A recap of speech given on August 3rd, 2016 at Evercore ISI Quantitative Symposium
Why do Fundamental Investors need to think more Quantitatively? 90%+ of fundamental managers we’ve interviewed do not have their 5 best ideas as their 5 largest positions. The primary reason for this is:
1. QUALITY MEASUREMENT: Fundamental investors generally do not have a repeatable process for measuring idea quality
2. POSITION SIZING: Fundamental investors generally do not have a repeatable process for sizing positions
Quantitative investors “score” or measure the quality of an idea and use that score, in concert with portfolio constraints, to size positions. Most fundamental investors try to do this heuristically and fail. The failure has been overlooked for years because:
1. CLOSE ENOUGH: Fundamental investors are smart and they can get pretty close in their heads. Yes, there will be big mistakes at times when emotions get in the way, but that’s more the exception than the rule.
2. WIDE MARGINS: Since the publishing of “Security Analysis” after the Great Depression, fundamental investors have been able to take advantage of “Mr. Market” by holding true to fundamental investing axioms.
With every passing decade, fundamental margins shrink and are quickly approaching a point where “close enough” is no longer sufficient to generate positive returns. Many fundamental investors and allocators are recognizing this trend and seeking out ways to be more precise and maximize their fundamental advantage. Process is the key.
PROOF OF THE NEED FOR CHANGE
If you want to see how an industry can be transformed by process, look to the recent revolution in sports. Sports managers are just like great fundamental investors. They try to add great players (investments) to their team (portfolio) to maximize their chance of winning (generating positive alpha). Moneyball created a process around each step by making assumptions explicit and measuring their impact on the desired outcome. It’s not any more complicated than that. This simple concept revolutionized all of sports in a matter of 20 years. Investing is in the early years of Moneyball adoption and, if sports is an apt proxy, it will change rapidly.
Over the past 60 years (dating back to Paul Meehl’s “Clinical versus Statistical Prediction” paper), scientists have studied the judgement of experts. There are hundreds of published studies that have a similar theme. Give an expert any and all available data that they want and ask them to make a judgement germane to their field of expertise (ex. Oncologist – how long will a patient live, Parole Board – who is most likely to recidivate, Wine Expert – price of wine at auction, etc.) The one request, is that they tell the scientist which variables are most important in their decision.
The scientist goes off and builds an improper (equal weights all factors) or proper (regressed) model and compares their model to the forecasts of the “experts.” Over the hundreds of expert studies for 60 years, the expert beats the simple model a paltry 6% of the time. And when the expert does forecast more accurately, it is usually by a very small margin.
We are not as good as we think we are at making complicated decisions. But we’re very good at determining the variables that matter. The logical conclusion from those facts is that we need to follow Bob Jones of System Two’s advice (spoke after my presentation at the ISI event with a similar message):
1. Decompose a complex decision into its critical components
2. Evaluate each individually
3. Combine algorithmically1
(1) Weights used are not critical in most cases
The importance of process is evident in our analysis of client data. Below, we show positions where explicit price targets and probabilities were forecast outperformed those positions without forecasts.
In the graph below, we show that clients who are more process oriented (as measured by having price targets, frequency of review, and diligence at updating position size based on their forecasts) outperformed our clients who were less diligent.
We also show below, that had our clients followed their own model their performance would have been 13% vs 7%. Our clients know the variables that matter and following their own process would improve outcomes. Sounds just like the conclusions from the Expert Studies mentioned earlier.
SO WHAT DO WE PROPOSE?
“Objectivity is gained by making assumptions explicit so that they may be examined and challenged, not by vain efforts to eliminate them from analysis.” – Richards Heuer, Psychology of Intelligence Analysis
I believe the changes required to be more process oriented are completely intuitive to investors but require repetition and time before habits are formed. The catalytic change that ignites the whole process is the simple switch from implicit to explicit assumptions.
Step 1: EXPLICIT FORECASTS: Some investors chafe at price targets because they smack of “false precision.” These investors are missing the point because the key to price targets is not their absolute validity, but their explicit nature which allows for objective conversation about the assumptions that went into them. Said another way, price targets improve the investment process because they foster great questions and force the team to be able to defend the methodology behind their calculations.
Step 2: EXPECTED RETURN: Apply probabilities to forecasts and convert them into expected returns. In Moneyball, the statistic that proved best at predicting win percentage was On-Base Percentage. In investing, it is Expected Return. It is the underpinning of good decision making in many scientific fields: actuaries, odds makers, poker players, physicists, etc. and is a requirement for making good decisions as an investor.
Step 3: TURN QUALITATIVE INTO QUANTITATIVE: Just like in the Expert Studies example, define the variables that are important in your decisions, analyze them independently, and combine them algorithmically.
Step 4: DEFINE RULES: Every investor has rules that guide their decision making. Make those rules explicit and construct a framework to measure your adherence to them.
Step 5: MAXIMIZE TRANSFER COEFFICIENT: Make sure all of your rules and assumptions are being transferred into the portfolio. To do that, create a model that expresses all of your rules and assumptions as a position size. This allows you to compare your a priori self with your actual decisions. Said another way, it creates an unemotional version of you. Said yet another way, it creates a system that looks at every position brand new every day and asks the question, “if I were investing in this asset for the first time today, what position size would I take?”
Step 6: ANALYZE RESULTS: Once you’ve done the first five steps, you can measure your explicit assumptions and model for correctness.
Step 7: REFINE PROCESS: Take the results from Step 6 and draw conclusions for ways to improve forecasts, inputs, and the model.
Step 8: REPEAT WITH IMPROVED PROCESS
These steps are straightforward. The adoption of this process is critical to success in the future where the edge for fundamental investors has dramatically shrunk. The difference between two equally skilled analytical minds will be the process applied to maximize that analytical prowess. The future of fundamental investing is clear if Moneyball is a true harbinger of things to come. Embrace the benefits of process today and be at the vanguard of investing. Ignore the benefits of process and slowly lose to competitors more adaptable to change.