There's been plenty of market volatility in the last few weeks, as if you did 't know. That's the time the major hedge funds are supposed to justify their keep – especially as the hedge fund industry has dramatically lagged the S&P for years. According to this Bloomberg article, now is the time the funds should really shine and outperform. So what happened in August?
David Einhorn’s Greenlight Capital flagship fund fell 5.3 percent in August, putting it at –14 percent so far for 2015, according to Bloomberg News. Third Point, run by Daniel Loeb, saw a 5.2 percent decline in its main fund in August. Ray Dalio of Bridgewater Associates saw his macro fund decline 6.9 percent last month. These superstars performed more dramatically badly than the HFRX Global Hedge Fund Index, which was down 2.2 percent in August and 1.42 percent for the year. The Standard & Poor’s 500-stock index lost 6.3 percent in August and is down about 4 percent in the year to date.
The intellectual capital of the industry has dwindled away. Too many people reading the same old academic finance journals, too much “me too-ism.” But it's still good at persuading other people to let it manage their money.
One of the most successful investors of recent times has been Howard Marks. I took a look at his book about markets here. You can’t outperform if you don’t think better.
Thus, your thinking has to be better than that of others—both more powerful and at a higher level. Since other investors may be smart, well-informed and highly computerized, you must find an edge they don’t have. You must think of something they haven’t thought of, see things they miss or bring insight they don’t possess. You have to react differently and behave differently. In short, being right may be a necessary condition for investment success, but it won’t be sufficient. You must be more right than others . . . which by definition means your thinking has to be different.
First-level thinking, he says, is just having an opinion or forecast about the future. Second-level thinking, on the other hand, takes into account expectations, and the range of outcomes, and how people will react when expectations turn out to be wrong. Second-level thinkers are “on the alert for instances of misperception.”
Here’s a parallel. Marks doesn’t put it this way, but in essence it’s a matter of seeing markets as a nonlinear adaptive system, in the sense I was talking about in the last post. Second-level thinking is systems thinking. Instead of linear straight lines, markets react in complex feedback loops which depend on the existing stock of perception ( i.e expectations). Some of the greatest market players have an instinctive feel for this. But because of the limits of the human mind when it comes to complex systems, most people have a great deal of trouble understanding markets.
That includes many mainstream economists. One obvious reason is price and price changes are one of the most important feedback loops in markets, but not the only feedback loop. A deeper reason is that most academics tend to be hedgehogs, interested in universal explanatory theories and linear prediction and “one big thing.” But complex systems frustrate and falsify universal theories, because they change. The dominant loop changes, or new loops or added, or new players or goals change the nature of the system.
There’s another implication if you have a more systems-thinking view of markets. Complex adaptive systems are not predictable in their behavior. This, to me, is a deeper reason for the difficulty of beating the market than efficient market theory. It isn’t so much that markets are hyper-efficient information processors that instantaneously adjust, as the fact they are complex. So consistent accurate prediction of their future state is impossible. It isn’t so much that markers are clearly mysteriously prone to statistically improbable 100- or 1000-year risks happening every 10 years. It’s that markets evolve and change, and positive feedback loops can take them into extreme territory with breathtaking speed that makes their behavior stray far from norms and equilibria.
“Tail Risks” are not the far end of a probability distribution, as standard finance theory and policy thinking believes. They are positive feedback loops: cascades of events feed back on each other and change the behavior of the underlying system. It’s not variance and volatility and fat-tailed distributions, but a matter of stocks and flows and feedback, and tipping points which shift the dominant loop, and the underlying structure and changing relationship between components.
This view also helps understand why markets and policy resist change and stay in narrow stable ranges for long periods. Balancing feedback loops tend to kick in before long, producing resistance and inertia and cycles and pendulums, and making “this time it’s different” claims frequently a ticket to poverty. Delays and time effects and variable lags and cumulative effects matter profoundly in a way that simply doesn’t show up in linear models. Differential survival means evolutionary selection kicks in, changing behavior.
How can you make money if you can’t predict the future in complex systems, then? It’s clearly possible. Marks is a dazzlingly successful investor whose core belief is to be deeply skeptical of people who think they can make accurate predictions.
Awareness of the limited extent of our foreknowledge is an essential component of my approach to investing. I’m firmly convinced that (a) it’s hard to know what the macro future holds and (b) few people possess superior knowledge of these matters that can regularly be turned into an investing advantage.
You might be able to know more than others about a single company or security, he says. And you can figure out where we might be in a particular cycle or pendulum. But broad economic forecasts and predictions are essentially worthless. Most forecasting is just extrapolation of recent data or events, and so tends to miss the big changes that would actually help people make money..
One key question investors have to answer is whether they view the future as knowable or unknowable. Investors who feel they know what the future holds will act assertively: making directional bets, concentrating positions, levering holdings and counting on future growth—in other words, doing things that in the absence of foreknowledge would increase risk. On the other hand, those who feel they don’t know what the future holds will act quite differently: diversifying, hedging, levering less (or not at all), emphasizing value today over growth tomorrow, staying high in the capital structure, and generally girding for a variety of possible outcomes.
In other words, a belief in prediction tends to go with a belief in making overconfident, aggressive big bets, sometimes being lucky – and then flaming out. The answer? Above all, control your risks, Marks says. Markets are a “loser’s game”, like amateur tennis. It’s extremely hard to hit winners. Instead, avoid hitting losers. Make sure you have defense as as well as offense.
Offense is easy to define. It’s the adoption of aggressive tactics and elevated risk in the pursuit of above-average gains. But what’s defense? Rather than doing the right thing, the defensive investor’s main emphasis is on not doing the wrong thing.
Thinking about what can go wrong is not purely negative, however. It’s not a matter of being obsessed with biases. Instead, it’s a way to be more creative and agile in adapting to change. If markets are complex systems, the key, as Herbert Simon puts it, is not prediction but “robust adaptive procedures.”
To stress the point again – people don’t intuitively understand systems. And many of our analytics and standard theories get them even less. But it’s the way markets and policy work.
This is a good Bloomberg story on how companies are increasingly investing in software, rather than more interest-rate sensitive longer-lived investments. Importantly, that also reduces the impact of Fed policy in the real economy.
Nobis’s strategy is being replicated at companies around the U.S., where investment in software is up 19 percent since the 2007 business-cycle peak, while spending on hard assets has slumped. Executives are taking less risk on physical assets such as computer hardware, machinery or warehouses, and using software to increase efficiency or reach customers on the Internet.
That shift has implications for the Federal Reserve. It suggests business spending may be less responsive to interest-rate policies, such as quantitative easing, aimed at encouraging investment in long-lived assets like structures, housing and equipment. Software purchases are typically financed out of cash on a month-to-month subscription basis, making the cost of borrowing less likely to influence the decision.
It is one more example of the steady shift of the economy from tangible activity, like manufacturing, to intangibles, where the value lies in the brand or design or intellectual capital. Intangibles are also much less location-specific. That makes it difficult to run policy for a single national economy without a large proportion of any stimulus leaking into activity abroad.
But much of our economic and accounting data still has roots in the closed-economy, manufacturing-centered economy of the 1940s and 1950s.
Financial markets prefer things to be hard and quantified, because they are ultimately measured by very quantifiable P&L.
But the most skilled financial market operators are often the most highly sensitive to the qualitative elements of how people see things. That is becoming more true as the easy pickings in arbitrage and purely quantitative strategy have become overexploited.
Here’s the thing. Many people instinctively know that human beings are flawed when it comes to perceiving issues and making decisions. So they try to take the human factor out of the matter altogether, by looking for mechanical, “rigorous” ways to eliminate the need for judgment. Or they search for information that is so unambiguous and certain that no judgement is needed at all. It has to be a slam-dunk.
Instead, you can see flaws in judgment and perception and ambiguity as an opportunity, indeed the most persistent opportunity in financial markets. This is essentially what George Soros did. His idea of reflexivity, as outlined in The Alchemy of Finance, has two elements: that people’s perception and efforts to understand a situation are extremely important; and that those efforts at understanding also then shapes events and situations. Perception helps create expectations in markets, and expectations help create reality. He wrote:
Scientific method is designed to deal with facts; but, as we have seen, events which have thinking participants do not consist of facts alone. The participants’ thinking plays a causal role; yet it does not correspond to facts for the simple reason that it does not relate to facts. Participants have to deal with a situation which is contingent on their own decisions: their thinking constitutes an indispensable ingredient in that situation.
He dismisses many of the pretensions of social scientists (especially economists) to produce rigorous general laws or quantified regularities.
Their endeavors have yielded little more than a parody of natural science. In a sense, the attempt to impose the methods of natural science on social phenomena is comparable to the efforts of alchemists who sought to apply the methods of magic to the field of natural science.
The upshot? How people think matters. Perception is one of the most critical factors in markets. If a situation is influenced by how people think, then it will be influenced by their blind spots. The most rigorous approach is to look for gaps and flaws and misunderstandings, not ignore them. Quantitative methods are indispensable in some situations, but have their limits.
CNBC decided to name a “Dirty dozen” of the very worst actively managed mutual funds in the US, which offered horrible performance for high fees. One name stood out for me: the Federated Prudent Bear Fund (BEARX). It has returned -10.48% annualized in the last five years, and charged investors 2.5% for the privilege. It’s associated with the Prudent Bear website, which is actually well-argued and has considerable influence in financial markets. It’s like a pure distillation of a bearish approach to markets. This isn’t intended to knock the bear fund, though. The website and blog was required reading for several years during the crisis. It has a consistent and serious set of ideas. Instead, it shows how dangerous it can be to get locked into one persepective in markets. It is painful being a bear, even in the worst five years the economy has seen in four or five generations. How could you not make stacks of money as a bear when we’ve seen such economic catastrophe in recent years? There was a brief bear heaven in 2008-9, and it has been downhill ever since. This is BEARX against the S&P over the last five years.
Of course, stocks have rallied massively from the troughs and are setting new record highs. The trick is to know when to stop being a bear, as well. Bears are close relatives of hedgehogs, who know “one big thing” and who find it hard to switch perspectives. For sure, if we get a massive market meltdown soon, there will be a brief moment of ursine elation and vindication again and these performance figures will look very different. But there’s two things to conclude. First, the odds are stacked against you long-term as a bear. The base rate is the economy and equities grow over time. It’s usually two steps forward, one step back. Bears have to be nimble and dance, not sit in a corner and growl that Armageddon is always just around the corner. Second, it shows why markets just don’t price future crises effectively. Let’s say the bears really are right that we’re about to drop off a cliff again. Indeed, the smart money is getting increasingly fretful about asset bubbles building up from excess liquidity. Most of the market is convinced the end of QE could lead to massive turbulence in equities, and the potential for a huge sell-off in bonds. But most asset managers just can’t afford to miss rallies, either. Timing is everything in markets. With a performance chart like the one above, there’s not that many pure bears left. They’ve died or evolved into something else. We are almost certain to get major market reversals again in due course. Predicting that isn’t hard. But bears need to survive until that happens, and not get too excited when it does. The next rally will be right behind.
Still on the subject of Warren Buffett, here’s an excellent article by Jason Zweig in the WSJ on how it is essential to get alternative perspectives as an investor:
A deliberate, lifelong effort to find people to tell him why he might be wrong is one of the keys to Mr. Buffett’s success. It doesn’t come naturally to most investors.
Mr. Buffett once noted about the scientist Charles Darwin that “whenever he ran into something that contradicted a conclusion he cherished, he was obliged to write the new finding down within 30 minutes. Otherwise his mind would work to reject the discordant information, much as the body rejects transplants. Man’s natural inclination is to cling to his beliefs, particularly if they are reinforced by recent experience.”
Another vehement believer in the importance of challenging your own investing ideas is Ray Dalio, founder of Bridgewater Associates, the world’s largest hedge-fund manager, which oversees more than $150 billion.
“When two intelligent parties disagree, that’s when the potential for learning and moving ahead begins,” Mr. Dalio told me last week. “The most powerful thing that [an investor] can do to be effective is to find people you respect who have opposite, different points of view [from yours]—and have an open-minded exchange with them about what’s true and what to do about it.” …
“When you think you’re right, you don’t go looking,” says Mr. Dalio. “When you think you’re right, your mind isn’t open to learning. The more you think you know, the more closed-minded you’ll be.”
Successful investment is about exploring misperception and understanding different perspectives, not simply predicting the short-run performance of the economic cycle (where few people, if any, have demonstrated consistent ability.)
People all too often do serious, long-lasting damage to themselves by neglecting to take the basic elementary steps that are necessary to make good decisions. One of them is being unaware of the limits of their knowledge. They don’t know what they don’t know, and this problem plagues investment and economic policy decisions. I’ve seen it many times among the most brilliant hedge fund investors and the most senior policymakers.
Good decision-making depends on having both good primary knowledge and good metaknowledge. Indeed, in one way a poor sense of what we do and don’t know (poor metaknowledge) poses a far greater danger to a decision-maker than limits on our subject-specific (primary) knowledge. When our subject knowledge is lacking, we realize that we must do additional research or hire a consultant (whether a business consultant, physician, or tax attorney.) But when metaknowledge is lacking, we blithely proceed to reach a conclusion without even noticing that we don’t have all the primary knowledge we need to make a sound decision.
In other words, this points to the starting point of Alucidate’s approach: you have to think about how you and other people think. Without that, there’s very strong evidence that you can’t make good decisions. And if you can’t make good decisions, then you are in trouble.
It’s metaknowledge where people usually make the biggest mistakes. As a rule, there is not a great deal of monetizable value in information or general expert opinion these days. Information wants to be free. A google search can bring up any amount of information or opinion on virtually anything from Cypriot statistics to trends in chemical engineering. And the record of “expert” opinion in predicting events is little better than a chimp throwing darts.
But mistakes in perception and preconception and misunderstanding other people’s framework and intention and goals are pervasive. If you want an edge, that’s where you have to look. The greatest investors have always known that. And this is why we can move the needle for clients.
Incidentally, in the light of the post below, academic economists tend to have especially acute problems with metaknowledge, especially when they make “policy recommendations.” They almost stereotypically have a limited grasp of the limits of their subject-matter expertise. In fact, central bank staffers know that if an academic economist is appointed to a policy position, it usually takes at least a year or two (if ever) for the professor to become “deprogrammed” enough to be useful.
Academic overconfidence in their particular “rigorous frameworks” and models can make it very hard for them to learn and adjust. Academic economists are temperamentally hedgehogs, devoted to one big thing, as Philip Tetlock describes, rather than foxes who will adjust their angle in response to new evidence. But there is little reason to expect universal results or universal laws in economics. What you can do is at least not fall into obvious traps.
David Brooks talks here about the limits of big data. Big data looks for correlation, rather than causation. The trouble is people are prone to “gigantic and unpredictable changes in taste and behavior”, and those are hard to anticipate in short runs of recent data.
Even more importantly,
Another limit is that the world is error-prone and dynamic. I recently interviewed George Soros about his financial decision-making. While big data looks for patterns of preferences, Soros often looks for patterns of error. People will misinterpret reality, and those misinterpretations will sometimes create a self-reinforcing feedback loop. Housing prices skyrocket to unsustainable levels.
If you are relying just on data, you will have a tendency to trust preferences and anticipate a continuation of what is happening right now. Soros makes money by exploiting other people’s misinterpretations and anticipating when they will become unsustainable.
Of course Soros has been talking of “reflexivity” and peope's beliefs for twenty years, since The Alchemy of Finance .
This is one reason why Alucidate is so focused on perception. People misinterpret reality, and they do do in patterns.
Bill Gross has a rather autumnal piece out today.
My point is this: PIMCO’s epoch, Berkshire Hathaway’s epoch, Peter Lynch’s epoch, all occurred or have occurred within an epoch of credit expansion – a period where those that reached for carry, that sold volatility, that tilted towards yield and more credit risk, or that were sheltered either structurally or reputationally from withdrawals and delevering (Buffett) that clipped competitors at just the wrong time – succeeded. Yet all of these epochs were perhaps just that – epochs. What if an epoch changes? What if perpetual credit expansion and its fertilization of asset prices and returns are substantially altered? .. What if there is a future that demands that an investor – a seemingly great investor – change course, or at least learn new tricks? Ah, now, that would be a test of greatness: the ability to adapt to a new epoch.
In the light of what I was talking about yesterday, you can’t ignore base rates. But what if the base rate changes? For the bond market, arithmetically we have come to the end of an era, as bond yields can hardly go substantially lower. There is bound to be a new epoch in bonds. But pronouncing a new epoch more widely is a stretch. After all, here we are five years after the crash which was supposed to change everything forever – and equities are hitting new highs, the banks are bigger than ever, and there’s worries about asset bubbles building.
The same story could be written about any number of calls in the last twenty years. The NYT looks at what happened with Apple shares in the last few months.
Last September, Apple shares hit a record $705. And to the overwhelming majority of Wall Street analysts, that meant one thing: buy.
By November, with Apple stock in the midst of a precipitous decline, they were still bullish. Fifty of 57 analysts rated it a buy or strong buy; only two rated it a sell. Apple shares continued their plunge, and this week were trading at just over $450, down 36 percent from their peak.
How could professional analysts have gotten it so wrong?
Buy recommendations are more likely to generate brokerage commissions than sells, because far more people will not own a stock than hold it at any point in time. Conflicts of interest are important, but are not the whole explanation.
But no one thinks conflicts alone can explain the analysts’ abysmal recent Apple performance. “There’s too much unanimity,” Bruce Greenwald, a professor of finance and asset management at Columbia Business School and a renowned value investor, told me this week. “That’s what’s so troubling. When that many analysts are in agreement, they can’t all be conflicted.”
He and other experts say there are additional documented factors that help explain why Wall Street analysts are so often wrong: they extrapolate from recent performance data; they chase momentum; they want to please their customers; and they show a tendency toward herd behavior. Which is to say, they fall into the same pitfalls that afflict most investors.
Another factor is that analysts have a tendency to tell their audience what it wants to hear. “The analysts are in the end sales people,” Professor Greenwald said. “Their credibility depends on their not upsetting their investors too much. Everybody loved Apple, everybody did well. The bears were always wrong. It took an enormous amount of courage to fight the tide.”
It is easy to have 20/20 hindsight, of course. Predicting the markets is just inherently difficult for even the most gifted individuals, as the record of average hedge fund performance also shows.
However, there are patterns here which keep being repeated from one incident to the next.There are plenty of lessons to learn – but they are rarely learned. There are patterns of misperception and flawed decisions. You can try to be aware of accummulated experience of potential mistakes and perception problems.
“Why aren’t they more sophisticated? You’d hope they would be,” Professor Kadan said. “But they always fall into the same traps.”
It is almost impossible to consistently make decisions which look perfect in hindsight. But by being more aware of potential problems with decisions, you can make slightly better ones.