How Much Is Enough?

Money is a funny thing. As a unit of exchange it is the raw material for consumption (or, if you prefer, the deferral of consumption). We express who we are through our spending. It’s no surprise then that the answer to “how much is enough?” varies wildly from person to person. But really what it boils down to is an optimization problem.

Contrary to what people think, the hard thing about answering “how much is enough?” is not calculating a dollar amount. The hard thing is deciding what constraints to apply to optimization. Once you do that, the calculations pretty much fall into place on their own.

At a high level, we are looking at the following function (let’s call it the Enough Function):

Enough = Present Value of (Future Lifestyle Spending + Future Basic Needs Spending + Desired Margin of Safety)

Obviously you can disaggregate each component (Basic Needs Spending would break down into line items like “Housing” and “Essential Food”). For the purposes of this post I’ve opted for brevity.

In principle optimizing the Enough Function is pretty straightforward. In practice people find it difficult for a couple of reasons. For one, most people live like sheep. They follow the examples set by advertisers, movies, TV shows and the people around them.

We can partly blame evolution for this. A million years ago if you didn’t fit in with the rest of your tribe you would be ostracized and could look forward to dying cold, hungry and alone. We are a long way from those days and yet our evolutionary programming dies hard. Most people have not spent much time thinking what actually gives their lives meaning. So they look for meaning elsewhere.

On a more mundane level, quantifying a margin of safety can also be tricky. There is just no way to gain absolute certainty. Margin of safety is best addressed with scenario analysis, which is beyond the scope of this post. In fact, for people who are totally lost when it comes to this stuff, a good reason to hire a professional financial planner is to delegate the analytical work to someone with expertise.

I don’t have a position on whether it’s “better” to live frugally or not. If we’re looking at the continuum of spending patterns, with Mustachianism on the frugal end and Kardashian-esque conspicuous consumption on the other, I suspect most people plot somewhere in the muddy middle.

Personally, I tilt a little more toward the frugal end of the spectrum. The main reason for this is that most of the things I enjoy doing (reading, writing) are not particularly expensive pursuits. But do I think people who want to drive nice cars and live in big houses and spend lots of money on clothes and jewelry are “doing it wrong?” No. Their Enough Functions are just optimized for a different set of constraints.

The Root Of All Most Financial Problems

Financial problems result from mismatches in the optimization of the Enough Function and the financial resources at hand.

It is okay to make a ton of money and live the high life. It is not okay to make very little money and live the high life. Unless you are optimizing for a crushing debt loan and eventual bankruptcy, of course. Fortunately, if you find yourself in this position there are a couple levers you can pull: spend less or make more money.

Like I wrote above, this stuff is really simple in principle. The challenge comes in the implementation, but it’s mostly a challenge of self-discipline (on the spending side) and hard work (on the income side).

The Trouble With Truth

A friend and I have been having a running conversation about the “post-truth era” and bias in the media. This post is an attempt to pull the ideas from those conversations together into a kind of mental model.

Essentially there are three issues in play here: epistemic uncertainty (the problem of induction), cognitive biases and incentive systems.

The first two help explain why otherwise intelligent and well-meaning people can come to inhabit echo chambers when they otherwise seek to reason objectively. Incentive systems then reinforce the sub-optimal behavior of well-meaning people and assist opportunists and charlatans in spreading outright falsehoods.

This post is not meant to address opportunists and charlatans as their motives are things like wealth, power and ideological fanaticism. For these individuals the truth is simply an inconvenient speed bump along the road to power. Rather, I am interested in how the uncertainty inherent in scientific reasoning leaves openings for multiple truths and seemingly contradictory bodies of evidence.

Epistemic Uncertainty

How can we know a thing is true in the first place? That seems like a good place to start.

Broadly speaking, we can reason deductively or inductively. Deductive reasoning is a process that arrives at a “logically certain conclusion.” Deductive reasoning is what you do in math class. The beauty of mathematics, which I did not properly appreciate as a kid, is that it is about the only discipline where you can know with certainty when you are right. Your conclusion must follow inevitably from your premises. It cannot be otherwise.

Inductive reasoning, on the other hand, takes specific observations and then infers general rules. Importantly, the scientific method is a form of inductive reasoning. All of the social sciences, including economics, utilize inductive reasoning. Inductive reasoning is subject to the so-called “problem of induction.” Namely: inferences are not “logically certain.”

The classic example involves swans. For a long time people believed all swans were white. This was an inference based on the fact that in every recorded observation of a swan, the swan had been white. Critically, this did not prove all swans were white. In order to prove all swans were white, you would have to observe every swan in existence, every swan that had ever existed, and every swan that ever would exist. That is of course impossible. And in fact, as soon as someone discovered a black swan (in Australia in 1697), the inference that all swans were white was immediately proven false.

That’s not to say the inference was a bad one. It was perfectly reasonable given the available data. You see how this presents issues for science, and any other truth-seeking endeavors. Even “good science” is often wrong.

If you have spent any time reading scientific research, you are familiar with the way hypotheses are formulated and tested. It is never a question of “true or false.” It is a question of “whether the null hypothesis can be rejected at such-and-such a confidence interval.”

What confidence interval is appropriate? The gold standard is 95% (a.k.a. within two standard deviations of the mean, assuming normally distributed results). However, there is a healthy debate over where that threshold should be set.

The probabilistic nature of induction results creates epistemic uncertainty. In that sense, there is no post-truth era. There has never really been an era of truth, either. Science has never really given us truth. It’s given us inferences, some of which have withstood many years of repeated testing (evolution, Newton’s laws, etc.), and to which we’ve assigned extremely high levels of confidence. In other words: we are pretty damn sure some things are true. Sure enough we can do things like send satellites out of our solar system. But it’s still not logical certainty.

In other areas, science has given us inferences where confidence levels are much lower, or where there is significant debate over whether the inference if of any significance at all. Many scientific studies don’t replicate.

The point of this is not to argue we should junk science or inductive reasoning. It’s to show how even if two parties use scientific reasoning in good faith and with the exact same methodology, they might arrive at different conclusions. How do you resolve the conflict?

To function properly, the scientific method requires friction. Replication of results in particular is critical. However, when we layer on cognitive biases and political and economic incentives, scientific inqiuiry and other inductive reasoning processes become distorted.

Cognitive Biases

Humans are funny creatures. Our brains evolved to deal with certain specific problems. It was not that long ago that the issues of the day were mainly things like: “can I eat this mushroom without dying?” and “that animal looks like it wants to eat me.”

Evolution did not optimize human brains for analyzing collateralized loan obligations.

I am not going to rehash the literature on cognitive biases here. If you are interested in a deep dive you should read Thinking, Fast and Slow, by Daniel Kahneman. Rather, I want to mention one bias in particular: confirmation bias.

Instead of looking for evidence that their inferences are false, people look for evidence that confirms them. The Wiki for confirmation bias calls it “a systematic error of inductive reasoning.” There is a saying among quants that if you torture data long enough it will say whatever you want it to. These days we have more data than ever at our fingertips, as well as new and exciting torture methods.

Importantly, confirmation bias does not represent a conscious decision to lie or deceive. People who consciously manipulate data to support a hypothesis they know ex ante to be false are opportunists and charlatans. We are not concerned with them here.

People aren’t evil or stupid for exhibiting confirmation bias. They just do. Intelligent people have to be especially careful about confirmation bias. They will be extra unconsciously clever about it.

You can probably see how combining this with inductive reasoning can be problematic. It creates a situation where everyone has “their” facts. What’s more, most people involved in research and reporting operate within incentive systems that encourage confirmation bias rather than mitigate it.

Incentives

If people tend to seek out information confirming their views, it is only logical that media businesses pander to that tendency. The media business is first and foremost an attention business. Either you have people’s attention or you don’t. If you don’t, the subscribers stop paying and the advertisers don’t want to be on your platform and pretty soon you are out of business. It behooves you to serve up the kinds of stories your readers like reading, and that align with their worldviews.

Likewise academics face their own pressures to conform with peers. Academic departments are subject to the same power games and politics as corporate boardrooms. Reputation matters. Particularly given the importance of tenure to young faculty. Also, if you are an academic star who has built a 40-year reputation on the back of a particular theory, how much incentive do you have to want to try and poke holes in that? If you think these dynamics don’t impact behavior, you don’t know very much about human behavior.

Closer to home for this blog, at hedge funds and mutual funds analysts often receive bonuses based on how their ideas perform once they are in a portfolio. But what if you are the analyst covering a weak opportunity set? The right thing to do is throw up your hands and say, “everything I am looking at sucks.” But if you go that route you can look forward to no bonus and possibly being fired. So instead you will sugar coat the least bad ideas and try to get them into the book.

Putting It All Together

So here we have it, from start to finish:

  • Many forms of “knowing,” including the scientific method, are forms of inductive reasoning. Inductive inferences are subject to uncertainty and potential falsification. This means there is always an opening for doubt or contradictory evidence. We accept certain scientific principles as true, but they are not actually logical certainties. Truth in the sense of logical certainty is not as common as many people think.
  • Due to cognitive biases, especially confirmation bias, people distort the process of scientific inquiry. Rather than seek information that could falsify their existing beliefs (the correct approach), they seek out information that confirms them. People have “their facts,” which they can back up with evidence, which in turn creates multiple, plausible versions of “the truth.”
  • Economically, media companies are incentivized to appeal to peoples’ cognitive biases. The economics of media incentivize a continuous feedback loop between content producers and consumers. Academics and other researchers are also incentivized to confirm their beliefs due to issues of reputation, professional advancement and compensation.

Nerd Stuff: Factor Valuation Edition

I have to give Research Affiliates some serious props for their online interactive (and, yes, free tools). I mentioned the asset allocation tool in a post from earlier this week. If you didn’t check out the tool then, you really should.

I did not realize until this morning that Research Affiliates also has a similar tool for factors, called Smart Beta Interactive. This allows you to slice and dice factor strategies and also the underlying factors themselves. I highly recommend checking this one out out, too.

Anyway, this post isn’t meant to be a Research Affiliates commercial. Instead, this is going to be a post on reflexivity. Behold, factor valuations for the US market:

RAFI_1Q18_Factor_Valuations
Source: Research Affiliates

Regarding their methodology, Research Affiliates states:

Just like stocks, bonds, sectors, countries, or any other financial instrument, equity factors and the strategies based on them can become cheap or expensive. We measure relative valuations of the long vs. short sides to estimate how cheap or expensive a factor is. We find that when relative valuation is low compared to its own history, that factor is positioned to outperform. When valuation is high it is likely to disappoint.

This is reflexivity in action. Briefly, reflexivity is a concept popularized by George Soros. The idea is that by taking advantage of perceived opportunities in the markets, we change the nature of the opportunities. Howard Marks likens this to a golf course where the terrain changes in response to each shot.

Here’s how this happens in practice:

Step 1: Someone figures out something that generates excess returns. That person makes money hand over fist.

Step 2: Other people either figure the “something” out on their own or they copy the person who is making money hand over fist.

Step 3: As people pile into the trade, the “something” becomes more and more expensive.

Step 4: The “something” becomes fairly valued.

Step 5: The “something” becomes overvalued.

Step 6: People realize the “something” has gotten so expensive it cannot possibly generate a reasonable return in the future. If prices have gotten really out of hand (and particularly if leverage is involved) there will be a crash. Otherwise future returns may simply settle down to “meh” levels.

Step 7: As the “something” shows weaker and weaker performance, it gets cheaper and cheaper, until some contrarian sees a high enough expected return and starts buying. The cycle then repeats. Obviously these cycles vary dramatically in their magnitude and length.

I do not consider myself a quant by any means, but I think the two most important things for quants to understand are: 1) why a factor or strategy should work in the first place, and be able to explain it in terms of basic economic or behavioral principles; 2) reflexivity.

Many people believe AI is going to push humans out of the financial markets. There is some truth in this. Big mutual fund companies that have built businesses on the old “style box” approach to portfolio construction are in trouble. The quants can build similar funds with more targeted exposures, in a more tax efficient ETF wrapper, and with lower expenses.

What I think people underweight is the impact of reflexivity. If the AIs aren’t trained to understand reflexivity, they will cause some nasty losses at some point. Personally, I think there will be an AI-driven financial crisis some day, and that it will have its roots in AI herding behavior. We are probably a ways away from that. But technology moves pretty fast. So maybe it will come sooner than I think.

Anyway, back to factor valuations.

What stands out to me is Momentum and Illiquidity at the upper ends of their historical valuation ranges. On the Momentum side this is stuff like FANG or FAANMG or whatever the acronym happens to be this week. On the Illiquidity side it’s private equity and venture capital. If you have read past posts of mine you know I believe most private equity investors these days are lambs headed to slaughter.

There tends to be a lot of antipathy between quant and fundamental people. Even (perhaps especially) if they are co-workers. The fundamental people are afraid of the quants. Partly because they are afraid of the math (a less valid fear), and partly because they see the quants as a threat (a more valid fear). Quants, meanwhile, tend to believe the fundamental people are just winging it.

In reality I think this is more an issue of language barrier and professional rivalry than true disagreement over how markets work or what is happening in the markets at a given point in time. In my experience, the best fundamental investors employ quant-like pattern recognition in filtering and processing ideas. Many quants, meanwhile, are using the same variables the fundamental people look at to build their models.

Personally, I think anyone who wants to survive in the investment profession over the next twenty years is going to have to be something of a cyborg.

Though, come to think of it, that probably applies to every industry.

How To Win

One of my favorite bits of life advice comes from Mark Cuban. A couple of years ago, Business Insider wrote a brief piece on his view that surprisingly few people are willing to put in the effort to gain a knowledge advantage in their fields. I remember it to this day, because it is consistent with much of my experience in the working world.

“I remember going into customer meetings or talking to people in the industry and tossing out tidbits about software or hardware,” he writes. “Features that worked, bugs in the software. All things I had read. I expected the ongoing response of: ‘Oh yeah, I read that too in such-and-such.’ That’s not what happened. They hadn’t read it then, and they still haven’t started reading it.”

Cuban says that despite a minimal background in computers, he was outperforming so-called experts in the field simply because he put time and effort in. It’s why, he writes, he still allocates a chunk of his day to reading whatever he can to gain an edge in the businesses he’s involved in.

“Most people won’t put in the time to get a knowledge advantage,” he writes.

Another quote that sticks in my head along these lines (I don’t recall exactly where I heard this, and it’s possible I’ve fused a couple different quotes together):

“If you aren’t passionate about what you’re doing, don’t ever make the mistake of competing with someone who is. You will lose every time.”

Betting Dark Side

In craps the best bet on the table (other than Odds) is Don’t Pass. The house edge is just a teensy bit narrower there than on the Pass Line. But no one really bets that way. And when people do, they are quiet about it, because they are betting for everyone else at the table to lose. That’s not the way you endear yourself to a bunch of degenerates at the casino. Betting Don’t Pass is also called betting “dark side.”

Personally, I have no interest in betting dark side in craps. The edge is pretty small to have to endure swarthy drunks shooting you sideways glances all night. But when it comes to investing I am plenty interested in opportunities to bet dark side.

In fact, sometimes I play a mental little game with myself called: What’s A Seemingly Obvious Trend Or Theme I Can Get On The Other Side Of?

For example right now everyone in the US is whining about how there are no cheap stocks. You know where stocks are cheap?

Russia.

In Russia you’ve got stuff on single digit earnings multiples paying 6% dividend yields. And it’s not even distressed stuff for the most part. Research Affiliates has got a phenomenal little asset allocation tool you can use for free. See those two red dots on the upper right in the double-digit return zone? That’s Russian and Turkish equities. (In case you are wondering, US large cap equity plots at about 40 bps of annualized real return)

RAFI_Cap_Markets201806
Source: Research Affiliates; Returns pictured are estimated real returns

Yeah. I know. Everyone hates Russia. You can probably rattle off at least five reasons why Russia is an absolute no-go off the top of your head. But I will happily bet dark side on Russian equity. I won’t bet the farm, but I’ll take meaningful exposure. The reason is I am getting paid pretty well to take Russian equity risk.

Risk assets are a pretty crappy deal here in the US. (40 bps real per year over the next decade, remember?) Here everyone’s convinced themselves stocks don’t go down anymore so they are willing to pay up. I guess some day that will be put to the test. We’ll see.

In the meantime, what other trends can we get on the other side of?

ESG might create opportunities. If you haven’t heard of ESG it stands for Environmental, Social and Governance. Big asset managers have become obsessed with ESG because it’s an opportunity to gather assets from millennials and women at a time when index funds and quants are hoovering up all the flows.

This is literally what the big asset managers tell allocators in presentations now: “millenials and women are going to inherit all the assets and they want to be invested in line with their values. Here are all our ESG products. Also here is marketing collateral to help you have ‘the ESG talk’ with your clients.”

So where do we go from here?

Well, for starters I am thinking a trillion dollars rotates into stuff that screens well on ESG. If this persists long enough and to a significant enough degree the stuff that doesn’t screen well on ESG is going to get hammered. With any luck it will get kicked out of indices and analysts will drop coverage and the bid-offer spreads will blow out.

Like Russian equities, the oil companies and the natural gas companies and the miners and the basic chemical companies and the capital intensive heavy manufacturers will trade on single digit earnings multiples with 6% dividend yields. All because they don’t score well on the asset gatherers’ screens.

So yeah, I think I’ll bet dark side when it comes to ESG, too.

For the record, I don’t have anything against ESG in principle. I am actually a big fan of  an extreme form of ESG, called impact investing, where you allocate capital with low return hurdles (like 0% real) to achieve a specific social objective. Maybe to fund development in a low income community in your city. Micro-lending is an example of this, and I think it’s a better model than philanthropy in many cases. But that’s a topic for another day.

This post is about how people’s emotional reactions to the securities they own create bargains. Here betting dark side is betting on something kind of icky. “Ick” is an emotional reaction. When people react emotionally to stuff, it has the potential to get mispriced. “Ick” is a feeling that encourages indiscriminate selling.

That’s where the Don’t Pass bet comes back into play. It’s one of the better bets in the casino, and it’s massively underutilized. Why?

Because it makes people feel icky.

“To the moon!”

From The McKinsey Global Private Markets Review 2018 (subtitle: “The rise and rise of private markets”):

McKinsey_PE_Rocket_Ship
Source: McKinsey

Your eyes do not deceive you. That is literally a rocket ship with stabilizer fins made of dollar bills, blasting off into the stratosphere. I like to imagine it’s headed off to join the crypto people and their lambos on the moon.

A few highlights from the introduction:

“Private asset managers raised a record sum of nearly $750 billion globally, extending the cycle that began eight years ago.”

“Within this tide of capital, one trend stands out: the surge of megafunds (of more than $5 billion), especially in the United States, and particularly in buyouts.”

“What was interesting in 2017, however, was how an already powerful trend accelerated, with raises for all buyout megafunds up over 90 percent year on year.”

“Investors’ motives for allocating to private markets remain the same, more or less: the potential for alpha, and for consistency at scale.”

This is what you see when an asset class gets frothy. And private equity is an asset class I have had my eye on for a while now. As I have written before, and as McKinsey says somewhat obliquely in their report, institutional investors have come to view private equity as a magical asset class.

We have seen this movie before. It happened with hedge funds in the early 2000s (spoiler alert: it ends with capital flooding into the space and diminished future returns). There are no magical assets. People ought to know better by now. I guess the allure is too powerful. Particularly for return-starved pension systems.

Anyway, when this thing turns there are going to be knock-on effects in a couple of other areas: namely high yield debt and leveraged loans. The gears of the private equity machine are greased with high yield debt. These days there is a strong bid for crappy paper. Especially crappy paper with floating rates.

The yield on the S&P/LSTA US Leveraged Loan 100 Index is something like 5%. Meanwhile, 2-year Treasuries yield 2.5%. And loan covenants suck, which means when defaults inevitably tick up recoveries are going to suck. Buyers are so fixated on interest rate risk they’re overlooking the credit component. You can keep your 250 bps of spread, thanks. Doesn’t seem like a great risk/reward proposition to me.

If I were a big institution, I would be swimming damn hard upstream against consensus on private equity.

If I were a financial advisor, I would steer clear of floating rate paper, rather than reach for a bit of yield so I can tell my clients they’re insulated from interest rate risk.

If I were a distressed debt investor I would make damn sure I had access to liquidity for when these deals start to explode (indeed, many distressed funds are out seeking commitments for exactly this purpose).

The institutional investors will screw it up, because they’re organizationally incapable of swimming upstream. Most of the financial advisors will screw it up, too, because they don’t really understand what they own in a bank loan fund and they tend to fixate on past performance data, which isn’t as relevant to the current environment. The distressed debt guys and gals will make a bunch of money for a few years picking through the shattered ruins of these deals. That, I admit, warms my heart. The distressed folks have had a rough go of it lately.

This whole dynamic is a great example of how investor psychology drives market cycles. To play off that tired old hockey analogy: investors don’t skate to where the puck is going, they skate toward the player who last handled the puck.

Here the puck is going to stressed/distressed debt.

It is most definitely not going to the moon.

What Is Academic Finance Good For, Anyway?

A certain subset of investors have nothing but contempt for academic finance. The late Marty Whitman summed up the arguments against academic finance quite nicely (incidentally, a large number of his shareholder letters are available for free here and you should read them). In April 2003, he wrote:

For MCT [Modern Capital Theory], the proof of the existence of an efficient market centers on the observation that no individual investor, or institution, has ever outperformed a market, or a benchmark, consistently. Consistently is, of course, a dirty word: It means “All The Time.” Academics seem to be absolutely right in their observation that no one outperforms any market consistently. However, it seems asinine to offer this as evidence that fundamental analysis is useless or nearly useless. Lots of investors, especially value investors, outperform markets or benchmarks on average, or usually, even if no one from Warren Buffet on down can outperform a market or a benchmark consistently. Further, many, if not most, MCT acolytes seem sloppy in their observations in that a good deal of the time they conveniently ignore the “consistently” condition in describing the uselessness of fundamental research.

EMH seems to be absolutely valid in a special case. The basic problem with MCT believers is that they assume wrongly that this special case is a general law. EMH describes the investment scene accurately only when two conditions exist in tandem:

1. The solitary goal of a passive, non­-control investor is to maximize a risk­-adjusted total return consistently.

2. The security, or commodity, being analyzed can be best analyzed by reference to a limited number of computer programmable variables.

[…] MCT misdefines risk. For MCT, the word risk means only market risk, i.e., fluctuations in prices of securities. In fact, one can’t really use the word “risk” without putting an adjective in front of it. There is market risk, investment risk (i.e., something going wrong with the company), credit risk, commodity risk, failure to match maturities risk, terrorism risk, etc. At TAVF, we try to avoid investment risk. We pay less attention to market risk.

I think Marty was pretty fair in his critique. But here’s the thing. His two conditions are descriptive of most institutional and retail investors. Especially #1.

Most retail and institutional investors are preoccupied with risk-adjusted returns, and they define risk as market risk, a.k.a volatility. Most of the advisors who serve these investors are likewise operating under the same conditions, whether they want to admit it or not. The reason is they have to manage career risk, a.k.a “you are in the bottom quartile of your peers so now you are fired.” Career risk turns us into phonies.

Broadly speaking, two types of people spit venom at academic finance:

  • People like Marty Whitman, who are successful investors and justifiably resent being lumped together with a bunch of phonies.
  • Phonies working through their own self-loathing.

How do you know you are a phony?

If you have ever made an investment decision based on how a client will respond in a meeting, or how that investment decision might impact your ability to raise or maintain your assets under management, you have at least a little bit of phony in you. The more these considerations dominate your thought process, the more of a phony you are. The word phony has a negative connotation but it’s really nothing to be ashamed of. For most of us, it comes with the territory of managing other people’s money.

But the end result is that we end up managing highly diversified portfolios for people concerned with asset price volatility. For us, the assumptions underlying academic finance and portfolio theory in particular are valid.

What Portfolio Theory Is Good For

Portfolio theory is pretty good at explaining the performance of diversified stock portfolios. I say “pretty good” because investment performance is non-stationary (the parameters of the data change over time) and the assumptions we make about the underlying distribution of expected returns are overly simplified (investment returns are not actually normally distributed).

When you own a diversified stock portfolio, your performance basically boils down to:

  • How much market exposure you have
  • Whether your portfolio is tilted toward value or momentum
  • Whether your portfolio is tilted toward big companies or small companies

Sometimes you will get a little residual boost from security selection, but it is tough to determine whether this represents luck or skill. Big mutual funds usually don’t like to break out multifactor attribution data because it is frequently unflattering and opens them up to (well-deserved) needling from quants who can provide the same exposures at lower cost.

Where Portfolio Theory Breaks Down

Portfolio theory breaks down if you are not diversified or if you change your definition of risk to something other than volatility, like permanent impairment of capital (which is how Buffett and Marty Whitman would define risk). Under these conditions, portfolio management is about maximizing expected value while managing down your risk of ruin.

Here you are not dealing with systematic exposures like value, size and momentum. Instead, you are exposed to the idiosyncratic risks associated with the specific securities you own (remember, you are not diversified enough for those risks to cancel each other out).

Reconciling The Conflict

Marty Whitman said it best: modern portfolio theory is valid for a particular set of conditions. When those conditions are violated it can seem comically silly. Yet, most professionals involved in money management are subject to the conditions that make portfolio theory valid. They are managing diversified portfolios for people who define risk as volatility.

So, like it or not, most of us are operating in a context where portfolio theory applies. Warren Buffetts and Marty Whitmans are few and far between.