Sunday, June 25, 2017

Maxims

My son is going to college this fall, and I wanted to give him a set of my favorite quotations. As his name is Max, I titled the book 'Maxims.' With only a little work in formatting, I was able to create a paperback and ebook on Amazon, so that I basically did this for the price of the New York Times.  I made it for him, but you can buy it on Amazon for $5, or $3 for the Kindle version (see here).

Obviously, we want our kids to appreciate the things we think are really important. Whether our kids will agree with us is up to them, but we can hope. But as I'm starting to lose him to the real world, I wanted comfort that if I get hit by a truck, he'd have access to some quotes that can help him even if I can't. So, I put a bunch of quotes (around 710) I've been compiling for decades into a book, in 7 sections: Wisdom, Purpose, Virtue, Life, Psychology, Science, Politics. About 15% of the quotes are unattributed, because when I wrote them down, I lost the source, and it doesn't show up in a Google search (some obscure, some probably my artistic license).

Here's a sample:

Wisdom
  • Good judgment comes from experience, and experience comes from bad judgment. ~ Barry LePatner
  • Know thyself. ~ Delphic Oracle
  • Only the simplest mind can believe that in a great controversy one side was mere folly. ~ AJ Kane
  • The art of being wise is knowing what to overlook. ~ William James
  • The beginning of wisdom is this: Get wisdom. Though it cost all you have, get understanding. ~ Proverbs 4:7
Purpose
  • Seek, above all, for a game worth playing.   ~ Robert S. de Ropp
  • The best use of life is to spend it for something that will outlast it.   ~ William James
  • The deepest principle in human nature is the craving to be appreciated.   ~ William James
  • Purpose is what gives life a meaning.  ~ Charles Henry Parkhurst
  • If you buy the why, the how is infinitely bearable.   ~ Friedrich Nietzsche
Virtue
  • It’s easy to have faith in yourself and have discipline when you’re a winner, when you’re number one. What you got to have is faith and discipline when you’re not a winner.   ~ Vince Lombardi
  • Love is joy accompanied by the idea of its cause.  ~
  • Love is the only virtue that is an end in itself.  ~
  • It has been my experience that folks who have not vices have very few virtues.   ~ Abraham Lincoln
  • Gratitude is the healthiest of all human emotions. The more you express gratitude for what you have, the more likely you will have even more to express gratitude for.  ~ Zig Ziglar
Psychology
  • Many can bear adversity, but few contempt.   ~ Thomas Fuller
  • We all have the strength to endure the misfortunes of others.   ~ Francois de La Rochefoucauld
  • All anger is self-righteous anger. There are few cynical opportunists, more often ideologues and moralists.   ~
  • Anything you're good at contributes to happiness.    ~ Bertrand Russell
  • Men need sex more than women, and this gives women power over men.   ~ Midge Decter
Life
  • First one must live, then one may philosophize.  ~ Latin Proverb
  • Criticism is always a kind of compliment.   ~ John Maddox
  • Everything ends badly, otherwise it wouldn't end.   ~ Koglan the Bartender
  • Everything is always decided for reasons other than the real merits of the case.   ~ John Maynard Keynes
  • What is said when drunk has been thought out beforehand.   ~ Flemish proverb
Science
  • When a debater’s point is not impressive, he brings forth many arguments.   ~ The Talmud
  • Real thinking is that which can force you into an answer whether you liked it or not, and fake thinking is that which can argue for anything.   ~
  • The best way to have a good idea is to have lots of ideas.   ~ Linus Pauling
  • Laymen feel that facts are easy and theory is difficult. It is often the other way around.   ~
  • Bayes' theorem suggests that given two persuasive speakers, you will find those which most agree with you as most persuasive.   ~ Richard Posner
  • A little inaccuracy sometimes saves tons of explanation.   ~ HH Munro
Politics
  • To be free is to be subject to nothing but the laws.  ~ Voltaire
  • Our country's founders cherished liberty, not democracy.   ~ Ron Paul
  • Justice is Equality…but equality of what?.   ~ Aristotle
  • The more corrupt the state, the more it legislates.   ~  Tacitus
  • The most melancholy of human reflections, perhaps, is that on the whole, it is a question whether the benevolence of mankind does more good or harm.   ~ Walter Bagehot
  • Never forget that everything Hitler did in Germany was legal.   ~ Martin Luther King Jr.



Tuesday, April 11, 2017

Jordan Peterson's Business Cycle Theory

University of Toronto psychologist Jordan Peterson has gained YouTube notoriety recently for speaking out against social justice warrior lunacy. However, it should be noted that his book, Maps of Meaning, is a very profound one. I heard him give a talk in which he mentioned initially that he was interested in economics, but was put off by the undefended assumption that people want to maximize their wealth. Most people want to be wealthy, of course, but they also want to have a fulfilling life, which is much more complicated. The larger existential question is, “How do I invest my time now to have the best life?” This does not lend itself very well to mathematical expositions.

His basic premise is that we develop maps of meaning to identify our best life. The world appears to us not in the form of objects, but rather things with an inherent valence or meaning according to how well they help us thrive. Thus, a cliff-edge does not represent a mere vertical descent, but rather, physical danger. Our world is filled with things, people, and ideas that all have meaning to us, and we arrange them in a map according to the way in which we see they have interrelated effects on our lives.

Human life is a narrative quest, focused on a story we find useful and true, an arc that leads to something profoundly good and beautiful. We attend to things we believe will lead us to this objective most efficiently, and thus, all of our tactics, strategies, and summum bonum are tied in a speculative endeavor. This conscious mode of thought is grounded in a metaphorical language that derives from narratives that use universal archetypes in the way that a good fictional character can describe a universal existential problem better than dry prose (e.g., Job, Holden Caulfield, or Anakin Skywalker).

Eventually, our maps develop anomalies, events that do not fit, because maps are always simplifications of reality. A sufficiently large anomaly is similar to finding that your microscope is out of focus: everything becomes a blur, chaos, and it is not obvious in which direction to adjust the lens. It is difficult for people to improvise solutions to their problems, as failure cascades through a complex system. In such a scenario, the first response is to freeze, as rats will freeze initially when moved to a new cage, because all they know is that their current environment is unmapped. Gradually, they begin to sniff around for predators, food, and other rats. Once they perceive that the danger is diminished sufficiently, the rat begins to explore the cage and do normal rat things.

Thus, when you learn that your partner is unfaithful or you are not good at your job, you have to rethink some fundamental assumptions about the meaning of your partner and job, and adjust your life accordingly. This happens until death, hopefully less frequently as one becomes older, but certainly at least several times. Alas, many ignore anomalies using cognitive dissonance, but the cumulative cost of not facing one’s errors leads to frustration and existential angst (which explains many angry bloggers).

What is interesting is the way in which this epistemological process is akin to Gould and Eldridge’s punctuated equilibrium theory, in which evolution consists primarily of stasis, but occasionally of significant change. The maps people use to understand the world do not change in a linear way, as if they were updating their Bayesian prior daily, but rather people update only when their prior fails massively.

Peterson and Business Cycles

This seems relevant to business cycles. Previously, I riffed on Batesian mimicry, the idea that business cycles focus on new things each recession because idiot imitators and outright frauds are drawn to businesses that seem most bulletproof to pointy-headed bosses. Thus, just as colorful poisonous snakes imitate colorful non-poisonous snakes (who then save on metabolic resources with the same benefit), putting “dot-com” after your company’s name was a great strategy circa 1999, and giving mortgages to anyone with a pulse was a great strategy in 2005. Failure is endogenous in humanity’s crooked nature.

However, a problem remained; why does investment decline across industries in each recession? Why do industry-specific problems spread into disparate sectors such as consumer, electronics, housing, etc.? Using the Peterson theory, when something sufficiently anomalous happens, investors see chaos because it portends a major epistemic flaw in their maps. Thus, it is best to wait and see where their maps are wrong before investing more. Finding a new paradigm takes time. One rarely understands an anomaly fully, even in hindsight. Even today’s economists disagree about the essential cause of the Great Depression of 1929, or the more modest 1990 recession.

Thus, in real-time, anomalies reach tipping points that halt investment, because deferral is not nearly as costly as is a bad investment. Recessions last approximately 12 months on average, so eventually, people determine the anomaly’s essence or at least become comfortable with a new, imperfect map of the business landscape in which chaos is contained, and activity resumes. Contractions are not equilibriums, because people naturally want to explore the uncharted, as exploring is one of Jaak Panksepp’s instinctive mammalian affects. The breadth of a business cycle is the result of the catastrophic nature in the way in which we update our worldviews.

Yet, every 10 years, economists develop a new model that the next cycle does not fit. Consider the recession in 1975, which focused on oil, in 1982, on interest rates, in 1990, on commercial real estate, in 2001, the internet bubble, and in 2008, residential housing. Each one is sui generis, as the prior indicators fail to work. In the most recent crisis, when mortgage indices began to falter in early 2007, no economist thought it was a major problem because these had not led to recessions in the past. A few funds failed, but everyone thought it was contained.

Then, larger mortgage-related firms continued to fail, and it became clear that the problem was bigger than anyone thought. In the fall of 2008, it seemed that the sky was falling, as people were unclear whether the problem was centered on residential housing, all securitizations via some arcane math error (copulas), or some unknown financial positive-feedback loop. Even now there is no agreement on the genesis of the 2008 recession, and like the fall of Rome, the list will probably remain forever long, even though I believe the only necessary and sufficient condition was the flawed assumption about diversified housing prices (no one thought a nationwide nominal housing price decline was possible).

Interestingly, the government caused another anomaly by trying to make things better. In an effort to help solve the problem, the government instituted many new rules that made it easier for delinquent homeowners to remain in their houses without paying, and required a costly legal process for lenders to evict anyone (foreclosure now averages over 600 days). Further, the government filed numerous lawsuits that punished banks for making the same loans it had encouraged previously, but such is politics. Thus, mortgages went from a 90% to a 20% recovery rate assumption, and half of total bank profits in this recovery went to pay fines, so that banks have had one of the weakest post-trough recoveries of any expansion. This has contributed to our anemic expansion, and highlights that one also must foresee the clumsy government response to any anomaly, which makes the problem much more intractable.

At some point, the initial mortgage bond anomaly was seen as evidence of a substantive flaw in people’s business models, but it was unclear what that flaw was. They saw chaos and turtled in, froze like rats in a new cage, and investment declined across the board, which is the essence of a recession. When bad information arises, this causes all investors to put less weight on their Bayesian prior about the future, so expectations shift more as new information arises, which is why volatility increases during incipient downturns.

The Maps of Meaning business cycle model highlights some key characteristics of recessions that standard business cycle theories do not:


  • The anomalies were real, and exposed profoundly flawed assumptions in certain common business practices. Specific business models were not viable, evidenced by large sustained exits in key sectors after each recession. This is important, because some economists believe recessions are caused by self-fulfilling, but groundless shifts in expectations; many Keynesians lie here (see, sunspot theories of recessions).
    • After 1990, commercial real estate remained depressed for several years 
    • After 2001, many internet companies exited
    • After 2008, mortgage companies and housing suppliers exited
  • Anomalies arise in different parochial aspects of the economy. 
    • A macro model that looks at aggregates will miss the essence of the shock
  • Attempts to counter business cycles via aggregate policy never work.
    • Simply lowering interest rates, or increasing government spending does not address the issue, which focuses on a particular business flaw, not an aggregate one
    • Once the shock is understood and contained, investment resumes, not because of any top-down stimulus, but rather simply because of cognitive adjustment


When investors perceive a large anomaly in their Weltanschauung, their natural reaction is to stop investing in new things, because regime shifts take place after every cycle and if one occurs in your business, you are toast if you build a new plant or keep those workers you hired in anticipation of growth in your now-discredited map.

The Next Big One

It is difficult to predict the future, but I would suggest several large areas that seem unsustainable, yet have witnessed fantastic growth over the past several recessions.

Municipalities are accumulating large pension deficits because it is easy to promise future retirement packages and allow the next generation of politicians to deal with it. Historically, muni bonds have been rock-solid investments, but if they become risky and all municipals face a new default premium, how much would they have to cut back their expenditures?
College tuition has outpaced the inflation rate for two generations, and the number of people going to college also has been increasing, creating a massive increase in college revenue. Yet now, many graduate lack skills for which people are willing to pay (e.g., journalism), so that many students will never recoup their tuition and opportunity costs. When prospective students begin to realize this, an unprecedented downsizing will occur.
The Fed has increased the money supply at an unprecedented rate over the past 10 years, and 8 years into an expansion, the US federal deficit is 4% of GDP. European nations are no better, and several, such as the PIGS, are worse. This portends a government bond collapse and rampant inflation.
40% of US corn is used for ethanol, a low-octane, corrosive fuel that exists only because of federal mandates, and 15% is used in high-fructose corn syrup, which is inefficient and less healthy than are other sweeteners, and is propped up by federal mandates as well. The effect on our aquifers is unsustainable.

When one of these starts to blow, people will believe at first that it will be no problem, because none of these areas has been key in any business cycle since WWII. However, when firms continue to fail, panic will ensue across the board, because if one of these areas goes down, another, or all of them, might, and the most sensitive businesses related to these areas are not obvious (land for farmers? computers for students?). Further, municipal debt, colleges, and corn could be accelerators rather than instigators of the next recession, in the same way unprecedented US state defaults prolonged the 1837 recession until the mid-1840s. Eventually, as the effects are contained in certain sectors of the economy, the economy will recover; again, recession is not an equilibrium.

Unfortunately, this time it all will occur with a banking sector that is precluded from backstopping obvious market overreactions because of the Volker rule. This will enhance the downturn, but unlike the May 2010 flash crash, it will last much longer.

The take home lesson? Buy cybercurrencies.

Monday, January 09, 2017

Robeco's Pim van Vliet has a new Low Vol book


Pim van Vliet runs one of the oldest and most successful Low Volatility funds in the world, which has now flowered into Robeco’s Conservative Equities brand of funds. It is noteworthy that it is not referred to as “low volatility,” because when he began this strategy in 2006, low volatility was not a ‘thing.’ High Returns from Low Risk is targeted at airport readers and casual investors, and is a quick read—36k words—that makes a profound point: objectively, high volatility stocks are bad investments. 

In contrast, students are taught that expected returns are an increasing linear function of risk. If investment in riskier assets generates a higher return, the only reason to focus on low or high volatility is one’s risk preference. When combined with the idea of efficient markets, this implies that investing is actually very simple: choose how much risk you can tolerate, which dictates your expected return, and diversify accordingly. 


Alas, within most asset classes, highly risky assets generate consistently lower returns than do those with average risk, and, after transactions costs are included, risky asset classes, such as options, are horrible investments for individual investors, the more so the riskier the option. Risky assets attract excessive interest from investors, and academics help them rationalize this adverse preference through their extensions of the Capital Asset Pricing Model (CAPM), all of which are very rigorous, but wrong. On the other hand, these same investors are skeptical about market efficiency, which causes them to burn money by trading too much and realizing too many short-term capital gains (which are taxed at higher rates than are long-term capital gains).

From High Returns from Low Risk

Aristotle noted that courage is a virtue situated between the extremes of cowardice—a deficiency of courage—and rashness—an excess. Courage is a good thing, and a good life requires a modest amount of it, but it is foolhardy to take too much or too little risk: so too in investing. Yet every year, new investors enter the market and are attracted to highly risky assets, and because they are taught that this should generate higher-than-average returns even if they have no alpha, they are emboldened. Yet investing in high risk assets is ill advised for two reasons: they generate higher wealth volatility and have a lower expected return.

Pim was introduced to the benefits of low volatility investing when he read Robert Haugen, who indicated in several papers that higher risk stocks do not generate higher-than-average returns. As I was researching this before it became popular, I have a strong opinion on the unimportant issue of who discovered the low vol anomaly first. Haugen did not know what he had discovered until approximately 2008, when he kept seeing his 1991 and 1995 papers referenced by the growing low volatility crowd. Haugen always emphasized that markets were inefficient, so he was an early proponent of the “factor zoo.” After all, George Douglas (1969) and Richard McEnnaly (1974) found no risk premium, yet no one mentions them.

In contrast, Bruce Lehman’s Residual Risk Revisited (1990) noted how strange it was that everyone was convinced the market index’s imperfect proxy of the “true” market was obscuring the CAPM, yet this implies residual risk should generate a sizable risk premium, which it did not. That was a big dog not barking. Then there was Ed Miller’s 1977 paper, where a greater diversity of opinions generates a lower return for high volatility assets, and that diversity of opinion correlates with volatility. In 2001, he wrote in the Journal of Portfolio Management that one should invest in low volatility equities, full stop. This is really the first academic publication to champion low volatility, and the fact that he was influenced by his earlier theory is important, because without a story that one really believes, any particular correlation becomes one of many, as with Haugen. 

That is clearly a rabbit trail quibble, however, as Pim is a gracious fellow, and is quick to give credit when he can. Another example of this is that in which he credits his colleague, David Blitz, for noting that relative rather than absolute performance affects investment manager: underperforming is a greater threat to a long-only portfolio manager than is losing money. That is, if you lose 10% in a market that is down 10%, you did average, and your assets under management will probably not be decreasing. However, if you make only 5% in a market up 15%, assets will go down. Risk is symmetrical: it can be too great or too little, because if you take too little risk, you will underperform in bull markets, which is just as bad as those who take too much risk and underperform in bear markets.

Pim noted his dismay at this finding: it would not be an easy sell to tell investors that his low-vol tilt generates better returns merely because they have lower volatility, because they would have higher relative volatility. Yet this could be precisely why the strategy presents an opportunity, in that, for a portfolio manager, low risk is actually average risk, so risk averse professionals do not invest in low, but rather in average beta stocks (aka, closet indexing).

One of my ideas that Pim highlights is that envy is more important than greed. That is, the relative risk preferences peculiar to investment professional contracting exist in individual investors themselves, as they also are not maximizing returns subject to volatility constraints, but subject to relative volatility constraints. This makes the low vol effect more fundamental and less ephemeral. If greater low vol performance were merely an institutional inefficiency, we should expect mechanisms to circumvent that, such using a Sharpe Ratio rather than total return. Yet over time, the end-of-year lists of best managers are ranked invariably according to simple raw returns within their focus, which always encourages risk-taking via the convex rewards of being at the top.

The most prominent methods used to explain the high returns on high beta assets are partial equilibrium results, ignoring the implications in a more general setting: 

1)      Frazzini and Pedersen (2010) relied on a leverage constraint, as investors reaching for the equity premium try to grab more via a higher beta with the same dollar investment.
2)      Harvey and Siddique (2000) focused on people’s preference for stocks with high co-skew, which are like lottery preferences, or risk loving preferences.
3)      Ed Miller (1977) showed how the winner’s curse implies that assets with a high diversity of potential outcomes have lower returns.
There is some element of truth in these approaches, yet they are all deficient as definitive explanations, because they imply very counterfactual things. Academics focus on rigorous solutions because general solutions do not lend themselves to the type of clean models that journals like to publish (and not coincidentally, what academics like to work on), and science is all about simplifying things to identify fundamental laws. This is a salubrious division of labor, where academics do their thing and practitioners then implement these findings in a more ad hoc way given all the realities academics assume away. Yet here these models are profoundly inconsistent with other stylized facts that suggest a deeper problem, in the same way Bruce Lehman’s noting that idiosyncratic risk having no risk premium highlighted a deep problem to the standard model.

If we take the following three empirical facts:

1.      The equity market return premium is positive (3-6% annually)
2.      High beta stocks have lower-than-average returns 
3.      Average investors choose to be long—not short—high beta stocks
You then need all of the following non-standard assumptions to generate such a result:
1.      There must be systematic factor risk for both high and low beta equities
a.       If high beta stocks did not have a systematic risk exposure independent of the market,  arbitrage would ensure any beta premium is a linear function of beta
2.      Some investors must be maximizing relative risk
a.       If all investors were maximizing absolute risk, no rational investor would be long in the high beta equities, because they have strictly dominated Sharpe ratios.
b.      If all investors were maximizing relative risk equities would not have a return premium.
3.      Some investors should exogenously prefer high beta assets
a.       Without such investors, the high beta assets would have higher-than-average returns, even with relative preferences, because of the effects of the absolute risk preference investors.
Now, this is a messy result, but such is reality (I show this in a paper here). Frazzini and Pedersen’s model implies beta arbitrage (there's only one 'factor'), but a zero-beta portfolio long low beta stocks and short high beta stocks will generate considerable volatility. If that is anticipated, their pricing formula would then include this factor, and the high beta assets would have a greater-than-average return because of the high residual, yet non-diversifiable, volatility in high beta assets. In Harvey and Siddique’s world, for skewness to have a strong effect (e.g., 3% expected return reduction for high vol assets), either investors are risk loving globally, or the equity risk premium should be 15% (Pim published a paper on this here). In Miller’s world, there is a massive arbitrage available to those sufficiently hyper-rational to see this behavioral bias, in that they will adjust their ex ante estimation in light of their knowledge of the subjective valuation prior distribution, which implies massive inefficiency. As it is very difficult to make money in asset markets, assuming that the market is patently irrational is not very compelling.

It is important to note that the current situation is really less of a low volatility than a high volatility puzzle. It is easier to explain why the low volatility stocks have higher returns than expected by the CAPM, yet remain lower than the mid-volatility stocks, than it is to explain why the high volatility stocks have lower-than-average returns, yet people clearly are generally long them (e.g., popular broad indices include these positions). Most importantly, because high volatility stocks have such low returns, low vol targeting actually outperforms the market as a whole.

The flatness of the risk premium, when combined with the incentives to go long in the volatile stocks above, creates higher-than-average low volatility returns. With respect to the reasons why risky equities draw individuals or fund managers outside of a mean-variance or mean-relative variance approach, the list of potential reasons is quite long:

·         Information cheap. Risky stocks such as Tesla are in the news a great deal, and their large price fluctuations are indicative of new information (i.e., news). That makes it easier to generate an opinion, long or short, and because of difficulties in shorting stocks, most long investors are looking at those stocks they want to buy.
·         Lottery preference. This could be called the skewness preference (Harvey and Siddique). They are simply lottery ticket preferences, in that, just as the most extreme lotteries with 100 million payouts generate the highest revenues because they offer the greatest upside, stocks that generate large upsides offer the most interest to investors. Robert Sapolsky has noted that monkeys generate spikes in dopamine when they perceive random rewards, highlighting the addictive quality of gambling, and the convex nature of the human brain’s preference for “more” in stochastic contexts.
·         Long bias compliment. As most long equity investors tend to think equities will rise—otherwise they would be out of the equity market—it then follows that the higher beta stocks will do better in those environments. Indeed, if you invest only in high volatility assets during up months for the market as a whole, your Sharpe dominates a low volatility tilt.
·         Alpha overconfidence. If you have alpha, it makes sense to focus on stocks that can go up 40% rather than 20%; the bias towards high volatility stocks is rational contingent upon this assumption.
·         Alpha signaling. Recognizing that those who know they have alpha are investing in highly volatile stocks, investing in them—and getting lucky—is a way to sell yourself to potential investors. Investors see one’s focus on high volatility stocks as a consistent signal that the asset manager knows he has alpha.
·         Alpha discovery. If you wonder whether you have alpha, it is best to buy some volatile stocks, as it will be obvious after a year whether or not you do.
·         Winner’s curse. People will tend to buy stocks for which they have the highest relative expectation. Stocks with the greatest disagreement will tend to have the greatest volatility, and their owners will be those who are most biased (Ed Miller’s paper).
·         Agency problems. Portfolio managers often receive a quasi-call option on their strategy. To take an extreme example: portfolio managers are fired if they lose money, but if they make money, they receive 10% of the profits. Such a payoff is maximized when the underlying strategy has the highest volatility. A fund complex also faces this payoff, in that fund inflows are convex, so have many risky funds within every style category, some of which will be category winners.
·         Representativeness bias. To get rich, you have to take risk. Some faulty, but plausible, logic then implies that taking a lot of risk will make you rich. 
·         Leverage constraints. If you are constrained by regulations or conventions (e.g., 60-40 equity-bond allocation), and think the market is going up, then you can increase your return by allocating your equity in the higher beta stocks (Frazzini and Pedersen’s “betting against beta” model).
·         Ignoring geometric return adjustment. People should look at the expected total return, which is reduced by the variance. People should anticipate this by making this adjustment, but often do not, which favors the higher volatility stocks.
In summary, there are many reasons other than the standard model that draw people to high volatility stocks, which then hurts their returns on average. Pim discusses his introduction to stock investing and highlights how the biases above directed his interest into a particular volatile stock, one that he could readily form an opinion upon and that could potentially generate out-sized returns.

Back to Pim’s book: he presents a “law of three”—omne  trium perfectum—all good things come in threes. In this context, the law of three is low vol, momentum, and value. His value metric is a form of price-to-income ratio, such as dividend yield or P/E. I am skeptical of a law stating 3 is the cardinality of attributes for Platonic forms, but agree that, in this case, it is a handful and not a factor zoo of dozens.

His basic formula for generating a good long equity portfolio is first, to look only at those stocks with lower-than-average risk. He uses volatility, but one could use beta as well (they give similar results), and the benefit of using beta is that, because it is normalized cross-sectionally, one merely has to remember to target stocks with betas less than 1.0, rather than knowing the current median stock volatility (in the US, 30%).  A simple filter of excluding stocks with betas higher than 1 is great advice: it lowers risk and increases returns, and helps you avoid getting sucked into the biases listed above. If you constrain your stock picking to low risk stocks, you are swimming with the tide.

His portfolio formulation is refreshingly clear. First, normalize momentum and value using percentiles, sum them, apply to the “low vol” half of stocks, and viola, you have a darned good portfolio. He shows you can even do this using Google’s stock screener. Alas, or fortunately for Pim, this is difficult, and so if you really want to do this, it would be better simply to pay Robeco a fraction of a percent to do so, as they will be more diligent in monitoring the portfolio and adjusting for many issues not mentioned merely because they distract from his presentation. 

Pim notes that this “low vol” anomaly is not restricted to developed country equities. He has found it in emerging markets, and within equity industry sectors. He notes it has been found in corporate bondsequity options, movies and private equities, but he could have added penny stocks, IPOs, real estate, currencies, futures, and sports books.

Investors would be wise to follow simple rules for investing. Those with the humility that comes from wisdom will be relieved to know that they can optimize their investments by merely focusing on lower-than-average risk stocks that make money, generate dividends, and have performed well. Those who need the advice most—average equity investors—are least likely to take it, so I am not worried that a regime shift is in play.

Personally, I am not a big fan of momentum, as while it works over time, it fails massively on occasion, as in 2009, when we had an adverse 4-standard deviation event in the US. Nonetheless, I can see how one can look outside this case and find, in the words of AQR’s Cliff Asness, that value and momentum are “everywhere.” I do, however, prefer his method of putting metrics into percentile space rather than a Gaussian variable, in that expected returns are more linear in a percentile z-score. I also appreciate the fact that he does not create a hierarchy of factors, as do many, in which a “value factor” is a combination of 6 metrics (e.g., the Bloomberg Equity Model), which is then added to 5 other such composite factors.

If there is no risk premium in general so many seminal economic models are extinguished that it simply will not happen ('no science ever defends its first principles' Aristotle). Further, the fact that people are not so much greedy as envious highlights the fact that economics has a profoundly limited relevance, because while in practice people merely want to out-do their neighbors, this is not something anyone admits they should be optimizing, and certainly is infeasible for a society. Economists can explain behavior using profit maximization or cost minimization, because each is consistent with both greedy and envious utility functions, so it is useful for many parochial applications. 

Yet this constitutes a partial equilibrium analysis. To the extent that economists want to prove certain macro policies are socially optimal, they return to a utilitarian world in which people do not care about relative wealth. This is simply untrue, and is relevant to why high marginal tax rates are popular regardless of whether they would bring in more revenue: bringing the top down is sufficient motive for most people (why most people loathe the Laffer curve, as it highlights their base interest in a higher marginal tax rate). With this flawed assumption, macro-economists are no more profound than are historians when analyzing a 'macroeconomy', as their fundamental motivator—individual wealth, however broadly defined—is defective and so does not generalize.

I used to think it would be good to convince others that the standard utility model is wrong, but now I am happy to let the consensus exist in perpetuity because of both the Serenity Prayer (“focus on what you can do”), and professionally, I am all-in on low volatility. Pim van Vliet is not keeping this powerful economic insight secret, but I am confident that most people will ignore his advice to their detriment.