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.