The job of a portfolio manager is to make decisions, all day, every day. Some of those decisions result in trades, but many more don’t. So an important question for a portfolio manager is which of your decisions help and which hurt performance? What kinds of decisions are they skilled at making, and which ones would be better made by someone or something else? And could they be using their own energy more efficiently by making fewer and better decisions? Get into decision attribution analytics, the largest and, for investors, most important area of behavioral analytics.
Until recently, these questions were almost impossible to answer. The best performance attribution analysis, the primary assessment tool for many investors and fund managers, starts with the result and works backwards to explain it by comparing it to the performance of an index alternative. But that doesn’t really help the manager: while useful in explaining why the portfolio performed the way it did over a given period, this analysis can’t identify what the fund manager might do differently to achieve a best result.
Decision attribution analysis has been greatly refined in recent years with the exponential growth of machine learning capabilities. Decision attribution is a bottom-up approach, compared to the top-down approach provided by performance attribution analysis. It examines the actual individual decisions a manager made in the reporting period, along with the context surrounding those decisions. Assess the value those decisions created or destroyed and identify evidence of skill or bias within them.
To be sure, managers make different decisions in different market environments, but there is more. Of course, fund managers choose different stocks at different points in the business cycle. But the selection decision is just one of many choices a fund manager makes during the life of a position. There are also decisions about when to enter, how fast to size up, how big to go, and whether to add or trim position as time goes on. Finally, managers make decisions about when to exit and how quickly to do so.
These decisions are less conspicuous, less scrutinized, and apparently far less variable. Having studied the behavior of stock portfolio managers for the better part of a decade, I have seen evidence, time and again, that while we change our selection behavior as the market environment changes, the rest of us our “moves” are more habitual and consistent.
Anyone with historical data on daily holdings in their portfolio has the raw material to see where they are empowered to make investment decisions and where they are consistently making mistakes. I would not like to mislead: decision attribution is a complex task. Any investor who has tried to do so can attest to this. And while it’s interesting to do it as a one-time exercise, it’s only really useful if it can be done on an ongoing basis; otherwise, how can we know if our skill (and not just our luck) is improving?
Only recently has technology made it possible to perform decision attribution analysis on a continuous and reliable basis. It is especially useful in today’s market: it helps managers understand what they can do not only to get a better performance result, but also to demonstrate their skills to investors when their performance is negative.
None of us is perfect at making decisions. Sophisticated capital allocators have no illusions about this. But as portfolio managers, being able to show our investors, with data-driven evidence, that we know exactly what we’re good at and the steps we’re taking to improve is very helpful. And given the availability of the underlying data and now the analytics toolkit, there really isn’t a good excuse not to do it.
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All messages are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.
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Clare Flynn Levy
