Michael Mauboussin on the Santa Fe Institute and Complex Adaptive Systems

Michael Mauboussin of Credit Suisse is one of the best strategists on Wall Street and a thought leader who consistently introduces some of the most compelling topics to the financial community. It is therefore no surprise Mauboussin is now Chairman of the Board of Trustees at the Santa Fe Institute, an organization which specializes in the multi-disciplinary study of complex adaptive systems. I recently had the privilege of interviewing Mauboussin about his involvement with the Santa Fe Institute and his thoughts on complexity. Enjoy (and be sure to follow the links to some fascinating further readings):

Elliot: Now that you’re Chairman of the Board of Trustees at the Santa Fe Institute, what are your goals and visions for how to more broadly inject SFI’s lessons on complexity into the financial community’s understanding of markets?

Michael: In my role at SFI, the primary goal is to make sure that the Institute does, and can, do great science. The unifying theme is the study of complex adaptive systems. But the goal is to have a place where there’s support for important, transdisciplinary research. 

That said, I would love to continue to see this type of thinking work its way into our understanding of financial markets. That is happening to some degree. One example is Andrew Lo’s work on the Adaptive Market Hypothesis. Another example is Blake LeBaron’s work on markets using agent-based models. I think it’s a more complete way of viewing markets than a standard rational agent model or the assumption of the absence of arbitrage. The problem is that modeling complex adaptive systems is a lot messier than those other approaches.

Elliot: When we last met at an event introducing The Success Equation to SFI members in New York, I asked you what the right “success equation” is for a young investor. Your response was to “keep coming to these events.” How did you first learn about the Santa Fe Institute? And how did you come to embrace the SFI?

Michael: I first learned about SFI in 1995 at a Baltimore Orioles baseball game, where Bill Miller was my host and the proselytizer. He explained how this new research group dedicated to the study of complex systems was coming up with cool and useful insights about business and markets. Specifically, he was taken with Brian Arthur’s work on “increasing returns.” This work showed that under some conditions returns actually move sharply away from the mean. This is counter to classic microeconomic thinking that assumes returns are mean-reverting. 

In many ways I was primed for the message. I had been doing a lot of reading, especially in the area of science, and so this way of thinking made sense to me from the beginning.

Elliot: Did you have a bias towards one market philosophy before you adopted the complex adaptive system mental model?  

Michael: Although I had a solid liberal arts background before starting on Wall Street, I had very little background in business or finance. As a result, I had few preconceived notions of how things worked. It’s a challenge to come up with clear conclusions based on an observation of what happens in markets. On the one hand, you see clear evidence that some people do better than the indexes and that there are patterns of booms and crashes over the centuries. These suggest that markets are inefficient. On the other hand, there’s also clear evidence that it’s really hard to beat the market over time, and that the market is more prescient than the average investor. So for me, at least, there was an intellectual tug of war going on in my head. 

I have to admit to being struck by the beauty of the efficient markets hypothesis as described by the economists at the University of Chicago. At the forefront of this, of course, was Eugene Fama, who recently won the Nobel Prize in part for his work in this area. What’s alluring about this approach is that it comes with a lot of mental models. You can equate risk with volatility. You can build portfolios that are optimal relative to your preference for risk. And so forth. Because you can assume that prices are an unbiased estimate of value, you can do a lot with it. The market’s amazing ability to impound information into prices impresses me to this day.

So it was with this mental tug of war as a backdrop that I learned about the idea of complex adaptive systems. Suddenly, it all clicked into place. A simple description of a complex adaptive system has three parts. First, there are heterogeneous agents. These can be ants in an ant colony, neurons in your brain, or investors in a market. Second, these agents interact leading to a process called “emergence.” The product of emergence is a global system that has properties and characteristics that can’t be divined solely by looking at the underlying agents. Reductionism doesn’t work. 

What instantly drew me to this way of thinking is that it describes markets very well and it is very common in nature. The central beauty of this approach is that it provides some sense of when markets are likely to be efficient—in the classic sense—and when inefficiencies will creep in. Specifically, markets tend to be efficient when the agents operate in a truly heterogeneous fashion and the aggregation mechanism is working smoothly. Diversity is essential, both in nature and in markets, and the system has to be able to take advantage of that diversity. There are some neat examples in experimental economics to show how this works. It’s really wondrous. 

On the flip side, when you lose diversity the system can become very inefficient. And that’s also what we see in markets—diversity loss leads to booms and crashes. Now the loss in diversity can be sociological, in which we all start to believe the same thing, or it can be technical, such as the winding up or winding down of a leverage cycle. But here we have a framework that accommodates the fact that markets are pretty darned good with the fact that they periodically go haywire. And SFI was at the center of this kind of thinking.

Elliot: It’s interesting that your answer on what theory of markets you subscribe to is not in the “black or white” vein whereby one must be in one camp and one camp only. It seems like much of the divisiveness in today’s discourse (in many arenas) stems from people’s unwillingness to see these kinds of shades of grey, though as you suggest, that mentality is not for everyone.  Do you meet resistance from people when explaining your stance? Is there a way to get others to embrace “complexity” when people have an innate desire for linear, orderly explanations that are essentially either/or answers? 

Michael: Most of us are uncomfortable with ambiguity—we’d rather just have a point of view and stick to it. But in markets, the real answer clearly lies between the folks who believe that markets are perfectly efficient and those who believe it’s largely inefficient. By the way, if you think the market is mostly inefficient there is no reason to participate because even if you have a sense that you are buying a dollar at a discount there is no assurance that the market will ever recognize that value. So some degree of market efficiency is essential even for those who believe that markets have inefficiencies. 

My goal is less to get people to change their view and more to establish a better understanding of how things work. Once you learn about markets as a complex adaptive system and appreciate its implications, I find it difficult to go back to a more traditional point of view.  

Elliot: In More Than You Know, you said, “The best way to describe how I feel following a SFI symposium is intellectually intoxicated.” Are there steps you take following these events to transform the ideas you’ve learned and the relationships you’ve built into expanding the scope of your own knowledgebase? And how are you able to harness this intoxication into productive output? 

Michael: I wish I could be more systematic in this regard, but I think it’s fair to say that the ideas from SFI have permeated every aspect of my work. Perhaps a couple of examples will help make the point.

I’ve already mentioned conceptualizing markets as a complex adaptive system. This alone is a large step, because rather than simply moaning about the limitations of standard finance theory, you have a framework for thinking about what’s going on.

I’ve also already mentioned Brian Arthur’s work on increasing returns. Many businesses are being defined less by their specific market segment and more by the ecosystem they create. And it is often the case that in a battle of ecosystems, one will come out on top. So this set of steps provides a mental model to understand the process of increasing returns and, as important, how to identify them in real time.

Ideas from SFI have inspired my work in many other ways, from understanding power law distributions in social systems to network theory to collective decision making to the processes underlying innovation. I could go on. But suffice it to say that there is hardly an area of markets, business, or decision making where your thinking wouldn’t be improved by learning, and internalizing, the kinds of ideas coming out of the SFI.

Elliot: In More Than You Know, you also introduce Charlie Munger and SFI as “Two sources in particular [that] have inspired my thinking on diversity. The first is the mental-models approach to investing, tirelessly advocated by Berkshire Hathaway's Charlie Munger. The second is the Santa Fe Institute (SFI), a New Mexico-based research community dedicated to multidisciplinary collaboration in pursuit of themes in the natural and social sciences.” It seems only natural that adopting Charlie Munger’s perspective to mental models would lead one to the SFI. Can you talk about the synergies between these two worldviews in making you a better analyst? What role did your adoption of Munger’s framework play in your attraction to the SFI?

Michael: Charlie Munger is a very successful businessman. Probably the first thing to note about him is that he reads constantly. He’s a learning machine. There’s bound to be a good outcome if you dedicate yourself to reading good stuff over a long period of time. That alone should be inspiring.

So as I think about the synergies between the worldviews, a few thoughts come to mind. First, it’s essential to provide your mind with good raw material. That means exposing yourself to a lot of disciplines and learning the key tenets. It also means spending time with people who think differently than you do. 

Second, you have to be willing and able to make connections. What are the similarities between disease and idea propagation? What can an ant colony teach me about innovation? What do physical phenomena, such as earthquakes, tell us about social phenomena, such as stock market crashes? You need good raw material to make connections, but you also have to be careful to avoid superficial links.

Finally is the idea of thinking backwards. Munger is a big advocate for this. You observe that something is where it is: How did it get there? Why did it get there? There are some fascinating challenges in this regard right now. We know, for example, that the sizes of cities and companies follow power laws. Why? By what mechanism does this happen? No one really knows, and the prospect of solving those kinds of challenges is exciting.   

But I have to finish with the point that this approach to the world is not for everyone. The interest or capability to work in this fashion is far from universal. So I wouldn’t recommend this to everybody. Rather, I would encourage it if you have a proclivity to think this way.  

Elliot: You talk of a benefit of the mental models approach as having a diverse array of models that you can fit a given situation, rather than fitting a given situation to a one-size-fits-all model.  Can you shed some insight on a) how you built up your quiver of models; b) how you organize these models (either mentally or tangibly); and c) how you choose which model to use in a given situation?

Michael: Yes, I think the metaphor is that of a toolbox. If you have one tool only, you’ll try to apply it to all of the problems you see. And we all know people who are just like that.

The mental models approach seeks to assemble a box with many tools. The idea is to learn the big ideas from many disciplines. What are the main ideas from psychology? Sociology? Linguistics? Anthropology? Biology? And on and on. In many cases you don’t have to be a deep expert to get leverage from a big idea. One of my favorite examples is evolution. Spend some time really understanding evolution. It is a mental model that applies broadly and provides insights that other approaches simply can’t.

I’m not sure I’m much of an example, but I have strived to read widely. This in part has been inspired by the people and ideas I have encountered at SFI. Most of my organization comes through writing or teaching. For me, that is a way to consolidate my understanding. If I can’t effectively write or teach something, I don’t understand it. Now I’m sure I write about things I don’t understand as well, but I try my best to represent the science as accurately as possible.

As for choosing the right model, the key there is to look for a fit. One concept that intrigues me is that nature has taken on and solved lots of hard problems, and there’s a lot we can learn from observing how nature works. So you might learn how to run a committee more effectively if you understand the basic workings of a honeybee colony. Or you might have insight about the resources your company should allocate to experimentation by examining ant foraging strategies. 

The risk is that you take the wrong tool out of the toolbox. But I think that risk is a lot smaller than the risk of using the same tool over and over. I’ll also mention that the work of Phil Tetlock, a wonderful psychologist at the University of Pennsylvania, suggests that so-called “foxes,” people who know a little about a lot of topics, tend to be more effective forecasters than so-called “hedgehogs,” those with a single worldview. So not only is this an intellectually appealing way to go, there’s solid evidence that it’s useful in the real world. 

Elliot: When you cite how Brian Arthur’s work “showed that under some conditions returns actually move sharply away from the mean. This is counter to classic microeconomic thinking that assumes returns are mean-reverting.” It makes me think about feedback loops and this passage from More Than You Know: “Negative feedback is a stabilizing factor, while positive feedback promotes change. Too much of either type of feedback can leave a system out of balance.” Positive feedback loops are seemingly the force that drives conditions away from the mean. How can we think about feedback loops in a more constructive way and are there steps that we can take to understand when/where/how they will appear?  As a follow-up, is there a good mental model for thinking about when and how breakpoints appear in feedback loops?

Michael: There’s been a great deal written about this idea, albeit not necessarily using this exact language. One classic work on this is Everett Rogers’s book, Diffusion of Innovations. He was one of the first to describe how innovations—whether a new seed of corn or an idea—spread. From this a lot of other ideas emanated, including the idea of a tipping point, where momentum for diffusion accelerates. 

The Polya urn model is also useful in this context. A basic version of the model starts with balls of two colors, say black and white, in an urn at some ratio. You then randomly select one ball, match it with a ball of the same color, and replace it. For example, say you started with 3 black balls and 3 white balls, so 50 percent of the balls are black. Now you draw a ball, observe that it’s black, and return it to the urn with an additional black ball. So the percentage of black balls is now 57 percent (4/7). 

This urn model is very simple but demonstrates the principles behind positive feedback nicely. Specifically, it’s nearly impossible in advance to predict what’s going to happen, but once one color gets ahead sufficiently, it dominates the outcomes. (You can play a little more sophisticated version here.) It’s interesting to hit the simulator over and over to simply observe how the outcomes vary.

Another area where this model pops up is in format, or standard, wars. The classic example is Betamax versus VHS, but there are plenty of examples throughout history. Here again, as one standard gets ahead, positive feedback often kicks in and it wins the war.   

Now I don’t think there’s any easy way to model positive feedback, but these are some of the mental models that may help one consider what’s going on.

Elliot: You talk about Munger’s advice to think backwards and invert. I think your first book was Expectations Investing which provided a framework for estimating the embedded assumptions in an equity’s price. Yet you also warn that this way of thinking isn’t for everyone. Was this something you realized after sharing the ideas with many or were you always aware of this? Do you have any ideas for why this has a relatively narrow audience? Is there a natural tie-in to the behavioral biases of humans and why this doesn’t work for everyone? (For example, the human proclivity towards the narrative bias to explain past events) And if so, how can we think backwards more rationally and overcome these biases?

Michael: Steven Crist, the well-known handicapper, has a line about horse race bettors in his essay, “Crist on Value,” that I love to repeat. He says, “The issue is not which horse in the race is the most likely winner, but which horse or horses are offering odds that exceed their actual chances of victory. This may sound elementary, and many players may think they are following this principle, but few actually do.” Take out the word “horse” and insert the word “stock” and you’ve captured the essence of the problem. 

Our natural tendency is to buy what is doing well and to sell what is doing poorly. But as Crist emphasizes, it doesn’t really matter how fast the horse will run, it matters how fast the horse will run relative to the odds on the tote board. Great investors separate the fundamentals from the expectations, and average investors don’t. Most of us are average investors.   

My advice, then, is to try to be very explicit about segregating the fundamentals and the expectations. Sometimes high expectations stocks are attractive because the company will do better still than what’s in the price. Great. That’s a buy. Sometimes there are stocks with low expectations that are dear because the company can’t even meet those beat down results. That’s called a value trap. So, constantly and diligently ask and answer the question, “what’s priced in?” Doing so is very helpful.  

Navigating the Global Economy - Buttonwood Gathering 2013

I had the privilege of attending The Economist’s Buttonwood Gathering 2013 replete with a stacked lineup of speakers and panelists. At a conference such as Buttonwood, one of the most interesting elements is the opportunity to exchange ideas with attendees who are generally pretty brilliant in their own right. I had numerous conversations with other Gatherers on topics ranging including Mexico’s pro-market reforms, Canada’s housing bubble, the European banking environment, and much more. Measuring consensus on such topics at Buttonwood provides a great glimpse into what the “Smart Money” is thinking. Sure enough, smart money seems abundantly optimistic in Mexico’s steps toward and capacity to successfully implement said reforms, Canada’s housing bubble is very real, and the European banking environment will have to pivot from an arena of nationalistic-driven excess to centralized decency.

These are simply some topics I conversed about with fellow gatherers. The panels themselves covered a wide range of topics, from the global economy, to the emerging market landscape and today’s optimistic venture capital environment for technology. While it’s impossible to completely cover each topic and the panelists’ thoughts in this post, I want to share some of the points that were more striking and relevant to me personally in the themes and topics that I focus on.

The two-day event kicked off with a conversation on the “global economic outlook” between José Manuel González-Páramo, Robert Rubin and Nemat Shafik, moderated by Zanny Minton Beddoes. All the panelists echoed the theme that Europe was improving and a decent coefficient of global growth was moving from emerging back to developed markets. Robert Rubin took a strikingly pessimistic tone towards the US growth outlook, given his belief that the conventional narrative of a fiscal drag was overstated and the real problem remains lack of demand and therefore anemic consumption. Shafik explained how there is increasing decoupling and dispersion amongst the various emerging markets and how each unique country thought of in its own unique way. Gonzalez-Paramo mused that Europe had the greatest potential to outperform estimates in the coming months should the relevant parties continue on the path towards a formalized banking union.

The discussion on Europe offered a natural segue into the second panel covering “Europe’s Burden” with José Manuel Campa, Bruce Richards and Nicolas Veron. Véron explained how the stress tests in Europe would be completely different this time around. Rather than pure stress tests, the exercise would be an intensive Asset Quality Review (AQR) done by the ECB instead of the European Banking Authority. Richards seconded this sentiment, and noted that the EBA tests were “laughed at.” Richards further explained how Europe’s banks have $42 trillion in assets compared to a GDP of $13 trillion, far larger than the US, which has $15 trillion in assets on a GDP just shy of $17 trillion. While Europe’s economy “has bottomed” it will take time for the banks to grow out of their size problem, with the US Savings and Loan Resolution Trust Corporation wind-down offering the best analog. Richards called Europe today “the largest asset disposition in the history of the world” and said the opportunity is in the very early stages, with assets like Spanish Non-Performing Loans available for 3 cents on the dollar.

Next, Roger Altman and Thomas Horton spoke about the changing corporate landscape in the US. Altman insisted that “uncertainty” in the business community stemmed predominantly from a shortfall in demand in the economy and not from Washington. The biggest trend Altman has been watching is the rise in activism amongst shareholders, and the willingness of institutional shareholders to embrace activist proposals. Meanwhile, Horton opined that US tax policy’s limitations on repatriation offered a significant hurdle to prudent balance sheet management in corporate America and that regulatory uncertainty has been a particularly large obstacle for him personally in helping American Airlines emerge from bankruptcy.

Day two started with an interesting discussion on monetary policy between Mohamed El-Erian and Vincent Reinhart. Both gentlemen generally agreed that central bank policy cannot create supply, but that it can move demand. In this context, the risk/reward balance of further quantitative easing has shifted decisively towards the direction of risk, with little reward. While the Fed has emphasized the importance of forward guidance, they completely underestimated the market’s interpretation as to when tapering would begin. El-Erian worries that in this environment, people are being “pushed, not pulled into trades.” Reinhart stressed that in the future Yellen Fed, there will place a greater focus on the dual mandate. Further, she will take it as her responsibility to provide guidance that is both broader in scope and deeper in explanation.

Next, Jim Millstein and Mary Schapiro talked about the financial regulatory environment. Millstein highlighted how in Too Big To Fail, there is no market discipline happening in either the equity or debt markets for banks. As such, there is no natural free market check on these institutions considering debt is subsidized with the TBTF guarantee and equity is too large for an activist to impose changes. Ultimately, Millstein sees finance heading towards a more utility-like role in the economy. Schapiro expressed some concern that while a stronger regulatory regime has been constructed, it has effectively been rendered toothless by a lack of funding, but that ultimately she was optimistic regulators will find a middle ground and bridge some of the gaps present between political goals and regulatory reality.

Japan was next up in the Gathering’s coverage of global economies. Koichi Hamada and Paul Sheard both shared their belief that Abenomics so far is working, particularly on the monetary policy side. Hamada noted that excess capacity to GDP declined from 3% to 1.5% and inflation actually started moving in the right direction for once. The problem, both agreed, is that little light has been shed and little progress made on supply side reforms that are ultimately necessary for Abenomics to truly work. Both believe that in time this will happen, but for now, Abe will have to combat an entrenched and powerful bureaucracy to get his way. Sheard made the point that no central bank in world history has tried to dislodge deflation expectations knowing it will inevitably have to re-anchor inflation to a 2-2.5% target. Japan has plenty of room to do more when compared to the Fed, as the US central bank increased its balance sheet by 250% during the course of the crisis, in contrast to Japan’s 54% increase. Both explained how while many worry about Japan’s “demographic” challenges,” Japan does have an opportunity in that women make up a smaller percentage of the workforce than in most developed countries and there is considerable room to improve.

Robert Shiller and Lewis Alexander then held an interesting discussion about bubbles. Shiller started with a definition of a bubble: they are a price-mediated feedback between prices and market participants, with excessive enthusiasm, media participants, and regret from those who are not involved. The “psycho-economic phenomenon” is a defining characteristic that becomes ingrained in a culture and is related to long-term expectations that cannot be pinned down quantitatively. Alexander offered a distinction between those bubbles that are a systemic risk verse those that are not. Bubbles carry systemic risk only when they have a credit component. Thus, in the absence of a credit component, the risks of a bubble are not all that severe for society at large. The housing bubble was one such systemic risk event, though both emphasized this was clearly not the fault of the Federal Reserve Bank (as many skeptics proclaim). Home prices began their rise in 1997 and continued to rise even during periods within which the Fed was raising interest rates. Shiller explained that there simply was no correlation at all between the path of rates and home prices, and that the efficient market hypothesis was the real culprit for inducing a sense of complacency in market observers that all prices are rational. Further, right now, people are calling for bubbles everywhere and they can’t all be the Feds fault, as is evidenced by what Shiller said is “most likely” a bubble in Brazilian real estate. Though Alexander cautioned that the problem with monetary policy is how it is a “blunt tool” and influences all or nothing with regard to price, so some distortions can happen. These distortions are mainly in interest rate risk, not credit risk right now and he does not see accompanying systemic risk as a result.

The two Bagehot Lectures were given by Agustín Carstens and Alan Greenspan. Carstens discussed the role of emerging market central banks in a crisis environment. Central banks should continue to focus on keeping inflation under control, and could use some macroprudential policies to offer a countercyclical buffer, though such policy should be used “like tequilia--only in moderation.” EM central banks also should play a supervisory role to regulate the flows of currencies and help mitigate volatility, but monetary policy can’t do all this on its own. Many EMs need serious structural reforms and it’s unfortunate that these needs are only recognized on the down side of the cycle, not the up. This is equally true in other areas. For example, Mexico opened a permanent line of credit with the IMF when times were good, while now countries who would benefit from such a line don’t want to do so for fear of appearing to “need” it and in the process, looking vulnerable. Alan Greenspan then took to the stage. He explained how there is a significant bifurcation in our economy whereby capital investment of a less than 20 year duration is doing quite well and of greater than 20 years is in a deep slump. Greenspan believes this is the result of uncertainty in long-term planning and blames tax policy as the culprit. Right now in the US we are seeing one of the greatest spreads ever in term structure between 5 and 30 year Treasuries and this is a reflection of the gap between the short and long-term economies.

In the ensuing panel on fiscal priorities with Roger Ferguson, Laura D’Andrea Tyson and Carmen Reinhart, D’Andrea Tyson quickly launched into her rebuttal of Greenspan’s argument. She explained how the fiscal stimulus relative to GDP was rather small, and the premature austerity undertaken by the government since emerging from crisis has made the recovery slower than it needs to be. There is considerable excess capacity in our economy, and this is a far bigger culprit in weak long-term investing than anything else and this uncertainty is over demand, not politics. Carmen Reinhart agreed with most of these points and added that private sector deleveraging continues to be a headwind to growth. She also noted that the US has done particularly well relative to others around the globe, but worries about how the US will unwind it’s large fiscal deficit when all is said and done. Ferguson elaborated on how big the private sector short-fall was during the crisis and how much more the government could have stimulated the economy instead of leaving monetary policy as the “last man standing” to help. He complained that “politicians are acting Ricardian in a Keynesian world” and hurting, rather than helping our cause. He and D’Andrea Tyson remarked on how the negative real interest rates on Treasuries offer a serious opportunity for the government to borrow and invest in much-needed infrastructure projects, but unfortunately everyone in a position to do something is focused on discretionary spending as a problem when it’s really entitlements. If only discourse were more rational.

While this is hardly an exhaustive summary of the Buttonwood Gathering, these were some of the more relevant discussions on topics that I am concerned with. I took fairly extensive notes during the two days, and if anyone would like some more insight on any of the specific panels discussed here (or those that I didn’t mention), please feel free to leave a comment below or email me and I will be sure to answer.

Links for Thought -- October 18th, 2013

What Happens When you Don't Buy Quality? (Fundoo Professor) -- This is a great presentation on the investment benefits of buying so-called "quality" companies (in contrast to pure "value"). Quality companies are those with high returns on invested capital and a sustainable business model. This post is replete with great quotes from Warren Buffett and anecdotes from Fundoo's own investments. A true must read.

Millennials: Coming of Age in Retail (Goldman Sachs) -- h/t to @montoyan for sharing this great piece on the brand affinities and trends amongst millennial shoppers. As we know, the retail landscape is changing dramatically with the transition from big boxes to the web. This report takes it to the next step by highlighting the concurrent impact of this huge demographic shift on the retail industry. 

You're Probably Overpaying to Invest (Jason Gilbert CPA) -- My partner at RGA Investment Advisors wrote this great piece explaining how people who invest in what they think are "low-fee" endeavors are really just missing exactly when/where/how many levels of fees are taken from their account each year. People really need to pay more attention to the fees embedded within investments on top of those they pay to advisors.

The Economic Implications of Corporate Financial Reporting (NBER) -- h/t to @jesse_livermore for the find. It was shocking to learn that "55% of managers would avoid initiating a very positive NPV project if it meant falling short of the current quarter's consensus earnings." Positive NPV projects by definition increase the intrinsic value of a business and yet the majority of managers who are entrusted with a fiducuary duty to do just that will turn down projects for what? Meeting BS analyst estimates. What an outrage!

Look for Value...in Price Signals? (Capital Spectator) -- It's amazing how closely 5 year rolling returns track the inverse CAPE. This is a point Antti Ilmanen made very effectively in his lecture at this year's Santa Fe Institute Risk Conference (see my notes on it here). Here is another good look at this same effect, also analyzing the role that value and momentum play in returns.

Taxes Raise Bar for a Hedge-Fund Bet (Bloomberg) -- Matthew Klein takes a great look at how comparing hedge fund returns is not really an apples to apples kind of thing. I wrote about this very same topic in my recent "Buffet, Soros and Uncle Sam" post, and it's great to see one of the better writers in the mainstream financial media pick up on this topic. After-tax returns are what most investors really make, so why is more emphasis not placed on this reality? Probably because a lot of people make a lot of money off of limiting our awareness to the topic.

Vox's New Mega-Round Puts a Bow on Content's "Holy Shit" Moment (Pando Daily) -- An interesting look at the massive sums some young web-based content companies are raising. We are definitely seeing the rise of new media, though this reeks of froth to me. How is it that new companies garnering the same, or even fewer eyeballs than traditional media stalwarts are raising money at valuations in excess of some profitable old guys? 

Horizon Kinetics Q3 Commentary (Horizon Kinetics) -- These guys write some of the best commentaries. Here is a really important look at how indexing and ETFs lead investors into investments they simply are not aware of. For example, when you buy EWP you're not really buying Spain, you're actually making a big investment in Latin America. Take a look at some other areas where what you buy is not what meets the eye.

The Soaring Cost of a Simple Breath (NY Times) -- This one hits home, as I was a childhood asthmatic who luckily outgrew the problem. The last time I was using inhalers, they were generics. Fast forward a decade, I now have an asthmatic cat (Freckles) whose inhalers cost about $300 a pop. I never realized how/why this all happened until reading this article covering some big catalysts for our cost problems here in the US.

10 Years Later, Steve Bartman Remains a Tragedy (Deadspin) -- I really feel bad for the guy. Seriously, there were players in the game, with a role in controlling the outcome who messed up far worse than Bartman (looking at you Alex Gonzalez), yet the young fan ended up the goat. How is it that not one player stepped up to take responsibility for the team's collapse in order to protect what really is just an innocent fan?

I've been loving the band Little Feat lately and can't believe I didn't listen to more of them until now. Here is a great version of Dixie Chicken, until next week, enjoy:

Learning Risk and the "Limits to Forecasting and Prediction" With the Santa Fe Institute

Last October, I had the privilege to attend Santa Fe Institute and Morgan Stanley's Risk Conference, and it was one of my most inspiring learning experiences of the year (read last year's post on the conference, and separately, my writeup of Ed Thorp's talk about the Kelly Criterion). It's hard not to marvel at the brainpower concentrated in a room with some of the best practitioners from a variety of multi-disciplinary fields ranging from finance to physics to computer science and beyond and I would like to thank Casey Cox and Chris Wood for inviting me to these special events.  

I first learned about the Santa Fe Institute (SFI) from Justin Fox's The Myth of the Rational Market. Fox concludes his historical narrative of economics and the role the efficient market hypothesis played in leading the field astray with a note of optimism about the SFI's application of physics to financial markets. Fox highlights the initial resistance of economists to the idea of physics-based models (including Paul Krugman's lament about "Santa Fe Syndrome") before explaining how the profession has in fact taken a tangible shift towards thinking about markets in a complex, adaptive way.  As Fox explains:

These models tend to be populated by rational but half-informed actors who make flawed decisions, but are capable of learning and adapting. The result is a market that never settles down into a calmly perfect equilibrium, but is constantly seeking and changing and occasionally going bonkers. To name just a few such market models...: "adaptive rational equilibrium," "efficient learning," "adaptive markets hypothesis," "rational belief equilibria." That, and Bill Sharpe now runs agent-based market simulations...to see how they play out.

The fact that Bill Sharpe has evolved to a dynamic, in contrast to equilibrium-based perspective on markets and that now Morgan Stanley hosts a conference in conjunction with SFI is telling as to how far this amazing multi-disciplinary organization has pushed the field of economics (and importantly, SFI's contributions extend well beyond the domain of economics to areas including anthropology, biology, linguistics, data analytics, and much more). 

Last year's focus on behavioral economics provided a nice foundation upon which to learn about the "limits to forecasting and prediction." The conference once again commenced with John Rundle, a physics professor at UC-Davis with a specialty in earthquake prediction, speaking about some successful and some wrong natural disaster forecasts (Rundle operates a great site called OpenHazards). Rundle first offered a distinction between forecasting and prediction. Whereas prediction is a statement validated by a single observation, forecasting is a statement for which multiple observations are required for a confidence level.

He then offered a permutation of risk into its two subcomponents. Risk = Hazard x exposure.  The hazard component relates to your forecast (ie the potential for being wrong) while the exposure relates to the magnitude of your risk (ie how much you stand to lose should your forecast be wrong). I find this a particularly meaningful breakdown considering how many colloquially conflate hazard with risk, while ignoring the multiplier effect of exposure.

As I did last year, I'll share my notes from the presentations below. Again, I want to make clear that my notes are geared towards my practical needs and are not meant as a comprehensive summation of each presentation. I will also look to do a second post which sums up some of the questions and thoughts that have been inspired by my attendance at the conference, for the truly great learning experiences tend to raise even more questions than they do offer answers.

Antti Ilmanen, AQR Capital

With Forecasting, Strategic Beats Tactical, and Many Beats Few

Small, but persistent edges can be magnified by diversification (and to a lesser extent, time). The bad news is that near-term predictability is limited (and humility is needed) and long-term forecasts which are right might not setup for good trades. I interpret this to mean that the short-term is the domain of randomness, while in the long-term even when we can make an accurate prediction, the market most likely has priced this in.

Intuitive predictions inherently take longer time-frames. Further, there is performance decay whereby good strategies fade over time. In order to properly diversify, investors must combine some degree of leverage with shorting. Ilmanen likes to combine momentum and contrarian strategies, and prefers forecasting cross-sectional trades rather than directional ones.

When we make long-term forecasts for financial markets, we have three main anchors upon which to build: history, theory, and, current conditions. For history, we can use average returns over time, for theory, we can use CAPM, and for current conditions we can apply the DDM. Such forecasts are as much art as they are science and the relative weights of each input depend on your time-horizon (ie the longer your timeframe, the less current conditions matter for the inevitable accuracy of your forecast).

Historically the Equity Risk Premium (ERP) has averaged approximately 5%, and today's environment the inverse Schiller CAPE (aka the cyclically adjusted earnings yield) is approximately 5%, meaning that 4-5% long run returns in equity markets are justifiable, though ERPs have varied over time. Another way to look at projected returns is through the expected return of a 60/40 (60% equities / 40% bonds) portfolio. This is Ilmanen's preferred methodology and in today's low-rate environment the prospects are for a 2.6% long-run return.

In forecasting and market positioning, "strategic beats tactical." People are attracted to contrarian signals, though the reality of contrarian forecasting is disappointing. The key is to try and get the long-term right, while humbly approaching the tactical part of it. Value signals like the CAPE tend to be very useful for forecasting. To highlight this, Ilmanen shared a chart of the 1/CAPE vs. the next five year real return.

Market timing strategies have "sucked" in recent decades. In equity, bond and commodity markets alike, Sharpe Ratios have been negative for timing strategies. In contrast, value + momentum strategies have exhibited success in timing US equities in particular, though most of the returns happened early in the sample and were driven more by the momentum coefficient than value. Cheap starting valuations have resulted in better long-run returns due to the dual forces of yield capture (getting the earnings yield) and mean reversion (value reverting to longer-term averages). 

Since the 1980s, trend-following strategies have exhibited positive long-run returns. Such strategies work best over 1-12 month periods, but not longer. Cliff Asness of AQR says one of the biggest problems with momentum strategies is how people don't embrace them until too late in each investment cycle, at which point they are least likely to succeed. However, even in down market cycles, momentum strategies provided better tail-risk protection than did other theoretically safe assets like gold or Treasuries.  This was true in eight of the past 10 "tail-risk periods," including the Great Recession.

In an ode to diversification, Ilmanen suggested that investors "harvest many premia you believe in," including alternative asset classes and traditional capital markets. Stocks, bonds and commodities exhibit similar Sharpe Ratios over long time-frames, and thus equal-weighting an allocation to each asset class would result in a higher Sharpe than the average of the constituent parts. We can take this one step farther and diversify amongst strategies, in addition to asset classes, with the four main strategies being value, momentum, carry (aka high yield) and defensive.

Over the long-run, low beta strategies in equities have exhibited high returns, though at the moment low betas appear historically expensive relative to normal times.  That being said, value as a signal has not been useful historically in market-timing.

If there are some strategies that exhibit persistently better returns, why don't all investors use them? Ilmanen highlighted the "4 c's" of conviction, constraints, conventionality and capacity as reasons for opting out of successful investment paths.

Henry Kaufman, Henry Kaufman & Company

The Forecasting Frenzy

Forecasting is a long-term human endeavor, and the forecaster in the business/economics arena is from the same vein as soothsayers and palm readers. In recent years, the number of forecasters and forecasts alike has grown tremendously. Sadly, forecasting continues to fail due to the following four behavioral biases:

1. Herding--forecasts minimally fluctuate around a mean, and few are ever able to anticipate dramatic changes. When too many do anticipate dramatic changes, the path itself can change preventing such predictions from coming true.
2. Historical bias--forecasts rest on the assumption that the future will look like the past. While economies and markets have exhibited broad repetitive patterns, history "rhymes, but does not repeat."
3. Bias against bad news--No one institutionally predicts negative events, as optimism is a key biological mechanism for survival. Plus, negative predictions are often hard to act upon. When Kaufman warned of interest rate spikes and inflation in the 1970s, people chose to tune him out rather than embrace the uncomfortable reality. 
4. Growth bias--stakeholders in all arenas want continued expansion and growth at all times, even when it is impractical.

Collectively, the frenzy of forecasts has far outpaced our ability to forecast. With long-term forecasting, there is no scientific process for making such predictions. An attempt to project future geopolitical events based on the past is a futile exercise. In economics, fashions contribute to unsustainable momentums, both up and down, that lead to considerable challenges in producing accurate forecasts.

Right now, Kaufman sees some worrying trends in finance. First, is the politicization of monetary policy, and he fears this will not reverse soon. The tactics the Fed is undertaking today are unprecedented and becoming entrenched. The idea of forward guidance in particular is very dangerous, for they rely entirely upon forecasts. Since it's well established that even expert forecasts are often wrong, then logic dictates that the entire concept of forward guidance is premised on a shaky foundation. Second, monetary policy has eclipsed fiscal policy as our go-to remedy for economic troubles. This is so because people like the quick and easy fixes offered by monetary solutions, as opposed to the much slower fiscal ones. In reality, the two (fiscal and monetary policy) should be coordinated. Third, economists are not paying enough attention to increasing financial concentration. There are fewer key financial institutions, and each is bigger than what used to be regarded as big. If/when the next one fails, and the government runs it through the wind-down process, those assets will end up in the hands of the next remaining survivors, further concentrating the industry.

The economics profession should simply focus on whether we as a society will have more or less freedom going forward. Too much of the profession instead focuses on what the next datapoint will be. In the grand scheme of things, the next datapoint is completely irrelevant, especially when the "next" completely ignores any revisions to prior data.  There is really no functional, or useful purpose for this type of activity.

Bruce Bueno de Mesquita, New York University

The Predictioneer's Game

The standard approach to making predictions or designing policy around questions on the future is to "ask the expert." Experts today are simply just dressed up oracles. They know facts, history and details, but forecasts require insight and methods that experts simply don't have. The accuracy of experts is no better than throwing darts. 

Good predictions should use logic and evidence, and a better way to do this is using game theory. This works because people are rationally self-interested, have values and beliefs, and face constraints. Experts simply cannot analyze emotions or account for skills and clout in answering tough geopolitical questions. That being said, game theory is not a substitute for good judgment and it cannot replace good internal debate.

People in positions of power have influencers (like a president and his/her cabinet). In a situation with 10 influencers, there are 3.6 million possible interactions that exist in a complex adaptive situation (meaning what one person says can change what another thinks and does). In any single game, there are 16 x (N^2-N) possible predictions, where N is the number of players.

In order to build a model that can make informed predictions, you need to know who the key influencers are. Once you know this, you must then figure out: 1) what they want on the issue; 2) how focused they are on that particular problem; 3) how influential each player could be, and to what degree they will exert that influence; and, 4) how resolved each player is to find an answer to the problem.  Once this information is gathered, you can build a model that can predict with a high degree of accuracy what people will do.  To make good predictions, contrary to what many say, you do not need to know history. It is much like a chessmaster who can walk up to a board in the middle of a game and still know what to do next.

With this information, people can make better, more accurate predictions on identified issues, while also gaining a better grasp for timing. This can help people in a game-theory situation come up with strategies to overcome impediments in order to reach desired objectives.

Bueno de Mesquita then shared the following current predictions:

• Senkaku Island dispute between China and Japan - As a relevant aside, Xi Jinping's power will shrink over the next three years. Japan should let their claims rest for now, rather than push. It will take two years to find a resolution, which will most likely include a joint venture between Japan and China for expropriation of the natural gas reserves.
• Argentina - The "improvements" in today's business behavior are merely aesthetic in advance of the key mid-term elections. Kirshner is marginalizing political rivals, and could make a serious move to consolidate power for the long-term.
• Mexico - There is a 55% chance of a Constitutional amendment to open up energy, a 10% chance of no reform, and a 35% chance for international oil companies to get deep water drilling rights.  Mexico is likely to push through reforms in fiscal policy, social security, energy, labor and education, and looks to have a constructive backdrop for economic growth.
• Syria with or without Assad will be hostile to the Western world.
• China will look increasingly inward, with modest liberalization on local levels of governance and a strengthening Yuan.
• The Eurozone will have an improving Spain and a higher likelihood that the Euro currency will be here to last.
• Egypt is on the path to autocracy.
• South Africa is at risk of turning into a rigged autocracy.

Aaron Clauset, University of Colorado and SFI

Challenges of Forecasting with Fat-Tailed Data

(Please note: statistics is most definitely not my strong suit. The content in Clauset's talk was very interesting, though some of it was over my head. I will therefore try my best to summarize the substance based on my understanding of it)

In attempting to predict fat-tail events, we are essentially trying to "predict the unpredictable." Fat tails exhibit high variance, so the average of a sample of data does not represent what is seen numerically. In such samples, there is a substantial gap between the two extremes of the data, and we see these distributions in book sales (best-sellers like Harry Potter), earthquakes (power law distributions), market crashes, terror attacks and wars. With earthquakes, we know a lot about the physics behind them, and how they are distributed, whereas with war we know it follows some statistical pattern, but the data is dynamic instead of fixed. This is true with war, because certain events influence subsequent events, etc.

Clauset approached the question of modeling rare events through an attempt to ascertain how probable 9/11 was, and how likely another one is. The two sides of answering this question are building a model (to discover how probable it was) and making a prediction (to forcast how likely another would be). For the purposes of the model, one would care only about large events because they have disproportionate consequences. When analyzing the data, we don't know what the distribution of the upper tail will look like because there simply are not enough datapoints. In order to overcome these problems, the modeler needs to separate the tail from the body, build a multiple tail model, bootstrap the data and repeat.

In Clauset's analysis of the likelihood for 9/11, he found that it was not an outlier based on both the model, and the prediction. There is a greater than 1% chance of such an event happening. While this may sound small, it is within the realm of possible outcomes, and as such it deserves some attention. This has implications for policymakers, because considering it is a statistical possibility, we should pursue our response within a context that acknowledges this reality.

There are some caveats to this model however. An important one is that terrorism is not a stationary process, and events can create feedback loops which drive ensuing events. Further, events themselves that in the data appear independent are not actually so. When forecasting fat tails, model uncertainty is always a big problem. Statistical uncertainty is a second one, due to the lack of enough data points and the large fluctuations in the tails themselves. Yet still, there is useful information within the fat tails which can inform our understanding of them. 

Philip Tetlock, University of Pennsylvania

Geopolitical Forecasting Tournaments Test the Limits of Judgment and Stretch the Boundaries of Science

I summarized Tetlock's talk at last year's SFI Risk Conference, so I suggest checking out those notes on the IARPA Forecasting Tournament as well. IARPA has several goals/benefits: 1) making explicit one's implicit theories of good judgment; 2) getting people in the habit of treating beliefs like testable hypothesis; and, 3) helping people discover the drivers of probabilistic accuracy. (All of the above are reasons I would love to participate in the next round). With regard to each area there are important lessons. 

There is a spectrum that runs from perfectly predictable on the left to perfectly unpredictable on the right, and no person or system can perfectly predict everything. In any prediction, there is a trade-off between false positives and correct hits. This is called the accuracy function. 

With the forecasting tournament, people get to put their pet theories to the test. This can help improve the "assertion-to-evidence" ratios in debates between opposing schools of thought (for example, the Keynesians vs the Hayekians). Predictions would be a great way to hold opposing schools of thought accountable to their predictions, while also eliciting evidence as to why events are expected to transpire in a given way.

In the tournament, the participants are judged using a Brier Score, a measure that originated in weather forecasting to determine accuracy on probabilistic predictions over time. The people who perform best tend to have a persistence in good performance. The top 2% of performers from one year demonstrated minimal regression to the mean, leading to the conclusion that predictions are 60% skill and 40% luck on the luck/skill spectrum.

There are tangible benefits of interaction and collaboration. The groups with the smartest, most open-minded participants consistently outperformed all others. Those who used probabilistic reasoning in making predictions were amongst the best performers. IARPA concentrated the talent of some of the best performers in order to see if these "super teams" could beat the "wisdom of crowds." Super teams did win quite handily. Ability homogeneity, rather than a problem, was an enhancer of successes. Elitist algorithms were used to generate forecasts by "extremizing" the forecasts from the best forecasters, and weighting those most heavily (5 people with a .7 Brier would upgrade to approximate a .85 based on the non-correlation of their success. Slight digression: it was interesting sitting behind Ilmanen during this lecture and seeing him nod his head, as this theme resonated perfectly with his points on diversifaction in a portfolio resulting in the portfolio's Sharpe Ratio being above the average of its constituent parts). 

There are three challenges when thinking about the value of a forecasting tournament. First, automation from machines is getting better, so why bother with people? While this is important, human judgment is still a very valuable tool and can actually improve the performance of these algorithms. Second, the efficient market theory argues that what can be anticipated is already "priced in" so there should be little economic value to a good prediction anyway. Yet markets and people alike have very poor peripheral vision and good prediction can in fact be valuable in that context. Last, game theory models like Buena de Mesquita's can distill inputs from their own framework. While this may be a challenge, it's probably even better as a complementary endeavor.

Links for Thought -- October 11th, 2013

The Secrets of Shopping (Chicago Booth, Capital Ideas) -- This is one of the more interesting reads I've come across in a while. Nielson, in conjunction with Booth collected years of household purchase data on products ranging from groceries to painkillers. Some of the early analysis clearly demonstrates that without the proper knowledgebase, consumers are easily steered astray from rational decisions for behavioral reasons. This piece is amazingly informative for investors and shoppers alike.

Mogul's Plan to kill Netflix (Quartz) -- John Malone earned his fortune building a cable empire and made plenty more money dabbling in content. Now Malone is trying to use some of these lessons in order to empower the cable companies to unite (um collude) to drive down content costs and squeeze Netflix out from its successful niche as a low-cost distributor in its own right.

Eurozone debt crisis: Timeline (The Telegraph) -- Why is this timeline from 2011 relevant today? I was trying to avoid any mention of the debt ceiling this week, but here goes: today people speak of the Summer of 2011 as if the market's turmoil was entirely based on "The Debt Ceiling Crisis" when in fact, the market's gyrations were far more attuned to the very real (as opposed to manufactured) crisis that was unfolding in Europe. 

Guy Spier - Build your life in a way that suits you (Graham & Doddsville) -- Skip the needlessly apologetic feature on Koch Industries and their value and read Guy Spier's interview. Guy is one of the most self-aware investors out there in explaining how his investment process, and his role in the investment industry fits with his personality.  Many of us young up-and-comers in the industry can learn so much from someone like Guy, and we should be thankful he is open to sharing these insights as he is.

How the feds took down the Dread Pirate Roberts (ArsTechnica) -- Fascinating feature on how the FBI tracked down and built a case against the man behind Silk Road, a Bitcoin exchange often used for selling drugs. The Austrian econ/libertarian/anarchist connection to the Bitcoin story makes this even more interesting and goes to show that even in a pseudo-economy founded on the principles of fragmentation and individuality, some kind of centralization of power is inescapable 

The FBI and the legitimation of the bitcoinverse (Felix Salmon) -- Have I told you I'm fascinated by Bitcoin yet? There are so many interesting elements, ranging from crime drama to econ experiment. Here Felix talks about how the aforementioned FBI bustup of Silk Road helps legitimize Bitcoin and I cannot help but agree.  With this cleanup, and venture capitalists like Fred Wilson and Marc Andreesen taking interest, there is now a clearer path to a digital economy built on the currency. This all has great potential, until the inevitable deflationary collapse comes crashing in, but until then... (I'll have to expand on this entire theme into a blog post soon).

Bobby Orr is focused on doing good -- quietly (The Boston Globe) -- Today, when even B-listers are Twitter heroes, there is something to be said about a quiet, understated humility. Bobby Orr is one of the all-time great hockey players (and many would argue he's the single greatest), yet because he pursues his charitable and humanist endeavors in such a quiet manner, we rarely hear about his important contributions to society. Simply put, he's a great guy.

Speaking of hockey, here is Tomas Hertl, who some are calling "The Next Great One" scoring what just might stand as the goal of the season when all is said and done.