Saturday, January 14, 2017

Musings about Behavioral Finance and Valuation after reading The Undoing Project

Still working through the linkages of  The Undoing Project, Behavioral Finance and Valuation
As I prepare for the spring semester – and a new module on Behavioral Finance – I must admit that I’m still somewhat of a skeptic on the topic following my reading of The Undoing Project (See my previous blog entry on the review of Michael Lewis’ The Undoing Project). While each year more research is providing interesting results about the irrational behavior of individual investors and financial analysts, I remain committed to the ‘rationality’ assumptions that support classical finance theory and the belief in the efficiency of markets in the long-run.
I don’t want to ignore the reality that all individuals do not always act as wealth maximizing, risk adverse investors - and that we weigh losses more than equivalent gains. So my conclusion is that economists need to better specify the von Neumann-Morgenstern expected utility function; however, that's for another time...  

What has me interested in behavioral finance is its ability to hopefully improve investor performance. The annual Dalbar study, which follows the overall performance of the ‘average’ investor, certainly backs up the need for improvement. Note the dismal performance of average investors (yellow bar) over a recent 20-year period (the graphic below shows average annual returns of only 2.1% versus 7.2% for a 60/40 portfolio). [The table is from JP Morgan’s Q1 2017 Guide to the Markets].
20-Year Annualized Average Performance
Indicates Poor Returns for the 'Average Investor'

So, to be clear, I believe it is important for students of finance to understand the common behavioral investment mistakes made by individual investors and analysts (i.e. framing, overconfidence, anchoring, etc.). If they can learn to avoid these mistakes – and even better yet, help their clients avoid them as well - then it is a worthwhile endeavor. It would be great if over time the ‘average investor’ earned returns more in line with the 60/40 returns shown above – it would help minimize some of the concern about the looming retirement crisis forecast for Western economies.
I realize that the real world is untidy and not every investor or financial analyst has the same information or acumen to act rationally, so learning some basic rules of thumb (or heuristics) of behavioral finance might help some individuals avoid serious blunders. That’s a good thing.
I do find the chore of reconciling behavioral finance with traditional discounted cash (DCF) valuation to be challenging. Each semester I teach my students how to conduct fundamental investment analysis using various valuation approaches (DCF models, arbitrage pricing, and relative valuation). I continue to believe that the models hold up well and that there is a substantial body of empirical research that supports long-term market efficiency; so, I am not about to abandon classical finance theory.
As behavioral finance continues to make major inroads in academia and the investment industry, I don't believe that traditional intrinsic valuation approaches will change much at all. True, the expected future cash flows and the required discount rate in a DCF model still must be forecast; however, if an analyst has skewed or biased expectations that lead them to inaccurate valuations, then they’ll eventually be working in at another occupation.
It can be argued that assessing future cash flows is an undertaking that is subject to all sorts of behavioral deceptions and traps; however, in the end the truth comes out and either the analyst has a good batting average or not. So, while traditional, fundamental analysis can be rife with behavioral issues, the valuation process is worth doing. The fine line is to avoid being too mechanical versus too instinctive.  Forming expectations and adjusting for risk can be skewed by behavioral components; however, the key to doing good valuation work is to be aware of your own behavioral biases, how they might impact the modeling, and to learn how to counteract your prejudices.
Here is where I believe behavioral finance has value for investment students and future research analysts:
  • It can help explain why analysts arrive at different prices for the same stock. By studying the interaction between psychology and valuation, behavioral finance can help to identify systematic analysis errors (i.e. herding, availability bias, hindsight bias, etc.) in the valuation process.
  • It is useful in understanding why a stock price differs from an analyst’s estimate of intrinsic value. The price can diverge from the intrinsic value because the analyst makes estimation mistakes or because they missed or under-weighted some relevant information. The analyst could behave in an irrational manner (refusing to deviate from the crowd or exhibiting overconfidence, familiarity or home bias) that can cause prices to depart significantly from their estimated values.
  • Overconfidence can be detrimental to the analyst’s stock-picking ability in the long run and can lead to excessive trading. Analysts, like any other professional, tend to exaggerate their abilities and underestimate the likelihood of bad outcomes over which they have no control. This combination of overconfidence and optimism can cause analysts to overestimate the reliability of their knowledge, underestimate risks and exaggerate their ability to control events, which often leads to excessive trading.

The one area of behavioral finance that I concede is not properly or adequately addressed by classical “rationality’ economics is loss aversion. This refers to an individual's tendencies to prefer avoiding losses over acquiring equivalent gains. In common terms it could be said that most people feel it is better to not lose $100, than it would be to gain $100. Amos Tversky and Daniel Kahneman conducted studies and suggested that losses are at least twice as powerful, psychologically, as are equal gains. This observation (loss aversion) needs to be incorporated into expected utility theory.

As an investment professor, it behooves me to help my students become conversant with the concepts and findings of behavioralists so that they can recognize when their instincts or prejudices could lead to poor outcomes. The CFA Institute is also adding more content on behavioral finance.
So, you ask, if most analysts and investors have behavioral biases, then how can markets be efficient? The answer for me is clear... while any analyst can be wrong about their price estimate for Amazon, the many independent analysts who cover Amazon are likely to collectively get the valuation correct, on average, in the long-term – the trick as an analyst or investor is to have fewer missed estimates!

1 comment:

  1. A major source of forecasting precision is the analyst's understanding how the industry works. This is easier for managers who are in the business than for analysts who sometimes do not have access to the same information