Internet Appendix to Picking Winners? Investment Consultants Recommendations of Fund Managers

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1 Internet Appendix to Picking Winners? Investment Consultants Recommendations of Fund Managers TIM JENKINSON, HOWARD JONES, and JOSE VICENTE MARTINEZ * This Internet Appendix includes the following additional information: I. Details on the Greenwich Associates (GA) survey II. III. Details on the evestment database Additional results Table IA.I Additional information on time variation in fees. In Section II (including Table II) of the main article we discuss the time-series variation in fees. In this table we provide more detail on the stability of fees are over time. In 92% of our sample observations, the product fee is exactly the same as in the previous year. This corresponds to a change in fees, on average, no more than once every 12 years. The average absolute annual change in product fee in the sample is less than 1 bp. Table IA.II Additional information on cross-sectional variation in fees. In Section II (including Table II) of the main article we discuss the intrastyle cross-sectional variation in fees. This table shows that there is little intrastyle cross-sectional variation in fees. * Citation format: Jenkinson, Tim, Howard Jones, and Jose Vicente Martinez, Internet Appendix to Picking winners? Investment consultants recommendations of fund managers, Journal of Finance, DOI: /jofi Please note: information supplied by the authors. Any queries (other than missing material) should be directed to the authors of the article. 1

2 Table IA.III Determinants of consultants recommendations using alternative measures of fees. In Section III.A (including Table III) of the main article we discuss various drivers of consultants recommendations, including fees. In this table we reproduce the main results of Table III, using evestment fees (as in the main paper), time-averaged IIS fees (the approach favored by Busse, Goyal, and Wahal (2010)) and actual year-on-year IIS fees (for which the sample is the smallest). Table IA.IV Determinants of consultants recommendations robustness checks. In Section III.A (including Table III) of the main article we discuss the drivers of consultants recommendations, including assets under management (AUM) at time t-1. In Table IA.IV we first interact past performance and soft investment factors (columns (1) and (2)). We next include additional past performance measures in the Poisson regression, specifically one- and three-year excess return rankings and one- and three-year alpha rankings (columns (3) and (4)), although these measures are highly collinear. We also report results of using the GMM estimator of Mullahy (1997) to address potential endogeneity of AUM(t- 1) using AUM(t-2) as an instrument (columns (5) and (6)), to account for the possibility that current recommendations might have been issued a number of months before they appear in the survey, thus affecting AUM(t-1). Table IA.V Investment products and benchmarks. In many of the tables in the main article we report the performance of portfolios of recommended and nonrecommended products against a benchmark. This table shows the benchmark used for each style-size category included in our analysis. 2

3 Table IA.VI Performance of the most versus least recommended funds. In Section III.C (including Table V) of the main article we analyze the performance of recommended versus nonrecommended funds. In this table we run a similar analysis, but only on the recommended funds, separating these funds into the 50% that received the most and the 50% that received the fewest recommendations. Table IA.VII Performance of recommended and nonrecommended products using alternative measures of fees. In Section III.C (including Table V) of the main article we analyze the performance of recommended and nonrecommended products, showing the results gross and net of fees. The fees used are the endof-period fees provided by evestment. In this table we supplement those results by reporting the gross-of-fees and net-of-fees performance of recommended and nonrecommended products using evestment fees (as in the main paper), time-averaged IIS fees (the alternative followed by Busse, Goyal, and Wahal (2010)), or actual year-on-year IIS fees (for which the sample of products is the smallest). Table IA.VIII Additional information on the performance of products recommended by large versus small consultants. To compare the recommendations of large versus small consultants (see Section III.C) of the main article, we make use of a sample of large versus small consultants recommendations that is available only for 1999, 2000, 2001, 2009, 2010, and In each of these six years, Greenwich Associates classifies the 11 to 13 largest consultants, depending on the year, in the large group and the rest in the small group, and keeps a separate 3

4 record for both groups. As we show in Table IA.VIII (Panel A), there does not seem to be much difference between recommendations issued by large versus small consultants. Average product size, average asset management fees, and past performance rankings of recommended products are similar. Large consultants recommend marginally larger and more expensive products but the difference is negligible. In fact, large and small consultants recommendations appear to be highly correlated: the correlation coefficient between the number of recommendations received by a given product from large and small consultants equals More importantly, in the six years we analyze, we do not find any significant differences in the performance of these two sets of recommendations (Panel B). The net-of-fees performance of the portfolio of U.S. actively managed equity products recommended by large investment consultants is indistinguishable from the net-of-fees performance of the products recommended by the smallest consultants in our sample. 4

5 I. The Greenwich Associates Survey Greenwich Associates (GA) has conducted annual surveys of investment consultants since It carries out three main surveys: Domestic Equity, International Equity, and Fixed Income. These surveys focus on actively managed funds; GA does not conduct a separate survey of investment consultants views on passive fund managers. The data we use come from the GA Domestic (U.S.) Equity survey, in which investment consultants are asked to evaluate fund managers, and provide details of their recommendations on fund products. The structure of the GA survey has varied somewhat over time. For our analysis we obtain three types of data from the survey: 1. Rating of each manager, on a five-point scale, on what we term soft investment factors (from which we select clear decision-making, capable portfolio manager, and consistent investment philosophy, which occur in every survey). 2. Rating of each manager, on a five-point scale, on what we term service factors (from which we select capabilities of relationship professionals, usefulness of reports prepared by the fund manager, and effective presentations to consultants, which occur in every survey). 3. A list of four to six fund managers recommended in the following size-style categories: Large Cap Growth, Large Cap Value, Small Cap Growth, Small Cap Value, Mid Cap Growth, Mid Cap Value, and Domestic Equity Core. The GA surveys also include certain non-u.s. categories, as well as categories that are only available for a few years or are not clearly defined in a way that would allow a clean matching with our return data (e.g., U.S. Equity Active Quantitative products); we do not use these categories in our analysis. 5

6 The sections that we do not use include questions related to problems that consultants have with particular managers, the skills of managers in particular areas (some of which, for example, managing country allocation, are irrelevant to a single-country analysis), and the ability of managers to prepare useful thematic studies. We obtain survey data for the 1999 to 2011 period. Prior to 1999 the GA survey does not contain information on investment consultants recommended products. Each year the survey is carried out over a two- to four-month period starting between late November of one year and early January of the next. The number of products recommended is smaller in the early years of the sample because there is no recommendation data on products in the Mid Cap (Value and Growth) and Equity Core product categories in these years. Approximately 21% of the products receive at least one recommendation each year, a percentage that is relatively stable over the entire sample period (apart from the first year). During the same period, an average of 29 investment consultants answered the survey each year, each of them making, on average, 55 recommendations per year across the seven equity categories in our sample. This means that each recommended product received an average of four recommendations. End-of-year asset size data are available for approximately 68% of the total sample. 6

7 II. The evestment Database The returns we obtain for the products in the evestment database are composite returns. The individual returns earned by each client may deviate from these composite returns, but deviations are typically small. 1 Composite returns are net of trading costs, but gross of investment management fees. The data are self-reported by the fund managers, but constant scrutiny from clients using these data guarantees a high degree of accuracy. The return data are free from survivorship bias: like the GA survey, the evestment database retains data for funds that have been discontinued (e.g., because they have been acquired or closed). For each product, the databases also provide cross-sectional information (as of June 2012) on investment style and capitalization bracket, manager-designated benchmark, and the latest fees. See Jones and Martinez (2014) for further information on the evestment databases. 1 For example, some investors may require that their part of the overall portfolio is purged of, say, the influence of tobacco companies or arms manufacturers. 7

8 Table IA.I Product Fees: Time Variation This table shows the fees, in percent per year, for investments of $10 million, $50 million, and $100 million using data provided by Informa Investment Solutions (IIS). We present this information for recommended products, nonrecommended products, and all products in our sample. Fees $10M Fees $50M Fees $100M Rec. Not Rec. All Rec. Not Rec. All Rec. Not Rec. All

9 Table IA.II Product Fees: Cross-Sectional Variation This table shows quintile one (lowest) and quintile five (highest) average fees for an investment of $50 million, and their difference, for the institutional investment products used in our study. These statistics are computed using fees as of 2011 from the evestment database (the last year of our sample), and using fees averaged over the 1999 to 2011 period from Informa Investment Solutions (IIS). Fees are in percent per year. evestment - $50M IIS - $50M Q1 Q5 Diff Q1 Q5 Diff Large Cap Growth Large Cap Value Mid Cap Growth Mid Cap Value Small Cap Growth Small Cap Value Core Average All Cat. Combined

10 Table IA.III What Drives Consultants' Recommendations: Alternative Fee Measures This table reports results of pooled time-series cross-sectional Poisson and negative binomial regressions of the number of investment consultants recommendations received by a product on past gross performance measures, alternative asset management fee measures, and variables capturing soft investment and service characteristics of the asset managers as perceived by the consultants. Soft investment factors and service factors are expressed using the fractional rank of each asset manager in the sample. An asset manager's fractional rank, for a given variable, represents its percentile rank relative to other asset managers in the same period, and ranges from zero to one. Past gross performance measures (excess returns over benchmarks and three-factor alphas) are expressed using the fractional rank of each product in its investment category. Fee measures are also expressed using the fractional rank of each product in its investment category and include evestment fees as of the end of the sample period (or latest available), average IIS fees, and IIS fees as at the end of the year preceding each survey. All regressions also include a lagged measure of return volatility, lagged assets under management (in $ billions), and a full set of time dummies. Each column represents a separate regression. z-scores based on standard errors clustered at the product level are included in parentheses. The second part of the table displays model-implied average marginal effects and the bottom part shows the squared correlation between observed recommendations and modelpredicted ones together with the number of observations in each regression. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. evestment (T) IIS (Av. t) IIS (t-1) (1) (2) (3) (4) (5) (6) Poisson Poisson Poisson Poisson Poisson Poisson Soft Investment Factors (t) (17.18)*** (17.25)*** (14.43)*** (14.54)*** (14.19)*** (14.32)*** Service Factors (t) (3.49)*** (3.43)*** (3.28)*** (3.22)*** (3.23)*** (3.17)*** Past Performance Rank - Return (t) (3.03)*** (3.01)*** (3.18)*** Past Performance Rank - Alpha (t) (2.23)** (2.35)** (2.58)*** Fees (T, Av. t, and t-1) (4.08)*** (4.07)*** (2.87)*** (2.82)*** (2.55)** (2.51)** Assets Under Management (t-1) (9.22)*** (8.83)*** (8.95)*** (8.48)*** (9.11)*** (8.64)*** Return Volatility (t-1) (1.01) (0.73) (0.68) (0.41) (0.74) (0.45) Average Marginal Effects Soft Investment Factors (t) 5.78*** 5.84*** 6.30*** 6.37*** 6.35*** 6.42*** Service Factors (t) 1.04*** 1.03*** 1.28*** 1.27*** 1.27*** 1.26*** Past Performance Rank - Return (t) 0.56*** 0.67*** 0.71** Past Performance Rank - Alpha (t) 0.39** 0.49** 0.55*** Fees (T, Av. t, and t-1) 1.16*** 1.16*** 1.06*** 1.04*** 0.91** 0.90** Assets Under Management (t-1) 0.05*** 0.05*** 0.05*** 0.05*** 0.05*** 0.05*** Return Volatility (t-1) Squared Corr (Y; Ŷ) Number of observations 3,507 3,507 2,675 2,675 2,624 2,624 10

11 Table IA.IV What Drives Consultants Recommendations: Robustness Checks This table reports results of pooled time-series cross-sectional Poisson regressions of the number of investment consultants recommendations received by a product on past gross performance measures, asset management fees, and variables capturing soft investment and service characteristics of the asset managers as perceived by the consultants. Soft investment factors and service factors are expressed using the fractional rank of each asset manager in the sample. An asset manager's fractional rank for a given variable represents its percentile rank relative to other asset managers in the same period, and ranges from zero to one. Past gross performance measures (excess returns over benchmarks and three-factor alphas over several horizons) and fees (as at the end of the sample period) are expressed using the fractional rank of each product in its investment category. All regressions also include a lagged measure of return volatility, lagged assets under management (in U.S. $ billions), and a full set of time dummies. Each column represents a separate regression. Columns (5) and (6) report the results using the GMM estimator of Mullahy (1997) to address potential endogeneity of AUM(t-1) using AUM (t-2) as an instrument. z-scores based on standard errors clustered at the product level are included in parentheses. The second part of the table displays model-implied average marginal effects and the bottom part shows the squared correlation between observed recommendations and model-predicted ones together with the number of observations in each regression. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. (1) (2) (3) (4) (5) (6) Poisson Poisson Poisson Poisson IV Poisson GMM IV Poisson GMM Soft Investment Factors (t) (10.54)*** (11.47)*** (17.18)*** (17.03)*** (19.13)*** (19.54)*** Service Factors (t) (3.52)*** (3.43)*** (3.49)*** (3.66)*** (1.75)* (1.60) 1 Y Excess Return Rank (t) 0.09 (0.91) 2 Y Excess Return Rank (t) (-0.61) (2.08)** (2.27)** 3 Y Excess Return Rank (t) 0.19 (1.37) 1 Y Alpha Rank (t) -0.31*** (-3.30) 2 Y Alpha Rank (t) (-0.17) (-0.08) (0.72) 3 Y Alpha Rank (t) 0.26 (1.92)* 2 Y Exc. Ret. Rank (t) * SIF (t) 0.52 (1.66)* 2 Y Alpha Rank (t) * SIF (t) 0.29 (0.95) Fees (T) (4.08)*** (4.05)*** (4.11)*** (3.89)*** (7.02)*** (7.06)*** Assets Under Management (t-1) (9.44)*** (8.88)*** (9.24)*** (9.25)*** (7.58)*** (7.51)*** Return Volatility (t-1) (1.04) (0.72) (1.00) (1.22) (2.79)*** (2.91)*** 11

12 Table IA.IV (continued) Average Marginal Effects Soft Investment Factors (t) 5.12*** 5.48*** 5.78*** 5.81*** 7.20*** 7.34*** Service Factors (t) 1.05*** 1.03*** 1.04*** 1.10*** 0.64* 0.59*** 1 Y Excess Return Rank (t) Y Excess Return Rank (t) ** 0.57** 3 Y Excess Return Rank (t) Y Alpha Rank (t) -0.76*** 2 Y Alpha Rank (t) Y Alpha Rank (t) 0.63* 2 Y Exc. Ret. Rank (t) * SIF (t) 1.26* 2 Y Alpha Rank (t) * SIF (t) 0.70 Fees (t) 1.16*** 1.16*** 1.17*** 1.11*** 1.65*** 1.65*** Assets Under Management (t-1) 0.05*** 0.05*** 0.05*** 0.05*** 0.14*** 0.14*** Return Volatility (t-1) *** 9.14*** Squared Corr (Y; Ŷ) Number of observations 3,507 3,507 3,507 3,399 3,268 3,268 Large Cap Growth Large Cap Value Mid Cap Growth Mid Cap Value Small Cap Growth Small Cap Value Table IA.V Investment Products and Benchmarks Investment Product Benchmark Russell 1000 Growth Russell 1000 Value Russell Midcap Growth Russell Midcap Value Russell 2000 Growth Russell 2000 Value Domestic Equity Core - All Cap Russell 3000 Domestic Equity Core - Large Cap Russell 1000 Domestic Equity Core - Mid Cap Russell Midcap 12

13 Table IA.VI Performance of Most Recommended and Least This table shows the performance of portfolios of U.S. actively managed equity products recommended by the investment consultants in our sample during the 1999 to 2011 period. Portfolios analyzed are the portfolio of the 50% most frequently recommended products, the portfolio of the 50% least frequently recommended products, and their difference. Performance is measured using raw returns, returns in excess of a benchmark chosen to match the product style and market capitalization, and one-, three-, and four-factor alphas (corresponding to the CAPM, the Fama-French three-factor model, and the Fama-French-Carhart model). Excess returns and alphas are expressed in percent per year. These statistics are computed on monthly returns and annualized by multiplying returns and alphas by 12. All reported figures are net of management fees. The first part of the table shows the results for equally weighted portfolios of products and the second part of the table shows the same statistics for portfolios of products weighted using total net assets at the end of the previous year. t-statistics based on standard errors, robust to conditional heteroskedasticity and serial correlation of up to two lags as in Newey and West (1987), are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Equally Most Frequently Least Frequently Avg. Returns Avg. Excess Ret. over Benchmark One-Factor Alpha Three-Factor Alpha Four-Factor Alpha 5.98% 0.41% 1.29% 0.15% 0.16% (1.18) (0.74) (1.43) (0.18) (0.19) 7.89% 1.01% 3.16% 1.59% 1.53% (1.51) (1.81)* (2.71)*** (1.60) (1.50) Difference -1.91% -0.61% -1.87% -1.44% -1.37% (-2.59)** (-1.96)* (-2.59)** (-2.22)** (-2.23)** Value Most Frequently Least Frequently 4.28% 0.38% -0.44% -0.17% -0.18% (0.85) (0.46) (-0.52) (-0.20) (-0.21) 4.95% 0.58% 0.30% -0.18% -0.13% (0.99) (0.77) (0.36) (-0.20) (-0.14) Difference -0.67% -0.21% -0.74% 0.01% -0.05% (-0.77) (-0.33) (-0.84) (0.01) (-0.06) 13

14 Table IA.VII Net Performance of Recommended and Nonrecommended Products This table shows the net-of-fees performance of the portfolio of all U.S. actively managed equity products recommended by the investment consultants in our sample during the 1999 to 2011 period, as well as the net-of-fees performance of institutional products not recommended by any of the consultants. The table also shows the difference in performance between the two. Performance is measured using raw returns, returns in excess of a benchmark chosen to match the product style and market capitalization, and one-, three-, and four-factor alphas (corresponding to the CAPM, the Fama-French (1993) three-factor model, and the Fama- French-Carhart (1997) model). Excess returns and alphas are expressed in percent per year. These statistics are computed on monthly returns and annualized by multiplying returns and alphas by 12. Results reported in Panel A are based on end-of-sample evestment fees, results in Panel B are based on Informa Investment Solutions (IIS) time-averaged product fees, and results in Panel C are based on IIS product fees (not averaged). The table shows the results for equally weighted portfolios of products and for portfolios of products weighted using total net assets at the end of the previous year. t-statistics based on standard errors, robust to conditional heteroskedasticity and serial correlation of up to two lags as in Newey and West (1987), are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Equally Value Nonrecommended Products Recommended Non recommended Products Nonrecommended Products Recommended Nonrecommended Products Avg. Returns Avg. Excess Ret. over Benchmark One-Factor Alpha Three-Factor Alpha Four-Factor Alpha Panel A: evestment Fees 6.31% 0.51% 1.62% 0.39% 0.39% (1.24) (0.95) (1.77)* (0.48) (0.46) 7.43% 1.67% 2.82% 1.36% 1.30% (1.51) (2.58)** (2.87)*** (1.70)* (1.58) -1.12% -1.17% -1.21% -0.97% -0.92% (-2.29)** (-3.16)*** (-2.70)*** (-2.41)** (-2.38)** 4.29% 0.36% -0.43% -0.18% -0.18% (0.85) (0.46) (-0.52) (-0.21) (-0.22) 4.51% -0.11% -0.10% -0.97% -0.88% (0.93) (-0.16) (-0.11) (-1.23) (-1.16) -0.22% 0.48% -0.33% 0.79% 0.70% (-0.18) (0.58) (-0.27) (0.82) (0.76) 14

15 Table IA.VII (continued) Equally Value Panel B: IIS Average Fees 6.78% 0.84% 2.09% 0.75% 0.76% (1.33) (1.50) (2.13)** (0.89) (0.88) Nonrecommended Products 8.01% 1.99% 3.42% 1.80% 1.75% (1.64) (3.05)*** (3.17)*** (2.14)** (2.02)** Recommended Nonrecommended Products -1.23% -1.15% -1.33% -1.05% -0.99% (-2.78)*** (-3.10)*** (-3.51)*** (-3.07)*** (-3.00)*** 4.35% 0.47% -0.37% -0.16% -0.16% (0.86) (0.58) (-0.45) (-0.20) (-0.20) Nonrecommended Products 4.99% 0.13% 0.39% -0.56% -0.43% (1.03) (0.18) (0.38) (-0.63) (-0.50) Recommended Nonrecommended Products -0.64% 0.34% -0.76% 0.40% 0.27% (-0.48) (0.40) (-0.58) (0.39) (0.27) Equally Value Panel C: IIS Fees 6.78% 0.84% 2.09% 0.75% 0.76% (1.33) (1.51) (2.13)** (0.89) (0.88) Nonrecommended Products 8.02% 2.00% 3.42% 1.80% 1.76% (1.64) (3.08)*** (3.15)*** (2.14)** (2.03)** Recommended Nonrecommended Products -1.23% -1.16% -1.33% -1.05% -0.99% (-2.83)*** (-3.12)*** (-3.57)*** (-3.13)*** (-3.04)*** 4.36% 0.47% -0.36% -0.16% -0.15% (0.86) (0.58) (-0.44) (-0.19) (-0.19) Nonrecommended Products 4.90% 0.02% 0.32% -0.63% -0.48% (1.01) (0.02) (0.28) (-0.67) (-0.54) Recommended Nonrecommended Products -0.55% 0.45% -0.68% 0.47% 0.32% (-0.39) (0.52) (-0.50) (0.45) (0.32) 15

16 Table IA.VIII Large Versus Small Consultants Panel A of this table presents descriptive statistics on the sample of institutional investment products recommended by large and small investment consultants. Average asset size is in millions of U.S. dollars. Fees, for the management of U.S. $50 million, are in percent per year (source: evestment). All averages are weighted by the number of recommendations. Panel B shows the net-of-fees performance of the portfolio of U.S. actively managed equity products recommended by large and small investment consultants in our sample. Performance is measured using returns in excess of a benchmark chosen to match the product style and market capitalization, and one-, three-, and four-factor alphas (corresponding to the CAPM, the Fama-French three-factor model, and the Fama-French-Carhart model). Excess returns and alphas are expressed in percent per year. The table shows the results for equally weighted portfolios of products and for portfolios of products weighted using total net assets at the end of the previous year. t-statistics based on standard errors, robust to conditional heteroskedasticity and serial correlation of up to two lags as in Newey and West (1987), are reported in parentheses. Sample period: 1999 to 2001 and 2009 to ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Equally Value Average Product Asset Size Panel A - Descriptive Statistics Average Product Fee ($50M) Average Past Gross Performance - Excess Return Percentile Average Past Gross Performance - Alpha Percentile Large Consultants 5, Small Consultants 5, Large Consultants Small Consultants Difference Large Consultants Small Consultants Difference Avg. Excess Ret. over Benchmark Panel B - Recommendations' Performance: Net Returns One Factor Alpha Three Factor Alpha Four Factor Alpha 1.07% 2.97% 0.99% 1.00% (1.26) (1.83)* (0.89) (0.88) 1.62% 3.38% 1.36% 1.40% (1.86)* (2.05)** (1.36) (1.39) -0.55% -0.42% -0.38% -0.41% (-1.70)* (-1.12) (-1.13) (-1.25) 1.33% -0.14% 0.40% 0.41% (0.83) (-0.06) (0.18) (0.46) 1.36% -0.63% -0.08% 0.01% (0.94) (-0.37) (-0.05) (0.01) -0.03% 0.48% 0.47% 0.40% (0.10) (0.58) (0.72) (0.64) 16

17 REFERENCES Busse, Jeffrey, Amit Goyal, and Sunil Wahal, 2010, Performance and persistence in institutional investment management, Journal of Finance 65, Carhart, Mark, 1997, On persistence in mutual fund performance, Journal of Finance 52, Fama, Eugene, and Kenneth French, 1993, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics 33, Jones, Howard, and Jose Martinez, 2014, Institutional investor expectations, manager performance, and fund flows, Working paper, University of Oxford. Mullahy, John, 1997, Instrumental-variable estimation of count data models: Applications to models of cigarette smoking behavior, Review of Economics and Statistics 79, Newey, Whitney K., and Kenneth D. West, 1987, A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix, Econometrica 55,

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