Monday, November 3, 2014

Ervolini, Managing Equity Portfolios

Active portfolio managers are judged by the outcomes of their investing strategies, such as risk adjusted returns. More often than not, these returns come up short when measured against standard benchmarks. Understandably, investors ask why they should pay management fees for subpar active management when they could park their money in passive index funds at a significantly lower cost.

In Managing Equity Portfolios: A Behavioral Approach to Improving Skills and Investment Processes (MIT Press, 2014) Michael A. Ervolini, CEO of Cabot Research, offers the beleaguered portfolio manager some suggestions on how to go about improving. That such a book is deemed necessary is somewhat worrisome. Retail investors and traders, whose portfolios are dwarfed by their institutional brethren’s and who don’t collect hefty salaries for managing their own money, have been deluged with works offering much of the same advice—beware of behavioral traps, focus on process rather than outcome, keep a trading journal. Fortunately, since Cabot Research provides “innovative analytics to help money managers improve portfolio performance,” the book also highlights some of the metrics that all traders and investors can use to assess and improve their own performance.

Instead of rehashing the findings of behavioral finance, which I’ve written about on numerous occasions, I’ll focus on the book’s main organizing principle and some of its attendant metrics.

Investing, stripped to its bare essentials, involves three skills: buying, selling, and position sizing. “Familiar as these terms are,” the author writes, “few professionals know with any degree of confidence how much of their portfolio alpha comes from just the buying.” Or the selling, or the position sizing. “Not knowing how effective each skill is means that any attempt to improve is, at best, based on a hunch.” The author advocates adopting an analytical framework that regards performance as “a portfolio of decisions, rather than holdings. … The goal is to rigorously measure skills, process, and behaviors so that managers can do more of what they do well and have the necessary information to make small refinements that have a high likelihood of helping them improve.” (p. 158)

How should a money manager proceed? First, he has to collect data—the more granular the better. “For example, when making adds to existing positions, you might jot down answers to the following questions: Is the position currently a winner or loser? Prior to the add, is it small, mid-sized, or large relative to your typical full weight? How is it performing relative to the two or three basic attributes you use to gauge a stock’s alpha potential … ? How are you feeling—optimistic, pessimistic, confident, fearful, etc.?”

The manager must then analyze the data. “For example, to look deeper into your adds to winning positions, you might create a graph with an x-axis indicating time and two distinct y-axes, the one on the left indicating cumulative return, and the one on the right indicating the size of the adds.” (p. 236) And finally, he must draw up an improvement plan.

Ervolini walks the reader through a few ways to analyze buy, sell, and position sizing decisions. Sell decisions can be critiqued through the lens of holding time—whether positions harvested early are helping or hurting performance. As for buying or adding to positions, you can analyze whether your “high conviction” purchases are outperforming those in which you have less confidence.

The upshot is that you can only know your strengths and weaknesses if you find a way to measure your investing behavior—a way that is, the author recommends, simple (otherwise you won’t do it) and granular (otherwise the results will be too general to be useful). Separate out your trading activities, and here, I would suggest, you can include such things as order type and fill, response to market volatility, source of investment idea, etc. You never know what kinds of analytics might be helpful until you start collecting data and mining the results for performance gold.

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