ClearBridge Investments 2Q15 Market Commentary: Richard Thaler And Behavioral Finance

ClearBridge Investments 2Q15 Market Commentary: Richard Thaler And Behavioral Finance

ClearBridge Investments market commentary for the second quarter ended June 30, 2015.

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ClearBridge Investments - Market Commentary

“Markets are constantly in a state of uncertainty and flux and money is made by discounting the obvious and betting on the unexpected.” - George Soros

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I’m extremely fortunate to have the rewarding challenge of raising two boys. From an early age we have played all kinds of games to spend quality time together, and games have also served as a great medium to teach them lessons about life. In particular, we have played a lot of poker together as an effective way to gain appreciation of probability and the complex dynamics of interactive games. If you ask either one of my boys whose cards they are playing they will immediately answer: “the other guys’!”

What my boys do not yet realize, is that understanding how people interact is something I must give a lot of thought to as an investor. After all, one of the key dynamics of markets, and in fact all of economics, is the interaction of people (economic agents) as they react to various incentives and to each other’s actions. One of the great contributors to understanding this interactive dance was John Nash, who helped develop game theory and specifically the theory of non-cooperative games through his Nash equilibrium. With the recent death of Dr. Nash, I was compelled to review the core premise of game theory: end-game outcomes are often extremely difficult to predict, and often not intended by ANY of the player agents. I believe this vexing takeaway is one of the reasons markets are typically impossible to predict. However, you can derive some insight by studying how different investors actually make decisions, and thus trying to understand the different games that often drive markets in parallel.

ClearBridge Investments: How economic theory says decisions should be made

To help crystallize competing decision-making frameworks, I will use an example from Richard Thaler’s excellent new book on behavioral economics, Misbehaving. In the book, Thaler provides many examples of how economic theory says decisions should be made, and contrasts them with how decisions are actually made in the messy real world. In one example, two railroad tracks are laid down end to end, nailed down at the end points and meet in the middle. Each track is one mile long (5,280 feet) and expands to one mile plus one inch (5,280.08 feet) when it gets hot. Assuming the tracks maintain their linear shape, Thaler asks how high the expanded track is in the middle. The “right” way to answer this question is to realize that the expanded tracks form an isosceles triangle, where each half is a right triangle with a base of 5,280 feet and a hypotenuse of approximately 5,280.08 feet.

You can then simply plug this into the Pythagorean Theorem: a2 + b2 = c2 where you are solving for b. The correct answer is almost 30 feet. The problem is that most people’s answer is around two inches. What’s a gap of almost 360 inches or an error of over 99% among friends?

What this example, and several other fantastic examples in the book, illustrates is that we often frame the financial market as a well-ordered machine, where people converge on some concrete truth as they assess a set of objective probabilities. The reality is that people rely heavily on intuition and a subjective set of rules or heuristics when making decisions. This process is further complicated by the interplay and feedback of each decision on other competing decisions. This is why markets are a complex adaptive system, and we reflect this framing into our investment process. How so?

ClearBridge Investments: Investment process

First of all, as fiduciaries and long-term valuation investors, we are consistently trying to solve for business value using a disciplined process: typically by discounting the future free cash flows that we think a business will generate over time. This gets to the heart of our process, which is trying to find stocks where price is well below business value, and we generate returns from price-to-value convergence. In the spirit of the example, we are trying to find opportunities where other investors are pricing something at two inches, while our business-value math suggests 30 feet is the right answer. In most cases, the market gets pretty close to our illustrative 30 feet. In these cases, we have no differentiated view and we naturally don’t bet. However, the market sometimes gets it wrong, at times by a large margin, and we always stand ready to take advantage of such opportunities.

The key here is that doing simple valuation math is critical, but it is not enough to ensure a long-term opportunity. Ultimately, we are in the judgment business, and given the zero-sum competitive nature of stock picking, we must try and solidify what the person on the other side of a given transaction is getting wrong. Essentially, why are our expectations for a given business different than what the market has priced into the stock?

To provide some rigor to our judgment and to reflect the inherent uncertainty of looking into the future, we rely on subjective probabilities of what might happen to a given business over several quarters and years. Basically, we create a probability tree that assigns  different business values to a whole range of scenarios, varying from the nightmare to the best-of-all dreams. This range of potential outcomes allows us to calculate a probability-weighted expected value for a given stock, explicitly detailing the scenario that the market weights most highly and visualizing the magnitude of our variant perception. In many ways, our subjective probabilities reflect our knowledge of a given stock, but they also help quantify our ignorance.

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