broader repercussions here?

Analysts warn that Peru’s political paralysis could soon begin to wear down the country’s steady economic growth, which has been fueled by mining and infrastructure investment.

Peru’s 3.9 percent economic growth forecast this year by the International Monetary Fund is a sign of financial health on a continent plagued by stagnation, stock market runs and outright collapse in nearby Venezuela.

However, Peru’s basic economic model will likely remain untouched, regardless of which contender for the presidency comes out ahead.

“There are no signs at present that the favorable regulatory environment for the extractive sector, or the treatment of ongoing operational projects, will be adversely affected,” said Diego Moya-Ocampos, a political risk analyst with IHS Markit in London.

Peru’s foreign policy is also unlikely to change significantly. Both Mr. Vizcarra and Ms. Fujimori’s party have taken a tough stance against the government of Venezuela, whose economic collapse has triggered the biggest geopolitical crisis in the region in decades.

But a breakdown of the constitutional order triggered by this week’s events could come to haunt the country’s political center in the next elections, said Abhijit Surya, an analyst at the Economist Intelligence Unit.

“The latest developments significantly heighten the risk that a radical, anti-establishment candidate will win the 2021 presidential elections,” he wrote in a note to clients Tuesday.

ECB launches new lending benchmark based on overnight deals

Europe’s €STR rate part of global drive to achieve more reliable pricing in capital markets

Philip Stafford in London

(FILES) This file photo taken on December 13, 2018 shows the headquarters of the European Central Bank (ECB) in Frankfurt am Main, western Germany. - The European Central Bank is set to unveil fresh monetary easing at a meeting Thursday, September 12, 2019, analysts agree, under pressure from markets to deliver support to a flagging eurozone economy. (Photo by Daniel ROLAND / AFP)DANIEL ROLAND/AFP/Getty Images
The European Central Bank published the €STR rate for the first time on Wednesday © AFP

Global authorities’ efforts to move capital markets on to a sounder base took a step forward on Wednesday after European authorities launched a new benchmark based on overnight deals.

The new rate, called €STR, is intended to replace Eonia, a daily reference rate that reflects unsecured overnight lending between banks in the EU and which prices about €24tn of deals. The European Central Bank published the €STR rate for the first time on Wednesday, at a rate of minus 0.549 per cent based on Tuesday’s trading.

Watchdogs around the world argue that well-known interbank lending rates such as Libor and Eonia have outlived their usefulness because there are so few transactions that underpin the market, and banks no longer want to contribute to it.

More than $300tn of contracts are priced against Libor, for example, but the rate is largely composed by banks’ estimates rather than market transactions. Regulators would prefer investors and borrowers to price their thousands of loans, bonds and derivatives contracts on more reliable overnight lending rates.

The overnight rates are closely watched because the “front end” of European interest rate curves are derived from them. €STR is a “risk-free” rate and is based on deals concluded the previous day.

Several banks have been lining up deals referencing the new rate in preparation for the new benchmark. Earlier this week, JPMorgan and HSBC conducted a privately negotiated interest rate swap with a notional value of €100m and a week’s maturity. The European Investment Bank is also set to price a bond in coming days, with TD Securities acting as a bookrunner.

Eonia will still be published daily as a notional rate until the start of 2022 but it will be equal to €STR plus a fixed spread of 8.5 basis points. A basis point is 100th of a percentage point.

That means older, existing contracts will still have an economic value, said Murray Longton, principal consultant at Capco, a financial markets consultancy.

“In the next three to six months there’s unlikely to be much change in the market in general,” he said. “You aren’t forcing anyone to change the legal basis of the contract. When you stop deals transacted with Eonia [that] is when it matters.”

The pilot scheme for the past six months indicates that the new benchmark has traded at around five basis points below the ECB’s deposit rate of minus 50 basis points, according to UniCredit.

“Pre-€STR data suggest that this is a very stable benchmark and has displayed low volatility so far,” said Luca Cazzulani, deputy head of fixed income strategy at the bank.

Market participants are still testing new infrastructure intended to deepen liquidity in the market, such as swaps and futures contracts. Clearing of €STR-priced swaps is set to begin later this month.

European authorities have been further behind counterparts in the US, UK and Switzerland in developing a new rate, in part because a planned alternative rate was unlikely to meet the EU’s new standards.

Without reform, neither Eonia nor Euribor would meet tougher EU standards on benchmarks that come into effect in January.

The Federal Reserve Needs to Play Offense Before It’s Too Late, Analyst Says

By Teresa Rivas

Federal Reserve Chairman Jerome Powell. Photograph by Win McNamee/Getty Images

When Stifel’s head of equity strategy, Barry Bannister, spoke with Barron’s in June, he argued the Fed would at the very least need to cut interest rates by 50 basis points. That has since happened, but he still says the central bank isn’t being bold enough.

Bannister notes that while the Fed’s quarter-point interest-rate cut on Wednesday was largely expected, he’s still concerned that the “Fed is willing to play defense instead of offense, and anything they do could end up being too late.”

He believes the Fed has already skirted too close to the line in terms of allowing the funds rate to get too close to his calculation of what the neutral rate (one that is neither too tight or loose) is. History tells us, he says, that the closer that rates get to neutral, the closer we are to recession.

The problem is these two cuts bring us back to square one, Bannister says. The Fed has only undone what Bannister (and others) have called erroneous rate increases in 2018. He tends to agree with St. Louis Fed President James Bullard, who has said his job is to “un-invert” the yield curve and was the one dissenting voice calling for a 50-basis-point rate cut.

“The September and December rate increases were a bridge too far, so all they’ve done is put us back where we were,” while economic concerns have only grown, Bannister says. That requires more action—playing offense—that the Fed doesn’t seem willing to do, he warns. And for those who say the central bank should keep some bullets in the chamber in case of a recession, Bannister argues that “rates are not the only ammunition that the Fed has.” He points to “rampant quantitative easing” taking place in other parts of the world; it would be easy, he says, for the Fed to follow suit if needed, thanks to a relatively small balance sheet as a percentage of gross domestic product.

To Bannister’s point, the S&P 500is hovering at just 100 points higher than it closed the day of the September 2018 rate increase, he notes. Nor does he expect a lot of room for stocks to keep running: The fair value for the S&P 500 without factoring in the chance of a recession is 3,000 he says. Accounting for a 38% chance of a downturn (per the New York Fed yield curve model), his year-end target for the index is 2,900, about 3.6% lower than Wednesday’s close.

So does that mean the fourth quarter this year will be a repeat of 2018’s painful showing? Not necessarily he says: For one, he argues the magnitude of the Fed’s mistake last year is greater than the error he believes the central bank is making today. In addition, President Donald Trump now understands “the need to backpedal on the trade war,” he says. “Whether or not China obliges is the question, but if it did, it would improve business sentiment, particularly if China follows up with a slightly stronger yuan and more stimulus.”

But, given this target, he wouldn’t be surprised if we do see a selloff in the fourth quarter—one that could reverse, however, on the actions not only of monetary policy makers, but also those of the White House and China.

Jerome Powell’s Dilemma

There is a reason that the US Federal Reserve chair often has a haunted look. Probably to his deep and never-to-be-expressed frustration, the Fed is setting monetary policy in a way that increases the likelihood that President Donald Trump will be reelected next year.

Carmen M. Reinhart , Vincent Reinhart

reinhart39_ Sha HantingChina News ServiceVisual China Group via Getty Images_jerome powell

CAMBRIDGE – Once a year, the leadership of both the European Central Bank and the United States Federal Reserve go to the mountains for policy enlightenment. The ECB conducts a forum every June in Sintra, a town in the foothills of the eponymous Portuguese mountain range. And the Fed convenes in late August in Jackson Hole, Wyoming, for the Kansas City branch’s economic symposium.

In retrospect, this year’s remarks from on high by ECB President Mario Draghi and Fed Chair Jerome Powell provide insight into the global outlook and the two banks’ recent policy actions, which have been coincident, but not coordinated.

In Jackson Hole, Powell named the challenge to the global economic outlook, not personally (US President Donald Trump), but operationally: heightened trade uncertainty, he said, presented a new drag on aggregate demand. Back in 2018, most Fed officials believed that 3% annual real GDP growth was unsustainable, because resource utilization was already taut. That assessment led the Fed to hike the policy interest rate by a quarter point four times.

That episode demonstrates the pitfalls of real-time policymaking. One year later, the Bureau of Economic Analysis trimmed almost half a percentage point from GDP growth for 2018, and the Bureau of Labor Statistics revised downward its estimate of monthly employment gains.

Among the mechanisms by which an increase in interest rates slows aggregate demand is the foreign-exchange market. When the Fed is set on tightening as other central banks hug the effective lower bound of their nominal policy rates, the dollar’s value rises.

Essentially, dollar appreciation is a channel through which policymakers “donate” domestic economic strength to US trading partners that now have weaker, more attractive currencies.

With the ECB’s policy rate distinctly negative and its asset-purchase program running out of steam, Draghi especially appreciated the gift of easier European financial conditions last year.

Of course, the transfer of domestic economic strength by an independent agency, the Fed, displeased the chief executive, and withering criticism ensued. But it was not Trump’s carping about dollar appreciation that led the Fed to change course.

Rather, Trump’s trade policies elevated uncertainty about investment and growth. Investment in long-term capital is always risky for a business. When doubt about such an investment emerges before concrete is poured, less concrete will be poured.

By early 2019, the Fed viewed this new economic headwind as obviating the need to continue raising the federal funds rate. As the year unfolded and the trade winds intensified, Fed officials switched course and began to ease policy.

Some economic mechanisms, however, are asymmetric. When the Fed tightens its policy, other central banks do not always follow, preferring to allow their currencies to depreciate. In contrast, when the Fed eases its policy, far fewer international partners are willing to let their currencies appreciate so that the dollar can depreciate.

No one volunteers because everyone fears upward exchange-rate pressure. An earlier generation of central bankers would have relied on direct intervention in the currency market to pursue the same goal. But while this is still done in emerging-market economies, the use of reserves by an advanced economy would draw its peers’ opprobrium. Instead, they achieve the same end by changing policy interest rates to deflect appreciation and welcome modest depreciation.

As a consequence, when the Fed pivoted, all other major central banks followed. Draghi pushed the ECB in that direction in Sintra and followed through with further easing on September 12.

This similarly drew Trump’s ire, as he viewed the move as directed toward the exchange rate.

He is right, indirectly. A weaker euro is the intermediate result Draghi seeks in order to support a flagging economy and move inflation up to the ECB’s target of near, but below, 2%.

The ECB’s response, of course, means less dollar depreciation, weakening the stimulus effect of the Fed’s move. And the consolation that by easing policy, the Fed single-handedly induced worldwide monetary accommodation does not get much credit from the White House. Trump would prefer that Powell were faster than his counterparts in the race to the interest-rate bottom.

Powell’s problem is that the US economy apparently does not require such stimulus. Job gains remain robust, and wages are ticking up. Global trade may be in recession, but the US economy is not as dependent as its trading partners on global trade.

Probably to Powell’s deep and never-to-be-expressed frustration, the Fed is setting monetary policy in a way that increases the likelihood that Trump will be reelected next year. That instruction is not contained in the Federal Reserve Act, of course, but the Fed is supposed to deliver maximum employment and stable prices. Its mandate of sustainable economic growth thus requires Powell to attempt to offset the effects of policy uncertainty under Trump.

Fed officials are not thinking of intentionally letting the economy stumble between now and the 2020 election. Thus, if Powell succeeds, Trump will not bear the cost of his words and actions. This will invite more of the same.

There is a reason that Powell often has a haunted look, and not just at Jackson Hole.

Carmen M. Reinhart is Professor of the International Financial System at Harvard University's Kennedy School of Government.

Vincent Reinhart is Chief Economist and Macro Strategist at BNY Mellon.

Taking Up Running After 50? It’s Never Too Late to Shine

People who start running competitively in their 50s can become as swift and well-muscled as older runners who have trained lifelong.

By Gretchen Reynolds

Credit Richard Baker/Getty Images

Men and women who start running competitively when they are in their 50s can be as swift, lean and well-muscled within a decade as competitive older runners who have trained lifelong, according to a buoying new study of the physiques and performances of a large group of older athletes.

The findings suggest that middle age is not too late to take up intense exercise training and begin banking many of the health and aging benefits of being an athlete.

Already, a wealth of research indicates that older athletes — known as masters — age differently than older people who are sedentary. Past studies show that competitive athletes in their 60s, 70s, 80s and even 90s tend to have more and healthier muscle mass, stronger hearts and much less body fat than non-athletes of the same age.

In essence, masters athletes represent “the model of healthy aging,” says Jamie McPhee, a professor of musculoskeletal physiology at Manchester Metropolitan University in England, who led the new study. “They have fewer long-term health conditions” than aging non-athletes, he says, “take fewer medications, have fewer hospital or medical visits, and their physical function is excellent.”

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But many past studies of masters athletes have looked at participants, mostly men, who have trained for decades. Whether those of us whose work and family commitments, disinterest, ill health or long-term procrastination discouraged us from being serious competitive athletes earlier can start training in midlife and catch up to longer-term competitors’ performance and health has not been clear.

So, for the new study, which was published in August in Frontiers in Physiology, Dr. McPhee and his colleagues decided to find out. For more than a decade, he and his colleagues already had been studying the muscle and bone health of masters athletes, especially runners. Each of these runners had been at least 60 when he or she joined the study, and the researchers’ data showed that most displayed substantially greater muscle mass — although not necessarily bone density — than less-active older people.

Now they turned to this existing trove of data to look into whether it mattered, for health and performance, when athletes started training. The scientists focused on older distance runners for whom they already had extensive data about body composition, including their bone densities, muscle mass and body-fat percentages, as well as answers to lengthy questionnaires detailing how often and intensely the athletes had trained in every decade of their lives, beginning at the age of 18.

The records also tracked each runner’s times and placings in major races from the past two years. The runners had competed in a variety of distances, from the 800 meters to the marathon.

The scientists now gathered records for 150 of these masters runners and divided them into two groups, depending on when the athletes had begun training. One group, the early starters (mostly men), had been racing throughout adulthood, having often begun running as teenagers.

The latecomers, on the other hand, had not begun training and racing until they were at least 50, leaving them 20 or 30 years behind the early starters. Interestingly, while both groups included men and women, the latecomers featured a much larger percentage of women.

The researchers also gathered data about the body compositions of 59 inactive older men and women from a separate, long-term study of health and aging to provide a control group.

Finally, the researchers compared the health and performance parameters of the runners in each group to one another and to the controls.

And they found surprisingly few distinctions among the athletes. The runners’ finishing times barely differed, whether they had been training for 30 years or fewer than 10. Both groups of runners also showed about 12 percent greater muscle mass in their legs than the inactive control group and about 17 percent less body fat.

Only with bone density were the latecomer runners at a disadvantage. Their spinal bone density tended to be lower than among the control group or the long-term runners (whose spinal densities were comparable). The reasons are not clear, Dr. McPhee says, although they could relate to this group’s gender makeup.

The upshot is that it does seem to be “possible to catch up with those who have trained several decades longer,” Dr. McPhee says.

There are caveats, though. This study did not look at sports other than running or at markers of health apart from muscles and bones. It also did not delve into participants’ genetics or their physiques before they joined the study, either of which could have uniquely fitted them to become older athletes and might not apply to the rest of us.