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Getting the Drift
Ron Surz, President, PPCA Inc.
Dan Thatcher, President, Thatcher Resources

Executive Summary

With knowledge and awareness of style drifts, consultants can be proactive in protecting their clients' assets, rather than reactive to the harm after it is done. A recent innovation makes this possible. A new type of point-in-time attribution analysis, that classifies portfolio holdings into styles, alerts you to style drift immediately and provides insights that were never before attainable. "I didn't know" is not an acceptable explanation to the client anymore. Get the drift?

Style Drift

Growth stock investing had been in favor through the first quarter of last year, but that all changed with the recent correction, especially the correction in Technology stocks. Now we're all reading about some investment managers losing business because they succumbed to the resurgence of growth stocks at precisely the wrong time. Many of these unfortunates felt they had to make the leap into growth to stay in business, but the real unfortunates are the clients.

Style drift is a serious problem for clients because it distorts asset allocation and undermines performance when styles rotate. Value managers who have drifted over the past three years toward more favored growth stocks are regretting those moves, but not as much as their clients. Most sophisticated investors hire style specialists and, most importantly, design their policies around style stability. To guard against the deleterious effects of style drift, consultants need to become more proactive. They need to protect their clients. Firing a manager after the harm is done does not bring back the loss.

Being Proactive

A consultant's foremost responsibility is to protect a client's assets. It's the "do no harm" counterpart to the medical doctor's creed. To provide this protection a consultant must be diligently aware of the current and prospective consequences of an investment manager's actions. This due diligence is best accomplished with point-in-time attribution analysis that incorporates style, which can provide insights into:

  • Current allocation across styles and economic sectors - Do the present allocations to styles conform to the manager's style mandate? What economic sectors are being emphasized or de-emphasized relative to the style mandate?
  • Skill in picking stocks and sectors - Is the investment manager selecting superior stocks? Superior industries? Why is he adding or subtracting value?
  • Sources of investment return - The market, the manager's style, his individual stock picks, and her industry concentrations all contribute to performance. How much each contributes is an indication of the manager's strengths and weaknesses.
  • Success or failure - Performance is evaluated relative to the manager's assignment, which is usually style-related. Care must be exercised to evaluate skill and not style. A manager whose style is in favor should beat his style benchmark, not just the market.
  • Style drift - Is the manager's style profile similar to what it was in the previous quarter? Is it consistent with the client's policy for this manager? If not, why not?

Point-in-time attribution analysis, incorporating style, is to financial consulting what early detection testing is to medicine.

Despite the superiority of point-in-time analysis, the most popular approach currently used to detect style drift is returns-based style analysis, which uses mathematical regression of investment returns to estimate the manager's effective style mix. Since returns-based style analysis requires only returns, it can do a lot with very little, but the price paid for this convenience is a significant delay in identifying style shifts. For example, consider a returns-based style analysis that uses 30 observations to ascertain style fit. The first period after a style shift from value to growth produces 29 weightings of value versus one weighting of growth (29/1). The next period will provide weightings of 28/2, the next 27/3, and so forth. As you can see, identifying style drift using returns-based style analysis takes a long time. Consequently, this method can only be reactive. Waiting for performance to deteriorate and then confirming that style drift is the culprit means we are too late. The damage has already been done.

Point-in-time style analysis, on the other hand, requires portfolio holdings. This approach takes more time and effort to set up, but the needed information is readily available. The reward for making these additional data entries is immediate identification of style drifts and sector changes and, just as importantly, evaluation of the sources of value added by the manager, enabling consultants to be proactive.

This isn't to say that point-in-time analysis should always be used instead of returns-based analysis. Returns-based analysis is very good at capturing the history of on-average effective style allocation. The resulting style profile is usually quite good for establishing the benchmark in point-in-time attribution. In other words, the point-in-time backdrop should rarely be a standard off-the-shelf index, but rather ought to be a blend of style indexes derived from a returns-based regression analysis. The returns-based analysis establishes the norm against which point-in-time attribution measures value added or subtracted. In this way, the two methods are in fact complementary, and both should be used. Continuing with the medical analogies, returns-based analysis establishes the acceptable range for the test, like your blood pressure, while point-in-time analysis gives you your current reading.

Stock Classifications

At this point you may say, "It isn't easy to classify stocks into styles, and these classifications probably change over time", and you'd be right. In the November 1999 issue of Senior Consultant, one such classification method was described in "Incorporating Style in Attribution Analysis". The appendix to the November article, which details classification rules, is repeated at the end of this article. There are certainly alternative schemes, and the industry may never agree on one single classification methodology, but similar concerns haven't stopped us in the past from using economic sectors, such as Health Care and Technology. The point is: We need to start doing something that makes sense and to perfect it as we learn. We encourage you to revisit the November article and to peruse the following examples of how some S&P500 stocks were classified at the beginning of the third quarter of 2000.

Sample Security Classifications
Large Value Large Core Large Growth
Mid-Cap Value Mid Core Mid Growth
Small Value Small Core Small Growth

Classifications like the ones above are used to determine portfolio style allocations at a point in time and to perform attribution analyses that are far superior to the old-fashioned sector-based approaches that we've been using for the past 30 years. Unlike sector classifications that seldom change, style classifications change frequently. Philip Morris was a growth stock not too long ago, now it's value. Microsoft and General Electric have been hovering on the cusp between core and growth. The world of styles is very dynamic.

Doing the Job Right

It may appear that getting the drift is much too complicated, but like the skilled surgeon, the job can be routine with the best tools. The best tool for getting the drift is point-in-time attribution analysis incorporating style. This type of attribution is relatively new. It's been in use by some consultants for about a year now to be proactive to their clients' needs. Importantly, this sophisticated new tool distills the complex down to the easily understood. You don't need to worry about calculating sources of return, style classifications, and style drift. It's all done for you. Like the medical doctor, you just need to read the report, confident that the technology used is best-in-class. It's your expert opinion that the client is paying for. Isn't it time you got the drift? Don't your clients deserve it?

APPENDIX: Style Groupings

Style groupings are based on data provided by Compustat. Two security databases are used. The U.S. database covers more than 8,000 firms, with total capitalization exceeding $14 trillion. The non-U.S. database coverage exceeds 10,000 firms, 20 countries, and $18 trillion - substantially broader than EAFE.

To construct style groupings, we first break the Compustat database for the region into size groups based on market capitalization, calculated by multiplying shares outstanding by price per share. Beginning with the largest capitalization company, we add companies until 60% of the entire capitalization of the region is covered. This group of stocks is then categorized as "large cap" (capitalization). For the U.S. region, this group currently comprises 180 stocks, all with capitalizations in excess of $16 billion. The second size group represents the next 35% of market capitalization and is called "mid cap". Finally, the bottom 5% is called "small cap" or "mini cap".

Then, within each size group, a further breakout is made on the basis of orientation. Value, core, and growth stock groupings within each size category are defined by establishing an aggressiveness measure. Aggressiveness is a proprietary measure that combines dividend yield and price/earnings ratio. The top 40% (by count) of stocks in aggressiveness are designated as "growth," while the bottom 40% are called "value," with the 20% in the middle falling into "core."