Lean Experiments

“In theory there is no difference between theory and practice. In practice there is.”

— Yogi Berra

The Scientific Method: A good idea is a hypothesis that has been validated in an experiment.

Human brains are wired for story-telling and for fight-or-flight decisions, not statistical uncertainty. We tell ourselves simple stories to explain complex things we do not and, most importantly, cannot know. The truth is that we have no idea which way the market is going most of the time, and whatever prediction we make is sure to be over-simplified, if not flat out wrong. Unaided, most humans traders perform poorly in today’s electronic markets.

This understanding is, of course, nothing new. Four hundred years ago, Francis Bacon warned that our minds not only struggle to interpret the world around us but also are wired to deceive us. “Beware the fallacies into which undisciplined thinkers most easily fall – they are the real distorting prisms of human nature.” Indeed, the human minds are programmed toward: “assuming more order than exists in chaotic nature.” We are especially susceptible to self-deception when it comes to statistics. The problem exists because we place too much weight on the odds that past events will repeat, when unrepeatable chance is a better explanation.

As an extension of common sense, mathematical reasoning allows us to see the hidden structures underneath the messy and chaotic surface of our world. It’s a science of not being wrong, hammered out by centuries of hard work and argument. Humans are prone to confirmation bias – trying to confirm what we want to be true. So a good antidote to human biases would be: instead of looking for reasons why you could be right, look for reasons why you could be wrong. In other words, determine where your biases are and attempt to remove them.

Taking the above into considerations, we now have a reasonable basis for running experiments that screen for good ideas, i.e., hypotheses that have been validated in a battery of experiments. But how do we formulate such hypotheses in the first place? And from where do we get ideas, i.e., lots of them?

Quantum Leap: How a newer breed of quants are predicting the future. (Image Credit: WSJ).

One approach that is used by firms like Two Sigma Investments involve programming its machines to cull torrents of information from sources like newswires, earnings reports, weather bulletins, and Twitter; and then build trading models and algorithms that make trading decisions based on “signals” extracted from those data. Practitioners of this newer approach to quantitative investing differs from traditional “quants,” who program their machines to bet on statistical relationships among security prices but don’t bother much with real-world information. Instead, the goal of these newer breed of practitioners is to get an advantage over human fund managers by writing algorithms that are smarter and faster than any human could be in scouring the world’s information, finding patterns, and making trading decisions in stocks, bonds, options, futures, or currencies.

To see how models are built using this approach, let’s consider supply and demand for coconuts in our island economy. Let's suppose that our island economy has by now developed the concept of money and has its own unit of currency. How the coconut market behaves is determined by the elasticities of supply and demand which, respectively, tell us how price sensitive coconut growers and coconut buyers are. Now suppose that from time to time there are spells of bad weather which make growing coconuts difficult. A casual observer on the island would notice that when the weather gets bad, the price of coconuts rises and the quantity produced and purchased falls. When the weather is good, prices are low and quantities are high. This islander will notice such patterns and the patterns will become part of her beliefs about the world she lives in. Of course, the islander may not understand why this pattern exists – she merely understands that the pattern does exist.

Now, let’s extend our supply and demand model a bit. Let’s now suppose that weather conditions on the island are somewhat persistent from year to year. If the weather is bad one year then it is likely to be bad the next year. In this case, when prices are high one year, they will tend to be high next year. High prices this year, for example, means that the weather must have been bad recently. Again, our casual observer will incorporate this pattern into her beliefs and again she would not be required to understand why this pattern exists. Suppose we add a futures market which co-exists with the coconut market. The futures market on the island operates every Friday and sells claims on future coconuts.

Suppose now the island soothsayer comes up with a model which explains the quantity and price variations in terms of supply and demand. Unbeknownst to this soothsayer, the model is actually true. The model provides a meaningful and accurate description of how the coconut market works. However, the model is not particularly useful for predicting future prices. The model says that if there is an adverse shift in supply, then quantities should fall and prices should rise. The amount of quantity and price change are governed by two parameters, i.e., elasticities of supply and demand. However, predicting future prices in this environment boils down to predicting the weather. On that score, the supply and demand model, despite being true, is of little help.

In contrast, quantifying the observable patterns in the data is definitely helpful for the purpose of forecasting future prices. In fact, the current price contains valuable information on the likely future price. A simple regression of the current price on the past price will provide futures market participants with enough information to price bets on future prices. If quantities and prices are measured with error, then the best forecast will make use of both quantity and price to predict the future price. In this environment, futures traders have no use for the supply and demand model, even though it provides key insights into how this coconut market works.

There is a caveat in that a change in weather pattern on the island would render useless statistical patterns that had prevailed in the past. If there were a subsidy to coconut growers on the island, the island soothsayer's supply and demand model would correctly predict that the average quantities would rise and average prices would fall. So the soothsayer's model is useful after all, but only during such times when the regime changes. During normal times, ad hoc statistical forecasting methods – methods devoid of any structural economic content but which have substantial predictive power – work reasonably well in making market predictions.

With the increasing reliance on such a “big data approach,” critics warned of the dangers of “backtest overfitting,” in which random correlations are interpreted wrongly as strong relationships, and of placing big bets on “spurious” relationships that are non-existent in the real world. Such criticisms can be easily understood through the lens of the coconut market example above. “Pseudo-mathematics,” according to David Bailey, a research fellow at the University of California, Davis, “is a large part of the reason why so many algorithmic and systematic hedge funds do not live up to the elevated expectations generated by their managers.” Of course, not all firms are alike. At firms that are setup to work like laboratories, scientifically rigorous methodologies are applied. And one could easily tell from results.

Look Ma! No Hands!

A more interesting approach to consider, we think, might be to constrain the universe of possible relationships based on a priori domain knowledge so as to allow an automated feedback loop to be introduced into the process workflow. For example, in the domain of currency trading, we might consider a universe of macroeconomic relationships amongst exchange rates, interest rates, capital flow, economic growth, inflation, unemployment, savings, and investment. Correlations and/or causal chains can be traced among these concepts based on our structural understanding of macroeconomics, augmented with published data and statistics. The corresponding strength of relationships can then be objectively measured and their significance ranked for automated decision-making by various currency trading models in the system.

We feel that trading strategies that are based on a deeper understanding of the logical cause-and-effect economic relationships that drive markets just seem a more prudent approach. Within such a constrained yet knowledge-rich universe, we can confidently let the machines perform independent experiments. I.e., machines can come up with macroeconomics-driven hypotheses based on recognized data patterns, test them with powerful computers, interpret findings without human guidance, and learn to make improved hypotheses in the next iteration. In other words, machines drive the entire process of running scientific experiments on multiple sources of real-time market data, with human oversight but no human intervention. As a key benefit, continuous improvements to the trading models can be made in real time based on performance results even as real-world events unfold.

Looking ahead, we believe the re-invention of invention in computational finance can be realized by expanding the epistemic base of financial technology so it can deliver improved quantitative tools for integrated study of a wide range of financial trading models embedded into a common macroeconomic simulation framework, which would in turn contribute to our deeper understanding of computational finance within the context of quantitative macroeconomics.

“The problem with QE is that it works in practice, but it doesn’t work in theory.”

— Ben Bernanke (2014)

References:

1. Hope, Bradley (2014, April 1). How Computers Trawl a Sea of Data for Stock Picks. Wall Street Journal. Retrieved from: http://www.wsj.com/articles/how-computers-trawl-a-sea-of-data-for-stock-picks-1427941801
2. Strasburg, Jenny (2012, May 21). Computer Trading Takes Human Turn. Wall Street Journal. Retrieved from: http://www.wsj.com/articles/SB10001424052702304791704577418363843211828
3. Schrage, Michael (2014). The Innovator’s Hypothesis: How Cheap Experiments are Worth More than Good Ideas. MIT Press.

4. Ellenberg, Jordan (2014). How Not to Be Wrong: The Power of Mathematical Thinking. Penguin Press.

Collective Intellect

“Anyone who has followed science in recent years has noticed something odd: science is less and less about a solitary scientist working alone in a lab. Scientists are working in networks, and those networks are gaining scope, speed, and power through the internet.”

— Carl Zimmer

Is there a better way to harness collective wisdom of the tribe? How might information and communication technologies (ICT) mediate interactions and collate diverse perspectives from tribe members?

In mathematical research, for example, there is an interesting case of how massively collaborative mathematics can rapidly improve upon the landmark result sparked by the insight of a solitary genius. Yitang Zhang, a lecturer at the University of New Hampshire, settled a long-standing open question about prime numbers on May 13, 2013 by demonstrating that even though primes get increasingly rare further out along the number line, one will never stop finding pairs of primes separated by at most 70 million.

Zhang’s finding was the first time anyone had managed to put a finite bound on the gaps between prime numbers. His result represents a major leap toward proving the centuries-old twin primes conjecture, which posits that there are infinitely many pairs of primes separated by only two (e.g., 11 and 13).

But why 70 million? There is nothing magical about the number, other than it served Zhang’s purpose and simplified his proof. While Zhang went for a nice round number, his method in fact gives 63,374,611 with just a little optimization. Soon, other mathematicians quickly realized that it should be possible to push this separation bound quite a bit lower, although not all the way down to two.

On May 28, simple tweaks to Zhang’s method brought the bound below 60 million. A flurry of activity ensued over a span of 5 days from May 30 to June 3, as mathematicians vied to improve on this number, setting one record after another: from 59,470,640 to 58,885,998 to 59,093,364 to 57,554,086 to 48,112,378 to 42,543,038 to 42,342,946 to 42,342,924 to 13,008,612 to 4,982,086 to 4,802,222. By June 4, Terence Tao, a Fields Medalist, set up a “Polymath project,” an open, online collaboration to improve the bound that attracted dozens of participants. For weeks, the project moved forward at a breathless pace. At times, the bound was going down every thirty minutes, according to Tao. By July 27, the Polymath team had succeeded in reducing the proven bound on prime gaps to a mere 4,680.

In a rather interesting turn of events, a post-doctoral researcher, James Maynard, working independently at the University of Montreal, has upped the ante. On November 19, just months after Zhang announced his result, Maynard presented an independent proof that pushes the gap down to 600. Maynard’s approach applies not just to pairs of primes, but to triples, quadruples, and larger collection of primes as well.

“Zhang’s work and, to a lesser degree, Maynard’s fits the archetype of the solitary mathematical genius, working for years in the proverbial garret until he is ready to dazzle the world with a great discovery. The Polymath project couldn’t be more different — fast and furious, massively collaborative, fueled by the instant gratification of setting a new world record.”

— Erica Klarreich (2013)

According to Tao, the solitary and collaborative approaches each have something to offer mathematics. “It’s important to have people who are willing to work in isolation and buck the conventional wisdom,” Tao said. Polymath, by contrast, is “entirely groupthink.” Not every math problem would lend itself to such collaboration, but this one did.

Fast and Furious: A whirlwind of mathematical activities bringing down the prime gap bound from 70 million to 600 in just half a year.

It turns out that Zhang’s constructive proof is very modular and involved three separate steps, each of which offered potential room for improvement on his 70 million bound. First, Zhang invoked some very deep mathematics to figure out where the prime numbers are likely to be hiding. Next, he used his result to figure out how many “teeth” his “comb” would need in order to guarantee that it would catch at least two prime numbers with its teeth infinitely often. Finally, he calculated how large a comb he had to start with so that enough teeth would be left after it has been snapped down to a condition of “admissibility” needed for catching prime numbers. The fact that these three steps could be separated made improving Zhang’s bound an ideal project for a crowd-sourced collaboration. People with different skills squeezed out what improvements they could.

So what is the secret formula of collaborative success through Polymath? The Polymath project attracted people with the right skills, perhaps more efficiently than if the project had been organized from the top down. “A Polymath project brings together people who wouldn’t have thought of coming together,” Tao said.

The culture of a Polymath project is such that everything was out in the open, so anybody could potentially contribute to any aspect. This allowed ideas to be explored from many different perspectives and allowed unanticipated connections to be made. Furthermore, a bedrock principle of the Polymath approach is that participants should throw any idea out to the crowd immediately, without stopping to ponder whether it is any good. It goes against people’s instincts, but great mathematicians make stupid mistakes, too. This makes the project much more efficient when everyone was more relaxed about saying “stupid things” in a supportive environment conducive to exploration and experimentation.

Is there any reason to think that a Polymath approach might not work for computational finance? What would be an ideal project for crowd-sourced collaboration in financial modeling or algorithmic trading?

References:

1. Klarreich, Erica (2013, May 19). Unheralded Mathematician Bridges the Prime Gap. Quanta. Retrieved from: https://www.quantamagazine.org/20130519-unheralded-mathematician-bridges-the-prime-gap/
2. Klarreich, Erica (2013, November 19). Together and Alone, Closing the Prime Gap. Quanta. Retrieved from: https://www.quantamagazine.org/20131119-together-and-alone-closing-the-prime-gap/
3. Surowiecki, James (2004). The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. Little, Brown.
4. Shirky, Clay (2008). Here Comes Everybody: The Power of Organizing Without Organizations. Penguin Press.
5. Nielsen, Michael (2008, July 17). The Future of Science. Retrieved from: http://michaelnielsen.org/blog/the-future-of-science-2/
6. Nielsen, Michael (2009, January 26). Doing Science Online. Retrieved from: http://michaelnielsen.org/blog/doing-science-online/
7. Gowers, Tim (2009, January 27). Is Massively Collaborative Mathematics Possible? Gowers’s Weblog. Retrieved from: https://gowers.wordpress.com/2009/01/27/is-massively-collaborative-mathematics-possible/
8. Gowers, Tim and Nielsen, Michael (2009, October 15). Massively Collaborative Mathematics. Nature, 461(7266), pp. 879-881. Retrived from: http://michaelnielsen.org/papers/mcm.pdf
9. Nielsen, Michael (2011). Reinventing Discovery: The New Era of Networked Science. Princeton NJ: Princeton University Press.
10. Klein, Gary (2013). Seeing What Others Don’t: The Remarkable Way We Gain Insights. Public Affairs.

Wisdom of the Tribe

“All of this has happened before. But the question remains, does all of this have to happen again?”

— Six and Baltar ("Battlestar Galactica", 2009)

Insight: Seeing what others don't.

Recently, Ben Bernanke inaugurated his new blog with a simple question: “Why are interest rates so low?” Bernanke argues that low interest rates are not a short-term aberration, but part of a long-term trend. Has the world economy entered a period of ‘secular stagnation’? So what are the macroeconomic forces and dynamics that underlie global capital flow in today’s modern economy? How might that affect currency exchange rates going forward?  The following excerpts from recent public discourse among the world’s leading economists reflect diverse, and sometimes contradictory, viewpoints that may provide interesting clues to our future:

“In modern economies short-term safe interest rates cannot fall appreciably below zero because of the possibility of currency substitution. So interest rates are not fully flexible in modern economies. Hence the possibility exists that no attainable interest rate will permit the balancing of saving and investment at full employment. This is the secular stagnation hypothesis first put forward by Alvin Hansen in the 1930s.”

— Lawrence Summers (2014)

Diagnosing an Economic Malaise: Global Savings Glut or Secular Stagnation or Both? (Image Credit: Economist).

“Wage and price flexibility may well exacerbate the problem. The more flexible wages and prices are, the more they will be expected to fall during an output slowdown, leading to an increase in real interest rates. Indeed, there is the possibility of destabilising deflation, with falling prices leading to higher real interest rates leading to greater output shortfalls leading to more rapidly falling prices, and onwards in a vicious cycle.”

— Lawrence Summers (2014)

“Currently, many major economies are in cyclically weak positions, so that foreign investment opportunities for US households and firms are limited. But unless the whole world is in the grip of secular stagnation, at some point attractive investment opportunities abroad will reappear.”

“If that’s so, then any tendency to secular stagnation in the US alone should be mitigated or eliminated by foreign investment and trade. Profitable foreign investments generate capital income (and thus spending) at home; and the associated capital outflows should weaken the dollar, promoting exports. At least in principle, foreign investment and strong export performance can compensate for weak demand at home.”

— Ben Bernanke (2015)

“In a world with integrated capital markets real rates anywhere will depend on conditions everywhere. If there are more countries tending to have excess saving than there are tending towards excess investment, there will be a global shortage of demand. In this case countries able to devalue their currencies will benefit from generating more demand. Policies that seek to stimulate demand through exchange rate changes are a zero-sum game, as demand gained in one place will be lost in another. Secular stagnation and excess foreign saving are best seen alternative ways of describing the same phenomenon.”

— Lawrence Summers (2015)

“There is some similarity between the global saving glut and secular stagnation ideas: Both posit an excess of desired saving over desired capital investment at “normal” interest rates, implying substantial downward pressure on market rates. Both can account for slower US growth: Secular stagnation works through reduced domestic investment and consumption, the global savings glut through weaker exports and a larger trade deficit.”

— Ben Bernanke (2015)

“Slow or negative growth in the working-age population means low demand for new investments, both in housing and in productive capital, and therefore reduces the natural rate of interest still further.”

— Paul Krugman (2014)

“Numerous items have disappeared from GDP because they are already provided for free with a smart phone – not only the print dictionary or encyclopaedia, but the music-playing capability that makes the separate iPod obsolete, the restaurant locator that makes the printed Zagat guide obsolete, the growth in companies like Uber and Lyft that may make the urban taxi obsolete, and many more… GDP has always been understated.”

— Robert Gordon (2014)

“Indeed, this proliferation of inventions should make us quite nervous about the price indices used to compute GDP figures. The theory of price indices is that an individual should be indifferent between living today and living in the past with the same real income. How many people would really be indifferent between earning $23,000 in 1984 and earning $50,000 in 2014?”

— Edward Glaeser (2014)

“Lower-priced capital goods means that a given level of saving can purchase much more capital than was previously the case. Information technology continues to decline rapidly in price and to account for a larger share of total capital investment. It is revealing that the iconic cutting-edge companies have traditionally needed to go the market to support expansion. Today, leading-edge companies like Apple and Google are attacked for holding on to huge cash hoards.”

— Lawrence Summers (2014)

“Start with the fact that people can do three things with their incomes. They can:

1. buy things to consume,
2. invest – that is, buy things that will boost their income in the future,
3. hoard – that is, hold on to some of the cash they were paid, or park more of their wealth in something else they value not because it gives them utility or boosts their future income, but rather simply serves as a safe-and-liquid-store-of-value, so they can boost their spending above their income at some point in the future.”

“Continue with John Stuart Mill’s 1829 insight when the quantity demanded of safe-and-liquid-store-of-value assets to hoard is greater than the quantity supplied, demand for consumption goods and services and for real produced capital assets will be less than the supply. Businesses will then lose money and people will get fired. When people get fired and you lose full employment, incomes and planned spending both drop economy wide.”

— Brad DeLong (2015)

“Another way of looking at the secular rise in joblessness is that it represents a failure of entrepreneurial imagination. Why haven’t smart innovators figured out ways to make money by employing the jobless?”

— Edward Glaeser (2014)

“Let the situation stew for long enough, and eventually somebody in the private sector will probably figure out some circuitous and way to put the unemployed to work making the safe-and-liquid-store-of-value assets people want to buy to hoard. But that may take a very long time. And it takes an especially long time when nobody sane trusts the promises of anybody in the private sector that this is in fact a safe-and-liquid-store-of-value asset that you can hoard and then sleep easy on.”

— Brad DeLong (2015)

"A bubble [is] an asset whose price exceeds the present value of its associated income stream. ... Bubbles are an alternative way for society to deal with excess saving when fiscal policy does not take up the challenge. Buying bubbly assets with the intention of selling them at a later date is an alternative route of saving for future consumption. When nobody wants to invest because r is below g, and hence buys bubbly assets, the price of these assets goes up, yielding windfall profits to their sellers who are therefore able to increase their consumption. This additional consumption restores the balance between supply and demand for loanable funds on the capital market. This explains why so many high-valued apartments in Shanghai are vacant. They are just bubbly assets, stores of value."

— Coen Tuelings and Richard Baldwin (2014)

“Low interest rate environments are known to be prone to speculative episodes and the emergence of financial bubbles. … financial bubbles increase wealth and asset values, alleviate the shortage of assets, and stimulate the economy. But the stimulus is temporary: the economy returns to the zero lower bound as soon as the bubble bursts. A financial bubble can therefore arise as an imperfect market solution to a shortage of financial assets. The solution is no panacea because it is temporary and comes with risks to financial stability.”

— Ricardo Caballero and Emmanuel Farhi (2014)

"The more price elastic the supply of a bubbly asset, the greater the risk that a bubble goes bust, as more and more people start investing in the production of the bubbly asset."

— Coen Tuelings and Richard Baldwin (2014)

“Infrastructure investments, even if not immediately paid for with new revenue sources, can easily contribute to reductions in long-term debt-to-income ratios because they spur economic growth, raise long-run capacity, and reduce the obligations of future generations. It is an accounting convention, not an economic reality, that borrowing money shows up as a debt, but deferring maintenance that will inevitably have to be done at some point does not.”

— Lawrence Summers (2015)

“… but there are reasons to be sceptical. Much infrastructure investment is now capital intensive. America’s infrastructure programmes have often been criticised for waste and inefficiency.”

— Robert Gordon (2014)

“A safe asset is one that is expected to preserve its economic value following bad macroeconomic shocks. It is extremely difficult for the corporate and financial sector of a shell-shocked economy to produce such assets. … when the securitisation capacities of the economy (understood to be the physical, institutional, legal, and reputational resources that are required to isolate safe financial assets from risky real assets) have been impaired.”

“On one hand, safe asset shortages shape corporations’ capital costs and create incentives to cut back on risky investment and to either accumulate cash, return money to investors through equity buybacks and dividend payments, or substitute towards safer or easier-to-securitise forms of investment, sacrificing output for safe asset production.”

“On the other hand, safe asset shortages also create strong incentives for the financial system to engage in subprime-like forms of financial engineering, which can be thought as the process of extracting a ‘safe’ tranche from inherently risky loans backed by systemically exposed real estate collateral. And as the recent crisis demonstrated, this process can go to extremes, leading to waves of ‘fake’ safe asset creation, followed by sudden and violent episodes of collective realisation of their actual riskiness.”

— Ricardo Caballero and Emmanuel Farhi (2014)

“In a national economy, if someone is saving money or paying down debt, someone else must be borrowing and spending the same amount for the economy to move forward. But after the bursting of a nationwide asset price bubble, those with balance sheets under water are not interested in increasing borrowing at any interest rate. There will not be many lenders either, especially when the lenders themselves have balance sheet problems. The lack of borrowers means a significant portion of the newly saved and deleveraged funds that are entrusted to the financial sector are unable to re-enter the real economy. This in turn means that those unborrowed savings become a leakage in the income stream and a deflationary gap for the economy. If left unattended, this deflationary gap will push the economy ever deeper into balance sheet recession, a highly unusual recession that happens only after the bursting of a nationwide asset price bubble.”

— Richard Koo (2014)

“We may be headed into a world where capital is abundant and deflationary pressures are substantial. Demand could be in short supply for some time.”

— Lawrence Summers (2015)

“Certainly, population growth is starting to fall in many countries, especially in the more advanced economies. Yet, the global population is still increasing. This would suggest that globally, there should still be ample investment opportunities if framework conditions are put right. This is where the role of the integration of Asian and African economies into the global economy becomes central. More than half of the world’s population is concentrated in a small circle in Asia, including China and India. The more they are integrated into the global economy, the more they should increase global demand, and the more opportunities for profitable investment should exist. To achieve this, a well-functioning financial system is critical. It would need to prevent excessive risk-taking while channeling savings to the right countries and deployments.”

— Guntram Wolff (2014)

“Fixed exchange rates facilitate business and communication in good times but intensify problems when times are bad. … The gold standard and the euro are extreme forms of fixed exchange rates, and these policies had their most potent effects in the worst peaceful economic periods in modern times. The point is that an exchange rate system is a system, in which countries on both sides of the exchange rate relationship have a responsibility for contributing to its stability and smooth operation. The actions of surplus as well as deficit countries have systemic implications. Their actions matter for the stability and smooth operation of the international system…”

— Barry Eichengreen and Peter Temin (“Fetters of Gold and Paper”, 2010)

“The Eurozone is in the process of transitioning towards a permanently smaller and more streamlined banking sector. This is leading naturally to the deepening of capital markets – if intermediation between savings and investment is taking place less through banks, then it must take place more elsewhere. This is a welcome development as it provides the impetus not only for a more diversified financing mix in Europe, but for the development of a genuine single capital market.”

— Juan Jimeno, Frank Smets, and Jonathan Yiangou (2014)

Connections that make sense of it all.

The supply of safe assets has fallen by half since the financial crisis, according to Caballero and Farhi. The price of safe assets such as the U.S. Treasury bonds depends, inter alia, on their supply and the safety preferences of financial investors. There are good reasons for supposing that both have shifted. During that time, pension funds, banks, and insurance companies were forced by regulators to increase their holdings of safe assets, leading to massive excess demand for safe assets. However, the financial technology for producing risk-free assets proved to be inadequate.

Not surprisingly, the risk-free interest rates dropped to a historic trough. What's more, negative interest rates have arrived in several countries. Indeed, the biggest effect of negative interest rates may be on currencies. Low interest rates pull down yields on all manner of local investments, encouraging both natives and foreigners to put their money elsewhere. As capital takes flight, the currency falls. To wit, the euro has fallen against the dollar by nearly 20% since the ECB introduced negative deposit rates; the krona fell to a six-year low against the dollar after Sweden adopted negative rates.

Is secular stagnation something to worry about, or just another passing fad? Will growth in the next decade or two be much lower than it was in the past? Predictions are hard to make, in particular about the future.

“This time I bet no. Mathematics. Law of averages. Let a complex system repeat itself long enough and eventually something surprising might occur.”

— Six ("Battlestar Galactica", 2009)

References:

1. Hansen, Alvin (1939, March). Economic Progress and Declining Population Growth. The American Economic Review, Vol. 29, No. 1, pp. 1-15. Retrieved from: http://www.jstor.org/discover/10.2307/1806983?sid=21105867837671&uid=2&uid=4
2. Summers, Lawrence H. (2014). U.S. Economic Prospects: Secular Stagnation, Hysteresis, and the Zero Lower Bound. Business Economics, Vol. 49, No. 2. Retrieved from: http://larrysummers.com/wp-content/uploads/2014/06/NABE-speech-Lawrence-H.-Summers1.pdf
3. Tuelings, Coen and Baldwin, Richard (2014). Secular Stagnation: Facts, Causes and Cures. Centre for Economic Policy Research (CEPR). CEPR Press. Retrieved from: http://www.voxeu.org/sites/default/files/Vox_secular_stagnation.pdf
4. Rognlie, Matthew (2015). Deciphering the Fall and Rise in the Net Capital Share. BPEA Conference Draft (March 19-20, 2015). Retrieved from: http://www.brookings.edu/~/media/projects/bpea/spring-2015/2015a_rognlie.pdf
5. Eichengreen, Barry and Temin, Peter (2010, July). Fetters of Gold and Paper. NBER Working Paper 16202. Retrieved from: http://www.nber.org/papers/w16202.pdf
6. Krugman, Paul (1998, August 14). Baby-Sitting the Economy. Retrieved from: http://www.pkarchive.org/theory/BabySittingCantAvoidRecessions.html

Neither Brains nor Brawn

“Without the industrialisation of agriculture the urban Industrial Revolution could never have taken place – there would not have been enough hands and brains to staff factories and offices. … The modern capitalist economy must constantly increase production if it is to survive, like a shark that must swim or suffocate.”

— Yuval Noah Harari (“Sapiens”, 2015)

The wheels of industry grinds on for about two centuries after the Industrial Revolution and before long, according to Yuval Harari, humanity encounters a new problem: “As those factories and offices absorbed the billions of hands and brains that were released from fieldwork, they began pouring out an unprecedented avalanche of products. Humans now produce far more steel, manufacture much more clothing, and build many more structures than ever before. In addition, they produce a mind-boggling array of previously unimaginable goods, such as light bulbs, mobile phones, cameras and dishwashers. For the first time in human history, supply began to outstrip demand. And an entirely new problem was born: who is going to buy all this stuff?”

The Great Apes have brains and brawn aplenty.

Who needs humans for organized production, anyway?

Recall that industrialists and investors alike will go bust if there is not enough people to buy whatever new stuff industry produces. The wheels of industry grinds to a halt if there is no further growth on planet Earth. Consumerism can only do so much to delay, but not to forestall, the inevitable. After all, there are only seven billion of us on planet Earth, and not everybody can afford to, or would want to, pay for everything that industry produces. There is in addition a biological limitation to human consumption of food, drinks, clothing, books, movies, games, and toys. What this means is that the capitalist creed of always to ‘invest for growth’ would one day becomes untenable for an increasingly larger swath of the capitalist economy.

What would happen when surplus capital accumulated over the last two hundred years begins to chase after increasingly smaller returns or riskier opportunities for investment around the globe? The phenomenon of zero interest rates that we have witnessed in recent times may well be the harbinger of a new normal for capital in the 21st century. When this day approaches, capital for production reverts to wealth for consumption, the long-term uptrend of the stock market disappears and the economy goes back to being a basically zero-sum game just as it was a mere two hundred years ago before the Industrial Revolution.

“Capitalism distinguishes ‘capital’ from mere ‘wealth’,” explains Yuval Harari in his book Sapiens: A Brief History of Humankind. According to Harari, “Capital consists of money, goods and resources that are invested in production. Wealth, on the other hand, is buried in the ground or wasted on unproductive activities. A pharoah who pours resources into a non-productive pyramid is not a capitalist. A pirate who loots a Spanish treasure fleet and buries a chest full of glittering coins on the beach of some Caribbean island is not a capitalist. But a hard-working factory hand who reinvests part of his income in the stock market is.”

Indeed, the idea that ‘profits from production should be reinvested in increasing production’ sounds simple enough today. Yet, according to Harari, it was an alien concept for most people throughout history. Before the modern era, people believed that production was more or less constant. So why reinvest your profits if production won’t increase by much, no matter what you do? Better to spend revenues on tournaments, banquets, palaces and wars, and on charity and monumental cathedrals, than reinvest surplus into increasing their manors’ output, developing better kinds of wheat, or looking for new markets. And that’s exactly what medieval noblemen did. Capitalism later changed all that.

Capitalism began as a theory about how the economy functions. It offered an account of how money worked and promoted the idea that reinvesting profits into production leads to faster economic growth. Historically, labor as an economic factor of production has always been in critical shortage since the dawn of human civilization. Therefore, it would be quite unimaginable to kings, priests, or merchants a thousand years ago that peace would reign across most of the world’s population for any period of time and that human unemployment would be a social and economic problem despite the benefits of higher education. By extension, for industrialists and investors today to imagine a world thirty to fifty years into the future where capital would be ‘under-deployed’ because there are comparatively fewer places to put surplus capital into productive use would be equally unthinkable. But it is a plausible scenario, short of wars (i.e., arms-dealing) or natural disasters (i.e., reconstruction), that could have potentially destabilizing socio-economic consequences. After all, our modern capitalist economy on planet Earth depends on an unwavering belief in long-term growth with seemingly no end in sight. But just how realistic is this expectation of never-ending growth on planet Earth?

The prevailing optimism and unquestioned belief in a technology driven world of the future generating bounties for the masses of humanity is comforting thought. Evolving technology continues to drive down the marginal costs of basic goods and services in a race to the bottom. However, the underlying premise of such a techno-worldview skews towards addressing technological bottlenecks of production, and ignores the inherent biological limits of consumption by humans. Throughout most of history, humans live in conditions of scarcity. Therefore, the assumption by classical economists that human wants and needs are unbounded and will never be satisfied may seem reasonable and logical. However, in the coming age of ever greater abundance in production when supply outstrips demand for the first time in human history, can we still make such a general assumption about human wants and needs? It might be argued that there is no easy cultural remedy for moderating human consumption beyond simple affordable luxuries. Conspicuous consumption by definition incurs massive waste of resources that would be unsustainable at a global scale; and the mass media is certainly of no help in this area. Whichever way one chooses to study the situation, the problem of the 21st century remains: who is going to buy all this stuff, despite their falling prices?

Now imagine also a future some time in this century where humans are no longer needed for production. The machines have taken over and everything is fully automated, from tricorders in every home to self-driving cars to hamburger-flipping robots. The paramount question then becomes one of existential purpose: what are humans still good for, beyond consumption of goods and services that industry produces? Do we as humans then simply live to consume, just so the industrial machinery and the capitalist economy continues to hum along? And how does one make a living in such a futuristic world where neither human brains nor brawn are needed anymore? What remains of humanity as we know it? There are no easy answers. We simply do not know, but we can certainly speculate.

Let’s consider a representative brainy profession, e.g., a white-coated scientist working in a lab, along with a traditionally blue-collar profession that requires mostly brawn but also human skills and judgment, e.g., a hard-hat machine operator at a construction site. We look for objective evidence today that might give us some clue as to what the future might hold for these two professions at opposite ends of the spectrum from brains to brawn. For example, are the days far off before machines would start building scientific hypotheses all by themselves – unassisted by humans – which they in turn can proceed to test automatically? When and how might machines operate in a construction site that currently employs human operators in hard hats? The answer actually came sooner than anyone might expect; it had already happened. In fact, we are in the midst of a quiet revolution, i.e., industrial automation, that sees humans becoming gradually displaced by machines across many industries in the name of lower costs and greater efficiency.

Meet Adam: World's First Robot Scientist (and its Creator)...

... and Eve: World's Second Robot Scientist (by the Same Creator).

Meet Adam, the world’s first robot scientist. Built at the Universities of Aberystwyth in 2009, Adam is the first machine ever to discover novel scientific knowledge independently of its human creators. Adam is able to perform independent experiments, up to 1,000 a day, with the genome of baker’s yeast to test hypotheses and interpret findings without human guidance. To his credit, Adam had already uncovered three novel genes that together coded for an orphan enzyme. A second generation robot scientist, Eve, follows in 2015 and is designed at the University of Manchester and Cambridge to automate early-stage drug development, i.e., drug screening, hit conformation, and cycles of hypothesis learning and testing. Eve is capable of screening over 10,000 compounds per day, and has already discovered a compound that could be used to fight malaria.

The future lab may very likely be staffed by “teams of humans and machines,” according to Ross King who led the design of Adam and Eve. “Robots will be doing more and more of actual experimental work and simple cycles of hypothesis generation. Humans would migrate to more strategic and creative positions.” But with advances in artificial intelligence, it is conceivable that the role of robots would sooner or later encroach upon the human realm, progressing from lab technician to lab head. After all, as King commented, “there isn’t any intrinsic reason why that wouldn’t happen.”

Skycatch + Komatsu: A lethal combination of brains and brawn in construction sites around the world.

But what about work at a construction site that require both human brawn and brains? To improve productivity while also solving a potential shortage of construction workers in Japan, construction-equipment maker Komatsu developed the idea of letting drones and driverless bulldozers do part of the work. It is said that bulldozers are more difficult to operate than many other construction equipment. Typically, it takes about three years of experience before a human worker is considered qualified and competent to operate bulldozers. According to Komatsu, a unmanned team of drones, bulldozers and excavators can automate much of the early foundation work on construction sites. Under Komatsu’s plan, drones made by Skycatch would scan job sites from the air and send images to computers to build three-dimensional models of the terrain. Komatsu’s unmanned bulldozers and excavators would then use those models to carry out design plans, digging holes and moving earth. Both the drones and construction equipments would move along largely pre-programmed routes. All that a human needs to do is to program the machines, then simply push a button to send the machines to work and monitor their progress.

The upshot of it all is that in the future there will be fewer scientists and bulldozer operators who will be buying stuff that industry produces. But the alternate scenario arising from an apocalyptic world could easily be far worse.

Final destiny? Matrix human farm harvests humanity's energy: attention, intention, enthusiasm, dreams, and desires.

Woe to us humans when neither brains nor brawn nor blood matter anymore in the brave new world of silicon and software and steel. Should this fateful day eventually arrive, from among the many possible outcomes of our future history, know that we have brought it upon ourselves through our collective actions and complicity in the name of technology and progress, and shed no tears. The slippery slope of industrial automation may well turn out to be another trap for humanity after all. Only time will tell.

“The two most important days of your life are the day you are born and the day you find out why.”

— Mark Twain (1835-1910)

References:

1. Harari, Yuval Noah (2015). Sapiens: A Brief History of Humankind. Harper.
2. Piketty, Thomas (2014). Capital in the Twenty-First Century. Belknap Press.
3. Rifkin, Jeremy (2014). The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism. Palgrave Macmillan. Talk at: https://www.youtube.com/watch?v=5-iDUcETjvo
4. Buchen, Lizzie (2009, April 2). Robot Makes Scientific Discovery All by Itself. Wired. Retrieved from: http://www.wired.com/2009/04/robotscientist
5. Nicas, Jack (2015, January 20). Drones’ Next Job: Construction Work. Wall Street Journal. Retrieved from: http://www.wsj.com/articles/drones-next-job-construction-work-1421769564
6. Construction Equipment for the Future and with a Future. Komatsu Report 2013. Retrieved from: http://www.komatsu.com/CompanyInfo/ir/annual/pdf/2013/ar13e_03.pdf