I have been working in the investment industry for my entire sixteen year professional career and have had the opportunity to meet and advise hundreds of portfolio managers, analysts, and traders. In addition, I’ve read as much psychology, investment, and decision process research as I could get my hands on. Because of this background, clients often ask for best practices I’ve observed. I’ll usually rattle off a few that are top of mind, but I thought a more thorough list of the Best Practices was warranted.
Some of the best practices are narrowly applicable to Alpha Theory but most are broadly applicable to investing and even to decisions we make in everyday life. The best practice list is a living document that continues to grow and improve. I suspect that I’ll never stop refining this list but I believe there are a few central tenets:
1. Process is important
2. Good decisions can have bad outcomes…and vice versa
3. Emotion is the enemy of good decisions
4. Only explicit assumptions can be properly judged and evaluated
5. A simple model almost always beats an educated guess
Over the coming months, I will memorialize some of the Best Practices through a series of blog posts. I’ll start out with what we’ve observed to be the single most important Best Practice:
Best Practice #1: PROBABILITY WEIGHTED RETURN
“Objectivity is gained by making assumptions explicit so that they may be examined and challenged” – Richards Heuer, CIA Head of Analytic Methods and author of Psychology of Intelligence Analysis
It will come as no surprise to anyone that knows Alpha Theory’s work that using Probability Weighted Return1 is the first Best Practice. So many ills are healed by using Probability Weighted Return that it is unacceptable for a fund not to use it. Here is a litany of reasons why:
1) Decision Tool. Probability Weighted Return is the ultimate culmination of the research process. Every piece of information gathered through the research process can be incorporated into a probability weighted analysis. Every new piece of information will alter it. A Probability Weighted Return effectively conveys the learned information in a form that can be used to make subsequent decisions like, should I buy this asset, and if so, how much?
2) Explicit. Probability Weighted Return is explicit (see quote above). In a conversation without a Probability Weighted Return, the important data can lose context because the listener (or reader) is required to build their own mental model of how to think about risk and reward. Explicit estimates of reward, risk, and probability allow for the information learned through the research process to have context with regards to how they impact either the risk, reward, or probability.
3) Accountability. Explicit estimates create accountability and auditability. Implicit assumptions can be misinterpreted or allow equivocation. Accountability is gained when estimates are written down, tracked, and audited.
4) Downside. Downside is disproportionately more important than upside in a fund because of compounding. Downside estimation is critical to position sizing and is often given short shrift. Of course, downside is discussed in research overviews, but is it effectively accounted for? Probability Weighted Return requires an explicit estimate of downside that must be justified and defended.
5) Thesis Myopia. It is easy to get lost in the story of an idea and forget about the value. Stories are enticing but without value, there is no inefficiency to take advantage of. When an analyst is forced to describe both reward and risk, the myopia that comes with focusing on a single thesis is stripped away and the importance of price paid becomes paramount.
6) Maximize Fund Return. If the goal of the fund is to maximizeProbability Weighted Return then it is imperative that it be calculated for each position. How else could you calculate theProbability Weighted Return of the fund?
This is the first installment of Alpha Theory Best Practices. Stay tuned for more over the coming weeks and months (maybe even years). As we release these, we’d love to know some of your Best Practices and where you may disagree with our conclusions.