Getting the Drift. Ron Surz, President, PPCA Inc. Dan Thatcher, President, Thatcher Resources 1/12/01. Summary

Transcription

1 Getting the Drift Ron Surz, President, PPCA Inc. Dan Thatcher, President, Thatcher Resources 1/12/01 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. Point-intime attribution analysis incorporating style was not available a few years ago, so consultants could not be proactive, but today they can be. I didn t know is not an acceptable explanation to the client anymore. With the new tools that are now available, consultants can help their clients much more today than they could a few years ago. 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. We re all reading now 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 recently, 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, rather than reactive, to the problem. They need to protect their clients. Firing a manager after the harm is done does not bring back the loss. 1

2 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, 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 2

3 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. For these reasons, Dr. William F. Sharpe, Nobel economics laureate and creator of returnsbased style analysis, has stated that point-in-time style analysis is superior to the method he created. This isn t to say that point-in-time analysis should always be used instead of returnsbased analysis. Returns-based analysis is very good at capturing the history of onaverage 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-intime 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 returnsbased 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-intime analysis gives you your current reading. Stock Classifications But, 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 3

4 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 Sample Security Classifications Large Value Large Core Large Growth BANK OF AMERICA AMERICAN INTL GRP AMERICA ONLINE ANHEUSER-BUSCH GENERAL ELECTRIC CISCO SYS CHEVRON MICROSOFT DISNEY CO PHILIP MORRRIS PFIZER INC AT&T YAHOO Mid-Cap Value Mid Core Mid Growth ARCHER-DANIELS BED BATH & BEYOND ALLIED WASTE ALCAN ALUMINUM ECOLAB PHELPS DODGE BLACK & DECKER PERKIN ELMER SAPIENT COOPER INDUSTRIES STARBUCKS NCR CORP TEKTRONIX Small Value Small Core Small Growth GREAT A&P TEA RUSSELL ARMSTRONG SPRINGS 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 oldfashioned 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 point-in-time attribution analysis incorporating style is StokTrib, from PPCA, Inc. ( Inc.com). Consulting professionals like yourself have been using StokTrib for more than a year now to be proactive to their clients needs. Importantly, this very sophisticated tool distills the complex down to the easily understood. You don t need to worry about 4

5 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-of-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." 5

6 StokTrib Holdings based point-in-time attribution analysis against a style customized benchmark

7 Investment Performance Knowledge Attribution Knowledge Skill versus Luck Data Facts Measurement Rate of Return Information Evaluation Success versus Failure Judgment

8 Sources of Return Market : Habitat Sector or Style Allocation Stock Selection Activity and Currency

9 Performance Attribution Using Styles Portfolio Market Selection PCT OF MARKET VALUE A RATE OF RETURN B PCT OF MARKET VALUE C RATE OF RETURN D STOCK STYLE TOTAL LARGE VALUE CORE GROWTH MID VALUE CORE GROWTH SMALL VALUE CORE GROWTH 21.5% % % 73.4% % % 5.1% % % % 12.8% 20.9% 16.3% 7.2% 11.5% 2.6% 1.3% 1.1% % % STOCK = A x (B-D) / 100 STYLE = (A-C) x (D-TOTAL INDEX RETURN) / 100 4

10 Attribution Approaches Total Market Style-Specific Market Style Groups Economic Sectors Economic Sectors (new) (old) (newest & best)

11 Performance Attribution Using Styles Portfolio Market Selection PCT OF MARKET VALUE A RATE OF RETURN B PCT OF MARKET VALUE C RATE OF RETURN D STOCK STYLE TOTAL LARGE VALUE CORE GROWTH MID VALUE CORE GROWTH SMALL VALUE CORE GROWTH 21.5% % % 73.4% % % 5.1% % % % 12.8% 20.9% 16.3% 7.2% 11.5% 2.6% 1.3% 1.1% % % STOCK = A x (B-D) / 100 STYLE = (A-C) x (D-TOTAL INDEX RETURN) / 100 6

12 Total Market Style Groups

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14 Style-Specific Market Industry sectors

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16 History

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20 About the Universes Portfolio Opportunity Distributions (PODs)

21 The Problems with Benchmarks and Peer Groups Benchmarks -- Defining -- Waiting Peer Group Biases Survivor Classification Composition size membership

22 (Computerized Universes) Portfolio Opportunity Distributions (POD s POD s) Investment Sub-Universe Portfolio Construction RULES Portfolio Opportunities

23 World Global Global ex Japan Global ex UK Global ex USA Global ex USA & Japan POD Regions Continents North America Far East Far East ex Japan Europe Europe ex UK Emerging Markets Countries USA Japan UK Canada Ireland Australia Germany France Switzerland Netherlands Other

24 Surz Style Definitions Capitalization E/P + y Large : Top 65% of $s Value : Top 40% Mid : Next 30% Core : Middle 20% Small : Bottom 5% Growth : Bottom 40%

25 Mobius Research Candidates : Russell, Surz, Pru, Wilshire, Vestek Fundamental : All win except Wilshire Tracking error : Surz & Russell Multicollinearity : Surz Coverage : Surz & Russell Winner : Russell????

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27 Page 12 SENIOR CONSULTANT November 1999 Performance Evaluation: Incorporating Style in Attribution Analysis Ronald J. Surz Ronald J. Surz is a Managing Director of Roxbury Capital Management, an investment management firm in Santa Monica. He has earned an MBA in Finance at the University of Chicago and an MS in Applied Mathematics at the University of Illinois, and holds a CIMA (Certified Investment Management Analyst) designation. Ron can be reached at or Ron@RoxCap.com. Performance attribution determines the reasons for performance being good or bad. Attribution goes beyond performance evaluation to uncover manager strengths and weaknesses, thus empowering us to make informed hire/retain/fire decisions. This article describes a popular attribution approach that has been used for the past two decades and recommends two superior approaches. We know much more today than we did two decades ago and today's computer technology makes it possible to do much more. The Need to Modernize Performance Attribution Attribution attempts to differentiate luck from skill and to identify the types of skill. Erroneous inferences based on antiquated methods can be quite costly to investors when they lead to wrongful firings and/or misguided hirings. Investment managers benefit most from enlightened attributions through improved client communications and decision making. Managers use attribution to see what works and why, and to make appropriate adjustments. To properly separate luck from skill we know today that style is usually an important consideration. Ignoring style can lead to false conclusions such as attributing to skill when it s really an in-favor style, or damning the skillful manager whose style is out of favor. Antiquated attribution methods ignore style effects entirely. Contemporary attributions properly separate the effects of style from skill in sector and stock selection. Except for sector fund managers, all domestic managers manage to a style, either explicitly or implicitly. Even managers who profess style neutrality tend to exhibit an identifiable and consistent style profile. Accordingly, the performance of most managers should be attributed to sector and stock selection within their style. The assessment of luck or skill within the style is then based on the probability that these selections are nonrandom, or statistically significant. Old-Fashioned Sector-Based Attribution Table 1 shows an example of a popular attribution approach that uses economic sectors to differentiate sector selection from stock selection. The first two columns of the table show the subject portfolio's commitment and return to each economic sector, respectively. The second column also shows return rankings in parentheses with 1 being the best and 100 the worst. This ranking is the probability that return is due to skill or lack thereof. The derivation of these rankings is discussed below. The next two columns show the corresponding market commitments and returns, where the market in the table is the entire Compustat database (over $11 trillion comprising approximately 8,000 stocks). The last two columns use differences in commitments to attribute sector selection effects and differences in performance to attribute stock selection effects. The example is for a real live midcap growth manager in the third quarter of As can be seen in the table, the manager's buy-and-hold return of % significantly lags the market's % return, and most of this underperformance (-5.48%) is attri0butable to poor stock selection. Compounding this problem, activity during the quarter subtracted 1.52%, bringing the actual return (-19.21%) ranking down to the 91st percentile, or the bottom decile. Activity is the difference between the actual return and the buy-and-hold return, and is so named because it arises from trading during the quarter. The arithmetic for arriving at these conclusions is straightforward and is shown at the top of the last two columns. It would seem that a serious conver- Table 1. Old-Fashioned Sector-Based Performance Attribution Portfolio Market Attribution (D-m) A B C D (A-C)* A(B-D) Cmtmt Return (Rk) Cmtmt Return Sectr Slctn Non-Dur (95) Durabls (99) Mat&Ser (99) Cap Gds (1) Tchnlgy (81) Energy (99) Transpo (99) Utility Finance (74) (86) Activity m (91) Source: PPCA 1999 PCT Publishing Circulation and Editorial Offices 1457 Crystal Springs Lane Richmond, VA Fax

28 November 1999 SENIOR CONSULTANT Page 13 sation with this manager is in order, and perhaps more. But, you will see that this manager, who is actually quite skillful, did not mess up nearly as badly as it would appear. Style-Based Attribution As mentioned above, we know that this manager is mid-cap growth. Could this be a possible explanation for his apparent abysmal stock selection? More importantly, shouldn't his known style of management be incorporated into the attribution analysis? We know something today that we didn't know 20 years ago when sector-based attribution was developed: manager style is a powerful determinant of investment performance. Table 2 presents style-based attribution for the same manager and time period shown in Table 1. The analysis proceeds along the same lines as Table 1, but now stocks are classified by style rather than by economic sector. The definitions used for these style classifications are provided in Appendix 1. Table 2 tells a completely different story than Table 1. The style-based attribution indicates that virtually all of the underperformance is attributable to the manager's style mid-cap growth was out of favor in the quarter. Actually, only large cap was in favor for the quarter. This manager's stock selection is actually average rather than abysmal. In addition to superior and accurate inferences, style-based attribution monitors manager adherence to style. In Table 2, we see that the manager is predominantly mid-cap growth, with a 61.3% allocation, but that 37% of his portfolio is allocated between mid-cap value and mid-cap core. This is quite acceptable since very few managers allocate 100% to their stated style. Of course, there would be cause for concern if a pattern of style divergence had emerged or if there were a significant departure from the style mandate. Style-Specific Attribution We could stop with style-based attribution as a superior alternative to sector-based attribution, but there's still room for improvement. We can combine styles with sectors to achieve the best inferences of manager strengths and weaknesses. To do so, we redefine "market" to be the market for the manager's style. Here we can define the style independently (e.g. 100% mid-cap growth), or we can use the profile from the style-based attribution (61.3% midcap growth, 22.8% mid-cap core, 15.6% mid-cap value, and 1.2% small-cap value). In Table 3, we use the style profile derived in Table 2 to analyze the manager's sector and stock selection within his style universe. As can be seen, the style-specific return of % is fairly close to the manager's buy-andhold return, ranking it in the 52nd percentile against its style-specific universe. Adjusting for the 1.52% subtracted from performance by activity during the quarter, the manager's ranking declines somewhat to the 58th percentile. These style-specific rankings are substantially better than the bottom decile rankings suggested by comparison to the broad market. In other words, this manager underperformed relative to his style, but not by much. Table 2. New Style-Based Performance Attribution Portfolio Market Attribution (D-m) A B C D (A-C)* A(B-D) Cmtmt Return (Rk) Cmtmt Return Sectr Slctn LrgeVal LrgeCor Lrge Gro MidlVal (96) MidlCor (72) MidlGro (34) SmlVal (2) SmlCor SmlGro (86) Activity m (91) Table 3. New Style-Specific Performance Attribution Portfolio Market Attribution (D-m) A B C D (A-C)* A(B-D) Cmtmt Return (Rk) Cmtmt Return Sectr Slctn Non-Dur (70) Durabls (99) Mat&Ser (91) Cap Gds (1) Tchnlgy (28) Energy (99) Transpo (91) Utility Finance (69) (52) Activity m (58) Source: PPCA You can also see how style matters within sectors. For example, consider the technology sector. As shown in Table 1, the total technology sector returned -2.6% in the quarter. By contrast, the mid-cap growth portion of the technology sector returned the much lower % shown in Table 3. You needed to be in the big caps to avoid the hit to technology in the third quarter of About the Rankings Rankings are shown in the tables (under the "Rk" columnar heading) for performance in each sector/style and for total performance. These rankings are derived from Portfolio Opportunity Distributions (PODs) where the computer generates all the possible portfolios that could be held in the stocks comprising the sector, style or relevant market. 1 Among the features of 1999 PCT Publishing Circulation and Editorial Offices 1457 Crystal Springs Lane Richmond, VA Fax

29 Page 14 SENIOR CONSULTANT November 1999 PODs are their lack of bias and their early availability. POD universes, or cyberclone peer groups, are free from numerous biases that plague traditional peer groups. Also, since PODs rely solely on security data, they are available within days of quarter-end, so evaluations can be performed much sooner than those approaches that wait for live manager databases to be assembled, and they are constructed over cumulative time periods as well as the single periods shown here. A Summary So Far Table 4 summarizes the results of Tables 1 through 3. Here we see that the manager's overall ranking is near the bottom decile, but his style-based ranking is near median. We also see that his style returned -17.9% in the quarter versus the market's % return, resulting in much of his underperformance being attributable to style. Extensions This discussion has focused on domestic equity performance attribution before taxes for a single quarter. Extending the analysis to other asset classes over time after taxes is straightforward. Cumulative period analyses are complicated by the fact that returns are compounded, but it is desirable to have attribution components added. Algorithms developed to solve this problem have been applied in the past to old-fashioned sector-based approaches, 2 and these algorithms have now been applied to the style-based approaches described here. Tax effects are assimilated into the attribution analysis by subtracting the difference between actual taxes and those taxes that would have been due from holding the manager's style passively. Non-U.S. attributions follow a similar style-based framework with an additional component added for currency effects. These currency effects are evaluated using an appropriate style-based POD for currency returns. Some argue that non-u.s. managers have no style, but there's increasing evidence that despite statements to the contrary, non- U.S. managers do, in fact, frequently exhibit an identifiable and consistent style profile. For U.S. managers investing abroad, this style profile tends to be a mix of large cap value and large cap core (surprised?). Bond attributions proceed along similar style lines too, with different style definitions, namely low, middle and high quality, segmented by long, intermediate and short durations. The Cross of Changes Mark Twain said "Everyone hates change but a wet baby." So it is with the ideas presented in this article. I asked everyone I could think of what they thought of style-based and style-specific attributions. All agreed that stylespecific attribution is indeed a new idea. Most thought it was a pretty good idea, but some felt otherwise. Here are some of the criticisms and my brief responses to these misconceptions. 1. Evidence Criticism: There is no evidence that styles explain any more or less than sectors. Response: Zephyr Associates and The Möbius Group have both conducted research on the relative explanatory powers of styles versus sectors. Using Table 4. Summary of Tables 1 Through 3 Overall Rank Style-Specific Rank Buy & Hold Return Activity Actual Return Attribution Market Return Sector/Style Allocation Stock Selection Overall Market Style-Based Sector-Based Style-Specific Market Sector-Based Allocations LrgeVal LrgeCor LrgeGro MidlVal MidlCor MidlGrov SmlVal SmlCor SmlGro Actual Market StylMkt Non-Dur Durbl Mat&Ser Cap Gds Tchnlgy Energy Transpo Utility Finance Actual Market StylMkt Allocation Attribution LrgeVal LrgeCor LrgeGro MidlVal MidlCor MidlGro SmlVal SmlCor SmlGro Market Non-Dur Durbl Mat&Ser Cap Gds Tchnlgy Energy Transpo Utility Finance Market StylMkt Selection Attribution LrgeVal LrgeCor LrgeGro MidlVal MidlCor MidlGro SmlVal SmlCor SmlGro Market Non-Dur Durbl Mat&Ser Cap Gds Tchnlgy Energy Transpo Utility Finance Market StylMkt PCT Publishing Circulation and Editorial Offices 1457 Crystal Springs Lane Richmond, VA Fax

30 November 1999 SENIOR CONSULTANT Page 15 returns-based optimizations to explain the performance of a large cross-section of funds, both firms conclude that style indexes do a much better job of explaining performance that do sector indexes. 2. S&P Rules Criticism: At the end of the day, the client only cares about beating the S&P 500. Response: The whole focus of performance evaluation ought to be on developing expectations for the future. For an individual manager these expectations evolve from an understanding of the manager's form and substance which, in today's world, translates into style and skill. It's hard to know in advance if the manager's style will be in favor, but you can still develop expectations many today are expecting large cap growth to end soon. The skill part is the manager's crystal ball. Bill Sharpe and others have demonstrated that even the best crystal balls can go unappreciated when style effects are ignored because they are so powerful. Comparisons to the S&P 500 can easily miss a skillful manager whose style is out of favor, and this can happen for an extended period of time long enough to result in unwarranted termination. 3. Agreeing on Classifications Criticism: No one will ever agree on the classification of stocks into style segments. Response: It is true that there will probably never be a universally accepted mapping of stocks into style groups, but there has never been such a mapping into economic sectors either. Yet, sector-based analysis is the accepted way of doing things. The style allocations used here are described in the Appendix. They have been compared to other approaches by the Möbius Group and found to be superior. 4. The Generalist Criticism: Style-based analyses don t mean much to a manager with a broad market mandate. Response: Style-based analysis may be less meaningful for a generalist manager, but it never hurts to observe how the manager implements his broad mandate. Most evaluators use sector analysis to make these observations. Why not use styles as well for additional insights. Conclusion Manager style really is a very powerful determinant of performance. Ignoring this fact, by using old-fashioned sector-based attribution analysis can lead to significantly misleading inferences, as shown in our real-life example. Style-based attribution produces much more reliable indicators because most investment managers manage to a style rather than to some mix of economic sectors. Style-specific attribution, which combines styles with economic sectors, is even better since we gain a better understanding of the manager's unique market as well as the manager's successes and failures within this security habitat. Investment managers stand to benefit most from Style-based attribution produces much more reliable indicators because most investment managers manage to a style rather than to some mix of economic sectors modernized investment performance attribution as a result of the superior insights obtained from style-based analyses. Since superior insights lead to superior decisionmaking the client benefits as well. Free software for the approach described here can be obtained at so you can experience the difference. 1 Surz, Ronald J. (summer 1994), "Portfolio Opportunity Distributions: An Innovation in Performance Evaluation", Journal of Investing. Surz, Ronald J. (winter 1996), "Portfolio Opportunity Distributions: A Solution to the Problems with Benchmarks and Peer Groups," Journal of Performance Measurement. Surz, Ronald J., (winter 1998), "Cyberclone Peer Groups," Journal of Investing. 2 Carino, David R. (summer 1999), "Combining Attribution Effects Over Time", The Journal of Performance Measurement. Many thanks to the following for their helpful comments and suggestions: William F. Brock of Evaluation Associates, Dr. Michael Edesess of Lockwood, Steve Hardy of Zephyr Associates, Dr. Robert Haugen of Haugen Systems, Rex Macey of Macey- Holland, Jack Marco of Marco Associates, Kenneth Phillips of Money Management Partners, Troy Schell of Jeffrey Slocum & Associates, Dr. William F. Sharpe of Financial Engines, and two anonymous reviewers from The Journal of Performance Measurement. Appendix. Style Groupings Style groupings are based on data provided by Compustat. Two security databases are used. The U.S. database covers more than 7,500 firms, with total capitalization exceeding $11 trillion. The non-u.s. database coverage exceeds 5,500 firms, 20 countries, and $10 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 150 stocks, all with capitalizations in excess of $13 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." 1999 PCT Publishing Circulation and Editorial Offices 1457 Crystal Springs Lane Richmond, VA Fax