Asset Manager Funds
∗Joseph Gerakos Juhani T. Linnainmaa Adair Morse
February 17, 2015
Abstract
Using data on 20,461 funds representing $35 trillion in assets under management, we document that institutional investors pay $177 billion in annual fees to asset managers. These fees comprise the second largest securities investing component of Philippon’s (2014) cost of financial interme-diation. Asset managers earn positive gross (and net) alphas in market models. The average non-intermediated dollar thus loses to the market even before fees. We next estimate a tactical beta model based on Sharpe (1992) and find that alphas attenuate and tracking errors decrease. In fund-specific fee and flow data, we show that institutional investors pay higher fees for larger factors exposures and tilt their flows toward “good” factor exposures. Overall, our results suggest that asset manager funds provide investors with tactical factor loadings and low tracking errors.
∗
Gerakos is with the University of Chicago, Linnainmaa is with the University of Chicago and NBER, and Morse is with the University of California Berkeley and NBER. We thank Jeff Coles (discussant), Scott Richardson, Julio Riutort (discussant), workshop participants at University of California at Berkeley, Emory University, University of Oregon, University of Colorado, University of Chicago, and conference participants at the 2014 Western Finance Association Conference, the 7th International Finance Conference at the Pontificia Universidad Cat´olica de Chile, and the 2014 MSUFCU Conference on Financial Institutions and Investments for their comments. We thank the Fama-Miller Center at the University of Chicago Booth School of Business for financial support.
1
Introduction
Philippon (2014) establishes that the annual cost of U.S. financial intermediation is 1.87% of investable assets. Applying these numbers to worldwide investable assets suggests that the 2012 global cost of financial intermediation is $3.2 trillion.1 Likewise, using Greenwood and Scharfstein’s (2013) ratios of the value-added component of financial services relative to the economy suggests that the U.S. finance sector accounts for $1.3 trillion in value-added, a magnitude consistent with Philippon’s (2014) esti-mates. These figures are not trivial, and economists are just beginning to explore the components that comprise the cost of financial intermediation and the extent that the components represent value-add to the economy.
In this paper, we study the securities investing component of financial intermediation.2 Greenwood and Scharfstein (2013) find that securities intermediation in the U.S. represents 22% of the income in finance, which implies that worldwide income from securities intermediation amasses to $726 billion annually. Estimates from prior literature can be used to break this magnitude down into the following components (adjusted to 2012):
• $100 billion for U.S. mutual funds (French (2008) and Bogle (2008)),
• $45 billion for U.S. hedge funds (French (2008) and Bogle (2008)),
• $313 billion for worldwide individual trading (Barber, Lee, Liu, and Odean, 2009).3
1Our estimate of worldwide investable assets for 2012 is $173 trillion. We describe in the Appendix how we estimate worldwide investable assets. We obtain a similar estimate of $175 trillion worldwide investable assets for 2012 if we extrapolate Philippon’s (2014) U.S. estimates to the world.
2
Other components include, for example, banking services, investment banking services, real estate transactions, and insurance provision. We define the securities-investing component as the costs of investing and trading.
3
Barber, Lee, Liu, and Odean (2009) estimate that commissions cost individual investors 0.7% of GDP in Taiwan. If we adjust for the high turnover in Taiwan, Barber et al.’s estimate suggests that individual traders incur $313 billion in fees annually worldwide. We thank Brad Barber and Robin Greenwood for data and guidance with these calculations.
Missing from these estimates are the fees that asset managers receive for the services that they provide to institutional clients. We use the term asset manager to refer to firms such as Blackrock and Goldman Sachs Asset Management that provide investment services to institutional investors. We fill this void by establishing facts about the size, fees, and performance of the investment funds of asset managers. As of 2012, we estimate that asset managers worldwide held $47 trillion in institutional assets under management, representing approximately 27% of worldwide investable assets.4 Despite the size of this industry, academics pay scant attention to asset managers.5 For instance, little is known about the asset manager fund structures through which this capital enters the market and how these funds perform. Data limitations likely hinder research, but more fundamentally, a perception exists that asset managers are pass-through vehicles in the sense that they assist institutional clients and high net worth individuals in implementing clients’ investment strategies. But asset managers charge fees. And these fees are sizable.
We estimate that investors paid asset managers $177 billion in fees for 2012, half of which was for asset manager funds focused on U.S. capital markets. Hence, the cost of asset managers is larger than that of mutual funds and two-thirds of that of individual brokerage services. Clients paying these fees are often large (e.g., pension funds) and could likely manage their portfolios in-house, especially given that institutional investors increased the professionalism of their in-house capabilities over our sample period (Bikker and De Dreu (2009) and Cochrane (2013)). Nonetheless, institutional investors choose to pay $177 billion in fees per year, and we set out to understand what they get in return.
We obtain data on asset manager funds from a worldwide consulting firm (“the Consultant”). The 4We base the estimate of asset under management on surveys carried out by Pensions & Investments magazine. We describe these surveys and how we adjust for institutional assets in the Appendix. For comparison, the Investment Company Institute (2013) reports that worldwide mutual fund assets under management were $26.8 trillion as of the end of 2012.
5Notable exceptions include Lakonishok, Shleifer, and Vishny (1992), Goyal and Wahal (2008), Busse, Goyal, and Wahal (2010), which we discuss momentarily.
Consultant maintains a database of 20,461 asset manager funds from 2,970 asset managers over the period 2000–2012. Asset manager funds have attributes ranging from institutional mutual funds to client-specific (“segregated”) investment strategies.6 Clients use the database to evaluate and choose asset manager funds in their portfolio selection. The data include assets under management (AUM), client counts, fees, and performance. The Consultant divides the asset universe into roughly 400 strategies, which we collapse into 235 strategies in six asset classes.7
Because asset managers market their investment products through consultants, the asset man-ager fund is the central unit of asset manman-ager’s promotion of their offerings, and thus of tracking holdings and performance. Although some proprietary portfolios are likely excluded, the database nonetheless covers over half of this sector’s total assets under management. The missing data reflect funds not available for investment (i.e., they have proprietary strategies or are not taking new invest-ments). In other words, our sample represents the set of investable products that comprise clients’ information sets. The same argument applies to fees and performance. Whatever inferences we draw from these data about the performance of asset managers in different asset classes, they are similar to the inferences that would be drawn by institutional investors who use the Consultant. Overall, we have fund-specific data on quarterly assets and monthly returns for over $23 trillion in delegated institutional assets under management as of June 2012.
We start by characterizing fees. We estimate that annual fees are highest for hedge funds (113 basis points), followed by global public equities (63 basis points), U.S. public equities (52 basis points), and asset blends (48 basis points). The cheapest asset manager funds are in U.S. fixed income (24 basis points) and global fixed income (33 basis points).
6
Examples of investment funds are ABC Capital Management—Pacific Basin large cap public equities and ABC Capital Management—Australian government bonds.
7
We start from 270 strategies in eight asset classes, but we exclude “cash” and “other” asset classes from further analysis after introducing the data.
We next measure the extent to which asset managers offset these fees through superior investment performance. To start, we estimate a simple market model that subtracts asset class returns from asset manager fund returns. Gross of fees, we find significantly positive alphas. This finding is important. Together with the adding-up constraint, it implies that the average non-intermediated dollar loses to the market even before fees. That is, if we study the before-fee performance of asset managers and other investors within, for example, U.S. equities, our results imply that asset managers extract money from other investors.
Differences in exposures to asset class-wide risk do not explain this wealth-transfer. We find larger net and gross alphas in CAPM-style regressions of fund returns against asset class returns because asset managers’ betas are consistently less than one in these regressions. These single-factor models, however, leave substantial tracking errors. The information ratios are thus low, implying either that the portfolios offered by asset managers are riskier than the market, and the alpha compensates for these additional (non-market) risk factors, or that the models are mis-specified in capturing what asset managers do. These points are related.
A different perspective on this initial puzzle of positive alpha is that a market-indexed portfolio may not be the counterfactual portfolio that an institutional client would choose if not delegating. Clients of asset managers are often pensions, insurance companies, and wealthy individuals, all of whom are typically long-horizon investors. Such investors may indeed want more long-run risk in their portfolio. A historical example of this comes from the loosening of the Blue Sky laws8 in the late 1970s, after which pension funds shifted their portfolio allocations to more asset classes to increase risk and thereby returns.
8
Blue Sky laws are U.S. state-level laws governing how a state pension plan can invest, enacted to protect the state population from “excessive” pension risk.
We therefore next evaluate performance under a much narrower vision of the asset class allocation. In particular, we redefine the asset classes to 235 strategy-level markets. In this revised analysis, alphas attenuate; beta estimates move more toward unity; and some of the tracking errors decrease. Nonetheless, such strategy-level benchmarking is also unsatisfactory in terms of measuring the value that asset managers provide. In particular, allocating assets across 235 “markets” is likely difficult and costly to implement. Furthermore, these strategy-level market models leave betas far from one and non-negligible tracking errors. Furthermore, positive alphas remain.
We build on these results and implement a “tactical-beta” model based on Sharpe (1992).9 The Sharpe (1992) approach evaluates performance using multiple factors and constrains the tactical betas (that is, factor loadings) to sum to one. The performance estimates we obtain from this model yield two main take-aways. First, tactical beta portfolios materially reduce tracking error. Second, tactical factor portfolios entirely explain away the positive alphas. The results are consistent with how asset managers market their services to clients: they provide institutional clients with the value proposition of achieving a tactical beta portfolio with minimal tracking error. Our tactical-beta estimates imply that the positivemarket-model alphas ultimately stem from profitable systematic deviations from the broad asset classes that asset managers implement. That is, what look like alphas in market models are their tactical betas.
We offer evidence from the converse side of this finding. Using fund-specific fee and flow data, we find that institutional investors pay for the services of tactical loadings, and funds that provide exposures against factors with positive returns—consider, for example, a fund investing in value stocks within U.S. equities when value stocks do well—receive higher asset flows.
Whether this performance represents skill is an open question. A traditional view is that asset 9
managers should not be credited with skill for providing tactical-beta exposures to broad asset classes because such style tilts were available to investors. According to this view, our results imply that investors pay close to $200 billion for services they could do themselves. In contrast, Berk and Binsbergen (2014) suggest that such deviations from broad asset class benchmarks represent skill because the benefit of such deviations may not have been known to investors or that such deviations might not have been available to investors through passive vehicles. Although the benchmarks are widely available, the loadings on each tactical factor are fund-specific and thus not known ex ante to investors. Thus, a mixture of the loadings on tactical factors can represent skill. We leave this question as open for further work.
We add to the literature on asset managers. Lakonishok, Shleifer, and Vishny (1992) use detailed data on all-equity pension funds and find that over 1983–1989 these funds significantly underperform the S&P 500 Index. The authors attribute this underperformance to an agency problem. Consistent with this intuition, Goyal and Wahal (2008) find that pension plan sponsors terminate managers not only because of underperformance, but also for a host of reasons unrelated to performance. Busse, Goyal, and Wahal (2010) examine the performance of actively managed U.S. equity funds, who find that their aggregate and average estimates of alphas are statistically indistinguishable from zero.
2
Data
We obtain data over the period 2000–2012 from a large global firm that consults pension funds, endowments, and other institutional investors on the allocation of capital to asset managers (the “Consultant”). Asset managers self-report quarterly holdings and monthly performance data to the Consultant to market their services. The Consultant aggregates the data into a database, which its own
consultants and clients use to shop for and evaluate asset manager funds, by asset class, by geography, by performance, by cost, or by a host of other filters, much in the way mutual fund databases are aggregated.
The Consultant’s business model depends on data reliability. It therefore employs a staff of over 100 researchers and performs regular audits of each manager and the manager’s funds. These audits are part of investors’ subscriptions; as they shop for asset manager funds, they can read audits, compare the fund to benchmarks, or read the credentials of the people running the asset manager funds. Managers who do not report full details of fees, assets under management, and performance can receive less attention when the Consultant makes recommendations to its clients.
We define the study’s primary unit of observation as an “asset manager fund.” These funds follow a specified investment strategy and are marketed to institutional investors by asset managers. Institu-tional investors can access asset manager funds through several channels. First, some asset manager funds are institutional mutual funds, with large numbers of arms-length, small institutional investors. Institutional mutual funds, however, represent only a small subset of both the number of funds and assets under management in the Consultant’s database. For example, institutional mutual funds rep-resented less than 3% of total funds and 10% of total assets under management in the database for 2012.
Segregated accounts are the largest channel through which institutional investors access asset manager funds. Such funds reflect an investment vehicle set up for a single client. An investment strategy, however, can extend to multiple clients who have segregated accounts. The numbers of clients in a strategy is therefore not informative of whether an asset management fund is a segregated account. For 2012, segregated accounts represent about 28% of funds and 40% of assets under management in
the Consultant’s database. The remaining asset manager funds consist of trusts, closed end funds, overlays, SICAFs, and SICAVs.
Although the services of asset managers provided by asset managers are well known, the mecha-nisms through which asset managers market their services are not well understood, and the charac-teristics of their investment funds are not described in the academic literature. We therefore devote a subsection below to describing the role of pooled capital in institutional asset management and how asset managers administer their services through these asset manager fund vehicles. We begin, however, with an aggregate picture of our data.
2.1 Aggregate assets under management
We begin by reporting statistics that allow us to characterize the total industry of asset manager funds and gauge the coverage of the Consultant’s database. We use survey data from Pensions & Investments magazine, described in the Appendix, to measure the total size of the industry. Each year, Pensions & Investments magazine surveys managers about their assets under management. These surveys are important to asset managers because they provide size rankings to potential clients. According to Pensions & Investments, nearly all medium and large asset managers participate.10
The first column of Panel A of Table 1 presents our estimates of total institutional assets under management based on the Pensions & Investments surveys at the end of each year between 2000 and 2012. The total AUM increases from $20 trillion to $47 trillion from the beginning to the end. The next column presents the numbers of asset managers that comprise the estimates of total institutional assets under management. The numbers range from 595 managers in 2012 to 748 managers in 2003. The third
column reports our estimates of worldwide investable assets11During our sample period the amount of
worldwide investable assets rose from $79 trillion to $175 trillion, but the share of institutional assets held through asset managers remained almost the same through the sample period. Institutional assets intermediated through asset managers represent approximately 26% of the worldwide investable assets. Panel B summarizes the Consultant’s database. The amount of institutional assets under manage-ment increases from $6 trillion to $26 trillion from 2000 to 2012. The coverage of the data starts at 31% of the total industry in 2000, increases to above 60% in 2007, and then says above this level to the end of the sample period. The number of managers in the Consultant’s database is substantially higher than those in the Pensions & Investments surveys because the Consultant covers small managers that are not included in the Pensions & Investments surveys. The total number of active managers in the Consultant’s database is approximately 2,000 towards the end of the sample period. The total number of managers who are active at any point during the sample period—defined as managers who offer at least one fund—is 3,186. When we match the names of the asset manager firms in the Consultant’s database with the managers included in the Pensions & Investments surveys, three-quarters of the managers covered in the Pensions & Investments surveys show in the Consultant’s database as of 2012.
The last two columns in Panel B report the total institutional assets under management that we will use in this study, which are a subset of that reported in the first column. We restrict data on two fronts. First, 10.5% of the manager-level assets under management included in the database lack corresponding returns. Second, even when the database includes returns, we remove backfilled data. In particular, we know the date when an asset manager fund was first added to the Consultant’s database. These data will not have been audited with rigor by the Consultant and can suffer from
11
performance (incubation) and survivorship biases. We therefore exclude them throughout our analysis. The total institutional assets under management for funds with returns and without backfill are on average 72% of the full series, and become similar to total institutional assets under management without these restrictions after 2003.
2.1.1 Selection and survivorship bias tests
Although Table 1 suggests that we have comprehensive coverage of asset manager firms, we lack information on the performance of some funds. According to discussions with the Consultant, the majority of the missing data represent funds that are closed to investment. By reporting all of their fund data to the Consultant, managers can promote their size and breadth of experience. One factor, however, likely impedes full disclosure. Asset managers exclude some asset manager funds whose strategies are proprietary to the client. Thus, our missing data, for the most part, likely reflect funds that are closed and non-reproducible. For our purposes, although the data are incomplete, they nonetheless represent an institutional investor’s information set for deciding among asset manager funds that are open for investment.12
If our analyses cover close to the universe of investable products, then potential biases in asset class selection, strategy selection, or performance are relevant for speaking to an institutional investor’s decision set. Nonetheless, we test for selection biases below. The results from these tests suggest that our data appear to be representative of the industry and devoid of biases. Therefore, our empirical results likely generalize to the entire asset management industry.
We begin by noting again that the data are free of survivorship bias because the Consultant records a “creation date” for each asset manager fund. This field represents the date that the asset
12
Ang, Ayala, and Goetzmann (2013) make a similar point with respect to the beliefs of endowments about the performance of alternative investments.
manager fund was first entered into the system. At the initiation of coverage, the manager can provide historical returns for the asset manager fund. Hence, such backfilled returns can be biased upward if better performing funds are more likely to survive and/or provide historical returns. Importantly, dead funds and strategies are left in the system. In our analysis of performance, we always analyze returns generated after the creation date, thereby ensuring that our tests are free of survivorship concerns.
The more pressing issue is the potential that managers selectively choose which funds to report to the Consultant. We address this point in two ways. First, the Pensions & Investments Money Manager Directory survey reports broad asset class weights (equity, fixed income, cash, and other) for the U.S. tax-exempt institutional assets held by each asset manager who responds to their annual survey.13 We therefore match the names of asset managers in the Consultant’s database with those who responded to the Pensions & Investments survey. Panel A of Table 2 compares the value-weighted asset class weights for managers who report to both Pensions & Investments and the Consultant. Except for fixed income, the broad asset class weights are similar across the two data sources. Any differences are likely due to differences between non-U.S. and U.S. asset allocations.
Second, we regress fund-level monthly returns on the percentage of assets under management for which the manager provides returns data to the Consultant in order to test whether managers refrain from reporting strategies with worse future performance. The regressions include interactions of strategy and month fixed effects; standard errors are clustered at the month-strategy level. Panel B of Table 2 presents results for these regressions. If managers engage in strategic reporting, then lower levels of coverage should be associated with higher returns. In fact, we find the opposite—managers who provide higher levels of coverage have slightly higher performance.
13
We contacted Pensions & Investments and their analysts stated that it is rare for a large asset manager to not respond to their survey.
2.1.2 Aggregate Fees
The Consultant’s database includes fees and fee structure by asset manager fund. Asset managers provide and update the Consultant with two fee parameters per asset manager fund: (i) the baseline fee for assets under management and (ii) discounts available at different asset thresholds. For example, a particular U.S. fixed income-long duration strategy charges 40 basis points for investments up $10 million, 30 basis points for investments up to $25 million, 25 basis points for investments up to $50 million, and 20 basis points for investments above $50 million. These parameters are static in the sense that the database records only the latest input of fee data from the asset manager. However, because these fees are in percent rather that dollars, the use of the static structure should only be problematic if fees over the last decade have materially changed per unit of assets under management. If anything, one might think that fees would have come down over time, rendering our estimates conservative.
However, our fee estimates could be too high. Institutional pools of capital may negotiate side deals on fees that shift their placement in the fee schedule up (as if they are getting more scale pricing than their assets invested in the fund would suggest), or, in the case of the largest investors, shifting the fee rate lower than any price on the fee schedule. The first of these scenarios is easily handled. We can calculate afee schedule lower bound estimate of the fees paid, which uses the lowest fee charge in the schedule for all the capital held in that asset management fund. In the example above, we would apply the rate 20 basis points to all capital invested in the fund. Thisfee schedule lower bound estimate does not, however, handle the possibility that large investors pay less than 20 basis points. Such instances are likely few in number, given that the $50 million threshold is a high hurdle at a fund level, assuming that investors diversify across funds and strategies.
for whether fees are negotiable. Hence, we set the lower bound estimate to fee schedule lower bound estimate for funds with non-negotiable fees. For funds with negotiable fees, we apply the fee schedule lower bound estimate for the closest match in fund strategy and size. The Consultant classifies funds in one of approximately four hundred strategies. Within these strategies, we match funds on fund size and the number of clients.
Figure 1 plots our annual estimates of aggregate fees received by asset managers for these three measures, all aggregated to the total global market. (We aggregate by taking the weighted average fees in the Consultant’s data and then multiplying by the estimates of worldwide assets under management based on Pensions & Investments.) Based on these scalings, we estimate that fees received by the top global asset managers range from $170 to 220 billion in 2011.
2.2 Statistics at asset manager fund level
Our data start with a total of 44,643 asset manager funds over the period 2000–2012. For each as-set manager fund, the database includes monthly returns and quarterly asas-sets under management. In the analysis from here forward, we drop funds whose strategies are classified as either cash or other/alternatives, because these classes are relatively small and either represent short-term alloca-tions (the cash holdings) or heterogeneous investment strategies that make benchmarking infeasible (alternatives). Alternative investments may indeed use the traditional asset managers we study, but often more in consulting than for asset placement in private equity, real estate, infrastructure, or real asset funds directly. Thus, the assets under management we see in alternatives may have a different selection, exacerbating benchmarking problems. In addition, for statistic purposes, some of the 44,643 funds are not active in our sample period. A few also are active but do not report either AUM or returns which are not backfilled. When we use these filters, we have a sample of 22,020 funds across
3,186 asset manager firms. Our sample encompasses 1,165,957 monthly return observations and with 70.5% of the funds being alive as of 2012. The total AUM for this sample is 20.8 trillion U.S. dollars in 2012.
2.2.1 Fund-level assets under management
Panel A of Table 3 reports descriptive statistics of asset manager fund characteristics (AUM, clients, AUM per client, age, and fee per unit of AUM). We report the mean, standard deviation, and quartile statistics for each characteristic. The statistics are panel-averaged cross-sections; in the sense that we calculate time series averages for each fund, and then we report the cross sectional statistics across funds.
The average fund has slightly less than $1.6 billion in assets under management, while the median fund $298 million, reflecting a noteworthy right skew. Clients per fund are, however, more skewed with the average fund having 189 clients, while the median fund has only six clients. Similarly, when we examine AUM per client, the mean fund has $259 million per client, while the median fund, has only $50 million. In terms of age, the funds in the database are relatively established with the average fund having an age of ten years, and the median fund having an age of seven and a half years.
We next categorize the asset manager-specific asset manager funds into six broad asset classes: (1) U.S. public equities, (2) global public equities, (3) U.S. fixed income, (4) global fixed income, (5) asset blends, and (6) hedge funds. As in the aggregate statistics in Panel A, we first consider (in the last column of Panel B) the number of managers in the database who offer at least one fund in the broad asset class over the sample period, the total number of funds that exist in the broad asset class over the sample period, the percentage of funds that exist as of June 2012, and total AUM as of June 2012 in the asset class. The largest asset classes in total assets under management are U.S. and
global fixed income, each with $5.1 trillion in AUM as of 2012, closely followed by global equities ($4.4 trillion) and U.S. equities ($3.5 trillion). The smaller asset classes are asset blends ($2.1 trillion) and hedge funds ($1.4 trillion).
Moving to the main table columns, we consider the per fund statistics. Here we see differences between fixed income and equities funds. On average, the largest funds are in both fixed income classes ($2.6 billion for U.S. and $3.0 billion for global), followed by asset blends ($1.9 billion), both types of equities ($1.1 billion for U.S. and $1.4 billion for global), and finally hedge funds ($943 million). These differences are more muted at the median. Assets under management per client is also larger for fixed income funds than for equities. The average (median) per client investment in a U.S. fixed income fund is $256 ($74) million, whereas the average (median) U.S. public equities investment per client is $134 ($23) million.
2.2.2 Fund-level fees
We next examine the distributions of fees by asset class and assets under management per client. Panel A of Table 4 reports that the mean fee per AUM is 62 basis points, and the median fund charges 50 basis points. This distribution comes from differences in the number of funds in each asset class. We have fewer fixed income funds, for example, relative to equities. The mean (24 basis points) and median (20 basis points) fee for U.S. fixed income funds are less than half of the mean (52 basis points) and median (50 basis points) for U.S. public equities. Global fixed income and equities have similar means but more right-skewed distributions and thus larger means. Hedge funds have the largest fees. The mean hedge fund fee is 113 basis points and a median of 101 basis points. The cost of investing in hedge funds is double that of U.S. public equities. None of the fee distributions displays an economically significant skew.
A natural question arises of who is paying these fees. Although we cannot see the client in each fund, we can, however, examine the distribution of fees conditional on the fund’s assets under management per client. These conditional distributions provide insight into the price breaks that larger clients receive. Panel B of Table 4 presents these conditional distributions. In general, throughout the percentiles, fees trend downward in assets per client. For example, when the assets per client is less than $1 million, the mean fee is 73 basis points but less than 50 basis points when the asset per client is greater than $1 billion. We will return to the question of who pays the fees when we put the performance results into context.
2.3 Summary of Descriptive Results
Our goal has been to both provide an overview of our data leading into performance estimations and establish some facts about asset management funds in order to speak to the scale of intermediation. We summarize with a list of take-aways:
• Institutional asset managers intermediate over a quarter of worldwide investable assets with a 2012 holding in these asset classes of $47 billion. The instrument of such intermediation is a small fund, either client-specific or pooled with similar institutional investors.
• In 2012, we observe over 2,000 active asset manager funds, holding $1.6 billion per fund on average, and charging 62 basis points per AUM.
• In aggregate, financial intermediation into these funds costs institutional investors approximately $200 billion per year in the later years of our sample.
This final point sets up our agenda: what do investors get in terms of performance for these $200 billion in fees?
3
Performance
3.1 Baseline Performance Results
The statistics are not comparable because of time averaging. Thus, in Figure 2, we plot the annual value-weighted returns of the asset manager funds versus the benchmark.
The motivation for plotting this simple relationship is to understand whether there are any time anomalies in our short panel that might account for the results we present going forward. The esti-mates suggest that the performance is relative stable over time. Asset manager funds outperform the benchmark by approximately one percent per year before fees for the first five years of the sample, break even or lose slightly for the next for years, and then outperform again by approximately 2 per-cent per year for the last four years of the sample. This stability is notable given that the January 2000 through June 2012 sample period contains the collapse of the dot-com valuations, the run-up to the financial crisis and the crisis itself, and the first years following the crisis. The estimates show that asset managers’ performance relative to the benchmark does not vary considerably across the good and bad times.
We start by estimating market models on a monthly basis. In Panel A of Table 5, we run value-weighted regressions of asset manager fund returns on the asset class benchmark returns, constraining the market beta to be equal to one. We cluster standard errors by month. The point estimates and the standard errors we obtained from this procedure are therefore identical to what would be obtained by first computing value-weighted cross-sectional average each month and the examining the time-series of these averages. We cast the estimation problem as pooled regression so that the tracking errors are defined consistently throughout the paper as the standard deviation of the residual in a model allowing for non-zero alpha. We estimate this model for each asset class and then in
aggregate, weighting the observations by the broad asset class’s assets under management for the month. Table A3 of the Appendix lists the broad asset classes and their associated benchmarks along with the mean return and standard deviation of returns for each benchmark and the funds in the the Consultant’s database. Alphas in this specification represent simple value-weighted, monthly excess returns over the benchmark index. For exposition, we annualize alphas and tracking errors in all of our tables.
The results show that overall asset manager funds exhibit an annual alpha in gross returns of 105 basis points with at-statistic of 3.12. This result merits a pause. Our estimation encompasses over 13% of the total market, even without aggregating to all asset managers. Assuming no selection in which asset managers we observe, our estimation represents approximately 27% of all global capitalization in these asset classes. Thus, given that, with the exception of hedge funds, these investments are simple long position holdings, a simple adding-up calculation suggests that if asset manager-intermediated holdings return a positive 105 basis points gross over the index, everyone else must return a gross 38 basis points below the index.14
Delving into the components of Panel A of Table 5, we see that global equities lead this overper-formance by 169 basis points. U.S. equities and U.S. fixed income also exhibit positive alpahs. In contrast, asset blends and hedge funds underperform their respective benchmarks. The estimates for for the individual broad assets classes are, however, noisy with none of the alphas statistically differ-ent from zero and all having relatively large traducing errors. Each asset class’s contribution to the aggregate outperformance of 105 basis points depends on how much capital the asset managers have in each asset class at different points in time. The decomposition estimates in columns “Components
14
The market-clearing condition implies thatwasset managersαasset managersˆ + (1−wasset managers) ˆαeveryone else≡0. We use this condition to get the estimate of ˆαeveryone else=−38 basis points.
of overall gross alpha” report the time-series average of the products of the (time-varying) weights and the performance over the benchmark. The asset class-level estimates in this column therefore add up to 105 basis points by construction. Although hedge funds, for example, lose 49 basis points per year on average, this asset class’s contribution to the aggregate outperformance is exactly zero because of how the weights vary, that is, hedge funds are smaller when their (future) performance is worse.
Are investors able to capture some of the overperformance, or do the fees completely absorb the overperformance? The right columns of Panel A report a significant overperformance of 58 basis points in net performance. Thus, this result provides evidence that asset manager funds overperform the market by enough to compensate investors for the fees.
The gross and net return performance tests presented in the leftmost and rightmost columns equally weight each broad asset class. To examine how the performance of each broad asset class aggregates to overall performance, we next decompose the gross alpha by multiplying the return over the benchmark for each asset class by the asset class’s relative size for the month. With this weighting, U.S. equities contribute a significant 35 basis points (t-value = 2.04) toward the overall overperformance of 105 basis points. Similarly, U.S. fixed income contributes 20 basis points with at-value of 1.89. Global equities and fixed income also contribute positively (45 and 11 basis points), but the contributions are not significantly different from zero. Interestingly, when we allow for weightings, the underperformance of asset blends and hedge funds disappear, implying that the allocations to these asset adjust with respect to underlying performance of these asset classes.
The simple market model presented in Panel A may not appropriately account for the riskiness of the fund’s portfolio relative to the market when evaluating whether asset manager funds overperform. In particular, the investors in asset manager funds are often long-horizon pools of capital such as
pension funds, endowments, and insurance holdings. Such long horizon investors could be willing to invest in asset manager funds that hold asset mixes with betas higher than one, in the traditional CAPM sense. In Panel B, we therefore implement a standard single factor CAPM-like estimation by regressing monthly asset manager fund excess returns over the one-month Treasury bill on the excess return of the broad asset-class benchmark. We again estimate these regressions separately for funds’ gross and net returns.
When well allow for betas to vary, overperformance continues to holds and becomes more precisely identified across the asset classes. For all of the asset classes except for global equity, we find a significantly positive alpha for gross returns. Moreover, tracking errors decrease for each of the broad asset classes. Perhaps the more interesting result in Panel B relates to the beta estimates. The overall beta is less than unity at 0.90, which implies that relative to Panel A, overperformance is larger. In this specification, asset manager funds overperform the market in gross returns by 188 basis points and return 141 basis points of this to investors.
Overall, these estimates imply that asset managers do not outperform the benchmark because they are holding securities with asset-class betas greater than one. Rather, asset managers outperform the market and, given that the average dollar’s beta against the benchmark equals one, they do so by taking on less market risk than “everyone else,” including individual investors, retail mutual funds, and institutional investors who enter the market without the aide of asset managers.
3.2 Hypotheses on Positive Performance
The take-away from the previous section is that asset manager funds overperform the market. We now turn to analyzing what asset manager funds do to generate such overperformance. To this point, we have said nothing about how well the market model and single factor models capture the variance of
asset manager portfolios. The R2s mostly mimic the beta results: where the beta is close to one, the
R2 approaches one. Tracking error, however, tells a different story. The tracking errors reported in Table 5 are large. As a comparison, Pet¨ajist¨o (2013) studies the tracking error implications of active investing in mutual funds. He reports that 6% of mutual funds are pure indexers, with tracking error less than 2%.
In contrast, asset manager funds’ tracking errors are in the range of 7–8%. Across all non-indexing mutual funds, Pet¨ajist¨o finds tracking errors of a similar magnitude that the tracking error across mutual funds is 7.1%. A key difference between mutual funds and asset manager funds, however, is that on average mutual funds do not overperform. This performance difference does not inhibit us from seeking guidance as to the source of tracking error by mutual fund types. Our goal is to then ask whether our overperformance is due to a style of investing.
Pet¨ajist¨o (2013) reports that concentrated mutual fund strategies have tracking error of 15.8%; factor bets, 10.4%; stock pickers, 8.4%, moderately active 5.9%; and closet indexers, 3.5%. In our Table 5, tracking errors are largest for the global equity class, which has a very diverse, often not very correlated set of strategies. In addition, our smallest beta estimate in Panel B is for global fixed income. Both of these findings lead us to our first hypothesis of explaining overperformance; some strategies are concentrated and thus not well approximated on average by a broad market index.
Hypothesis 1: Asset manager funds overperformance relative to the broad market because of strategy selection into higher beta strategies within the asset classes.
With the terminology “higher beta strategies,” what we have in mind is that if we implement a market or single factor model at a more granular level within the asset class, the average beta on more granular level benchmark in a single factor model would be be larger, while the alpha would disappear, and
the tracking error would decline. We should note that even if we find this result, we need to ask in interpretation whether the counterfactual portfolio that an institutional client would choose would reflect micro-level markets if the institutional client did not delegate management of its assets.
A second hypothesis emerges both from Pet¨ajist¨o’s (2013) decomposition as well as from the topics of asset managers’ advertisement. Namely, asset manager could provide tactical factor exposures. There are a host of terms to describe what Pet¨ajist¨o calls factor bets, such as smart beta, tactical beta, or exotic beta. The common ground is the term “beta,” reflecting the idea that factor loadings on well-chosen, well-weighted, and well-timed factors can provide what would look like alpha in either a broad market model or a CAPM-like, single factor model. Thus, in testing this hypothesis, we predict that the inclusion of tactical factors into our model will lower tracking error and eliminate positive alpha.
Hypothesis 2: Asset manager fund overperformance relative to the broad market is due to tactical factor loadings.
We note that we are agnostic as to whether the factors themselves are the source of positive returns or if asset managers have skill in timing factor exposures. We return to this point when we study fees and flows following tactical beta.
3.2.1 Strategy Benchmarking
The Consultant’s database classifies the asset manager funds into strategies within the broad asset classes. To choose benchmarks for each strategy, we use the modal benchmark covering asset manager funds in the same strategy unless the benchmark chosen has less than 10% coverage of all asset manager funds the strategy, in which case we use the benchmark covering the most assets under management
in the strategy. We list the 235 strategies and their benchmarks in Table A4.
We next estimate models based off the 235 strategy-level benchmarks (i.e., we assume that there are 235 asset class markets). That is, we re-define the relevant market at a more granular level to evaluate whether heterogeneity in strategies masks pockets of skill.
Table 6 reports strategy-level coefficient estimates again estimated within an asset class, but with the benchmark varying by strategy. To do so, we save for each strategy i-month t pair the estimate
ˆ
αi+ ˆeitin which ˆeitis the regression residual for montht. We compute both equal- and value-weighted
averages of these estimates for each broad asset class and report the means andt-values of the resulting series as the equal- and value-weighted alpha estimates in this panel. We correct the t-value for the loss of (# of strategies) + 1 degrees of freedom that arises from estimating the regressions separately for each strategy. The value-weighted beta estimate is weighted using time-series averages of assets in each strategy. The tracking error is the volatility of the value-weighted ˆαi+ ˆeit estimate. Alphas and
tracking errors are again annualized.
When we allow for strategy-level benchmarks, in general, tracking errors decrease andR2s increase. In terms of performance, we continue to find overall overperformance on both gross and net return ba-sis. Moreover, fixed income broad asset classes have positive and significant alphas on both a net and gross return basis. AlthoughR2s increase and tracking errors decrease, such strategy-level benchmark-ing is also unsatisfactory in terms of measurbenchmark-ing the value that asset managers provide. In particular, allocating assets across 235 “markets” is likely difficult and costly to implement. Furthermore, these strategy-level market models leave betas far from one and non-negligible tracking errors.
The claim that 235 strategy-level benchmarks reflect the appropriate “but for” portfolio (what a pension fund client would have otherwise done) seems a high hurdle for internal management of a
portfolio. At the same time, the claim that asset manager funds out perform the 235-level strategy benchmarks, with mixed results on tracking error, begs the question of whether risk is properly bench-marked in a single factor model. We therefore next examine whether performance is better explain by multiple, rather than by more granular, benchmarks. Specifically, we move to a tactical beta model (i.e., Sharpe (1992)) that allows for multiple tactical loadings to evaluate alphas while speaking to tracking error risk.
3.2.2 Sharpe analysis: Tactical beta
Sharpe (1992) formulates a method to estimate the loadings across tactical factors and then to assess performance of a portfolio relative to these tactical implementations. The intuition behind the Sharpe approach is to select a set of tactical factors and then estimate a constrained regression of fund returns against these factors with regression slopes (1) constrained to be non-negative and (2) constrained to sum to one. The estimated betas can be interpreted as portfolio weights. These weights describe the long-only portfolio—which we call the “style portfolio”—that tracks that fund the closest when the portfolio is built from the tactical factors alone. Sharpe (1992) proposes comparing the performance the fund against the style portfolio. The tactical factor estimation allows us to speak to whether asset manager fund are providingtactical beta to its clients. If managers generate their positive gross alphas in the market and one-factor models by providing exposure against tactical factors, then we would expect these alphas and tracking errors to decline in the tactical beta model. Such as result would be consistent with institutional investors purchasing tactical exposures from asset managers.
Sharpe (1992) and the recent application oftactical beta in practice suggest that fund performance (alpha and tracking error) might better be understood in terms of loadings on multiple tactical factors. Using open end mutual fund data, Sharpe (1992) shows that inference from a linear combination of
loadings on tactical factors (he calls them asset class factors) with non-negative weights inform about fund exposures and offer an interpretation of performance, more in line with the activity of money managers.
To implement our analysis, we augment modify and augment Sharpe’s original list of tactical factors. The following list describes the original factors used by Sharpe (1992) and those used in our analysis below:
Sharpe (1992) Our implementation
Sharpe/BARRA Value Stock Index S&P 500/Citigroup Growth Index
Sharpe/BARRA Growth Stock Index S&P 500/Citigroup Value Index
Sharpe/BARRA Medium Capitalization Stock Index S&P 400 Midcap
Sharpe/BARRA Small Capitalization Stock Index S&P 600 Small Cap
FTA Euro-Pacific Ex Japan Index S&P Europe BMI
FTA Japan Index MSCI Emerging Markets Free Float Index
Salomon Brothers’ 90-day Treasury Bill Index 30-day Treasury Bill Index
Lehman Brothers’ Corporate Bond Index Barclays Capital US Corporate Investment Grade
Lehman Brothers’ Long-term Government Bond Index Barclays Capital US Government/Credit Index
Salomon Brothers’ Non-U.S. Government Bond Index Barclays Capital Euro Aggregate
Lehman Brothers’ Intermediate Government Bond Index HFRX Absolute Return Index
Lehman Brothers’ Mortgage-Back Securities Index Wilshere US Real Estate Securities Index
UBS Bank Bill
BofA ML LIBOR 1-Month Average
UBS Global Infrastructure & Utilities Index Dow Jones UBS Commodity Index
JP Morgan EMBI Global Diversified Index
We start with the 12 factors used by Sharpe and then make several adjustments. First, we replace Japan-focused factor with an emerging markets factor to reflect changes market weights since Sharpe wrote his paper. Given the large number of hedge funds in our sample, we add the HFRX Absolute Return Index along with the Dow Jones UBS Commodity Index. Similarly, to explain the performance of private equity funds, we include the UBS Global Infrastructure & Utilities Index. We also add cash benchmarks and an emerging market fixed income benchmark.
With these factors, we implement the Sharpe analysis as follows. For each strategyi-month t, we regress the strategy returns against the 18 tactical factors using data for all months except month t. The first tactical factor (“1. Asset-class benchmark”) is the strategy’s broad asset class benchmark which is listed in Table A1. The remaining 17 tactical factors are those given above. The regression slopes are constrained to be non-negative and sum to one. We use the resulting slope estimates to compute the return on strategyi’s mimicking portfolio in montht. By estimating the model by using non-monthtdata, we ensure that our performance measurement is out-of-sample in statistical terms. We for each strategy-month the fund’s return in excess of the style portfolio and then compute monthly value-weighted averages for each broad asset class. The gross and net alphas and thet-values associated with these estimates are the time-series averages of these return differences. Tracking error is the standard deviation of the value-weighted return difference between the strategy and mimicking-portfolio returns. Alphas and the tracking errors are annualized. We compare Sharpe weights by value-weighting the average regression slope estimates obtained from the first-stage regressions. These weights sum up to 100% within each asset class.
We start by focussing on Panel A of Table 7, which presents the alphas and tracking errors from the tactical factor models. Our main take-aways are as follows. First, asset classes have some natural residual risk properties that neither a tactical beta model nor a granular benchmark market model can attenuate. We cannot say that we are explaining what everyone is doing. The large tracking errors seem to imply that funds are still doing something that is not captured by the tactical factors. Some of these deviations might be noise, other deviations might represent skill (or systematic lack of skill). Second, as for the alphas, we find little evidence of abnormal performance on a gross returns basis in the tactical beta analysis, which contrasts with the positive alpha results shown previously.15 Across
15
the rows, the overall gross return is basically zero. On a net return basis, asset manager funds can deliver negative performance, especially in public equities and hedge funds. Weighted across asset classes, the overall net performance of asset manager funds is negative. The reason the estimates are negative might be that there are costs associated with holding the indexes, and we are not accounting for those costs. So the “true” performance relative to the mimicking funds is probably even closer to zero than what we show here.
We next examine Panel B, which presents the estimates of the weights on the tactical factors. Our estimation is fund-by-fund and then we take the averages of the weights, which are presented in the table. Hence, within U.S. equities there are positive weights on both growth and value, because the sample includes both growth and value funds. The first row presents the average weight on the broad asset class benchmark. For example, the average weight on the Russell 300 for U.S. equity funds is 9.8%. The remaining rows present the deviations from the benchmark. For example, the average U.S. equities fund holds a 26% weight in the S&P 500/Citigroup Value benchmark. Overall, the weighted are sensibly distributed across the benchmarks.’
Overall, we can the market-model alpha of the average fund to the tactical factors. For the average fund, it is not a secret where the positive alphas are coming from. The funds deviate systematically from the broad asset class and they deviate in directions that enhance returns. This result raises the question of interpretation. There is skill involved in that asset managers implemented these deviations prior to observing the returns, that is, without knowing that these particular deviations would be profitable in 2000–2012 sample. Although we show here that one can “copy” these funds, we are doing the copying only after the fact. Constructing such clones in real time would be far more difficult because then you’d need to accumulate sufficient historical data to be able to estimate the that our tactical factors may be ill-equipped to pick up all of the strategies in fixed income.
weights.
These results are similar in spirit to Berk and van Binsbergen (2014) and Barber, Huang, and Odean (2014), who consider the proper benchmarking of mutual funds. If internal management by the client cannot reproduce a tactical exposure in an asset class, then these authors suggest that we should attribute that exposure loading to a value-added activity that the fund provides its clients. In our analysis, clients could replicate these funds by trading a particular basket of these benchmarks. Cochrane, however, offers an interpretation of the wordcould:
“I tried telling a hedge fund manager, “You don’t have alpha. Your returns can be replicated with a value-growth, momentum, currency and term carry, and short-vol strat-egy.” He said, “Exotic beta is my alpha. I understand those systematic factors and know how to trade them. My clients don’t.” He has a point. How many investors have even thought through their exposures to carry-trade or short-volatility. . . To an investor who has not heard of it and holds the market index, a new factor is alpha. And that alpha has nothing to do with informational inefficiency.”
Cochrane (2011)
Together, these results paint the following picture of asset managers. asset manager funds appear to offer clients exposures to tactical factors and, importantly, a minimization of tracking error. Once performance is adjusted to reflect the return on the tactical factors, the funds offer zero alpha on a gross return basis.
3.3 Paying for Tactical Beta: Fee Results
We next measure the correlation between fund fees and tactical beta exposures. The intuition behind our test is simple. The fees that investors pay could represent a compensation for the tactical factor exposures that (investors perceive) the managers as providing. If so, we would expect fees in the cross section of asset manager funds to correlate positively with the performance of the style portfolio. That is, investors would compensate asset managers for offering profitable exposures against tactical factors. The alternative possibility is that investors pay for “skill” that is not captured by the tactical beta exposures. Under this alternative investors would purge tactical factors from the reported fund returns and pay fees that are proportional to the unexplained measure of performance. Our analysis is therefore that of revealed preferences, that is, we measure the extent to which investors pay for the return on the style portfolio or for the residual-return component.
We implement this fee-tactical factor analysis by measuring how fees correlate with two components of performance: the gross return on the style portfolio from the Sharpe analysis (i.e., the returns from exposures to the tactical factors) and everything else (i.e., alpha), which is calculated as the difference between the fund’s gross return and the return on the style portfolio. We run panel regressions of fees on these two components of performance on both an equal-weighted and value-weighted basis. In the estimation that pools data across all asset classes, we include asset class-times-month fixed effects. The estimates therefore measure the marginal effect of within asset class-and-within month variation in the two components of performance on fees. In the asset-class specific estimates we include month fixed effects.
Table 8 presents the results for these regressions. The fees are positively and significantly correlated with the returns on the style portfolios in both the equal- and value-weighted specifications. In the
equal-weighted specification the slope on the style portfolio component is four times that on the residual-return component, and the t-values on these return components are 6.58 and 2.84. In the value-weighted regressions—in which the weights are proportional to each fund’s AUM share in the cross section of funds that month—only the return on the style portfolio is statistically significant. The slope estimate for the residual-return component, by contrast, is negative and close to zero. The asset-class specific estimates reveal some variation in these correlations. For example, neither the style-portfolio nor the residual return component is significantly associated with fees within U.S. fixed income and both return measures are positively associated with fees for hedge funds. This hedge-fund result is noteworthy. The significance of the residual-return component implies that investors pay hedge fund managers for providing exposures against some additional tactical factors. Our list of tactical factors, for example, does not include the returns earned through carry and low-volatility trades, and in our analysis the residual return therefore captures the returns that hedge funds earn by trading any such omitted factors.
The results in Table 8 do not support the view that investors on average pay asset managers for the “unexplained” part of performance, that is, for that component alphas that cannot be traced to tactical factors. Instead, our estimates suggest that fees are higher—or, rather, investors are willing to pay higher fees—for performance that is gained through tactical factor exposures.
3.4 Investing for Tactical Beta: Flow Results
We next carry out an analysis similar to the fee analysis by measuring whether capital flows are correlated with the style portfolios returns or with the unexplained part of performance. In this analysis, we replace the style portfolio return with the return on the strategy-specific benchmark, because the style portfolios obtained from the Sharpe analysis are based on forward-looking return
information. These 235 strategy-specific benchmarks, which are listed in Table A5, are simple and available in real time. This analysis asks, for example, whether investors allocate more or less capital to a small-cap value equity fund after its performance is good or bad due to (1) the performance of small-cap value segment of the market or (2) the abnormal performance of the fund relative to the small-cap value benchmark.
We estimate the performance-flow relation using data across all asset classes and separately within each asset class. We regress quarterly log flows on both the style portfolio return and the residual return. We measure flows as the change in the fund assets minus the fund’s net return over the same quarter. We include as controls lagged flows and the fund’s log assets under management as of the end of quarter q. We estimate two sets of regressions. The first set of regressions estimates the relation between flows in quarter q+ 1 and the style-portfolio and residual returns in quarter q. The second set of regressions adds to the regression the style-portfolio and residual returns from quarter q−1.
The estimates in Table 9 show that fund flows respond significantly to the unexplained component of performance in the previous quarter. These estimates indicate that if a small-cap value equity fund, for example, performs well relative to the small-cap value index, investors allocate more assets to this fund. This result is consistent with the Berk and Green (1995) model in which investors reallocate capital as they update their beliefs about managerial ability—assuming that managerial ability is about the ability to generate returns over the passive benchmarks. These results are therefore inconsistent with the view that investors would purely compensate managers for providing profitable factor exposures; under that view the asset flows would mostly correlate with the return on the strategy-specific benchmark—that is, more assets flow into cap value equity funds when small-cap equity does well—and not so much with the component of returns that cannot be traced back to
tactical factors. At the same time, style-portfolio and residual returns are of nearly same significance when these returns are measured with additional lag (in quarterq−1). These estimates suggest that investors may reallocate capital across asset manager funds based on their inferences about tactical factor exposures, but that such reallocation takes place more gradually.
Overall, the results in Table 9 suggest that both style-portfolio returns (that is, betas) and residual returns (that is, alphas) are associated with capital flows to asset manager funds. However, unlike in the analysis of fees, the connection between fund flows and unexplained performance is stronger than that between flows and the return on the style portfolio.
4
Conclusion
Although there is extensive academic research on the costs and benefits to financial intermediation in terms of individual trading, mutual funds, hedge funds, and private equity funds, there is limited research on asset manager funds offered by asset managers to institutional investors and high net worth individuals. Yet, asset managers intermediated over $46 trillion in 2012, representing about 27% of worldwide investable assets. Our aggregate fee estimates suggest that investors pay asset managers at least $177 billion per year. We estimate tactical beta loadings based on Sharpe (1992) and find evidence suggesting that investors pay these fees for tactical loadings and low tracking error. Our analysis of the correlations between fees, return components, and asset flows support this interpretation. The estimates suggest that institutional investors both pay higher fees to asset managers for providing factor exposures and tilt their flows toward “good” factor exposures.
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250
Schedule middle point
219.9
170.2 176.7 200
p Schedule lower bound Implied realized fee
100 150 e e ( in $ bi ll io ns) 0 50 F e 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Year
Figure 1: Aggregate fees. This figure presents the estimated fees based on information available in the Consultant’s database. The estimates are value-weighted average fees in the Consultant’s database multiplied by the institutional assets under management in the Pensions & Investments. Line“Schedule middle point” assumes that the average dollar in each fund pays the median fee listed on that fund’s fee schedule and “Schedule lower bound” uses the lowest fee from each fee schedule. “Implied realized fee” is estimated using data on funds that report returns both gross and net of fees. We take annualize the monthly return difference, take the value-weighted average, and then re-weight asset classes so that each asset class’s weight matches that in the full database.
3 llar o v er (%) 1 2 in te r m e d iated do l t-cla ss b e nc hmark -1 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 A v erage asse t Year Year
Figure 2: Performance of the average intermediated dollar over the asset-class benchmark.
This figure reports the value-weighted return across all the funds in the Consultant’s database over the asset-class benchmark from January 2000 through June 2012.
Table 1: Assets under management ($ in billions)
Panel A presents the annual total assets under management and the number of managers for asset managers in the Pensions & Investments Global 500 Survey and our estimate of worldwide investable assets. Panel B presents the total assets under management in the Consultant’s database, the number of managers in the Consultant’s database, the fraction of the P&I managers covered by the Consultant’s database, the assets in the Consultant’s database with matching return information, and the assets in the database excluding observations generated before a strategy was first added to the Consultant’s database (column “Without backfill”). The Consultant’s data cover the period 2000–2012.
Panel A: Worldwide investable assets and Pensions & Investments database Pensions &
Investments Worldwide investable assets
Number of % held by
Year AUM managers Total asset managers
2000 20,192 718 78,884 25.6% 2001 20,896 727 75,512 27.7% 2002 20,372 723 76,603 26.6% 2003 24,965 748 93,933 26.6% 2004 28,726 715 108,514 26.5% 2005 31,702 723 116,104 27.3% 2006 37,345 720 134,293 27.8% 2007 41,645 704 157,057 26.5% 2008 31,415 671 134,650 23.3% 2009 37,958 646 152,190 24.9% 2010 43,089 633 164,610 26.2% 2011 42,592 610 164,709 25.9% 2012 46,758 595 174,786 26.8%
Panel B: Consultant’s database
AUM with returns
% of Number of managers Without
Year AUM P&I Total % in P&I Raw backfill
2000 6,301 31.2% 579 50.7% 5,285 3,101 2001 6,573 31.5% 722 56.1% 5,467 3,670 2002 6,943 34.1% 840 59.5% 6,014 4,155 2003 9,611 38.5% 1004 61.5% 8,167 6,128 2004 11,351 39.5% 1120 65.3% 10,064 7,949 2005 12,923 40.8% 1213 67.1% 11,857 9,391 2006 15,959 42.7% 1398 67.9% 14,891 12,244 2007 27,776 66.7% 1596 70.5% 24,843 21,595 2008 22,123 70.4% 1758 72.4% 18,492 16,118 2009 25,340 66.8% 1864 72.8% 21,373 19,513 2010 26,394 61.3% 2011 74.6% 23,173 21,607 2011 25,877 60.8% 2067 75.6% 23,004 21,978 2012† 26,265 1974 73.9% 23,293 22,932 †
