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Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment Management
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Performance Persistence in Institutional Investment Management

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  • 1. Performance Persistence in Institutional Investment Management Jeffrey A. Busse Amit Goyal Sunil Wahal∗ March 2006 Abstract Using new, survivorship-bias free data, we examine the persistence in perfor- mance of 6,260 institutional portfolios managed by 1,475 investment managers between 1991 and 2004. Persistence in domestic equity portfolios is significant and economically large up to one year after portfolio formation. Unlike retail mu- tual funds, this persistence is entirely in winner portfolios. Similar patterns are evident in international equity portfolios, but persistence in fixed income portfo- lios lasts up to three years. Better performing portfolios are more likely to offer incentive fees and most-favored-nation clauses but also charge higher fees. Fee size is insufficient to eliminate excess returns. Top performers draw an influx of assets from plan sponsors, and in the year following such inflows, alphas sharply decline. Overall, the results are consistent with the supply-side quantity-based equilibrating process modeled by Berk and Green (2004). ∗ Busse is from the Goizueta Business School, Emory University, email: Jeff Busse@bus.emory.edu; Goyal is from the Goizueta Business School, Emory University, email: Amit Goyal@bus.emory.edu; and Wahal is from the WP Carey School of Business, Arizona State University, email: Sunil.Wahal@asu.edu. We are indebted to Margaret Tobiasen at Informa Investment Solutions, and to Jim Minnick and Frithjof van Zyp at eVestment Alliance for graciously providing data. We thank Byoung-Hyoun Hwang, George Benston, Narasimhan Jegadeesh, and seminar particpants at Emory University, UCLA and the University of Oregon for helpful suggestions.
  • 2. 1 Introduction Performance persistence in delegated investment management represents a significant challenge to efficient markets. Academic opinion on whether persistence exists is Bayesian – we update our priors based on the most recent evidence incorporating either new data or improved measurement technology. Although Jensen’s (1968) original examination of mutual funds concludes that funds do not produce abnormal performance, later stud- ies provide compelling evidence that relative performance persists over both short and long horizons (see, for example, Grinblatt and Titman (1992), Elton, Gruber, Das, and Hlavka (1993), Hendricks, Patel, and Zeckhauser (1993), Goetzmann and Ibbotson (1994), Brown and Goetzmann (1995), Elton, Gruber, and Blake (1996), and Wermers (1999)). More recently, Carhart (1997) shows that accounting for momentum in indi- vidual stock returns eliminates almost all evidence of persistence among mutual funds.1 However, Bollen and Busse (2005) find that performance persists over short quarterly holding periods, even after controlling for momentum. The attention given to persistence in retail mutual fund performance is entirely war- ranted. The data are good, and this form of delegated asset management provides millions of investors access to readily-built portfolios. As a result, at the end of 2004, there were 7,101 equity, bond, and hybrid mutual funds responsible for investing $6.1 trillion in assets (Investment Company Institute (2004)). However, there is an equally large arm of delegated investment management that receives much less attention but is no less important. At the end of 2004, over 50,000 plan sponsors (public and private retirement plans, endowments, foundations, and multi-employer unions) allocated over $6 trillion in assets to about 1,500 institutional asset managers (Money Market Direc- tory, 2004). In this paper, we comprehensively examine the performance persistence of portfolios managed by institutional investment management firms.2 Institutional asset management firms draw fixed amounts of capital (referred to as “mandates”) from public and private defined benefit retirement plans, endowments, foundations, unions, and trusts. Although these mandates span a variety of asset classes, including domestic equity, fixed income, international equity, real estate securities, and 1 One exception is the continued strong underperformance of the worst performing funds. Berk and Xu (2004) show that this persistence occurs in both the current and prior year. They argue that the unwillingness of investors to withdraw capital from these funds causes this remaining pocket of persistence. 2 Persistence in another largely unexplored form of delegated asset management, hedge funds, is studied by Jagannathan and Novikov (2005), among others. They estimate persistence in hedge returns to be approximately 1 percent per year. 1
  • 3. alternative assets, we restrict our attention to the first three. To our knowledge, we are the first to examine persistence in asset classes beyond domestic equity. Invest- ment styles within these asset classes run the gamut in terms of size and growth-value gradations for equity portfolios, and credit and maturity dimensions for fixed income portfolios. Investment style identification is important to plan sponsors because it al- lows plan sponsors to make better-informed asset allocation decisions. Conveniently, it also provides us with self-identified natural benchmarks to judge returns. Our data are drawn from two independent sources: Informa Investment Solutions (IIS) and eVestment Alliance. Both firms collect self-reported returns and other infor- mation from investment management firms and provide data, services, and consulting to plan sponsors, investment consultants, and investment management firms. IIS provides a longer time series of returns and has better cross-sectional coverage; it provides quar- terly returns for 6,260 portfolios managed by 1,475 institutional asset management firms from 1979 to 2004. Since these data suffer from survivorship bias prior to 1991, we de- rive our estimates from the post-1991 sample period. In addition to returns, eVestment provides detailed information about fee arrangements, schedules, and whether portfolios are closed to new investors. We use these data to add texture to our persistence analysis and sharpen inferences with respect to net of fee performance. We investigate persistence over both short and long horizons. We form deciles using one-year raw returns as well as benchmark-adjusted returns, and then evaluate the future excess performance of these deciles. Using raw returns to form deciles mirrors the typical method employed in mutual fund studies, and allows us to compare our results with those in such papers. Using benchmark-adjusted returns is consistent with performance evaluation methods used by plan sponsors, and also affords us the ability to show benchmark-adjusted returns and information ratios in the post portfolio formation periods. We estimate alphas using both unconditional and conditional three- and four- factor models. One quarter after deciles are formed based on raw returns, mean decile alphas are positive, large, and statistically signficant in the majority of winner portfolios. For in- stance, the one-quarter Fama and French (1993) three-factor alpha from the extreme winner decile is 2.88 percent per quarter with a t-statistic of 3.77. Accounting for mo- mentum using the Carhart (1997) procedure reduces the point estimate to 1.07 percent per quarter with a t-statistic of 1.85. Using a factor model that includes an aggregate bond index, the term spread, and the default spread, unconditional one-quarter alphas for the winner fixed income decile vary from 1.03 percent to 1.53 percent depending 2
  • 4. on the specification. Consistent with Christopherson, Ferson and Glassman (1998), we find that conditioning information materially influences, but does not reduce, estimates of alphas for fixed income portfolios. The alphas of the winner international equity decile vary from 2.04 percent to 2.67 percent per quarter. When we form deciles based on benchmark-adjusted returns, the alphas shrink somewhat but remain economically large and statistically signficant. Performance continues to persist over longer horizons. For domestic equity portfolios, persistence continues up to one year after portfolio formation; the quarterly alpha of the extreme winner decile from a three-factor model over a one-year evaluation period is 1.44 percent. Interestingly, after the first year, domestic equity alphas are statistically indistinguishable from zero. For fixed income portfolios, the first year alpha for the extreme winner decile is 0.62 percent. In the second and third year thereafter, the alpha shrinks marginally (to 0.60 and 0.51 percent respectively) and retains statistical significance. International equity portfolios show a pattern similar to domestic equity. One year after decile formation, the alpha for the extreme winner decile is 2.76 percent, but is indistinguishable from zero in the second and third year. Our persistence results are based on returns that are gross of fees (but net of trading costs). Investment management fees could potentially wipe out any gains to plan spon- sors from chasing prior winners. To determine if that is the case, we use fee schedules from our second data source (eVestment Alliance). These schedules provide a pro forma fee that typically declines with the size of the mandate supplied by the plan sponsor (as in a step function). The marginal fees for each breakpoint vary across asset classes, investment styles, and individual firms. We find that annual fees are a fraction of the alphas of the extreme winner portfolios and insufficient to eliminate excess returns. For example, using the most aggressive fee schedule for domestic equity portfolios, a $10 million mandate in small cap growth portfolios has an annual fee of 1 percent. For these portfolios, the annual alpha is 5.76 percent (a quarterly alpha of 1.44 multiplied by 4), which is an order of magnitude higher than fees. Moreover, the schedules themselves represent an upper limit on actual fees charged by investment managers; individual fee arrangements between plan sponsors and investment managers typically include rebates, some of which can be quite large. Beyond the sheer magnitude of fees, there is other interesting heterogeneity in fee arrangements. Two types of arrangements are more common among better performing portfolios: performance-based fees and most-favored nation (MFN) clauses. The former typically involve low or no minimum fees, and link actual fees to beating a prescribed 3
  • 5. benchmark. In domestic equity portfolios, almost 55 percent of portfolios in the extreme winner decile offer performance-based fees, compared with 47 percent for the extreme loser decile. By comparison, the use of incentive fees is less common in mutual funds. Elton, Gruber and Blake (2003) report that only 108 funds offered incentive fees in 1999 (see also Golec and Starks (2004)). MFN clauses require that the investment manager match fees for comparable mandates and plan sponsors. For example, if plan sponsor A negotiates a fee of x basis points, and plan sponsor B subsequently negotiates a fee of x-5 basis points for a similar sized mandate with similar restrictions, the investment management firm is obliged to reduce A’s fees by 5 basis points. Again, investment managers with portfolios in the extreme winner decile are more likely to offer MFN provisions than in the extreme loser decile (38 versus 33 percent). There is considerable economic and practical significance to persistence in institu- tional portfolios. From a practical perspective, if performance persists net of fees, then plan sponsors could benefit from picking winners. Interestingly, and in stark contrast to retail mutual funds, predictability in institutional portfolios arises entirely from the winner portfolios. Since loser portfolios cannot be shorted, the upshot of this asymme- try is that picking winners is a feasible strategy for plan sponsors. Consistent with this implication, Goyal and Wahal (2005) show that plan sponsors hire investment managers that have provided superior historical returns. The economic impact of chasing past winners is that capital inflows should follow returns. Del Guercio and Tkac (2002) and Heisler, Knittel, Neumann, and Stewart (2004) show that the flow-performance relation for institutional asset managers is linear, unlike the convex relation for mutual funds (Ippolito (1992), Sirri and Tufano (1998), and others). If there are diseconomies of scale in investment management, such inflows should cause future alphas to deteriorate. We investigate the relation between performance and flows across portfolios in each of the three asset classes. For domestic equity portfolios, capital flows one year after portfolio formation increase monotonically from loser to winner deciles. The spread in flows between the extreme winner and loser deciles is as much as 36 percent of total net assets. In dollar terms, the average portfolio in the loser decile loses $27 million of assets while the average portfolio in the winner decile gains $132 million. Monotonicity is also evident for fixed income and international equity portfolios. Winner deciles have considerably higher inflows than loser deciles; the extreme winner-loser spread in flows is 16 percent for fixed income and 24 percent for international equity. These large capital inflows appear to have severe consequences for future perfor- mance, at least for domestic and international equity portfolios. In domestic equity 4
  • 6. portfolios, the winner decile that had a Fama-French alpha of 1.44 percent per quarter in the first post-ranking year (with an accompanying inflow of 31.80 percent of assets), has an alpha of 0.28 percent (statistically insignificant) in the second post-ranking year. In international equity portfolios, the alpha for the extreme winner decile deteriorates from 2.76 percent per quarter in the first post-ranking year to 0.36 percent in the second year and -0.35 percent in the third year. Persistence and flows in fixed income portfolios remain an enigma: alphas in winner deciles remain high up to two years, and some- times three years, after decile formation. Moreover, flows do not appear to diminish significantly over time. Even for equity portfolios, making the causal leap that flows drive down alphas is a difficult, if not impossible, task. To do so, one would need to generate an estimate of capacity as well as diseconomies originating from investment ideas and/or execution costs – a task that is impossible without proprietary data. We can, however, make some progress on the issue at a more aggregate level. We do so by taking our extreme winner portfolio at the end of the first year and double sorting it with respect to total assets and capital flows. Thus, we split the extreme winner portfolio into four groups. If flows drive down alphas for portfolios that are closer to capacity, then the subsequent decline in alpha should be larger in the high total assets and high flow intersection. The advantage of this approach is that it holds pre-inflow alpha approximately constant and allows us to focus on alpha changes. We find that the decline in alpha is indeed higher for the high-assets/high-flow group than for the low-assets/low-flow group. For example, in the domestic equity asset class using a four-factor model, the decline in alpha is - 0.53 percent for the former group and -0.21 percent for the latter. While this certainly does not nail the causal issue, it does reinforce the idea that flows and performance are endogenous. This notion is central to papers that seek to understand the flow- performance relation (Lynch and Musto (2003), Berk and Green (2004), and others). It is this very endogeneity that, when combined with diseconomies of scale, produces the quantity-based equilibrating mechanism that is at the heart of Berk and Green (2004). Our results show that such equilibration is not instantaneous – some persistence is necessary to draw the flows that subsequently extinguish persistence. In retail mutual funds, redemptions and capital inflows are rapid. In contrast, in an institutional setting, capital flows are sticky. As a result, the equilibration process appears to take one to two years. Mechanisms exist to control asset flows that are endogenous to investment man- agers. For instance, investment managers that recognize potential diseconomies and 5
  • 7. understand the flow-performance relation could simply refuse capital inflows by shut- ting winner portfolios to new investors. In retail mutual funds, Bris, Gulen, Kadiyala, and Rau (2005) report that over a 10 year period, 143 equity funds that delivered posi- tive excess returns subsequently closed to new investors. In our sample, 208 out of 2,881 domestic equity portfolios are closed to new investors, a rate that is higher than that for retail mutual funds. More interestingly, portfolios that are closed to new investors have an average benchmark-adjusted quarterly return of 1.15 percent, compared to 0.65 percent for all portfolios. Thus, it appears that some investment management firms close portfolios to new investors due to concerns about diseconomies.3 Another way to con- trol flows is through fees. Although we can only observe fee schedules (not actual fees), we can determine if portfolios in the winner decile charge more than those in the loser decile. Our data provide some weak evidence that this is indeed the case: for similar sized mandates, marginal fees are higher for better performing portfolios. To the extent that better-performing portfolios are likely to offer smaller rebates than portfolios in the loser decile, the spread in fees may in fact be larger than we can detect. Our paper builds on the small but growing literature in institutional asset manage- ment. The progenitors in this area are Lakonishok, Shleifer, and Vishny (1992), who examine the performance of equity-only portfolios managed by 341 investment manage- ment firms between 1983 and 1989. Lakonishok et al. (1992) find that performance is poor on average, and although there is some evidence of persistence, they conclude that survival bias and a short time series prevent them from drawing a robust conclusion. Coggin, Fabozzi, and Rahman (1993) also focus on equity portfolios and find that in- vestment managers have limited skill in selecting stocks. Del Guercio and Tkac (2002) and Heisler, Knittel, Neumann, and Stewart (2004) examine the relation between flow of funds and performance and conclude that plan sponsors withdraw funds from poorly performing investment managers. They also find that flows are positively related to Jensen’s alpha but negatively related to tracking error. Goyal and Wahal (2005) exam- ine the selection and termination of investment managers by plan sponsors and find that investment management firms are hired after superior performance and, generally, but not exclusively, fired after poor performance. Post-hiring excess returns are zero, rather than positive, as one would expect if performance persists. However, the difference be- tween their results and ours is largely due to horizon: hiring decisions are conditioned on three-year returns, at which point persistence has died out. Ferson and Khang (2002) use portfolio weights to infer persistence, and Tonks (2005) examines the performance of 3 For example, Aronson+Johson+Ortiz, a prominent investment management firm, recently stopped accepting new capital into its large cap value portfolio precisely because of such worries. 6
  • 8. UK pension fund managers between 1983 and 1997. Both find some evidence of excess performance. Perhaps the closest study to ours is Christopherson, Ferson, and Glass- man (1998), who study persistence among 185 equity-only investment managers between 1979 and 1990. Although their sample suffers from survival bias, using a conditional approach, they find some evidence of persistence, particularly among poorly performing investment managers.4 Our paper proceeds as follows. Section 2 discusses our data and methodology. Section 3 presents results. Section 4 concludes. 2 Data and Methodology 2.1 Data As mentioned earlier, we obtain data from two independent data providers: Informa In- vestment Solutions (IIS) and eVestment Alliance. Both firms provide data, services, and consulting to plan sponsors, investment consultants, and investment managers. Since there are differences in composition of each of the databases, we describe them in detail below, noting issues that are particularly relevant for our results. IIS provides quarterly returns of portfolios managed by investment management firms from 1979 to 2004. Panel A of Table 1 presents some basic descriptive statistics of the IIS database. Prior to 1991, this database only contains “live” portfolios. Subsequently, data gathering policies were revised such that investment management firms that exit the universe due to closures, mergers, and bankruptcies were retained in the database. Thus, data over the 1979-1990 sample period suffers from survivorship bias, while the returns thereafter are free of such problems. We therefore report separate statistics for the two subperiods, 1979-1990 and 1991-2004. Not surprisingly, both the total and average number of firms (and portfolios) per year are much higher in the second part of the sample period. In general, coverage of the database is fairly comprehensive; we cross-check the number of firms with data contained in the Mercer Performance Analytic database and find that our database coverage is slightly better. Our coverage also corresponds favorably to that found in publications such as the Money Market 4 Using the structural break in survivorship in our sample (1979-1990 and 1991-2004), we later show that the magnitude of the survivorship can be quite large. For example, the survivorship-biased alpha computed using Fama-French procedures in the extreme loser portfolio is 0.82 percent per quarter versus -1.19 percent in the non-survivorship-biased period, a spread of 2 percent per quanter. 7
  • 9. Directory of Investment Advisors. As expected, the attrition rate of portfolios between 1979 and 1990 is zero. Carhart (1997) reports that one-third of all retail mutual funds disappear over a 31 year period, which corresponds to about 3 percent per year. Attrition in our non-survivorship biased sample period (1991-2004) is slightly higher and varies from 3.2 to 3.6 percent per year. Several features of the data are important for understanding the results. First, since investment management firms typically manage more than one portfolio, the database contains returns for each portfolio. For example, Aronson+Johnson+Ortiz, an invest- ment management firm with over $22 billion in assets, manages 10 portfolios in a variety of capitalizations and value strategies. The returns in our database correspond to each of these 10 portfolios, and our unit of analysis is each portfolio return. Second, the database contains “composite” returns provided by the investment management firm. The individual returns earned by each plan sponsor client (account) may deviate from these composite returns for a variety of reasons. For example, a public defined benefit plan may ask an investment management firm to eliminate “sin” stocks from its portfo- lio. Such restrictions cause deviations from composite returns, but these deviations are typically quite small. Therefore, composite returns are representative of actual earned returns. Third, the returns are net of trading costs but gross of investment management fees. The database also contains two other critical pieces of information: style assignments and assets at the end of the year. For domestic and international equity portfolios, each portfolio is associated with a primary style and a market capitalization. Twenty-nine primary equity styles and four market capitalization categories exist. The majority of the data reside in value, growth, and core-diversified styles. The market capitalization categories include micro (<$500 million), small ($500 million–$2 billion), medium ($2 billion–$7 billion), and large (>$7 billion). Geographic parameters are not available for international equity portfolios (e.g. EAFE or EAFE excluding Japan). Twenty- eight primary fixed income styles exist, but again, most of the data reside in just a few categories, including core, maturity controlled, government, and high yield. Fixed income maturity breakpoints are 1, 3, and 7 years. Unlike returns, total assets in each portfolio are only recorded at the end of the year. Moreover, asset information is only available for approximately 30 percent of investment management firms. The database contains both active and passive portfolios, but since our interest is in the performance persistence of active managers, we remove all passive portfolios from the sample. We break statistics down by the three major asset classes - domestic equity 8
  • 10. (including all size and value-growth intersections), fixed income (domestic fixed income portfolios containing corporate and/or government debt securities), and international equity (including global portfolios). This is in contrast to Lakonishok et al. (1992), Del Guercio and Tkac (2002), Heisler et al. (2004), and Christopherson et al. (1998), all of whom focus exclusively on domestic equity portfolios. Our secondary data source, eVestment Alliance, provides quarterly composite re- turns, fee information, and an identifier that tags portfolios that are closed to new investors. Unlike IIS, the names of investment management firms are not hidden. Panel B of Table 1 provides descriptive statistics for the eVestment data. The time series cov- erage is shorter, starting in 1991. The cross-sectional coverage is also smaller than the IIS database. For example, the IIS database covers 1,137 domestic equity investment management firms and 3,381 portfolios between 1991 and 2004. In contrast, eVestment provides data on 805 firms and 2,682 portfolios. The attrition rate is approximately 1 percent, substantially smaller than for the IIS database.5 Because of these differences, we generate estimates of persistence from the IIS database, and use eVestment data to provide a better understanding of fees and portfolio closures. 2.2 Methodological Approach Our empirical approach to measuring persistence follows the mutual fund literature with some minor adjustments to accommodate certain facets of institutional investment management. For instance, we follow Carhart (1997) and form deciles based on raw returns during a ranking period and examine returns over a subsequent evaluation period. However, we also form deciles based on benchmark-adjusted returns for two reasons. First, plan sponsors frequently focus on benchmark-adjusted returns, at least in part because expected returns from benchmarks are useful for thinking about broader asset allocation decisions in the context of contributions and retirement withdrawls. Second, sorting on raw returns could cause portfolios that follow certain types of investment styles to systematically fall into winner and loser deciles. For instance, small cap value portfolios may fall into winner deciles in some periods, not because these portfolios delivered abnormal returns, but because this asset class generated large returns over that period. Although alpha estimates from three- and four-factor models adjusts for these characteristics in the post-ranking period, they do not influence decile formation if 5 We note, however, that a direct comparison of the attrition rates is not possible because the cross- sectional coverage is also smaller; it could be that attrition rates in the firms not sampled are higher. 9
  • 11. assignments are based on raw returns. By contrast, using benchmark-adjusted returns to form deciles circumvents this problem. Beginning at the end of 1979, we sort portfolios into deciles based on the prior annual raw or benchmark-adjusted return.6 We then compute the equal-weighted return for each decile over the following one-quarter. As we expand our analysis to examine persistence over longer horizons, we compute this return over appropriate future intervals (for one- year results, we compute the equally-weighted return over quarters 1 through 4, and so forth). We then roll forward, producing a non-overlapping set of post-ranking quarterly returns. Concatenating the evaluation period quarterly returns results in a time series of post-ranking returns for each portfolio; we generate estimates of abnormal performance from these time-series. We assess post-ranking abnormal performance by regressing the post-ranking returns on K factors as follows: K U rp,t = αp + βp,k fk,t + p,t , (1) k=1 where r is the return on portfolio p, and fk is the k th factor return. For domestic equity portfolios we use the Fama and French (1993) three-factor model with market, size, and book-to-market factors. Since Carhart (1997) shows that incorporating individual stock momentum (Jegadeesh and Titman (1993)) removes most of the persistence evident in mutual funds, we also estimate models that include a momentum factor. We obtain these four factors from Ken French’s web site. For fixed income portfolios, we again follow Fama and French (1993) and estimate a three-factor model with the Lehman Brothers Aggregate Bond Index return, a Term Spread Return computed as the difference between the long-term government bond return and the T-bill return, and a Default Spread Return computed as the difference between the corporate bond return and the long- term government bond return. We obtain aggregate bond index returns from Mercer Performance Analytics, available from 1981 onwards. We obtain the default and term spread returns from Ibbotson Associates, available from 1979 onwards. For international equity portfolios, we employ an international version of the three-factor model. We obain the international market return and book-to-market factor from Ken French. We 6 We sort based on prior one-year returns for two reasons. First, this choice is consistent with that of the mutual fund literature and allows us to directly compare estimates of alpha. Second, longer ranking periods are more likely associated with large differences in fund size, and, given the evidence in Chen, Hong, Huang, and Kubik (2004), we might not expect performance to persist across large variations in fund size (due to diseconomies). 10
  • 12. compute the international size factor as the difference between the S&P/Citigroup PMI World index return and the S&P/Citigroup EMI World index return, both of which exclude the United States (see http://www.globalindices.standardandpoors.com). Christopherson et al. (1998) argue that unconditional performance measures are inappropriate for two reasons. First, they note that sophisticated plan sponsors pre- sumably condition their expectations based on the state of the economy. Second, to the extent that plan sponsors employ dynamic trading strategies that react to changes in market conditions, unconditional performance indicators may be biased. They advocate and show that conditional performance measures can improve inferences. We follow their prescription and estimate conditional models in addition to the unconditional models described above. We estimate the conditional models as: K L C 0 l rp,t = αp + βp,k + βp,k Zl,t−1 fk,t + p,t , (2) k=1 l=1 where the Z’s are L conditioning variables. We use four conditioning variables in our analysis. We obtain the 3-month T-bill rate from the economic research database at the Federal Reserve Bank at St. Louis. We compute the default yield spread as the difference between BAA- and AAA- rated corporate bonds using the same database. We obtain the dividend-price ratio, computed as the logarithm of the 12-month sum of dividends on the S&P 500 index divided by the logarithm of the index level, from Standard & Poor’s. Finally, we compute the term yield spread as the difference between the long term yield on government bonds and the T-bill yield, using data from Ibbotson Associates. 3 Persistence 3.1 Short-term Persistence Table 2 shows estimates of one-quarter evaluation period alphas where raw returns deter- mine decile assignment. The table reports results for both unconditional and conditional methods. The t-statistic reported next to the alpha estimate for each decile is based on the standard error of the regression coefficient. At the bottom of each column we report a Spearman correlation coefficient. 11
  • 13. Panel A of Table 2 shows results for domestic equity. Although we do not report 2 2 each of the individual R from the regressions, the average R is 0.91 (0.94) for the 2 unconditional three-factor (four-factor) model. The corresponding average R for the 2 conditional models are greater, 0.95 and 0.97 respectively. In general, the R are large and comparable to those in prior studies (Fama and French (1993) and Carhart (1997)). Three facts immediately stand out. First, the effect of survivorship bias is quite large. For instance, the alpha of the first decile (extreme losers) using the three-factor model over the survivor-biased 1979-1990 sample period is 0.82 percent per quarter with a t-statistic of 2.11. The corresponding alpha in the non-survivorship-biased period of 1991-2004 is -1.19 percent, a differential of 2.01 percent. For the four-factor model, the differential is 1.74 percent per quarter. Another way to see the effects of survivorship bias is to examine the spread between the extreme winner and extreme loser decile. Based on the unconditional three-factor model, this spread is 1.21 percent per quarter in the survivorship biased sample and 4.07 percent per quarter in the unbiased sample. Second, there is virtually no persistence among the worst performers. Concentrating on the survivor-bias free sample period, the alphas of loser deciles are statistically in- distinguishable from zero. This is in stark contrast to the evidence in mutual funds, where persistence is strongest in the extreme loser decile. Third, persistence in deciles 7 through 10 (and sometimes in decile 6) is statistically significant. The alphas across these deciles increase monotonically, with alphas in the extreme winner deciles ranging from 1.07 percent per quarter to 3.33 percent per quarter. Again, in contrast to the evidence for retail mutual funds, the addition of momentum to the factor model reduces but does not eliminate persistence; between 1991 and 2004, the unconditional (condi- tional) alpha drops from 2.88 percent (3.33 percent) per quarter to 1.07 percent (1.62 percent), but remains highly statistically significant. Even by conservative standards, the coefficients are economically large, generating annualized abnormal returns greater than 4 percent, which compares favorably to retail mutual funds. Panel B of Table 2 provides estimates of one-quarter alphas for fixed income port- 2 folios. The average R of the unconditional regressions is substantially lower than for domestic equity portfolios, 0.79, but improves to 0.91 for the conditional regressions. 2 This increase in R is perhaps not surprising since some of the conditioning variables capture the effects of variations in yields in fixed income securities. Since conditioning information is especially relevant for this asset class, we focus our attention on estimates generated by conditional models. Because factor information is not available prior to 1981, we present results for 1981-1990 and 1991-2004 subperiods. 12
  • 14. Evidence of persistence in the loser deciles does not exist in either subperiod. How- ever, the winner deciles persist considerably. In conditional models estimated for the 1991-2004 subperiod, alphas are positive, increase monotonically, and are statistically significant for deciles 6 through 10. For the extreme winner decile, the alpha from the unconditional (conditional) model is 1.03 percent (1.53 percent) per quarter with a t- statistic of 3.76 (4.68). These abnormal returns are economically large, particularly for fixed income portfolios. Panel C of Table 2 shows one-quarter alphas for international equity portfolios es- timated using three-factor models. Since we only have factor data from 1989 onwards, and since very few international equity portfolios exist prior to 1991, we calculate al- phas for the non-survivor-biased sample from 1991 onwards. Again, the performance of loser deciles does not persist. Winner deciles, by contrast, continue to show large positive alphas. For example, for deciles 9 and 10, unconditional and conditional alphas range from a low of 2.04 to a high of 2.67 percent per quarter, and three out of four are statistically signficant. In addition to forming deciles based on raw returns, we also form deciles based on benchmark-adjusted returns. Our returns data come with style assignments and self designated benchmarks for domestic equity and fixed income portfolios. For domes- tic equity, benchmarks are based on size and value-growth grids. For fixed income, benchmarks are based on credit and maturity terms. We use benchmark-adjusted an- nual returns to form deciles and estimate alphas in the subsequent quarter using the same methods as before. Unfortunately, benchmark designations are not available for international equity portfolios. In Table 3, we ignore the survivorship-biased sample period and present results us- ing benchmark-adjusted returns for 1991-2004. Compared to the earlier results, some changes are evident in the results for domestic equity portfolios. First, the point esti- mates of alphas for loser deciles are larger than those generated by raw returns sorts, but remain statistically insigificant. Thus, although portfolios in loser deciles do not perform as poorly, no evidence of persistence or reversal continues to exist among losers. Second, the alphas of portfolios in winner deciles are smaller by approximately 0.5 percent per quarter. In the extreme winner decile, the alpha from an unconditional three-factor model drops from 2.88 percent per quarter (based on the raw return sort) to 2.03 per- cent per quarter. In one case, the alpha loses statistical signficance (the unconditional four-factor model based on benchmark-adjusted returns). Despite these differences, the general pattern of results remains the same – loser deciles do not persist and extreme 13
  • 15. winner deciles do. For fixed income portfolios, ranking on benchmark-adjusted returns produces a sim- ilar set of results, at least for conditional alpha estimates. Portfolios in extreme loser deciles show no predictability. However, deciles 4 through 10 show positive and statisti- cally significant alphas. For deciles 4 through 9, alphas range from 0.10 to 0.31 percent, and the alpha for the extreme winner decile is 0.80 percent per quarter. 3.2 Longer-term Persistence Significant persistence in performance one quarter after portfolio formation is certainly economically meaningful. However, plan sponsors typically do not deploy capital in a portfolio for one quarter, since the transaction costs from exiting a portfolio after one quarter and entering a new one are large and potentially prohibitive. Even if plan spon- sors employ transition management firms that seek to minimize such costs, the frictions are simply too large to justify a performance-chasing strategy. In addition, adverse rep- utation effects likely exist from trading in and out of institutional portfolios. If excess returns remain high for a sufficient period of time after portfolio formation, however, then plan sponsors may be able to exploit performance persistence. Accordingly, in this section, we examine persistence in institutional portfolios over longer horizons. Methodologically, we follow the same procedure as before, with some minor adjust- ments. We roll forward annually and calculate post-ranking quarterly returns for three years following portfolio formation. Thus, we compute the alpha for the first year from the beginning of year 1 to the end of year 1, we compute the alpha for the second year from the beginning of year 2 to the end of year 2, and so forth. For domestic equity and fixed income, we assign deciles using benchmark-adjusted returns. Since benchmarks are unavailable for international equity portfolios, we assign their deciles using raw returns. Also, since conditioning information clearly affects inference for fixed income portfo- lios, we estimate conditional fixed income alphas; alphas for domestic and international equity portfolios are unconditional. We do not show results for two- and three-quarter evaluation periods so that we do not overwhelm the reader with too many results.7 Instead, we simply show alphas for each decile in years 1, 2, and 3 after portfolio formation. 7 The alphas for winner portfolios over two- and three-quarter holding periods are generally large and statistically significant. 14
  • 16. Panel A of Table 4 shows alphas for domestic equity. Using a three-factor model, the performance of winner deciles persists. For example, deciles 9 and 10 have alphas of 0.56 (t-statistic=2.51) and 1.44 (t-statistic=3.34) percent respectively. However, the four-factor model alphas for these portfolios shrink to 0.21 and 0.47 percent respectively and lose their statistical significance. In the second year, alphas sharply reverse: the loser decile alphas are actually positive (1.04 and 0.80 percent) and statistically significant, whereas the winner decile alphas are indistinguishable from zero using both three- and four-factor models. What appears to be reversal in the loser decile is most likely driven by greater attrition in that decile. The attrition rate in the extreme loser decile is 7.6 percent in the first year, 5.3 percent in the second year, and 5.6 percent in the third year. In comparison, corresponding attrition rates in the extreme winner decile are 1.9 percent, 2.7 percent, and 2.8 percent respecitvely. Since we do not assign a “delisting” return to the portfolios that exit a decile, the returns of loser deciles are positively biased. Three years after portfolio formation, almost all alphas have deteriorated to zero. Alphas in fixed income deciles appear to follow a U-shape in the first year (Panel B of of Table 4) – alphas are greatest in the extreme winner and loser deciles and weaker in intermediate deciles. Again, reversal in the extreme loser decile is most likely driven by higher attrition rates among loser portfolios and the fact that we do not assign a delisting return to exiting portfolios (the cumulative attrition rate over a three year period is 11.9 percent for the extreme loser decile and 7.4 percent for the extreme winner decile). In the second year, the alphas of the extreme deciles are dampened and by the third year, the alpha of the extreme loser decile is indistinguishable from zero. In all cases, the extreme winner decile has the highest alpha and it is economically quite large. For instance, the alpha is 0.62 percent per quarter in the first year and 0.58 percent per quarter in the third year. In winner deciles of international equity portfolios (Panel C of of Table 4), alphas are 1.98 and 2.76 percent per quarter for deciles 9 and 10 in the first year. These alphas decline sharply in the following years. The decile 10 alpha drops from 2.76 percent in the first year to 0.97 percent in the second year, and 0.48 percent in the third year. Interestingly, alphas for the loser deciles climb over time, although the t-statistic of the extreme loser decile never achieves significance. In general, persistence appears to last up to one year in equity portfolios (both domestic and international). The fixed income results, however, are puzzling to the extent that persistence continues up to three years after decile formation. It is possible that the composition of the extreme deciles is unusual and causes this pattern. We 15
  • 17. examine whether any particular types of investment styles load in these deciles. To do so, we calculate the percentage of each decile’s observations associated with each investment style, and then subtract this percentage from the unconditional mean across deciles. The nine styles are: money market, municipal, core, short-term, intermediate-term, long- term, mortgages, convertibles, and high-yield. This procedure produces a deviation for each decile and style, which sums to zero within a decile. We find that deciles 1 and 10 have the largest loadings on high yield portfolios.8 Since the default spread variable that we use is the difference between corporate and government bond returns, it likely poorly adjusts for high yield securities. Consequently, the persistence results that we observe are likely caused by imprecise factor adjustments. To help with inference, in addition to computing alphas based on factor models, we also compute benchmark- adusted returns and information ratios for each decile over the three-year period following decile assignment. Our hope is that benchmarks more precisely account for high yield securities in the high yield portfolios. Naturally, we sacrifice the regression approach and instead compute simple benchmark-adjusted returns and information ratios: e rt = rp,t − rb,t (3) e rt IR = e , (4) σ(rt ) where rp is the return on portfolio p, rb is the return on the corresponding benchmark, r e is the excess return, and IR is the information ratio. Table 5 shows these returns and information ratios each year for domestic equity (Panel A) and fixed income portfolios (Panel B). The domestic equity results mirror those from the regression approach - persistence lasts up to one year in the winner deciles and disappears thereafter. For fixed income portfolios, persistence only appears in the intermediate deciles during the first year, which represents an important departure from the alphas presented in Table 4, and is consistent with our suspicion that the fixed income factor model inadequately handles high yield portfolios. Overall, performance persists at least up to the end of the first year after portfolio formation for domestic and international equity portfolios. These excess returns revert to zero thereafter. Persistence in fixed income lasts longer in some specifications, and in many ways remains enigmatic. 8 Decile 1 (10) shows postive deviation of 5.3 percent (18.3 percent) from the unconditional mean. Additionally, the intermediate deciles all have negative loadings on high yield investment styles. We also check loadings of various investment styles in domestic equity and find no substantial deviations. 16
  • 18. 3.3 Persistence and Flows The evidence thus far suggests that the performance of institutional portfolios persists over short- to intermediate-term horizons. Excess returns are generally positive up to one year after portfolio formation and revert to zero thereafter. Although the magnitude of the excess returns and the reversals varies across asset classes, the general pattern appears widespread. Prior performance is an important screen used by plan sponsors in selecting investment management firms and allocating capital. Thus, new capital likely flows into extreme winner portfolios and flows out of extreme loser portfolios. If there are decreasing returns to scale in investment management, then capital flows could account for the patterns in persistence that we observe. We measure percentage asset flows, Cfp,t , for each portfolio p during the year t as: Ap,t − Ap,t−1 × (1 + rp,t) Cfp,t = , (5) Ap,t−1 where Ap,t measures the dollar amount of assets in portfolio p at the end of year t, and rp,t is the gross return on portfolio p during the year (not quarter) t. Measurement of flows in this manner (as fractional flows) is analagous to that typically employed in the mutual fund literature. We truncate flows from below by 1 so that small asset values in the denominator do not produce large outlier flows that could distort our results. Two important considerations exist in measuring institutional portfolio flows. First, total assets for each portfolio are only available on an annual basis, whereas returns are available quarterly. This mismatch could potentially induce discreteness into the flow-performance relation. In practice, however, it unlikely makes much of a difference, because investment management firms largely gain or lose assets when they are hired or fired by plan sponsors. Since such selection and termination decisions are in themselves discrete, no artificial discreteness is induced in the flow data. It is also worth noting that this capital flow process is quite different from retail mutual funds, where purchases and redemptions take place daily. Second, total asset data are available for a smaller sample than that of returns. To ensure that we assign portfolios to the correct decile, we assign deciles based on all portfolios that report returns, and then calculate the mean flow for each decile portfolio with available data. This procedure could in and of itself cause selection bias, an issue that we address in section 3.4. Table 6 shows average capital flows into domestic equity, fixed income, and interna- tional equity portfolios in each decile 1, 2, and 3 years following decile formation. In 17
  • 19. domestic equity, a monotonic relation exists between flows in year 1 and decile ranking based on the prior year return. In year 1, decile 10 receives a flow of almost 32 percent of assets whereas decile 1 loses almost 4 percent of assets, a spread of 36 percent. Since the average portfolio in decile 1 (10) has total assets of $466 million ($420 million), this implies a loss (gain) of $27 million ($132 million). In the following year (year 2), the alphas revert. For winner portfolios, the three- (four-) factor alpha declines from 1.44 percent (0.47) per quarter in year 1 to 0.09 percent (-0.22 percent) in year 2. Interest- ingly, the loser decile three- (four-) factor alpha goes from 0.05 percent (0.40 percent) in year 1 to 1.04 percent (0.80 percent) in year 2. Portfolios in the intermediate deciles follow similar patterns: deciles with the highest capital flows have the lowest alphas during the next year. In fixed income, alphas persist over longer horizons. Capital flows are smaller in percentange terms than domestic equity, but the spread between winner and loser deciles remain. Perhaps even more interesting is the fact that changes in flows over time are not as dramatic. For instance, in the extreme winner decile, the change in flow from year 1 to year 2 is only 4 percentage points, less than half the 9 percentage points for domestic equity. The flow patterns for international equity portfolios are more similar to those for domestic equity. Alphas in the first year are quite high (the extreme winner decile alpha is 2.76 percent) and followed by extremely high capital flows (29 percent). The year after these capital inflows, decile 10 alpha shrinks to 0.97 percent, following which capital inflows decline to 23 percent. Again, similar patterns exist in other deciles. Despite the barrage of statistics above, the general pattern that emerges for domestic and international equity portfolios is straightforward. Performance lasts up to a year after portfolio formation. Among winners, this persistence elicits capial inflows, and excess returns subequently revert. It is appropriate at this point to examine our re- sults in the context of the assumptions and implications of Berk and Green (2004). In Berk and Green’s model, performance does not persist, even though there is differential ability across fund managers. Investors rationally respond to past performance. Capital flows into superior performers, which in conjunction with assumed diseconomies of scale, causes future excess returns to disappear. We observe persistence. We cannot directly measure diseconomies of scale, although some evidence indicates it exists (Perold and Salomon (1991)). We also observe flows that appear to follow performance after which excess returns disappear. 18
  • 20. The individual moving parts of the evidence are consistent with flows affecting future persistence. To establish a clear causal link, one would need to know the capacity of an existing portfolio and then the impact of flows on future returns. That is an impossible task without proprietary data. We can, however, make a modest attempt in that direction. To do so, we separate the extreme winner decile into four groups at the end of the first year based on total assets and the degree of capital flows. Effectively, we do a double-sort on the winner portfolio using assets and flows. Our hope is that total assets provide a crude measure of capacity, and that if diseconomies of scale reduce future alphas, then changes in alpha should be larger for big portfoliso that experience larger inflows. We find that the decline in alpha is indeed greater for the high-assets/highflow group than for the low-assets/low-flow group. In domestic equity, using a four-factor model, the decline in alpha is -0.53 percent for the former and -0.21 percent for the latter. This evidence certainly suggests a link between flows and future performance. At a minimum, it provides circumstantial support for the quantity-based equilibrating process that is at the heart of Berk and Green (2004). The key difference is that in our setting it takes one year, and in some cases two, for the equilibrating mechanism to take effect, most likely because capital flows in the institutional arena are sticky (particularly when compared to retail mutual funds). 3.4 Fee Arrangements We base our results thus far entirely on returns that are net of trading costs but gross of fees. The possibility exists that investment management fees eliminate the post- ranking period excess returns. Cross-sectional variation in fees could also be so large that it swamps alphas in winner portfolios; in other words, average fees may not be high enough to eliminate excess returns across all portfolios, but only in the extreme winner deciles. In this section, we analyze fees charged by investment management firms for institutional portfolios. As noted earlier, to study fees we employ data from eVestment Alliance. This firm provides composite quarterly returns and fee information. The proto-typical fee struc- ture is such that management fees decline as a step function of the size of the mandate assigned to the investment management firm by the plan sponsor. Although variation undoubtedly exists in the breakpoints, eVestment collects marginal fee schedules using “standardized” breakpoints. Specifically, each investment manager identifies fees for $10, $25, $50, $75, and $100 million mandates. These marginal fees are based on fee 19
  • 21. schedules; actual fees are individually negotiated between investment managers and plan sponsors. Such individual negotiations involve rebates to the marginal fees as well as other structural fee arrangements (e.g. performance linkages). To our knowledge, no available database details individual fee arrangements. As a result, we regard our analy- sis as exploratory in nature and designed only to address issues pertaining to persistence. While our data are new and unique, they are not rich enough to provide a comprehensive understanding of actual fee arrangements in institutional investment management. Table 7 provides descriptive information on fee arrangements. In Panel A, we report the percentage of portfolios that offer performance-based fee clauses in contracts across each asset class and decile.9 Performance-based fees often have no minimum and link actual fees to performance above a prescribed benchmark. For domestic equity and fixed income portfolios, a substantial variation exists in the percentage of portfolios that offer performance-based fees across the deciles. In domestic equity, for example, only 47 percent of the portfolios in the loser decile offer performance-based fees while almost 55 percent in the winner decile offer such an arrangement. The corresponding percentages for fixed income are 38 percent for decile 1 and 46 percent for decile 10. Little variation exists in international equity portfolios. Panel B of Table 7 shows the percentage of portfolios that offer most-favored nation (MFN) clauses by asset class and decile. MFN provisions typically state that the invest- ment manager will charge the plan sponsor a fee that is the lowest of that charged to similar mandates from other comparable plan sponsors. If properly enforced, an MFN clause benefits incumbent plan sponsors in the sense that the investment manager is required to match lower fees provided to a new plan sponsors.10 Again, some evidence suggests that portfolios in winner deciles have greater propensity to offer MFN clauses compared to portfolios in loser deciles. The spread between extreme winner-loser deciles for domestic equity, fixed income, and international equity are 5, 4, and 3 percentage points respectively. Table 8 shows the distribution of annual fees across deciles for domestic equity, fixed income, and international equity (Panels A, B and C respectively). For each decile, we show the average marginal fee (in basis points) in each breakpoint described above. 9 The sum of the “Yes” and “No” columns does not add up to 100 percent because this information is missing for a small number of portfolios. 10 As with most such contracts and clauses, many of the benefits are dependent on the details of the contract and its enforcement. For instance, the investment management firm and plan sponsor might reasonably disagree on whether mandates from two-plan sponsors are comparable because of size or specific portfolio restrictions (e.g. no sin stocks or use of directed brokerage). 20
  • 22. Since fees vary widely across investment styles within domestic equity, we show four major intersections of the size and growth-value grid: large cap growth, large cap value, small cap growth, and small cap value. Similarly, for fixed income, we only show fees for four styles: municipal, high yield, intermediate term, and mortage-backed securities. The results in Table 8 display several important elements. First, the magnitude of the fees are such that they are unlikely to influence our inferences with regard to persistence; the fees are simply not large enough to eliminate the alphas. Take domestic equity, for example. The largest fee reported in the table is 100 basis points for the extreme winner decile corresponding to the smallest ($10 million) mandate in small cap growth. The quarterly alpha one year after decile formation using a three-factor model was 1.44 percent, implying an annual alpha of almost 6 percent. Even if one were to use the four-factor alpha of 0.47 percent (which is statistically insignificant), removing maximum fees still leaves an annual alpha of 1 percent. The results for fixed income and international equity are substantially stronger. The quarterly alpha one year after decile formation for the extreme winner decile was 0.62 percent, implying an annual alpha of 2.4 percent. The maximum annual fee (from the high yield investment style) is 59 basis points. The corresponding quarterly alpha for international equity was 2.76 percent (9.5 percent annually), where the maximum annual fee is 88 basis points. We have been quite conservative in assessing the impact of fees on two accounts. First, fees are based on reported schedules. Actual fees involve significant rebates and are likely to be substantially lower. Second, we have deliberatedly cherry-picked the largest fees in an asset class to shrink alphas. Despite this conservatism, investment management fees are clearly not large enough to eliminate the alphas discovered in the persistence regressions. One might legitimately ask whether better-performing portfolios charge more in fees than worse-performing portfolios. An examination of the variation in fees across deciles in each of the panels shows evidence that this is indeed the case. Reported fees are consistently higher for portfolios in decile 10 than in decile 1; the differentials are modest in domestic equity and fixed income, but larger in international equity. To the extent that investment managers are likely to offer larger rebates for worse performing portfolios and less likely to offer large rebates for better performing portfolios, the differences may in fact be larger than can be estimated using these data. 21
  • 23. 3.5 Alternative Mechanisms to Control Flows Portfolio managers cognizant of diseconomies of scale and wishing to preserve their reputation for providing consistent excess gross returns may prefer to restrict asset flows rather than suffer declines in alpha. Several possible mechanisms could be used to achieve this result. One obvious mechanism is price. Fee increases could be used to control asset flows, preserving gross performance and persistence. In retail mutual funds, fees and loads vary widely, affecting redemptions and capital inflows (see Nanda, Narayanan, and Warther (2000) for a model in which heterogeneous fees appear endogenously). To the extent that better-performing investment managers charge higher fees, there is some circumstantial evidence that fees are used to control flows. However, as noted earlier, actual fee arrangements between institutional investment management firms and plan sponsors are private. As a result, we cannot detect fee increases or cleanly observe the use of heterogenous negotiated fees to discourage asset inflows from particular types of (perhaps short-term) plan sponsors. Another, more direct, way to control asset flows is to simply stop accepting new money in winner portfolios. Bris, Gulen, Kadiyala, and Rau (2005) find that 143 re- tail equity mutual funds closed to new investors over a 10-year period, and document that these funds delivered positive excess returns prior to closing. We are anecdotally aware of some investment advisors that have followed this approach in the institutional marketplace. For example, returning to our example of Aronson+Johnson+Ortiz, this investment management firm closed its flagship large cap value portfolio to new institu- tional portfolios under the belief that it could not continue to generate superior returns with a larger asset base. Data from eVestment tags portfolios that have been closed to new investors, and we can use these data to see if this mechanism is employed by institutional investment man- agers. Out of 5,122 portfolios, eVestment reports that 277 are closed to new investors, a rate that appears higher than that for mutual funds. The majority of these closures (270) are in domestic and international equity portfolios (208 and 62 respectively, out of 2,881 and 910 portfolios in each of these asset classes). Only 7 out of 1,331 fixed in- come portfolios are identified as closed. More interestingly, the returns are substantially higher for closed portfolios than portfolios that are open to new investors. The average quarterly raw return for closed portfolios are 4.9 percent, 3.2 percent, and 3.8 percent respectively for domestic equity, fixed income, and international equity respectively. The corresponding returns for portfolios open to new investors are lower in each case: 3.9, 22
  • 24. 1.9, and 3.0 percent respectively. These differentials are also evident in benchmark- adjusted returns, which can be computed for domestic equity and fixed income only. The average quarterly benchmark-adjusted return in closed domestic equity portfolios is 1.15 percent, compared to 0.65 to open portfolios. The comparable and corresponding returns for fixed income portfolios are 1.05 and 0.13 percent respectively.11 3.6 Robustness and Other Issues Our results could be influenced by backfill bias, similar to that observed in hedge fund databases (Liang (2000)). Specifically, it could be that only portfolios that have been successful over some period of time enter the database. To determine how this bias may affect our results, we follow Jagannathan and Novikov (2005), and eliminate the first 2 years of returns for each portfolio in our sample, and then re-estimate our regressions. The results of this exercise are not reported in the paper, but our basic estimates of persistence are almost identical. Another possibility is that a selection bias exists in the investment management firms that report total assets under management per portfolio. Although ex ante the source of such a bias is hard to identify, it is a possibility that could cloud our inferences. In a perfect world, we would be able to observe characteristics of firms that report assets and those that do not, and then examine whether their flow-performance relations differ. Unfortunately, this is not possible, particularly since we do not have firm identities. We can, however, examine whether our persistence results differ for the subsample of firms that include both assets and returns data. We replicate our results for this subsample and find that the alpha estimates do not differ materially from those reported in Table 4, and in some cases they are even larger. Since the database used to generate estimates of fees differs from the data on which we base persistence estimates, the two databases may not be comparable. To examine this possibility, we estimate one-year alphas (equivalent to those reported in Table 4). The alpha estimates from eVestment data are very similar to those estimated using the larger sample obtained from IIS. As shown earlier, although our data is survivorship-bias free, greater attrition exists 11 Ideally, we would like to know the date on which a portfolio was closed to new investors so that we can compute returns before and after closure. Unfortunately, our data do not include closure dates. If investment management firms do in fact avoid diseconomies by closing portfolios, then our computed return differentials are downward biased. 23
  • 25. in the loser decile portfolios than in the winner deciles. Since portfolios are not assigned a delisting return, this implies that post-ranking returns in loser deciles are biased upward. If we were to somehow assign a delisting return, this might generate persistence (and negative alphas) in the loser decile. We choose not to do so for two reasons. First, there is no obvious choice of delisting return. Second, and more importantly, since institutional portfolios cannot be shorted, there is little practical advantage in doing so. Finally, a reconciliation of our results with those reported in Goyal and Wahal (2005) are in order. They report that one, two, and three-year excess returns of investment management firms prior to being hired by plan sponsors are significantly positive. Post- hiring excess returns are statistically indistinguishable from zero. On the surface, this may seem at odds with our evidence of persistence. However, the key lies in horizons. Hiring decisions reported by Goyal and Wahal occur after three years of superior per- formance. Our evidence of persistence is that after two years (the portfolio formation year and the subsequent evaluation year), reversals start to occur. Consequently, these two sets of results suggest that deciles based on three-year return rankings (rather than one-year return rankings) should not persist. To confirm this, we re-estimate the results in Table 4 after forming deciles based on three-year returns. This exercise (results not tabulated) shows that performance does not persist when deciles are formed in this way. 4 Conclusion In this paper, we examine the persistence in performance of 6,260 portfolios managed by 1,475 investment management firms between 1991 and 2004. These portfolios provide exposure to domestic equity, fixed income, and international equity asset classes to public and private defined benefit retirement plans, endowments, foundations, multi- employer unions, and trusts. A large number of active investment styles and strategies are included; for equity strategies, all size and growth-value gradations are represented, and all maturity and credit risk dimensions are included in fixed income portfolios. To our knowledge, we are the first to examine persistence beyond the traditionally studied domestic equity funds. We form deciles using raw and benchmark-adjusted returns over one year and ex- amine the persistence in performance thereafter using a variety of three- and four-factor models. The factor models do a good job of explaining the return series, but we find significant excess returns in the post-decile formation period. Unlike retail mutual funds, 24
  • 26. however, these excess returns are concentrated entirely in winner deciles. The magni- tudes of the alphas themselves are economically large. Moreover, typical investment fees are not large enough to eliminate net-of-fee excess returns. From a practical perspective, our results suggest that the widespread practice of hiring investment managers that have delivered superior returns is both rational and potentially profitable. Indeed, the organizational structure of institutional investment management and, in particular, the use of consultants to pick investment managers is conducive to this effort. However, the persistence that is the source of potential gains for plan sponsors is its very own death knell: we find that portfolios in the winner deciles draw an influx of capital from plan sponsors, and in the year following this capital inflow, the excess returns disappear. Berk and Green (2004) argue that diseconomies of scale in investment management, combined with capital flows following superior performance, could generate this result. Our results are consistent with their model and highlight the transmission mechanism by which this may take place. Particularly noteworthy is the fact that, within the institutional money management arena, it takes time for asset flows to eliminate persistence. 25
  • 27. References Berk, Jonathan and Richard C. Green, 2004, Mutual fund flows and performance in rational markets, Journal of Political Economy 112, 1269-1295. Berk, Jonathan and Jing Xu, 2004, Persistence and fund flows of the worst performing mutual funds, Working paper, University of California at Berkeley. Brown, Stephen and William Goetzmann, 1995, Performance persistence, Journal of Finance 50, 679-698. Bollen, Nicolas and Jeffrey Busse, 2005, Short-term persistence in mutual fund perfor- mance, Review of Financial Studies 18, 569-597. Bris, Arturo, Huseyin Gulen, Padma Kadiyala, and P. Raghavendra Rau, 2005, Clos- ing time? The signaling content of mutual fund closures, Working paper, Purdue University. Carhart, Mark M., 1997, On persistence in mutual fund performance, Journal of Finance 52, 57-82. Carhart, Mark M., Jennifer N. Carpenter, Anthony W. Lynch, and David K. Musto, 2002, Mutual fund survivorship, Review of Financial Studies 5, 1439-1463. Chen, Joseph, Harrison Hong, Ming Huang, and Jeffrey D. Kubik, 2004, Does fund size erode mutual fund performance? The role of liquidity and organization, American Economic Review 94, 1276-1302. Coggin, T, Frank Fabozzi, and Shafiqur Rahman, 1993, The investment performance of US equity pension fund managers: An empirical investigation, Journal of Finance 48, 1039-1055. Christopherson, Jon A., Wayne E. Ferson, and Debra A. Glassman, 1998, Condition- ing manager alphas on economic information: Another look at the persistence of performance, Review of Financial Studies 11, 111-142. Del Guercio, Diane and Paula Tkac, 2002, The determinants of the flow of funds of managed portfolios: Mutual funds versus pension funds, Journal of Financial and Quantitative Analysis 37, 523-557. Elton, Edwin J., Martin J. Gruber, and Christopher R. Blake, 1996, The persistence of risk-adjusted mutual fund performance, Journal of Business 69, 133157. Elton, Edwin J., Martin J. Gruber, and Christopher R. Blake, 2003, Incentive fees and mutual funds, Journal of Finance 58, 779-804. Elton, Edwin, M. Gruber, S. Das, and M. Hlavka, 1993, Efficiency with costly informa- tion: A reinterpretation of the evidence for managed portfolios, Review of Financial Studies 6, 1-22. 26
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  • 30. Table 1: Descriptive Statistics This table presents descriptive statistics on the sample of institutional investment management firms and their portfolios. Statistics are presented for the survivorship biased sample period of 1979 to 1990 and survivorship bias free sample period of 1991 to 2004. Firm and portfolio size is in millions of dollars. The attrition rate is calculated by summing the number of portfolios that disappear from the database during a year and scaling by the total number of portfolios at the beginning of the year. Domestic Domestic International Equity Fixed Income Equity Panel A: Data Source: IIS Sample: 1979–1990 Total # Firms 670 397 144 Total # Portfolios 1123 742 331 Avg. # Firms per year 359 204 57 Avg. # Portfolios per year 516 309 108 Avg. Size of Firm 823 1439 1188 Avg. Size of Portfolio 510 797 553 Attrition Rate 0.00 0.00 0.00 Sample: 1991-2004 Total # Firms 1137 602 330 Total # Portfolios 3381 1683 1196 Avg. # Firms per year 873 470 236 Avg. # Portfolios per year 2146 1192 746 Avg. Size of Firm 2530 3764 3648 Avg. Size of Portfolio 981 1427 1121 Attrition Rate 3.08 3.18 3.48 Panel B: Data Source: eVestment Alliance Sample: 1991-2004 Total # Firms 805 356 237 Total # Portfolios 2682 1290 821 Avg. # Firms per year 630 292 169 Avg. # Portfolios per year 1657 914 491 Attrition Rate 1.01 1.02 1.21 29
  • 31. Table 2: Post-Ranking One-quarter Alphas with Deciles formed using Raw Returns This table lists the post-ranking alphas for deciles of funds sorted according to the raw return during the ranking period of one year. The portfolio deciles are rebalanced at the end of every quarter and are held for one post-ranking quarter. Unconditional alphas are calculated from the factor model K rp,t = αU + p βp,k fk,t + p,t , k=1 while the conditional alphas are calculated from the factor model K L 0 l rp,t = αC + p βp,k + βp,k Zl,t−1 fk,t + p,t , k=1 l=1 where f are K factors and Z’s are L instruments. The list of instruments includes T-bill, dividend price ratio, term spread and default spread. The factors for domestic equity are the three Fama and French (1993) factors and the Carhart (1997) momentum factor. The factors for domestic fixed income are the Lehman Brothers Aggregate Bond Index returns, Term Spread Return, and Default Spread Return. The factors for international equity are the international versions of Fama and French (1993) factors. We report alphas for the survivorship biased sample period of 1979 to 1990 30 and survivorship bias free sample period of 1991 to 2004. All alphas are in percent per quarter and t-statistics are reported in parentheses next to alphas. Decile 1 contains the worst performing portfolio and decile 10 contains the best performing portfolios. Panel A: Domestic Equity Unconditional Conditional 1979–1990 1991–2004 1979–1990 1991–2004 Decile FF Carhart FF Carhart FF Carhart FF Carhart 1 0.82 (2.11) 1.59 (4.16) -1.19 (-1.64) -0.17 (-0.23) 0.38 (0.86) 0.72 (1.56) -1.13 (-1.62) -0.37 (-0.52) 2 0.26 (1.01) 0.68 (2.55) -0.81 (-1.56) -0.13 (-0.25) -0.00 (-0.00) 0.34 (0.92) -0.78 (-1.55) -0.08 (-0.16) 3 0.58 (2.77) 0.70 (2.89) -0.62 (-1.69) -0.12 (-0.34) 0.40 (1.48) 0.49 (1.42) -0.54 (-1.47) -0.06 (-0.16) 4 0.64 (3.56) 0.61 (2.92) -0.35 (-1.29) -0.06 (-0.23) 0.32 (1.53) 0.37 (1.47) -0.32 (-1.22) 0.07 (0.25) 5 0.74 (3.85) 0.65 (2.89) -0.01 (-0.03) 0.15 (0.76) 0.52 (2.66) 0.59 (2.53) 0.03 (0.23) 0.25 (1.54) 6 0.70 (4.07) 0.50 (2.61) 0.18 (1.30) 0.16 (1.08) 0.72 (3.33) 0.62 (2.55) 0.22 (1.84) 0.28 (2.01) 7 1.10 (5.29) 0.74 (3.45) 0.67 (3.47) 0.44 (2.22) 0.89 (3.81) 0.79 (3.78) 0.74 (3.75) 0.57 (2.79) 8 1.09 (4.81) 0.74 (3.07) 0.97 (3.52) 0.47 (1.86) 0.79 (3.57) 0.59 (2.64) 1.05 (3.69) 0.70 (2.61) 9 1.37 (4.34) 0.69 (2.31) 1.51 (3.70) 0.66 (1.89) 1.13 (2.93) 0.90 (2.56) 1.65 (3.98) 0.91 (2.36) 10 2.03 (4.41) 1.11 (2.46) 2.88 (3.77) 1.07 (1.85) 1.61 (2.85) 1.23 (2.18) 3.33 (4.13) 1.62 (2.59) SpCorr 0.79 (0.01) 0.13 (0.73) 1.00 (0.00) 1.00 (0.00) 0.93 (0.00) 0.65 (0.05) 1.00 (0.00) 1.00 (0.00)
  • 32. Panel B: Domestic Fixed Income Unconditional Conditional Decile 1981–1990 1991–2004 1981–1990 1991–2004 1 -0.53 (-1.77) 0.27 (0.98) -0.08 (-0.31) -0.33 (-1.62) 2 -0.06 (-0.35) 0.14 (1.38) 0.18 (1.14) -0.13 (-1.34) 3 -0.01 (-0.11) 0.09 (1.83) 0.16 (1.31) -0.01 (-0.25) 4 0.10 (1.26) 0.06 (1.43) 0.22 (2.38) 0.01 (0.29) 5 0.11 (1.30) 0.04 (1.09) 0.10 (1.16) 0.03 (0.79) 6 0.32 (3.18) 0.05 (0.94) 0.24 (2.58) 0.13 (2.35) 7 0.17 (1.43) 0.03 (0.56) 0.11 (0.93) 0.17 (3.33) 8 0.37 (2.12) 0.09 (1.42) 0.24 (1.37) 0.31 (5.77) 9 0.51 (2.29) 0.23 (2.28) 0.13 (0.69) 0.52 (5.52) 10 0.70 (2.23) 1.03 (3.76) 0.38 (1.62) 1.53 (4.68) SpCorr 0.99 (0.00) 0.02 (0.97) 0.47 (0.18) 1.00 (0.00) Panel C: International Equity (1991–2004 only) Decile Unconditional Conditional 1 -0.23 (-0.24) 0.02 (0.01) 2 -0.32 (-0.43) 0.19 (0.20) 3 -0.12 (-0.19) 0.23 (0.31) 4 0.46 (0.94) 0.68 (1.28) 5 0.55 (1.26) 0.77 (1.56) 6 0.98 (1.87) 0.96 (1.84) 7 1.37 (2.40) 1.10 (1.90) 8 1.60 (2.47) 1.08 (1.68) 9 2.37 (3.29) 2.05 (2.73) 10 2.67 (2.55) 2.04 (1.70) SpCorr 0.99 (0.00) 0.98 (0.00) 31
  • 33. Table 3: Post-Ranking One-quarter Alphas with Deciles formed using Benchmark-Adjusted Returns This table lists the post-ranking alphas for deciles of funds sorted according to the benchmark-adjusted return during the ranking period of one year. The portfolio deciles are rebalanced at the end of every quarter and are held for one post-ranking quarter. Unconditional alphas are calculated from the factor model K rp,t = αU + p βp,k fk,t + p,t , k=1 while the conditional alphas are calculated from the factor model K L rp,t = αC + p 0 βp,k + l βp,k Zl,t−1 fk,t + p,t , k=1 l=1 where f are K factors and Z’s are L instruments. The list of instruments includes T-bill, dividend price ratio, term spread and default spread. The factors for domestic equity are the three Fama and French (1993) factors and the Carhart (1997) momentum factor. The factors for domestic fixed income are the Lehman Brothers Aggregate Bond Index returns, Term Spread Return, and Default Spread Return. We report alphas only for survivorship bias free sample period of 1991 to 2004. All alphas are in percent per quarter and t-statistics are reported in parentheses next to alphas. Decile 1 contains the worst performing portfolio and decile 10 contains the best performing portfolios. Domestic Equity Domestic Fixed Income Unconditional Conditional Unconditional Conditional Decile FF Carhart FF Carhart FF FF 1 -0.08 (-0.21) 0.62 (1.63) -0.05 (-0.17) 0.34 (1.00) 0.35 (2.20) 0.19 (1.18) 2 -0.08 (-0.25) 0.43 (1.40) 0.01 (0.05) 0.50 (2.19) 0.19 (2.77) 0.08 (1.01) 3 -0.03 (-0.11) 0.25 (0.95) 0.05 (0.25) 0.33 (1.77) 0.11 (2.66) 0.06 (1.24) 4 0.05 (0.24) 0.25 (1.13) 0.16 (1.11) 0.42 (2.94) 0.11 (3.21) 0.10 (2.33) 5 0.08 (0.45) 0.13 (0.69) 0.16 (1.27) 0.29 (2.19) 0.09 (2.24) 0.11 (3.00) 6 0.09 (0.60) 0.11 (0.69) 0.15 (1.35) 0.26 (1.95) 0.10 (2.30) 0.16 (3.90) 7 0.28 (1.82) 0.10 (0.62) 0.32 (2.33) 0.21 (1.29) 0.11 (2.64) 0.16 (4.55) 8 0.30 (1.65) 0.12 (0.61) 0.35 (2.18) 0.24 (1.31) 0.14 (2.64) 0.26 (5.30) 9 0.61 (2.26) -0.05 (-0.28) 0.76 (3.23) 0.23 (1.21) 0.15 (2.13) 0.31 (4.81) 10 2.03 (3.56) 0.51 (1.42) 2.39 (4.47) 1.07 (2.94) 0.70 (2.58) 0.80 (4.31) SpCorr 1.00 (0.00) -0.52 (0.13) 0.95 (0.00) -0.36 (0.31) 0.03 (0.95) 0.73 (0.02) 32
  • 34. Table 4: Post-Ranking One- to Three-Year Alphas This table lists the post-ranking alphas for deciles of funds sorted according to the benchmark-adjusted return (for domestic equity in Panel A and domestic fixed income in Panel B) or raw return (for international equity in Panel C) during the ranking period of one year. The portfolio deciles are rebalanced at the end of every year and are held for one to three post-ranking years. Unconditional alphas are calculated from the factor model K rp,t = αU + p βp,k fk,t + p,t , k=1 while the conditional alphas are calculated from the factor model K L rp,t = αC + p 0 βp,k + l βp,k Zl,t−1 fk,t + p,t , k=1 l=1 where f are K factors and Z’s are L instruments. The list of instruments includes T-bill, dividend price ratio, term spread and default spread. The factors for domestic equity are the three Fama and French (1993) factors and the Carhart (1997) momentum factor. The factors for domestic fixed income are the Lehman Brothers Aggregate Bond Index returns, Term Spread Return, and Default Spread Return. The factors for international equity are the international versions of Fama and French (1993) factors. We report unconditional alphas for domestic equity and international equity and conditional alphas for domestic fixed income. The sample period is 1991 to 2004. All alphas are in percent per quarter and t-statistics are reported in parentheses next to alphas. Decile 1 contains the worst performing portfolio and decile 10 contains the best performing portfolios. Panel A: Domestic Equity (Unconditional alphas) First-Year Second-Year Third-Year Decile FF Carhart FF Carhart FF Carhart 1 0.05 (0.15) 0.40 (1.30) 1.04 (3.54) 0.80 (2.55) 0.73 (2.38) 0.31 (1.00) 2 0.16 (0.56) 0.24 (0.77) 0.56 (2.82) 0.39 (1.83) 0.40 (2.12) 0.20 (1.01) 3 0.15 (0.64) 0.21 (0.80) 0.38 (2.08) 0.27 (1.37) 0.14 (0.98) 0.03 (0.20) 4 0.06 (0.31) 0.19 (0.86) 0.46 (2.37) 0.47 (2.15) 0.13 (0.82) 0.18 (1.02) 5 0.12 (0.77) 0.17 (0.98) 0.06 (0.38) 0.11 (0.57) 0.17 (0.96) 0.23 (1.16) 6 0.12 (0.77) 0.15 (0.83) 0.08 (0.54) 0.15 (0.85) 0.11 (0.67) 0.18 (1.01) 7 0.16 (0.95) 0.20 (1.07) 0.09 (0.53) 0.21 (1.19) 0.18 (1.24) 0.18 (1.08) 8 0.32 (1.74) 0.16 (0.82) 0.17 (0.98) 0.32 (1.70) 0.09 (0.48) 0.12 (0.54) 9 0.56 (2.51) 0.21 (0.98) 0.05 (0.17) 0.13 (0.43) 0.32 (1.13) 0.22 (0.70) 10 1.44 (3.34) 0.47 (1.36) 0.09 (0.23) -0.22 (-0.53) 0.23 (0.58) -0.04 (-0.09) SpCorr 0.64 (0.05) 0.49 (0.15) 1.00 (0.00) -0.52 (0.13) 0.81 (0.01) -0.18 (0.63) 33
  • 35. Panel B: Domestic Fixed Income (Conditional alphas) Decile First-Year Second-Year Third-Year 1 0.45 (3.25) 0.37 (3.19) 0.18 (1.44) 2 0.23 (3.14) 0.16 (2.55) 0.08 (1.07) 3 0.13 (2.91) 0.10 (2.32) 0.10 (1.66) 4 0.14 (3.37) 0.10 (2.59) 0.07 (1.61) 5 0.11 (3.54) 0.10 (2.61) 0.07 (2.81) 6 0.15 (3.81) 0.05 (1.60) 0.09 (2.64) 7 0.10 (3.24) 0.09 (2.57) 0.07 (1.86) 8 0.11 (2.01) 0.14 (2.40) 0.09 (1.70) 9 0.17 (1.93) 0.23 (2.88) 0.11 (1.46) 10 0.62 (2.80) 0.60 (2.91) 0.51 (2.51) SpCorr 0.85 (0.00) -0.14 (0.71) 0.84 (0.00) Panel C: International Equity (Unconditional alphas) Decile First-Year Second-Year Third-Year 1 0.51 (0.48) 1.63 (1.90) -0.03 (-0.03) 2 0.22 (0.27) 0.67 (0.97) 0.39 (0.50) 3 0.04 (0.07) 0.47 (0.84) 0.75 (1.42) 4 0.39 (0.89) 0.31 (0.66) 0.72 (1.46) 5 0.22 (0.51) 0.40 (0.98) 0.77 (2.01) 6 0.96 (1.93) 0.51 (1.07) 0.70 (1.65) 7 0.86 (1.61) 0.52 (0.95) 0.67 (1.45) 8 1.41 (2.30) 1.10 (1.64) 0.96 (1.95) 9 1.98 (2.33) 0.93 (1.10) 0.87 (1.41) 10 2.76 (2.19) 0.97 (0.95) 0.48 (0.57) SpCorr 0.08 (0.84) 0.98 (0.00) 0.15 (0.68) 34
  • 36. Table 5: Post-Ranking One- to Three-Year Benchmark-Adjusted Returns This table shows descriptives on the post-ranking benchmark-adjusted returns for deciles of funds sorted according to the benchmark-adjusted return for domestic equity in Panel A and domestic fixed income in Panel B during the ranking period of one year. The portfolio deciles are rebalanced at the end of every year and are held for one to three post-ranking years. The sample period of 1991 to 2004. Mean returns are listed under the columns ‘Mean’ while Information Ratios are listed under the column ‘IR.’ Means significant at the 99% level are indicated by three stars, significant at the 95% level are indicated by two stars, and significant at the 90% level are indicated by one star. Decile 1 contains the worst performing portfolio and decile 10 contains the best performing portfolios. Panel A: Domestic Equity First-Year Second-Year Third-Year Decile Mean IR Mean IR Mean IR 1 0.12 0.04 0.96 0.23 0.80∗ 0.28 2 0.20 0.09 0.63∗ 0.25 0.50 0.24 3 0.20 0.14 0.43 0.21 0.34 0.20 4 0.30∗ 0.24 0.43∗ 0.28 0.38∗ 0.25 5 0.34∗∗ 0.35 0.38∗ 0.26 0.49∗∗ 0.32 6 0.47∗∗∗ 0.43 0.32∗∗ 0.30 0.40∗∗ 0.33 7 0.52∗∗∗ 0.45 0.33∗∗ 0.36 0.35∗∗ 0.30 8 0.65∗∗∗ 0.55 0.46∗∗∗ 0.59 0.27∗ 0.30 9 0.86∗∗∗ 0.54 0.49∗∗∗ 0.44 0.44∗ 0.28 10 1.55∗∗∗ 0.40 0.71∗∗∗ 0.41 0.70∗ 0.26 Panel B: Domestic Fixed Income First-Year Second-Year Third-Year Decile Mean IR Mean IR Mean IR 1 0.30∗ 0.23 -0.18 -0.11 0.03 0.03 2 0.11 0.18 -0.09 -0.12 -0.03 -0.03 3 0.10∗∗ 0.29 -0.03 -0.10 0.08∗∗ 0.31 4 0.08∗∗∗ 0.42 0.03 0.13 0.05∗∗ 0.35 5 0.05∗∗∗ 0.50 0.05∗∗∗ 0.45 0.05∗∗∗ 0.41 6 0.06∗∗ 0.29 0.06∗∗ 0.32 0.04∗ 0.27 7 0.05 0.20 0.09∗∗ 0.35 0.07∗∗ 0.37 8 0.02 0.04 0.09∗∗ 0.29 0.06 0.19 9 -0.04 -0.05 0.22∗∗∗ 0.43 0.05 0.08 10 0.11 0.08 0.50∗∗∗ 0.54 0.08 0.07 35
  • 37. Table 6: Post-Ranking One- to Three-Year Fund Flows This table lists the post-ranking fund flows for deciles of funds sorted according to the benchmark- adjusted return (for domestic equity in Panel A and domestic fixed income in Panel B) or raw return (for international equity in Panel C) during the ranking period of one year. The portfolio deciles are rebalanced at the end of every year and are held for one to three post-ranking years. Decile 1 contains the worst performing portfolio and decile 10 contains the best performing portfolios. The sample period is 1991 to 2004. Flows are in percent per year. Domestic Equity Domestic Fixed Income International Equity Decile Y1 Y2 Y3 Y1 Y2 Y3 Y1 Y2 Y3 1 -4.28 2.10 0.96 1.31 5.94 3.51 5.49 4.58 8.82 2 -2.79 0.45 1.68 2.91 4.79 6.09 7.12 5.74 7.15 3 2.54 2.88 4.01 6.47 3.93 4.73 8.27 6.26 7.16 4 6.05 3.51 3.39 5.74 3.10 3.43 12.24 10.09 6.10 5 8.68 5.71 7.49 5.73 5.33 6.45 15.26 13.05 10.91 6 9.32 9.41 8.37 8.25 9.34 5.41 19.38 18.22 16.23 7 14.77 12.30 9.67 8.43 6.71 7.60 19.39 18.20 9.57 8 19.52 15.70 12.19 10.58 7.80 7.46 19.25 19.38 16.79 9 23.49 18.34 12.46 10.09 10.42 8.63 23.65 22.68 21.04 10 31.80 23.01 17.36 17.25 13.42 12.28 29.40 23.60 19.03 36
  • 38. Table 7: Descriptives on Fees Incentives This table lists the descriptives on fees for deciles of funds sorted according to the benchmark-adjusted return (for domestic equity and domestic fixed income) or raw return (for international equity) during the ranking period of one year. The portfolio deciles are rebalanced at the end of every year and are held for one year. Decile 1 contains the worst performing portfolio and decile 10 contains the best performing portfolios. Panel A lists the fraction of funds that either charge or do not charge performance based fees (the remainder is fraction of funds for which information is not available). Panel B lists the fraction of funds that either do or do not have most-favored nation clause in fee schedules (the remainder is fraction of funds for which information is not available). The sample period is 1991 to 2004. Panel A: Performance based fees Domestic Domestic International Equity Fixed Income Equity Decile Yes No Yes No Yes No 1 47.3 41.2 37.7 41.9 64.6 20.2 2 46.6 41.9 38.0 46.4 62.2 22.7 3 49.5 39.9 39.3 44.3 65.5 24.7 4 50.1 38.7 35.9 48.3 60.9 26.1 5 51.5 37.2 39.2 46.0 61.3 24.9 6 53.8 34.5 41.4 44.2 66.2 23.0 7 53.8 35.7 42.5 44.5 59.2 28.4 8 52.8 37.9 44.8 42.1 61.8 28.3 9 53.9 36.6 45.9 39.9 64.2 23.1 10 54.9 36.4 46.3 35.8 65.8 20.8 Panel B: Most-favored nation clause Domestic Domestic International Equity Fixed Income Equity Decile Yes No Yes No Yes No 1 32.8 32.0 29.5 28.8 42.8 21.0 2 32.7 32.0 33.0 32.0 42.6 21.1 3 31.4 33.4 37.1 29.7 45.0 21.0 4 32.1 32.8 35.6 29.6 43.5 20.4 5 32.2 31.3 34.7 29.3 49.7 16.9 6 32.3 32.1 41.9 24.1 51.9 16.9 7 36.5 29.2 39.4 28.9 48.4 17.0 8 34.4 32.0 42.5 25.0 48.6 23.3 9 38.2 30.0 37.4 26.2 48.6 18.5 10 37.8 30.5 33.1 22.3 44.4 22.1 37
  • 39. Table 8: Fees This table lists the fees for deciles of funds sorted according to the benchmark-adjusted return (for domestic equity in Panel A and domestic fixed income in Panel B) or raw return (for international equity in Panel C) during the ranking period of one year. The portfolio deciles are rebalanced at the end of every year and are held for one year. Decile 1 contains the worst performing portfolio and decile 10 contains the best performing portfolios. The fees are based on fee schedules and are reported for mandate amounts of $10 million to $100 million. The fees are given separately for domestic equity, fixed income, and international equity, and are classified by deciles. Fees for domestic equity and fixed income portfolios are further classfied by styles. The sample period is 1991 to 2004. The fees are in basis points per year. Panel A: Domestic Equity Decile $10M $25M $50M $75M $100M $10M $25M $50M $75M $100M Large cap Growth Large cap Value 1 76 69 63 59 57 80 73 66 62 60 2 74 67 60 56 55 74 66 59 55 53 3 72 66 60 56 54 67 61 56 53 51 4 71 65 59 55 53 67 61 56 51 49 5 72 66 60 55 53 67 62 56 52 50 6 72 66 60 56 53 69 63 57 53 52 7 70 64 58 54 52 70 64 58 54 52 8 76 71 65 61 59 71 65 59 54 52 9 76 71 66 62 60 75 69 63 59 58 10 77 72 66 62 60 83 76 72 69 66 Small cap Growth Small cap Value 1 98 97 92 89 87 95 91 87 84 82 2 97 95 90 86 84 92 89 86 83 81 3 97 95 91 88 87 94 91 87 84 82 4 92 90 85 82 80 93 89 85 82 80 5 97 95 91 88 86 94 92 88 85 83 6 97 95 90 86 84 93 91 88 85 84 7 96 94 89 85 84 93 90 86 83 81 8 96 94 90 86 84 91 89 86 84 82 9 97 95 91 87 86 100 97 94 92 90 10 100 98 94 91 90 98 96 92 90 88 38
  • 40. Panel B: Domestic Fixed Income Decile $10M $25M $50M $75M $100M $10M $25M $50M $75M $100M Municipal High Yield 1 37 33 31 28 27 56 54 53 51 49 2 37 34 31 28 28 57 56 53 51 50 3 35 32 30 28 28 54 53 49 48 47 4 33 30 28 26 25 53 52 51 50 49 5 32 30 28 27 26 55 54 51 49 48 6 35 32 30 29 28 51 51 50 48 48 7 33 30 29 27 27 55 55 53 51 50 8 35 32 30 28 27 56 55 51 50 48 9 35 32 30 28 27 57 57 54 52 51 10 38 35 32 30 29 59 57 55 52 51 Interm Term Mortgages 1 39 36 34 32 30 40 39 38 38 37 2 37 34 31 29 28 26 26 25 25 25 3 35 33 30 28 27 29 28 27 26 26 4 35 33 30 28 27 27 27 26 25 25 5 36 33 30 28 27 26 25 24 23 23 6 35 33 30 28 27 29 29 28 27 26 7 36 34 31 29 28 29 29 28 27 26 8 36 34 31 29 28 31 30 29 27 27 9 38 36 33 31 29 30 30 29 28 28 10 43 41 38 36 35 53 53 52 53 53 Panel C: International Equity Decile $10M $25M $50M $75M $100M 1 81 80 75 70 68 2 80 78 74 69 66 3 77 75 70 66 63 4 74 72 67 63 60 5 75 73 68 63 61 6 78 76 70 65 62 7 79 77 72 67 64 8 80 78 73 68 66 9 85 82 77 73 70 10 88 86 81 77 75 39

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