The Active vs. Passive Management Debate

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Commerce Trust Company Research Group – 2020

The Active vs. Passive Management Debate

EXECUTIVE SUMMARY

There was a time when virtually all investors seeking exposure to the stock and bond markets held a limited number of individual stocks and bonds or achieved diversification through active mutual funds. Today, more than $8.5 trillion in assets are invested in index strategies (mutual funds and ETFs). The growth in indexing has coincided with an escalating debate over whether it pays to invest in active management, with the chance to outperform, or whether an investor is better off paying lower fees to perform generally in line with the index.

Proponents of indexing argue that markets are so efficient that active managers cannot benefit from mispricings. They also point to a lack of skill among active managers to distinguish future high-performing securities from future poor performers sufficiently to overcome the additional active management expenses. They often cite research that shows that the average active manager fails to outperform the target index.

This last point shouldn’t be a surprise, as active managers (of both funds and separate accounts) represent a large portion of assets in many markets. Therefore, the aggregate trading of these active managers has a profound effect on security and index returns. If the performance of the average manager largely determines the index performance before fees, we might expect the average manager to underperform the index after fees.

We initially explored this topic by analyzing long-term active manager historical performance. We used the entire universe of active equity funds in the Morningstar database (which reflects returns after fees) on a category-by-category basis. In our analysis, we asked the question: On average, what percentage of managers outperformed their benchmarks after fees over the five-year (or 10-year) periods?

Overall, the results would not support indexing across the board, or even in the majority of categories. In fact, the data suggest that large percentages of managers do outperform their index benchmarks over long periods of time – not for every period, but on average.

As we expected, the results in the value categories looked weaker than in growth categories with similar market capitalizations. The analysis points to the need to select better-than-average value managers in order to outperform the index. The growth numbers looked slightly better than we had expected, as did the results in small- cap value. It’s generally accepted that small-cap markets are less efficient than large-cap markets, so it came as no surprise that small-cap

categories had results that beat those of large-cap markets.

We further stated that manager research and due diligence performed by a trained staff can lead to more success when hiring a manager (i.e., be more successful than throwing a dart).

RESEARCH REPORT

THE BOTTOM LINE

■ The average active manager in a number of equity categories historically has not been able to outperform the index over long periods.

■ However, significant

percentages of active managers have been able to beat their benchmarks over long periods but not necessarily over shorter periods.

■ Investors need to select better-than-average managers to boost their chances of beating the index.

■ Virtually all active managers underperform the index over shorter periods of time, which may tempt investors to terminate a manager at precisely the wrong time (i.e., immediately before a period of outperformance).

■ One way of smoothing

excess return patterns and reducing the temptation to terminate a manager prematurely is to combine managers with uncorrelated patterns of out- or

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We continued our analysis with a focus on the question of active management consistency. It would appear that virtually all managers, even the ones that outperform over the long run, can experience shorter-term periods of underperformance. These shorter periods of underperformance can be painful enough to tempt an investor to terminate the manager. Furthermore, this temptation to terminate the manager can occur at exactly the wrong time – immediately before the manager begins to outperform.

We present a solution to the problem of “termination temptation.” The solution involves the combination of complementary managers with low correlation of excess returns. These types of combinations can reduce periods of underperformance as one manager “zigs” while the other manager “zags.” If we look at a total portfolio, which can comprise a number of manager combinations in different categories, presumably we may see that some combinations may be outperforming while other combinations may be underperforming.

With diversification of categories in a portfolio, the overall effect of active management can be additive even as some managers underperform, because others may be outperforming. Our philosophy leads us to use some passive managers in select areas, such as large-cap value, where even the best active managers can struggle. Of course, both the concept of endpoint sensitivity (defined on page 3) and the notion that past performance does not guarantee future results remind us that a single investor’s experience may vary significantly from our conclusions.

INTRODUCTION

There was a time when virtually all investors seeking exposure to the stock and bond markets held a limited number of individual stocks and bonds or achieved diversification through active mutual funds. It wasn’t until the first index funds appeared

in the 1970s that indexing, or holding all of the stocks in an index, became a viable investment approach for many investors. While index mutual funds were successful on their own, the advent in the 1990s of exchange-traded funds (ETFs), which are exchange-traded intraday, brought about another flood of assets into index investments.

Today, more than $8.5 trillion in assets are invested in index funds (mutual funds and ETFs). That compares with about $13.3 trillion in active mutual funds and many trillions more in individual stocks and bonds. Additional assets are invested in derivatives (such as futures) on indices. The vast majority of indexed assets are in equities, although bond index funds have made inroads in recent years.

As with actively managed funds, index funds and ETFs allow investors with even small investment portfolios to attain broad market diversification. Active funds provide the lure of outperforming the index, while indexed investments charge lower fees with the goal of matching the index performance.

The growth in indexing has coincided with an escalating debate over whether it pays to invest in active management, with the chance to outperform, or whether an investor is better off paying lower fees to perform in line with the index. In order to outperform an index, a manager must build a portfolio with different securities or portfolio weights from those of the index. The manager must be able to differentiate the securities that will perform better than the index average from those that will underperform.

Many proponents of indexing argue that markets are efficient – that is, information is incorporated into prices so quickly and perfectly that active managers cannot benefit from mispricings. Supporters of indexing also point to a lack of management skill needed to distinguish future

high performers from future poor performers consistently enough to overcome the additional active management expenses. They often cite research that shows that the average active manager fails to outperform the target index.

The conclusions of this type of research shouldn’t be a surprise to us, as active managers (of both funds and separate accounts) represent a large portion of assets in many markets. For example, we estimate that more than 50% of all large-cap stocks are managed as part of an active mutual fund or separate account portfolio. Therefore, the aggregate trading of these active managers has a marked effect on the index and individual security returns. The same holds true in mid- and small-cap stocks. If the performance of the average manager largely influences the index performance before fees, we might expect the average manager to underperform the index after fees.

In market areas where the return distributions are tight (i.e., the top-performing securities are

outperforming the bottom-performing securities by only a narrow margin), it becomes even tougher for active managers to overcome the hurdle represented by fees. In addition, even managers considered to have the highest level of skill can suffer periods of underperformance, further making the case for indexing.

The purpose of this paper is to investigate the debate over active management versus indexing. In the following pages, we analyze active manager historical performance to understand whether it pays to employ active management or whether investors are better off placing their assets in index products. We also explore whether there are ways to employ active managers in better ways. Our analysis focused on returns only, ignoring taxes and risk for purposes of simplification.

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3 Our first step is to examine the

historical performance of active managers versus index performance in order to assess the ability of active managers to outperform their index benchmarks after fees over the long run. We limit the scope of this paper to equity managers, and we use Morningstar’s active mutual fund universe (which reflects returns after fees) as our proxy for all active managers. For clarification, throughout this paper we use the term “manager” to refer to a manager strategy (such as a mutual fund) and not to the particular individual managing the strategy.

ACTIVE MANAGER HISTORY

We analyzed the entire universe of active funds in the Morningstar database on a category-by-category basis. We examined five-year and 10- year periods beginning January 1994, rolled quarterly. This resulted in a total of 81 rolling five-year periods and 61 rolling 10-year periods. We chose these longer rolling periods for a number of reasons:

■ Investment performance data tend

to be exceedingly sensitive to the time period chosen (“endpoint sensitivity”). A winner in one time period may be a loser in the next time period. We were interested

in overall performance over many time periods, particularly as investors are constantly moving in and out of markets. This prompted us to use rolling time periods.

■ Short time periods for active

managers can reflect considerable random “noise.” Our philosophy focuses on longer time periods and avoids short-term market timing. Manager strategies can swing in and out of favor over shorter time periods, and we were interested in removing that noise in order to draw conclusions over, say, a full market cycle.

■ Five years represents our

preference for the minimum time frame to allow a manager’s strategy to play out and prove its success, and the use of five-year periods provided more data points than the use of 10-year periods. However, we also were interested in understanding whether the use of an even longer time frame would yield significantly different results, perhaps altering our view on the appropriate measurement period. Therefore, we examined both five-year and 10-year rolling periods, although we acknowledge that our data points for 10-year periods would be fewer than the number for five-year periods.

■ We used source data that were

adjusted for survivorship bias. Survivorship bias implies that data could be skewed higher because performance of funds that did not survive (perhaps because

performance was inadequate to attract new assets) would not be included. Although our research on survivorship bias indicates that the skew is not dramatic, we used survivorship bias-free data for greater accuracy.

■ In this study, we used only

institutional share classes, which are generally the lowest-cost share classes available. This is consistent with our practice of only employing institutional share classes in client portfolios.

■ Managers are equal-weighted in all

of our results.

In our analysis, we asked the question: On average, what percentage of managers outperformed their benchmarks after fees for each of the five-year (or 10-year) periods? A 50% score, or “batting average,” would indicate that half the managers outperformed their respective index after fees for that time period. A higher batting average would indicate that most managers outperformed the index, and the selection of a manager might have been successful by throwing the proverbial dart (over many trials). A lower batting average would indicate that most managers did not outperform after subtracting fees. Then we averaged these batting averages for all of the five- and 10- year rolling time periods.

Our research did not directly address the question of magnitude of

outperformance (or underperformance), which we will call “excess return versus the benchmark,” although Exhibits 1 and 2 and charts in the appendix provide a rough picture for drawing conclusions. The topic of excess returns relates to such questions as:

■ Are the high performers

outperforming by more than the poor performers are underperforming?

■ Are investors being paid to bear

the risk of higher management expenses (i.e., per unit of risk)?

THE RESULTS

Given the growing voice of index strategy proponents, the results surprised us somewhat in some areas but only confirmed what we suspected in others. We expected to see mediocre results in the growth categories and worse results in the value categories, with the batting average weaker in the higher-capitalization categories of both growth and value. While that pattern did unfold, overall results were a bit better than we had expected.

CATEGORIES ANALYZED

DOMESTIC CATEGORY INDEX

Large-Cap Blend Russell 1000 Large-Cap Growth Russell 1000 Growth Large-Cap Value Russell 1000 Value Mid-Cap Blend Russell Mid Cap Mid-Cap Growth Russell Mid Cap Growth Mid-Cap Value Russell Mid Cap Value Small-Cap Blend Russell 2000 Small-Cap Growth Russell 2000 Growth Small-Cap Value Russell 2000 Value

INTERNATIONAL CATEGORY INDEX

Large-Cap Developed MSCI EAFE

Small-Cap Developed S&P Developed Small Cap _{ex U.S.} Emerging Markets MSCI Emerging Markets

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In Exhibits 1 and 2, we see charts from the large-cap growth category for both five- and 10-year rolling periods. The red line (at zero using the left scale) represents the benchmark. The “I-beam” shows the range of returns for the middle two quartiles of the managers in the category relative to the benchmark, with the line connecting the I-beams positioned at the median manager excess return for each time period. When the median manager line dips below the red line, the median manager has underperformed the benchmark over the five- or 10-year period ending at that point. The upper and lower thick lines (in gold) represent the excess return of the managers in the 5th and 95th percentiles and illustrate the dispersion of returns among the middle 90% of managers for the category (using the left axis labels). The bars at the bottom in brown provide the percentage of outperforming managers for each period, rolled quarterly and scaled to the right axis.

As you can see, over the 81 rolling five- year time periods, the batting average was 47%, meaning that on average, 47% of the managers beat the index for each rolling five-year period. For the 61 rolling 10-year time periods, an average

of 51% of managers outperformed. This indicates that, by throwing a dart, an investor stood a good chance of outperforming the Russell 1000 Growth Index for each period. Note the fall in performance in recent time periods. This pattern was exhibited in other areas of growth as well.

The large-cap value category presented a very different picture, as we expected. Exhibits 3 and 4 show two charts in this category, and the results stand in contrast from those of the large-cap growth category. On average, only 42% of the large-cap value managers outperformed for the rolling five-year periods, and only 46% of managers outperformed in the rolling 10-year periods.

The table in Exhibit 5 shows the batting averages for all the categories we analyzed.

The results in the value categories looked slightly weaker than in growth categories with similar market capitalizations. These figures point to the need to select better-than-average value managers in order to outperform the index. The small-cap growth numbers looked slightly better than we had expected, as did the results

in small-cap value. It is generally accepted that small-cap markets are less efficient than large-cap markets, so it came as no surprise that small- cap categories had results that beat those of large-cap markets. We had expected better numbers in emerging markets than in developed markets, again, because emerging markets are generally thought to be less efficient, but the results were similar.

It is important to understand that the median manager in one rolling period may not be the median manager in the preceding or subsequent period. In three of the nine domestic categories, the median manager has, on average, outperformed the index after fees if held five years. It also is interesting to note that the batting averages for the five-year periods showed a tendency to be lower than the comparable 10-year batting average in all growth areas, but only two of the value categories.

Overall, these results would not support indexing across the board, or even in the majority of categories. In fact, the data suggest that large percentages of managers do

outperform their index benchmarks over long periods of time.

EXHIBIT 1

Large-Cap Growth Funds Trailing Five-Year Excess Returns vs. Russell 1000 Growth Index On average, large-cap growth managers have a reasonable track record, as evidenced by many five-year periods with a batting average over 50% (for an overall batting average of 47%). Recent performance has been weaker, with lower batting averages and negative excess returns for the median manager.

-Average 47% 19% 31% 25%32% 64% 73%73%74%_72%77% 84% 79%78%80%81%85%85%87%83%83% 78%76%79%76% 68%_64%_64% 62%66%67% 53% 60% 69%68%68%67% 60% 54%58%54% 46%43%43%_40% 37% 31%33% 27%27%32%27% 18%19%21%24% 31%33%31%29% 21%28%27% 32% 25%26% 11%12% 27%25% 21% 29%_25% 19% 28%29% 18%16%22% 27% 19%17% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

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BOOSTING YOUR CHANCES:

THE NEED FOR MANAGER RESEARCH

In considering an investor’s experience when selecting an outperforming manager, we are taking a detour to discuss our belief in the need for manager research. Many investors do not have the expertise, time, or resources to fully understand the active managers in their portfolios. Investors can be lured by a recent period of outperformance, without knowledge of the “behind-the-scenes” infrastructure that can lead to future success or failure. Manager research is needed for a variety of reasons, including the following:

■ Good performance in one time

period does not necessarily serve as a predictor for subsequent time periods. Manager research can help deconstruct past performance and identify the elements that can lead to good future performance.

■ A manager may lose key staff

members, and these types of changes may go unnoticed by investors. Part of the manager research process is to monitor manager staffs to understand if

the team that was responsible for past good performance is intact.

■ The manager’s portfolio may not

stick to its mandate. For example, an investor may have invested in a high-growth manager that, over time, has transitioned to a value-oriented strategy. Manager research analysts can monitor portfolios for style drift.

■ Compensation packages can be

revised over time, changing the motivations of the manager’s investment staff. Manager research analysts can evaluate changes in compensation packages to understand the potential effects on performance.

THE CHALLENGE OF PURSUING ACTIVE MANAGEMENT

The results of our analysis may prompt some investors to index in certain areas, such as large-cap value and mid-cap value. In many other areas, the figures show that active management can beat the index. In this section, we focus on the question of active management performance consistency.

Exhibit 6 shows the percentage of managers with 100% batting

averages in rolling time periods by category from 12/31/1994 through 12/31/2019. This analysis excludes managers with less than 10 years of history. The data show that virtually no managers have a perfect record for rolling one-, three-, and five-year periods. It would appear that virtually all managers, even the ones that outperform over the long run, can experience shorter-term periods of underperformance.

These shorter periods of

underperformance can be painful enough to tempt an investor to terminate the manager. Furthermore, this temptation to terminate the manager can occur at exactly the wrong time – immediately before the manager begins to outperform. Investors who bend to this sort of pressure can place themselves in a position in which they are constantly “chasing performance,” investing with managers after periods of outperformance and before periods of underperformance, and then terminating the manager after underperforming and before subsequent periods of outperformance. We illustrate the very real concept of termination temptation by taking a look

EXHIBIT 2

Large-Cap Growth Funds Trailing 10-Year Excess Returns vs. Russell 1000 Growth Index

Large-cap growth managers fared better when measured over 10-year periods, with an overall batting average of 51%.

Average 51% 62%63%67%66% 72%73%74%74%75%78%80%79% 82%83%86%87%86%84%84%83%83%81%81%_78% 66% 58%60% 51%53%52% 34%37% 45%44%45%50%48%45%48%_42% 38%36%36% 30%_27% 17%21% 24% 19%21%25%19%_17%21%20%21%22%20%20% 14%13% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

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EXHIBIT 3

Large-Cap Value Funds Trailing Five-Year Excess Returns vs. Russell 1000 Value Index

Performance of large-cap value managers is somewhat weak when measured over five-year periods, with an overall batting average of 42%.

Average 42%

at some successful managers and their performance records. In the small- cap growth universe, we examine the performance of two managers we’ve been recommending since 2004.

The performance of the first manager, the T. Rowe Price New Horizons Fund, can be seen in Exhibit 7. This chart shows trailing one-year performance for the New Horizons Fund (the green line crossing above and below the zero line). As you can see, the fund exhibit swings in its excess returns over one-year periods, outperforming in some periods and underperforming in others. However, when examining performance over longer time periods, the fund’s batting average improves dramatically, as we shall see below.

The second manager is the

ClearBridge Small Cap Growth Fund. Its performance also swings from outperformance to underperformance and back again when measured over one-year periods (see Exhibit 8).

The table in Exhibit 9 shows the two funds’ batting averages for rolling one-, three-, five- and 10-year periods.

We also show statistics for the iShares Russell 2000 Growth Fund (IWO), an index exchange-traded fund (ETF). For rolling one-year periods, we see that T. Rowe Price’s batting average of 75% means it underperformed the index in 25% of rolling one-year periods. In contrast, the rolling 10-year batting average is 100%, which means T. Rowe Price outperformed the Russell 2000 Growth Index in 100% of the 10-year time periods rolled quarterly, an impressive record.

Even more striking is the worst cumulative excess return versus the index. The worst time period for the T. Rowe Price fund relative to the index since 1994 was an 18-month period beginning 8/31/1998. Over that 18-month period, the fund accumulated an excess return of -20.7%. If an investor bought the fund on 8/31/1998, the investor would have underperformed the index by -20.7% on a cumulative basis for that period. That type of underperformance might prompt even the most stalwart investors to throw in the towel. However, over the subsequent 102-month period, T. Rowe Price outperformed the index, for total

10-year performance that outpaced the index by 64.9% (or about 3.1% annually). Many investors might have sold the T. Rowe Price fund at precisely the wrong time.

T. Rowe Price is not an isolated case. As you can see in the exhibit below, the ClearBridge fund’s worst cumulative excess return was -17.6%. It also has an average 10-year record of 100%. In the case of one manager we found in the small-cap growth universe, underperformance of -50.2% accumulated before the manager clawed back to complete the 10-year period ahead of the index for that entire period.

This picture is not uncommon and occurs in all the style categories. The lesson is clear. When a good manager is underperforming, sometimes the worst thing an investor can do is sell the manager. The key is to ask why the manager is underperforming. It may be that the manager’s long-term philosophy is temporarily out of favor. It could be that key investment staff members have left the firm. In the former situation, holding the manager may be the right thing to 10%13% 20%25%23% 27%_25%_23% 38%_35%_35%42%44%44% 49%_46%51%49%52%_48%_49%49% 45%44% 40% 30% 25%24% 17%20% 25%23% 35%35% 42%_38%44% 60%63% 68%73%74%69%71%73%76%76%73%74%77% 63%62%_58%62% 55% 63% 54% 28% 23%30%22% 30%30%33%34%29% 20% 16%17%19%23% 31% 42%47%48%44%_41%43%42%41% 49% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

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EXHIBIT 4

Large-Cap Value Funds Trailing 10-Year Excess Returns vs. Russell 1000 Value Index

When measured over 10-year periods, large- cap value managers outperform the index 46% of the time.

Average 46%

do. In the latter situation, selling the manager may be appropriate. A robust manager research effort to analyze the underperformance can be very helpful in making the right decision.

Unfortunately, even with sufficient information about a struggling (but overall successful) manager, some investors can’t stomach extended periods of underperformance, and they decide to abandon active management. We believe investors should expect all managers, even the best ones, to experience periods of underperformance, perhaps extended periods of substantial underperformance. Furthermore, we believe there is a solution to this problem of weak stomachs.

THE MANAGER COMBINATION ANTACID

In a perfect world, all managers would outperform all the time, at least the good ones. However, investment styles come and go… and then come back again. Much of the investment world subscribes to the idea of long-term mean reversion, or the “self-correcting” that occurs in the financial markets over long periods of time. Many successful

active managers experience good and bad times relative to the index, much like the markets themselves experience on an absolute basis.

The good news is that, in a universe of many managers employing varied approaches, different managers may be outperforming at various times. In other words, different managers’ patterns of outperforming and underperforming can be

unsynchronized. Just as uncorrelated stocks can be combined in a stock portfolio to smooth returns (lowering portfolio volatility), uncorrelated managers can be combined within an asset class to reduce the volatility of

performance relative to an index. By “uncorrelated,” we are referring to the correlation of excess returns relative to the index. The combination of managers with uncorrelated excess returns (such that one manager “zigs” when another manager “zags”) can substantially reduce the number of periods of active manager underperformance as well as the duration and magnitude of underperformance. This concept is illustrated in Exhibits 10 and 11. In Exhibit 10, we see that an investment in a single manager can lead to multiple periods of underperformance, even if the manager is successful in the long term. The illustration in Exhibit

EXHIBIT 5 Average Percentage of Managers

Outperforming the Index Over Rolling Periods (12/31/1994 through 12/31/2019) Figures in red indicate the overall batting average is below 50%. DOMESTIC CATEGORY FIVE-YEAR BATTING AVERAGE 10-YEAR BATTING AVERAGE Large-Cap Blend 33% 31% Large-Cap Growth 47% 51% Large-Cap Value 42% 46% Mid-Cap Blend 37% 33% Mid-Cap Growth 47% 53% Mid-Cap Value 40% 35% Small-Cap Blend 60% 65% Small-Cap Growth 58% 68% Small-Cap Value 54% 62% INTERNATIONAL CATEGORY FIVE-YEAR BATTING AVERAGE 10-YEAR BATTING AVERAGE Large-Cap Developed 53% 56% Small-Cap Developed 57% 61% Emerging Markets 53% 51% 16%13%17%19%19%22%16%18%18%20% 23%26% 39%38% 49%46%50% 61%64%64%64%63%_57%58% 54%51%51%49% 45% 55%52% 47%49%46%48%49%53%51%49% 55% 51%56%57%58%56%54%51%58%60%59%56%59%58%60%57% 62% 53% 33%31%35%32% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

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OUR APPROACH TO MANAGER RESEARCH

We believe that a robust program of manager research can boost the chances of selecting a manager that will outperform in the future. Our manager research program employs both quantitative analysis and qualitative research. Our quantitative screening model employs 11 factors that seek to capture returns, risk, and risk-adjusted returns. It measures performance over many rolling time periods against both peers and the index. Our model constructs a customized benchmark for each manager in the universe in order to filter out the noise of a manager’s natural investment style biases. Then it ranks each manager in the universe, with the resulting rank reflected in a score that we call the PAR score (for “Performance Adjusted for Risk”). The purpose of our quantitative screen is to identify the managers who have historically performed consistently well in the past not only on an absolute basis but also on a peer-relative and risk-adjusted basis (i.e., have earned returns high enough relative to the risks they are taking).

Our qualitative research involves understanding the people behind the strategy (stability of the staff, credentials, resources, firm structure, etc.), their investment philosophy on sources of value in the marketplace, and their process for translating that philosophy into a portfolio (their discipline for purchases and sales, risk constraints, trading, etc.). The goal of our qualitative research is to understand whether past good performance is repeatable in the future. We perform this research through visits to managers, conference calls, analysis of portfolios and trades, and other methods. Then we monitor selected managers, looking for changes, evaluating performance, and gauging consistency.

We also provide research on index strategies, particularly for products that lie outside the realm of domestic equities. In domestic equities, index products can largely hold the entire universe. For many of the other asset classes, index products may take a sampling approach, which can produce surprisingly high tracking error. Considering the broad array of index products available today, it’s necessary to understand how well an index product tracks its benchmark.

11 shows that the combination of the managers leads to smoother returns and reduces periods of underperformance.

We see the effects of this concept with our two managers from the small-cap growth universe. In Exhibit 12, we see the performance of a 50/50 combination of the two.

In Exhibit 13, we compare the individual statistics of the T. Rowe Price and ClearBridge funds with the combination. The correlation of excess returns for the two funds is 0.46. This means that the two managers haven’t always outperformed (or underperformed) in a synchronized way. In the table, we see that the worst cumulative excess return of the combination was -7.3%, experienced in a period from 5/31/2012 through 6/30/2015. This -7.3% underperformance compares favorably with the underperformance of the single managers (-20.6% and

experienced its worst performance over a much shorter time period than did the ETF). The worst cumulative excess return for the combination was significantly better than those of the individual managers because of the “zigging” and “zagging” effect. In addition, the batting averages were overall higher for almost all rolling time periods (and equal for the 10-year periods).

EXHIBIT 6

If we consider a total portfolio, which can comprise a number of manager combinations in different categories, presumably we may see that some combinations may be outperforming while other combinations may be underperforming at any given time. This “total portfolio” perspective stands in stark contrast to a single- manager-by-single-manager evaluation and may further enable investors to hang on to active managers.

There is frequently cited evidence that a critical determinant of performance is fees. Of course, all else equal, managers with lower expenses should outperform those with higher expenses, so it is appropriate to focus on providing clients with the lowest underlying manager fees available. The above fund tickers represent institutional share classes of the funds, which usually carry the lowest expense ratios. Our standard practice is to use the lowest-cost share class available to us, so our clients’ portfolios are not burdened with sales loads, sales fees, etc.

CONCLUSION

The record of active managers appears to provide a somewhat mixed story that is compatible with the idea that the average manager largely reflects (or, perhaps more accurately, largely

-17.6%) and is not far removed from the worst excess performance of the ETF (although the combination

Percentage of Managers with 100% Batting Averages in Rolling Time Periods (12/31/1994 through 12/31/2019) These low percentages reflect the extreme difficulty of always outperforming the index, especially over shorter time periods. Note the weaker figures in the last column for domestic large-cap and mid-cap value managers versus their growth counterparts.

DOMESTIC CATEGORY 1-YEAR

PERIODS 3-YEAR PERIODS 5-YEAR PERIODS 10-YEAR PERIODS Large-Cap Blend 0.49% 0.52% 1.11% 4.04% Large-Cap Growth 0.24% 1.00% 3.67% 9.26% Large-Cap Value 0.00% 0.34% 1.44% 7.76% Mid-Cap Blend 0.00% 0.00% 0.00% 4.94% Mid-Cap Growth 0.00% 1.03% 1.11% 10.13% Mid-Cap Value 0.00% 2.38% 0.00% 0.00% Small-Cap Blend 0.64% 0.66% 0.67% 14.81% Small-Cap Growth 0.00% 0.00% 4.27% 15.03% Small-Cap Value 0.00% 0.00% 1.06% 16.47%

INTERNATIONAL CATEGORY 1-YEAR

PERIODS 3-YEAR PERIODS 5-YEAR PERIODS 10-YEAR PERIODS Large-Cap Developed 1.29% 1.33% 1.45% 9.02% Small-Cap Developed 0.00% 2.17% 2.27% 13.89% Emerging Markets 0.00% 1.33% 4.00% 10.45%

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EXHIBIT 7

T. Rowe Price New Horizons Trailing One- Year Excess Returns vs. Russell 2000 Growth Index

When measured over short (one- year) time periods, the T. Rowe Price fund (shown as the red line) exhibits periods of both outperformance and underperformance.

Average 75%

EXHIBIT 8

ClearBridge Small Cap Growth Fund Trailing One-Year Excess Returns vs. Russell 2000 Growth Index As we saw in the previous exhibit with the T. Rowe Price Fund, the ClearBridge Small Cap Growth Fund (shown as the red line) also exhibits periods of both outperformance and underperformance when measured over short (one-year) time periods. Average 62% -30% -20% -10% 0% 10% 20% 30% 40%

T. Rowe Price New Horizons I

-30% -20% -10% 0% 10% 20% 30% 40%

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EXHIBIT 9

The Challenge of Holding Even Successful Managers

The statistics for the two small-cap growth managers show that even strong managers experience periods of weak performance during which investors can lose faith. Note the differences in shorter-term batting averages versus the higher longer-term batting averages.

EXHIBIT 10

Illustration of the Volatility of Relative Performance for a Single Manager Virtually all managers experience periods of underperformance against the index when measured over the short term.

EXHIBIT 11

Illustration of the Volatility of Relative Performance for Manager Combinations By combining managers with different patterns of outperforming and underperforming (“uncorrelated excess returns”), performance versus the index can be less volatile. Outperformance Time Underperformance Time Outperformance Underperformance E xc es s Re tu rn s E xc es s Ret u rn s

T. Rowe Price New Horizons Legg Mason Clearbridge SmallCap Growth iShares Russell 2000 Growth

Batting Average vs. the Index (Periods Rolled Quarterly)

Start Date 12/31/1994 7/31/1998 7/31/2000

End Date 12/31/2019 12/31/2019 12/31/2019

Standard Deviation of Returns 20.4% 21.1% 21.0%

1-Year Periods Batting Average 75% 62% 55%

Worst Cumulative Underperformance

Worst Cumulative Excess Return -20.7% -17.6% -6.9%

# Months 18 68 219

Worst Underperformance Start Date 8/31/1998 5/31/2012 4/30/2001

Worst Underperformance End Date 2/29/2000 1/31/2018 6/30/2019

Fund Cumulative Return 117.3% 116.0% 314.2%

Index Cumulative Return 138.0% 133.5% 321.1%

Digging Out From Worst Cumulative Underperformance

Worst Underperformance Start Date 8/31/1998 5/31/2012 3/31/2001

Lesser of 10-Year End Date or Report Date 8/31/2008 12/31/2019 3/31/2011

Fund Cumulative Return 160.9% 176.9% #N/A

Index Cumulative Return 96.0% 161.9% #N/A

Cumulative Excess Return 64.9% 15.0% #N/A

Annualized Excess Return 3.1% 0.8% #N/A

Breakeven Period from Worst (Months) 4 4 Did not break even

Average 10-Year Performance

Average Annualized Excess Return 4.0% 1.0% 0.0%

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11

EXHIBIT 12

50/50 Combination of the T. Rowe Price New Horizons Fund and the ClearBridge Small Cap Growth Fund Trailing One-year Excess Returns vs. Russell 2000 Growth Index

The 50/50 combination exhibits a smoother pattern of excess returns than does either manager alone. Average 82% EXHIBIT 13 Legg Mason SCG (50%)/T. Rowe Price New Horizons (50%)

T. Rowe Price New Horizons Legg Mason Clearbridge SmallCap Growth iShares Russell 2000 Growth

Batting Average vs. the Index (Periods Rolled Quarterly)

Start Date 7/31/1998 12/31/1992 7/31/1998 7/31/2000

End Date 12/31/2019 12/31/2019 12/31/2019 12/31/2019

Standard Deviation of Returns 20.7% 20.4% 21.1% 21.0%

1-Year Periods Batting Average 82% 75% 62% 55%

Worst Cumulative Underperformance

Worst Cumulative Excess Return -7.3% -20.7% -17.6% -6.9%

# Months 37 18 68 219

Worst Underperformance Start Date 5/31/2012 8/31/1998 5/31/2012 4/30/2001

Worst Underperformance End Date 6/30/2015 2/29/2000 1/31/2018 6/30/2019

Fund Cumulative Return 74.9% 117.3% 116.0% 314.2%

Index Cumulative Return 82.2% 138.0% 133.5% 321.1%

Digging Out From Worst Cumulative Underperformance

Worst Underperformance Start Date 5/31/2012 8/31/1998 5/31/2012 3/31/2001

Lesser of 10-Year End Date or Report Date 12/31/2019 8/31/2008 12/31/2019 3/31/2011

Fund Cumulative Return 216.8% 160.9% 176.9% #N/A

Index Cumulative Return 161.9% 96.0% 161.9% #N/A

Cumulative Excess Return 54.9% 64.9% 15.0% #N/A

Annualized Excess Return 2.9% 3.1% 0.8% #N/A

Breakeven Period from Worst (Months) 6 4 4 Did not break even

Average 10-Year Performance

Average Annualized Excess Return 2.4% 4.0% 1.0% 0.1%

Std. Dev.of Annualized Excess Return 0.8% 1.1% 0.7% 0.1%

-30% -20% -10% 0% 10% 20% 30% 40%

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influences) the returns of the index and the underlying securities. After fees, the average manager may be expected to underperform slightly over time, necessitating the selection of above- average managers. Overall, however, it appears there are large percentages of managers that can outperform the index over the long term.

However, even good managers underperform from time to time. The temptation to terminate a manager during a period of underperformance can be great. A combination of managers with uncorrelated excess returns can provide for smoother index-relative performance and mitigate the “termination temptation.”

The results of our analysis appear to be somewhat at odds with the current trends. Active equity funds are experiencing outflows in favor of index funds. Our analysis shows that active managers deserve a look, especially in certain areas of the market-cap and style matrix, and particularly in growth and smaller-cap areas in domestic markets, plus the international markets, if the investor himself or herself can either perform the necessary due diligence or has a financial provider with those resources. In addition, the investor needs to have an appropriately long-time horizon and be able to tolerate periods of underperformance along the way.

With diversification of categories in a portfolio, the overall effect of active management can be additive even as some managers underperform because others may be outperforming. Use of passive managers should generally be associated with underperformance (due to fees), but that underperformance should be minimal. Our philosophy leads us to use some passive managers

in select areas where even the best managers can struggle (such as large- and mid-cap value). Of course, the concepts of endpoint sensitivity and the notion that past performance does not guarantee future results both remind us that an individual investor’s experience may vary significantly from our conclusions.

COMMERCE TRUST COMPANY IS A DIVISION OF COMMERCE BANK NOT FDIC INSURED | MAY LOSE VALUE | NO BANK GUARANTEE

Contact a Commerce Trust advisor today.

1-855-295-7821 | commercetrustcompany.com

3/2019 IM1312

DISCLOSURES

This overview is provided by Commerce Trust Company, a division of Commerce Bank, and is strictly for general informational purposes only. Investors should carefully consider the investment objectives, risks, charges and expenses of all funds. This and other important information is contained in the fund’s prospectus from your financial professional and should be read carefully before investing. Commerce Bank does not provide tax advice. Please contact your tax professional to review your particular situation before investing.

To ensure compliance with requirements imposed by the IRS, we inform you that any U.S. federal tax advice contained in this document is not intended or written to be used, and cannot be used, for the purpose of (i) avoiding penalties under the Internal Revenue Code, or (ii) promoting, marketing, or recommending to another party any transaction or matter that is contained in this document.

Past performance does not guarantee future results, and information provided is not to be construed as solicitation to buy or sell any particular security. Diversification does not guarantee a profit or protect against all risk. Monthly return streams have been supplied by Morningstar, Inc. and have not been verified for accuracy. © 2018 Morningstar, Inc. All Rights Reserved. The information from Morningstar contained herein: (1) is proprietary to Morningstar and/or its content providers; (2) may not be copied or distributed; and (3) is not warranted to be accurate, complete or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information. Morningstar is a registered trademark of Morningstar, Inc. and is not affiliated with Commerce Trust.

All other calculations and analysis from Commerce Trust Company Research Group.

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The red line (at zero using the left scale) represents the

benchmark. The “I-beam” shows the range of returns for

the middle two quartiles of the managers in the category

relative to the benchmark, with the line connecting the

I-beams positioned at the median manager excess return

for each time period. When the median manager line dips

below the red line, the median manager has underperformed

the benchmark over the five-or 10-year period ending at

that point. The upper and lower thick (gold) lines represent

the excess return of the managers in the 5th and 95th

percentiles, and illustrate the dispersion of returns among

the middle 90% of managers for the category (using the

left axis labels). The bars at the bottom in green provide the

percentage of outperforming managers for each period,

rolled quarterly and scaled to the right axis.

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APPENDIX 1: Intl. Emerging Markets Funds Trailing 10-Year Excess Return vs. MSCI Emerging Markets NR USD Index

Average 51%

APPENDIX 2: Intl. Foreign Blend Funds Trailing 10-Year Excess Return vs. MSCI EAFE NR USD Index

Average 56% 82% 73% 70%69%68%68%71%68% 63%63%65%65% 57% 54%52% 46%52% 57%58%61% 44%50%50%44% 40%40% 36%34%39%37%41%38%41% 46%48% 38%38%40%43%40%40% 32% 41% 47%50% 41% 50% 46%46%46%47% 58%_54%56%53% 47%53% 60%_57%58%62% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

% Funds that Outperformed Index (scaled to right axis)

76%75% 65%66% 72% 62% 52%_49%54% 46% 52%53%55%56%56% 61%60%64%63%64%62%62%_60%61%58%58% 56% 48% 55%55%55% 51%53%51%51%_48%56%54%51%54%56%55% 57%56% 48% 53% 59%59%_56%59%58%_55%55%63% 55% 47%_45%46%47%46%48% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

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APPENDIX 3: Intl. Small Cap Funds Trailing 10-Year Excess Return vs. S&P Developed ex US Small Index

Average 61%

APPENDIX 4: Large Blend Funds Trailing 10-Year Excess Return vs. Russell 1000 TR USD Index

Average 31% 100%100%100%100%100% 93%93% 88% 78% 83% 78% 68% 74%75% 69%72% 64%68% 60% 56% 29%29% 25%28% 38% 27% 39%43%43% 47% 55%58%55%55%58% 48% 58% 52%55%52% 58% 68%68% 58% 54% 50% 57% 65% 59%63% 68% 56%_53% 64% 56%54%53% 48%48% 57% 47% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

62%63%67%66% 72%73%74%74%75% 78%80%79% 82%83%86%87%86%84%84%83%83%81%81% 78% 66% 58%60% 51%53%52% 34%37% 45%44%45%50%48%45%48% 42% 38%36%36% 30%_27% 17%21% 24% 19%21% 25% 19%17%21%20%21%22%20%20% 14%13% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

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APPENDIX 5: Large Growth Funds Trailing 10-Year Excess Return vs. Russell 1000 Growth TR USD Index

Average 51%

APPENDIX 6: Large Value Funds Trailing 10-Year Excess Return vs. Russell 1000 Value TR USD Index

Average 46% 62%63%67%66% 72%73%74%74%75%78% 80%79%82%83%86%87%86%84%84%83%83%81%81% 78% 66% 58%60% 51%53%52% 34%37% 45%44%45%50%48%45%48% 42% 38%36%36% 30% 27% 17%21% 24% 19%21% 25% 19%17%21%20%21%22%20%20% 14%13% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

16%_13%17%19%19% 22% 16%18%18%20% 23%26% 39%38% 49% 46%50% 61%64%64%64%63% 57%58% 54% 51%51%49% 45% 55%52% 47%49%46%48%49%53%51%49% 55% 51% 56%57%58%56%_54% 51% 58%60%59%56%59%58%60%57% 62% 53% 33%31%35%32% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

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APPENDIX 7: Mid Cap Blend Funds Trailing 10-Year Excess Return vs. Russell Mid Cap TR USD Index

Average 33%

APPENDIX 8: Mid-Cap Growth Funds Trailing 10-Year Excess Return vs. Russell Mid Cap Growth TR USD Index

Average 53% 32% 41%41%41% 47%50% 53% 44% 39%43% 50% 38% 45% 56%55%59%58% 63% 58% 49% 55%59%57%57%54% 48% 33% 13% 10% 19%17% 9% 16% 25% 9% 17% 29% 15%14%13%20%21% 42% 34% 23% 29%_26%30% 39%_37% 32%34% 38% 29%31% 16% 5% 4% 6% 4%_1% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

53% 62%61%61%61% 68% 58% 63%68%68% 76%76%76%76%77% 80%82%81% 76%75%_73%75%78%76% 65% 54%59% 41% 50%53% 39%39%42% 46%49%51% 54%_52% 45%44% 40% 45% 52% 44%42% 34%35%35% 38%42%40% 34% 29% 36%37% 27% 19%18% 28% 25%28% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

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APPENDIX 9: Mid-Cap Value Funds Trailing 10-Year Excess Return vs. Russell Mid Cap Value TR USD Index

Average 35%

APPENDIX 10: Small-Cap Blend Funds Trailing 10-Year Excess Return vs. Russell 2000 TR USD Index

Average 65% 39%37% 26% 21% 26% 21% 15%14%14% 23%25%25% 64% 53%53% 48% 61%64%64%_58% 50% 44% 33%36% 44% 40%38% 35% 28%29%32%32%29%29%28%28% 34% 22%20%22% 29% 40% 48% 42%42% 35%33%37%39% 45% 39%43%42% 47%45%48% 24% 12%9% 17%_14% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

82%82%80%81%83%81%82%80%82%_79%84%83%84%_81%80%80% 78% 84%86%83%_81% 77%_75%74% 67%69%70% 58%59% 55% 52% 42% 49%50% 57%59%58%60% 63%63%_61% 50%51%50%50% 66% 54% 51% 58% 50%48%49% 55% 50%_48%54%49%49%_47%52% 43% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

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APPENDIX 11: Small-Cap Growth Funds Trailing 10-Year Excess Return vs. Russell 2000 Growth TR USD Index

Average 68%

EXHIBIT 12: Small-Cap Value Funds Trailing 10-Year Excess Return vs. Russell 2000 Value TR USD Index

Average 62% 96%96%96%95%95% 91% 87%90%91%92%92%93%91%92%92%_{86%87%85%85%84%} 78% 74%75%_71% 66% 74%78% 68%71%72% 53%51%56%54%55% 62%62% 69% 59%60%_56% 48% 41%45%43% 49%47% 42%45%43%_41%41%40%43%47% 57%57% 54% 67%65% 57% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%

% Funds that Outperformed Index (scaled to right…

46%44%44%48% 54% 48%48% 44%48%48%45%44% 66% 53% 67% 57% 50% 55% 71% 65% 55%56%57% 51% 60% 71% 65%69%65% 72% 78% 70%70%71% 76%_73%74%75%75%78% 73%73%77%76%76% 78%78%78% 70%70% 65% 63%63%62% 48% 72% 65% 52% 42%43%40% 0% 25% 50% 75% 100% 125% 150% 175% 200% 225% 250% -20% -10% 0% 10% 20%