Are N+1 heads better than one?

Journal of Economic Behavior & Organization Vol. 47 (2002) 103–120

Are N + 1 heads better than one? The case of mutual fund managers Larry J. Prather a,∗ , Karen L. Middleton b a

Department of Economics and Finance, East Tennessee State University, P.O. Box 70686, Johnson City, TN 37614, USA b Texas A&M University, Corpus Christi, TX, USA Received 9 August 1999; accepted 1 August 2000

Abstract Recent studies find that mutual funds exhibit differential and persistent performance which is frequently attributed to superior managerial decision making. We extend the literature by examining the impact of the fund’s management structure on performance outcomes. Specifically, we examine directly whether superior outcomes, in terms of risk-adjusted returns, may be explained by behavioral decision making theory that asserts that teams make better decisions than individuals. Empirical results are consistent with the classical decision making theory and the efficient market hypothesis. © 2002 Elsevier Science B.V. All rights reserved. JEL classification: D7; G0; M2 Keywords: Mutual fund performance; Behavioral decision making theory; Classical decision; Making theory; Team decision making

1. Introduction The investment of more than five trillion dollars into mutual funds has been a phenomenon of interest in both the popular press and the academic press. One particularly intriguing topic of research has been the performance and performance persistency of mutual funds. Recent studies by Brown and Goetzmann (1995), Grinblatt and Titman (1992) and Goetzmann and Ibbotson (1994) suggest that not only do performance differences exist among mutual funds, but these performance differences persist overtime. These findings of persistent performance differences are of interest to investors, investment advisors, mutual ∗ Corresponding author. Tel.: +1-423-439-5668; fax: +1-423-439-8583. E-mail address: [email protected] (L.J. Prather).

0167-2681/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 2 6 8 1 ( 0 1 ) 0 0 1 7 2 - X

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fund sponsors and academicians. Particularly important is finding an explanation for this documented superior performance. Recent literature recognizes the importance of examining managerial characteristics to ascertain common factors that explain superior outcomes since these outcomes are associated with investment management talent. Chevalier and Ellison (1999) examine managerial attributes such as the manager’s age, tenure, the required SAT of the managers undergraduate institution, and if the manager has an MBA degree. Their findings, as well as those of Golec (1996), suggest that managerial attributes influence performance outcomes. As influential as individual managerial characteristics may be, many of the security selection and asset allocation decisions for a mutual fund are not made by individual managers, but by groups or teams of managers. Yet little research has been conducted addressing the similarities and differences in performance outcomes when the mutual fund is managed by a team of decision makers rather than by an individual decision maker. Additionally, much of the extant research on individual versus group decision making and its relationship to performance such as that conducted by McNamara and Bromiley (1997), Schwarz (1994) and Zeckhauser (1987) has been completed in the context of a laboratory setting. This study contributes to the literature by examining whether the accumulation and interpretation of information is better made by individuals or teams. This is a crucial line of investigation since Arlen (1998), Kaufman (1999) and Mellers et al. (1998) have asserted that classical decision making theory and behavioral decision making theory are at odds concerning expected performance outcomes. Thus, our research question is “Will the performance outcomes, in terms of risk-adjusted returns, of a team of mutual fund managers differ from that of an individual mutual fund manager?” Empirical results, using the continuously compounded monthly net returns of 162 open-end mutual funds over a 13-year period, are consistent with the classical decision making theory and the efficient market hypothesis.

2. Review of the literature 2.1. Extant literature on mutual fund performance In one of the earliest studies that document persistent performance, Sharpe (1966) examined the net return of 34 mutual funds, and compared their relative performance and that of the Dow Jones industrial average (DJIA). He concluded that relative performance differences exist and that these relative differences persist over time. Goetzmann and Ibbotson (1994) investigated the persistency of raw returns and returns adjusted for risk. They found that performance was persistent using either raw or risk-adjusted returns. Additionally, these results were robust with respect to style. Brown and Goetzmann (1995) extended previous analysis by using a sample that consisted of defunct and surviving funds. They find relative performance persists for both winners and losers for both raw and risk-adjusted returns, even after controlling for investment objective. They conclude that historical performance data can be used as a guide in assessing future relative performance. However, the source of performance differentials was not identified.

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Golec (1996) and Chevalier and Ellison (1999), believe that managerial characteristics are important since superior outcomes are driven by superior investment decisions. A relatively untapped area, however, is the management structure of the fund. Golec examines managerial attributes expected to impact performance including team size. His findings suggest that the impact of team size on risk-adjusted performance is indeterminate. Two factors may contribute to the lack of statistical significance in Golec’s study. The first is that the sample time frame is only 36 months and the second is the use of only one market proxy. Brown and Brown (1987) and Lehmann and Modest (1987) show that the selection of the market proxy may be very important in accurately determining performance. This line of inquiry requires further exploration since decision making literatures propose two theories that may offer insight into the source of persistent risk-adjusted performance differentials of mutual funds. Those theories are discussed in the following sections and form the basis for our null and alternate hypotheses. 2.2. The classical decision making theory perspective Zeckhauser contends that classical decision making theory is founded on a rational choice model based on (1) the steady state of the markets in which all states of the world are known, (2) the continuous allocation of resources, (3) the presence of clear alternatives and their outcomes, and (4) the assumption that price taking on goods sold is subject to arbitrage. Hogarth and Reder (1987) and Kameda and Davis (1990) further propose that classical decision making research focuses on the decision making outcomes or the final state of wealth. The classical decision making model has been used extensively in multidisciplinary research. Much of the early research describes subjects as exhibiting a utility function whose decisions are defined as alternative uses of the resources he possesses. The expected utility model, an extension developed by von Neumann and Morgenstern (1944), assumes that decision makers have a utility function that describes the overall cost and benefit obtained from a specific choice. Thus, Hollenbeck et al. (1995) and Kahneman and Tversky (1984) define choice in this normative model of the ideal decision maker as a maximization process incorporating optimal decisions. Further, Hollenbeck et al. (1998) posit the ideal decision maker may be (1) an individual manager in the hierarchical structure where the support staff is not involved in the ultimate decision; or (2) a team of decision makers who must reach a consensus before enacting a final decision. Decision making structure aside, Arrow (1987) contends that from the classical utility theory perspective the ultimate decision should lead to the same maximizing choice and optimal performance outcome. Thus, we could expect that individual decision makers with absolute decision making power and group decision makers achieving a consensus decision would not vary in their performance outcomes. This discussion of the classical decision making model leads us to our hypothesis. Hypothesis 1. The performance of a mutual fund managed by a team of managers will not differ significantly from that of a mutual fund managed by an individual manager. Yet the study of human choice raises many questions left unanswered by the basic assumptions of classical decision making theory. For example, McNamara and Bromiley

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(1997) maintain business decisions are rarely made predicated on unconstrained individual information processing. El-Shinnawy and Vinze (1998) further suggest both permanent and temporary groups have become a tool for managing uncertainty. It is the behavioral decision making model that offers a contrasting perspective for the study of human choice behavior. 2.3. The behavioral decision making theory perspective Behavioral decision making theory is grounded in Simon’s (1982) bounded rationality model. Zeckhauser further defines the model as based on (1) the changing state of the markets in which all states of the world are unique and need to be discerned, (2) the discrete allocation of resources, (3) the fact that alternatives need to be identified, their outcomes can be moderated through strategic formulation and implementation, and prices can be negotiated, and (4) that values, ethics, and culture may impact the perceptions of risk and uncertainty. Thus, Hogarth and Reder (1987) and Kameda and Davis (1990) find behavioral decision making research focuses on the decision making process with outcomes coded as gains and losses over time. Early research in behavioral decision making by Hill (1982) focused on individuals in settings where interaction was limited. Findings implied that the superiority of groups over individuals was largely based on the pooling of pieces of information and the integration of these pieces to form a solution. This research was extended to the study of individual decision makers in the context of a group decision. Results of studies by researchers such as Vinokur (1971) and Burnstein and Vinokur (1977) suggested that individuals operating in a group decision making environment may be subject to the group polarization and risky shift phenomena. Findings by Isenberg (1986) and Myers and Lamm (1976) imply the introduction of multiple alternatives may produce polarization toward the alternative with the most numerable and favorable arguments in the array. Janis (1984) has argued that polarization is a negative consequence of high levels of group cohesion, often resulting in groupthink. More recently researchers have begun to think of the cohesion variable as a multidimensional construct. For example, Bernthal and Insko (1993) found that groups high in task cohesion and low in socioemotional cohesion are unlikely to develop groupthink. Thus, groupthink may not be an outcome of polarization when task-oriented cohesion supports a meticulous search and appraisal of information important to the group’s decision. Still other studies such as Sniezek and Henry (1989) compared individual and group decisions and found that groups recall and recognize relevant information better than individuals such that groups were more accurate in their information recall. Vollrath et al. (1989) concluded groups could reproduce the information verbatim as well as recall a larger volume of information. Further, when the task is complex and completed under high levels of uncertainty, Hinsz et al. (1997) contend group members tend to pool and integrate their resources and correct each other’s errors. Tindale (1993) suggests that such a shared belief system is one factor that may help to alleviate the uncertainty, resulting in reduced error bias. Finally, Bikhchandani et al. (1998) propose that employing a multiplicity of perspectives when reaching group decisions may allow for the initiation of information cascades that Barney (1996) argues are more difficult to imitate, allowing for superior returns over the long-run. This discussion of the behavioral decision making literature implies that the performance of a mutual fund managed by a team of mutual fund managers will be significantly greater

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than that of a mutual fund managed by an individual manager. Thus, it is in contrast to the hypothesis supported by the classical decision making literature and could explain the recent findings of persistent performance discussed earlier.

3. Methodology 3.1. Data To select the sample for analysis, a list of mutual funds that were in operation during the period September 1981–1994 was obtained from CDA Investment Technology, Inc. We then eliminated funds not in operation for the entire period. This resulted in a total sample of 377 funds for which return data were available over the 156-month period. Our selection process results in two unavoidable types of bias: “survivorship” bias and “omission” bias. Studies such as Brown et al. (1992) show that survivorship bias exists when extinct mutual funds are excluded when studying performance. This is particularly troubling when examining whether managers could outperform an unmanaged index since in efficient markets, poorly performing funds should become extinct. This leaves the better performing funds to compare against the index. Therefore, survivorship bias would cause the measured performance of the sample of funds to be overstated relative to the index. Our application is similar in some respects and different in others. The first similarity is that we are asking whether one group of portfolios outperforms another group of portfolios. The second similarity is that only surviving funds can be examined empirically. Therefore, our dataset and results are subject to survivorship bias. However, we believe that important theoretical differences exist between our study and studies that examine the relative performance of managed funds and an unmanaged index. These differences may cause bias to impact our study differently than it impacted other studies. The critical difference between the two types of studies is that when a study is undertaken that compares the performance of managed portfolios to an index, and poorly performing portfolios become extinct, only better performing portfolios remain to be examined. This poor performance could be explained by economic theory if it resulted from poor security selection, poor market timing, or inefficient operations. One example would be placing big bets on industry sectors, or several individual securities, that backfired. Another example would be charging high fees to support inefficient operations. Therefore, portfolio extinction is understandable in terms of economic theory and the effect of this bias is obvious. However, in a study of whether one group of managed portfolios outperforms another group of managed portfolios due to the type of management structure, the effect of this bias is less clear. This requires critically examining the theoretical reasons for extinction and the consequent impact of that extinction on the sample to be studied. If we assume that extinction is related to either poor portfolio management or poor operations, the degree of bias would hinge critically on the relative extinction of both sets of managed portfolios. First, assume that extinction is a result of inefficient operations. We can think of no a priori reason to believe that there is an inherent difference in operational inefficiency between team-managed funds and those managed by an individual. Therefore, to the extent that extinction is related to inefficiency, this facet of extinction could reasonably be expected to be random. Thus, it would also be expected to have an equal effect on both

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individually-managed and team-managed funds. If this is the case, survivorship bias should not be expected to have a material influence on our results. However, even an equal effect on an average does not necessarily translate into an equal effect in any given sample. Therefore, survivorship bias could impact the results of any empirical study. Second, extinction may be related to the quality of portfolio management. If extinction is related to the quality of portfolio management, theory may aid in understanding the impact on our sample. Portfolio performance is simply another way of stating performance outcomes. If performance outcomes can be assumed to be the result of the quality of portfolio management decisions, on an average, then extinction should be related to the quality of decision making. Thus, the classical and behavioral decision making theories may be used to form a priori expectations about relative extinction between team-managed and individually-managed funds. If classical decision making theory is correct, there would be no difference in the quality of decision making, on an average. Therefore, extinction and management structure would be expected to be independent, and consequently would not be expected to have a significant impact on our results. If however, the behavioral decision making theory is correct, teams make better decisions than individuals, on an average. This provides a basis for forming a priori expectations that funds managed by an individual have a greater likelihood of extinction. Under this reasoning, survivorship bias would act to increase the relative performance of the funds managed by an individual relative to that of team-managed funds, on an average. Therefore, to the extent that survivorship bias is important on our sample, the consequent impact would be to bias against finding statistical support for the behavioral decision making theory. Similarly, “omission” bias exists since newer funds are excluded from our analysis. This is potentially important since Arteaga et al. (1998) find that return bias can be created through mutual fund incubation. They argue that seed money could be used to create multiple funds, each taking multiple successive bets in an uncertain world. After the outcomes are known, winning funds would be marketed and losing funds dropped. That is, omission bias would serve to enhance the returns of managed portfolios relative to the index. If incubation exists, excluding funds that commenced operation after our sample period began strengthens the validity of our results. The reason for this is that it is unlikely that incubation bias is something new. Therefore, admitting “new” funds into our sample could bias results in favor of the subsample with the highest proportion of “new” funds. By deciding not to consider “new” funds, we reduce the effect of including funds that were marketed to the public because they became “winners by chance” during our sample period. On the other hand, if incubation does not exist, omission bias should not be expected to have an important impact on our results. To examine the impact of the managerial structure of the fund on performance, it was necessary to learn how each fund was managed. Morningstar was used to determine the management structure of the sample of funds. In total, Morningstar provided information about the management of 330 of the 377 funds in our sample. Thus, the 47 funds not covered by Morningstar were excluded from analysis. Morningstar reports management in several ways. To ensure proper coding of the data, we contacted Morningstar for clarification of the descriptors. Table 1 provides a classification of the initial sample. First, for funds managed by an individual, or where a single individual is the primary decision maker, the manager is reported by name (and tenure). We

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Table 1 Initial sample of mutual fundsa Investment objective Aggressive growth (AG) Growth (G) Growth and income (GI) Bond (B) Bond and preferred stock (BP) Municipal bond (MB) International (IN) Metal (ME) Combined

IND

TM

MUL

23 65 38 21 46 20 9 5

3 7 1 3 0 0 0 0

0 3 3 2 1 1 1 0

227

14

11

CO

1+1

0 4 1 2 3 0 2 0

0 2 1 1 0 1 0 0

8 17 7 7 9 7 15 3

34 98 51 36 59 29 15 8

12

5

49

330

ET

TOT

a

The values given provide the sample size for the different management structures reported by Morningstar. IND: funds managed by an individual; TM: team-managed; MUL: multiple managers; ET: et al.; CO: co-managed (two or more managers during the entire period of study); 1 + 1: funds that had two or more managers but at least one of the managers joined the fund during the sample period.

classified these as managed by an individual. Second, some funds are managed by teams, so Morningstar reports “team” in the manager section. Here, two or more managers manage together or the team approach is strongly promoted. Similarly, we classified these as team-managed. Third, the “et al.” term is used when there are multiple managers working together but one manager is considered the leader or principal decision maker. However, many funds have an individual manager supported by a research staff or assistants and it becomes unclear how the “et al.” classification differs from individual. Therefore, these funds were deleted from the final sample. Fourth, multiple is the term used when more than two managers work independently to manage the fund. An example of this may be a balanced fund where one manager makes equity decisions and the other makes bond decisions. These funds were also deleted from the final sample since the precise relationship is unclear and many possibilities exist. A fifth category, co-managed funds, lists the names of the managers if there are three or less. If the fund listed more than one manager but it was unclear that the fund was managed by two or more managers during the entire period, we classified it as “1 + 1”. To ascertain the proper classification of the funds classified as “1 + 1”, we called the appropriate mutual fund company and asked about the managers of the fund. Most firms provided the requisite information over the phone. However, several firms wanted either a written request or agreed to research our query and respond. Not all of them responded. For purposes of this paper, the final sample consists of funds classified as managed by a team, managed by an individual, and one fund classified as “1 + 1” that was confirmed to be managed by a team. 1 1 One limitation to this approach is that the final sample size of team-managed funds becomes small and several investment objective classifications cannot be evaluated. We also used an alternate classification scheme whereby funds classified as “et al.”, multiple, co-managed, or “1 + 1” (if the fund company indicated the fund was team-managed) were also classified as team-managed funds. This increased the sample size of team-managed funds to AG (4), G (18), GI (11), B (12), BP (6), MB (2), IN (4), and ME (2). Empirical results using that sample are consistent with those reported here. The major limitation to this approach is that the classification of some funds as team-managed can be questioned which leads to selection bias. This results from the inability to establish the precise relationship of the managers.

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Table 2 Final sample of individually-managed and team-managed mutual fundsa Investment objective Aggressive growth (AG) Growth (G) Growth and income (GI) Balanced (B) Bond and preferred stock (BP) Municipal bond (MP) International (IN) Metal (ME) Combined

Individually-managed

Team-managed

Total

23 65 38 21 46 20 9 5

4 7 1 3 0 0 0 0

27 72 39 24 46 20 9 5

227

15

242

a

The values given provide the sample size over the period September 1981–1994 for the different management structures reported by Morningstar.

Next, we sorted the funds by the eight CDA investment objectives. These investment objective classifications are external to the fund and based upon the legal constraints placed upon portfolio managers by the prospectus of the fund. Restricting our analysis to groups of funds within an investment objective is important since McDonald (1974) reported that funds sharing the same objectives have similar characteristics. Brown and Goetzmann (1997), and others show the importance of restricting analysis to funds sharing similar characteristics if true performance is required. Therefore, we follow the practice of comparing performance within each of those eight investment objectives. Table 2 provides the composition of our final sample. The useable sample consisted of 162 funds of which 147 were managed by an individual and fifteen managed by a team. This approach results in a sample that is as bias free as possible since the adverse impact of survivorship bias, omission bias, and classification bias have been minimized. However, eliminating classification bias resulted in no team-managed funds in some investment objective categories. This resulted in reducing the final sample of funds appreciably. Since no comparisons could be made between team-managed and individually-managed funds for those investments objectives, the individually-managed funds in those objectives were discarded. We acknowledge that the small sample of team-managed funds is undesirable. However, we do not believe it is possible to increase the sample size without admitting some unacceptable bias into the sample. 3.2. Computation of returns The data program we used allows hypothetical investments to be made and then the program generates the end of period (month) value of that investment for each period over the selected sample frame. The end of month values of the hypothetical investments made in these funds are computed assuming all interest, realized capital gains and dividend distributions are reinvested. Returns are also net of annual expenses and 12-b1 fees. Hypothetical investments were made in each fund in the sample and the return was calculated for each fund i during each of the 156 months in the sample period t. These continuously compounded monthly net returns are computed using Eq. (1) for each fund by taking the natural

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logarithm of the change in value over the holding period for each of the 156 months in our sample: 
valueit (1) Rit = ln valueit−1 where Rit is the continuously compounded return on fund i during the period t, valueit the value of an investment in fund i at time t and valueit−1 is the value of an investment in fund i at time t − 1. Once returns for each fund in the sample were computed, indices were constructed to represent each subsample. In total, eight equally-weighted indices were computed (one for funds in each of the four investment objectives studied that were managed by an individual manager, and another for funds in each investment objective managed by a team). Monthly returns for the individually-managed and team-managed subsamples (for each investment objective) are computed by summing the returns of the individual funds i, within the individually-managed or team-managed group m, for each investment objective o, and computing their average monthly return for period t using Eq. (2): n Rm,o,t Rm,o,t = i=1 (2) n where Rm,o,t is the equally-weighted index return for one of the eight investment objective and management structure combinations for each of the 156 months t. 3.3. Performance evaluation To determine whether group decision making affects performance, we first use the procedure of Jensen (1968) followed by a modified version of his procedure. Jensen uses the theoretical security market line (SML) equation of the capital asset pricing model (CAPM) to test risk-adjusted performance. The SML equation is written as Ri = R f + βi (R m − R f ), which suggests that in equilibrium, the return of any security (Ri ) should be equal to the risk-free rate of interest (Rf ) plus the market risk premium (R m − R f ) times the systematic risk of the security (β i ). By subtracting Rf from both side of the theoretical equation, Jensen shows that the theoretical SML equation can be transformed into a simple linear equation that can be tested empirically with ordinary least squares regression (OLS). Eq. (3) is the empirical version of the SML: Rit − Rf = α + βi (Rmt − Rf ) + ε

(3)

where Rit − R f is the excess return on the security (return above the risk-free rate), α the excess risk-adjusted return (commonly referred to as Jensen’s α), β i the systematic risk of the security, R m −R f the market risk premium and ε is a well-behaved error term. Under the joint hypotheses of the CAPM being correctly specified and the efficient market hypothesis (EMH) holding (securities being correctly priced), α should be zero (yielding the theoretical SML). If a security exhibits superior performance, the α would be positive and statistically significant. Roll (1978) criticizes CAPM (and therefore Jensen’s procedure) on the grounds that the market portfolio is unobservable and therefore performance measurement is subject

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to benchmark bias. Roll’s criticism is well founded since Brown and Brown (1987) and Lehmann and Modest (1987) provide empirical evidence that risk-adjusted performance is affected by the selection of the market proxy. Therefore, we mitigate this bias in two ways. First, we use multiple indices as proxies for the market. This allows examining the robustness of empirical tests. Second, we also slightly modify the procedure by comparing the excess returns of equally-weighted index returns of team-managed and individually-managed funds for a given investment objective. The reason for our modification is that funds with similar objectives are likely to have portfolios more similar in composition to each other than to any arbitrarily selected market proxy. Thus, the securities that make up these portfolios are likely influenced by the same macroeconomic factors. If the same macroeconomic factors affect the returns of these portfolios, the correlation of returns for funds sharing similar objectives should be high, producing a good model fit (coefficient of determination). There are two additional benefits of this approach. First, our central question is not whether active management is beneficial. The question is whether a group making active management decisions will outperform an individual making the same type of decision. The difference may be minute but potentially important. The importance is that investors cannot invest in the “market”. The best an investor may be able to do is invest in a market index fund. However, even the low expenses of an index fund ensure that realized returns are slightly less than market returns due to fees imposed by the fund to cover expenses. Therefore, direct comparison of risk-adjusted returns that are actually achievable is desired. The second benefit is that to the extent that investment objectives partition the security market line into risk segments as suggested by Sharpe (1966), if nonlinearities exist in the security market line (Sharpe, 1992), restricting evaluation to a small segment of the security market line in terms of risk differences would produce more accurate results since this bias would be reduced. A prime example of this bias may be the well-documented poor fit of the SML when bond portfolios are evaluated. Eq. (4) provides the modified model we used, team ind Rot − Rf = α + β(Rot − Rf ) + ε

(4)

team is the return on the team-managed fund subsample for a given investment where Rot ind the objective group o during period t, Rf the risk-free rate of interest (90-day T-bill), Rot return on the individually-managed fund sample for the same investment objective group o during period t, and α and β are the estimated excess risk-adjusted return and systematic risk coefficients. All we did to modify Jensen’s procedure is to substitute the return index for the individually-managed funds for the selected investment objective for the market index. This allows direct comparison of the risk-adjusted returns of both groups of funds that can actually be obtained by investors. This model is used to compare the performance of the individually-managed and teammanaged funds for each of the eight investment objective classifications. If α is insignificantly different from zero, the excess risk-adjusted returns of team-managed funds are insufficient to conclude that management teams add value to investors by making superior decisions. This would support the classical decision making theory (Hypothesis 1). Alternatively, if α is significantly greater than zero, the excess risk-adjusted returns of team-managed funds are sufficient to conclude that management teams add value to investors by making superior decisions, on an average. This finding would support the behavioral decision making theory. Finally, if α is significantly less than zero, the negative excess

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risk-adjusted returns of team-managed funds are sufficient to conclude either that management teams make inferior decisions, on an average, or that their marginal costs exceed their marginal benefits. Additionally, we use the Sharpe (1966) and Treynor (1965) measures to compare the relative performance, in terms of return per unit of risk, of team-managed and individuallymanaged funds for each investment objective. 2 The Sharpe measure, Eq. (5), provides the excess return per unit of total risk as follows: S=

Rp − Rf σp

(5)

where S is the Sharpe measure, Rp the return on the portfolio under investigation, Rf the risk-free rate, and σ p is the standard deviation of the portfolio under investigation. This method has both advantages and disadvantages when compared to Jensen’s α. An advantage is that the Sharpe measure provides the excess return per unit of total risk for a portfolio, not simply systematic risk. This can aid in visualizing the relationship between competing portfolios since a higher return per unit of total risk would enable an investor to allocate wealth between the risky portfolio and the risk-free asset in a manner that would permit dominating any other strategy. The disadvantage is that this measure does not permit testing whether differences are statistically or if they may have arisen simply from chance. The Treynor measure, Eq. (6), provides the return per unit of systematic risk as follows: T =

Rp − Rf βp

(6)

where T is the Treynor measure, Rp the return on the portfolio under investigation, Rf the risk-free rate, and β p is the systematic risk of the portfolio under investigation. It shares the same advantages and disadvantages, relative to Jensen’s α, as the Sharpe measure. The primary difference between the Sharpe and Treynor measures is that the Treynor measure examines the return per unit of systematic risk.

4. Empirical results Tables 3–6 provide the results of market model regressions of excess market returns on excess investment objective group returns where the S&P 500, DJIA, CRSP EW and CRSP VW indices are used as market proxies, respectively. Panel A of each table presents results for the mutual funds that are managed by a team, whereas Panel B presents the results for the mutual funds that are managed by an individual. In every case, the coefficient of determination is above 0.75 which suggests a good model fit and supports the capital asset pricing model (CAPM). Also, in every case the systematic risk, β, is positive and significant at the 1% level. Additionally, the systematic risk is highest for aggressive growth funds and 2 We have restricted our analysis to parametric tests that compare excess returns after adjusting for risk. Given the recent empirical contributions by Brown et al. (1996) and Najand and Prather (1999) which show that even investment objectives fail to capture all important elements of risk a potential inherent bias in favor of high risk funds exists. Therefore, non-parametric measures are not utilized.

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Table 3 Results of market model regressions using the S&P 500 as a market proxya Investment objective

R2

α

β

Panel A: team-managed Aggressive growth (AG) Growth (G) Growth and income (GI) Balanced (B)

0.820 0.937 0.901 0.917

−0.0018 (−0.836) −0.0006 (−0.622) −0.0003 (−0.322) −0.0007 (1.053)

1.304∗∗ 1.030∗∗ 0.863∗∗ 0.616∗∗

(26.477) (47.677) (37.397) (41.245)

Panel B: managed by an individual Aggressive growth (AG) Growth (G) Growth and income (GI) Balanced (B)

0.829 0.951 0.971 0.946

−0.0025 (−1.280) −0.0008 (−1.024) −0.0004 (0.923) −0.0012∗ (2.210)

1.195∗∗ 0.980∗∗ 0.764∗∗ 0.627∗∗

(27.304) (54.575) (71.372) (51.705)

a The regression results for the 156-month period are presented. The t-statistic is in parentheses with the parameter estimate. The model estimated is Rot − Rf = α + βo (Rmt − Rf ) + ε. ∗ Denotes significance at the 5% levels. ∗∗ Denotes significance at the 1% levels.

declines with movement to growth, growth and income, and balanced. This occurs for both team-managed and individually-managed funds irrespective of the selected market proxy. This finding is consistent with previous literature on mutual fund risk. The central issue of performance revolves around α. As shown in Table 3 (Panel A), none of the team-managed funds exhibit significant risk-adjusted performance. Panel B repeats the results for funds managed by an individual manager and shows that only balanced funds exhibit significant performance at the 5% level. With the exception of individually-managed balanced funds, these findings support the classical decision making theory and the efficient market hypothesis (EMH). However, to examine the robustness of these results, additional benchmarks must be employed. Table 4 Results of market model regressions using the DJIA as a market proxya Investment objective

R2

α

β

Panel A: team-managed Aggressive growth (AG) Growth (G) Growth and income (GI) Balanced (B)

0.753 0.871 0.849 0.853

−0.0026 (−1.004) −0.0012 (−0.889) −0.0009 (−0.708) 0.0003 (0.366)

1.235∗∗ 0.982∗∗ 0.828∗∗ 0.587∗∗

(21.760) (32.205) (29.473) (29.858)

Panel B: managed by an individual Aggressive growth (AG) Growth (G) Growth and income (GI) Balanced (B)

0.779 0.888 0.911 0.882

−0.0033 (−1.470) −0.0014 (−1.183) −0.0001 (−0.062) 0.0008 (1.009)

1.144∗∗ 0.936∗∗ 0.732∗∗ 0.599∗∗

(23.288) (34.974) (39.803) (33.890)

a The regression results for the 156-month period are presented. The t-statistic is in parentheses with the parameter estimate. The model estimated is Rot − Rf = α + βo (Rmt − Rf ) + ε. ∗∗ Denotes significance at the 1% levels.

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Table 5 Results of market model regressions using the CRSP EW as a market proxya Investment objective

R2

α

β

Panel A: team-managed Aggressive growth (AG) Growth (G) Growth and income (GI) Balanced (B)

0.828 0.841 0.824 0.787

−0.0028 (−1.245) −0.0008 (−0.526) −0.0009 (−0.637) 0.0008 (0.735)

1.239∗∗ 0.923∗∗ 0.776∗∗ 0.536∗∗

(26.421) (27.721) (26.055) (23.178)

Panel B: managed by an individual Aggressive growth (AG) Growth (G) Growth and income (GI) Balanced (B)

0.897 0.903 0.900 0.881

−0.0036∗ (−2.267) −0.0013 (−1.084) 0.0003 (0.292) 0.0012 (1.424)

1.175∗∗ 0.902∗∗ 0.694∗∗ 0.569∗∗

(35.458) (36.749) (36.221) (32.823)

a The regression results for the 156-month period are presented. The t-statistic is in parentheses with the parameter estimate. The model estimated is Rot − Rf = α + βo (Rmt − Rf ) + ε. ∗ Denotes significance at the 5% levels. ∗∗ Denotes significance at the 1% levels.

Table 4 repeats the tests shown in Table 3 using the DJIA as a market proxy. The coefficient of determination and systematic risk retain their established pattern. However, none of the groups of funds exhibit statistically significant risk-adjusted performance. This test also supports both the classical decision making theory and the EMH. Table 5 uses the CRSP EW index as a market proxy and repeats previous tests. Again, for the most part, α is statistically indistinguishable from zero. However, with the CRSP EW index, aggressive growth funds managed by a single manager now exhibit inferior performance. Table 6 Results of market model regressions using the CRSP VW as a market proxya Investment objective

R2

α

β

Panel A: team-managed Aggressive growth (AG) Growth (G) Growth and income (GI) Balanced (B)

0.837 0.944 0.913 0.922

−0.0032 (−1.475) −0.0016+ (−1.677) −0.0015 (−1.487) 0.0003 (0.412)

1.356∗∗ 1.064∗∗ 0.889∗∗ 0.631∗∗

(27.324) (49.588) (39.044) (41.421)

Panel B: managed by an individual Aggressive growth (AG) Growth (G) Growth and income (GI) Balanced (B)

0.847 0.961 0.978 0.958

−0.0037+ (−1.897) −0.0018∗ (−2.403) −0.0002 (0.449) 0.0008 (1.633)

1.243∗∗ 1.013∗∗ 0.787∗∗ 0.645∗∗

(28.348) (59.970) (81.173) (57.808)

a The regression results for the 156-month period are presented. The t-statistic is in parentheses with the parameter estimate. The model estimated is Rot − Rf = α + βo (Rmt − Rf ) + ε. + Denotes significance at the 10% levels. ∗ Denotes significance at the 5% levels. ∗∗ Denotes significance at the 1% levels.

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Table 7 Results of regressions of returns of funds managed by teams on returns of fund managed by individualsa Investment objective

R2

α

β

Aggressive growth (AG) Growth (G) Growth and income (GI) Balanced (B)

0.956 0.985 0.927 0.951

0.0001 (0.921) −0.0030 (−0.469) −0.0008 (−0.935) −0.0004 (−0.812)

1.073∗∗ 1.044∗∗ 1.129∗∗ 0.972∗∗

(58.036) (71.677) (44.276) (54.395)

a The regression results for the 156-month period are presented. The t-statistic is in parentheses with the team − R = α + β(R ind − R ) + ε. parameter estimate. The model estimated is Rot f f ot ∗∗ Denotes significance at the 1% levels.

Our final test for robustness is in Table 6 which uses the CRSP VW index. This index produces the greatest number of significant performance differentials and all of them are negative. Here, growth funds exhibit negative risk-adjusted returns for both individually-managed and team-managed funds. Additionally, aggressive growth funds managed by an individual manager exhibit negative performance. In summary, the results reported in Tables 3–6 suggest that differences may be driven by benchmark selection. Additionally, the central question of whether significant differences between the two management structures is not tested directly. To directly test for differences between the two management structures, we employ the modified market model, Eq. (3). Table 7 provides the results of the modified market model regressions for the four investment objective classifications of funds. As expected, in every case R2 is high (greater than 0.92). This is because investment objectives place some constraint on the portfolio mix. Therefore, funds with similar objectives are likely to have portfolios more similar in composition to each other than to any arbitrarily selected market proxy. Thus, the securities that make up these portfolios are likely influenced by the same macroeconomic factors. If the same macroeconomic factors affect the returns of these portfolios, the correlation of returns for funds sharing similar objectives should be high, producing a good model fit. Since our focus is on whether teams or individuals make better portfolio decisions, the α is our primary concern. The results in Table 7 suggest that no significant differences exist for any of the four classes of funds. 3 This is supportive of both the classical decision making theory and the EMH. However, one caveat must be considered. It is entirely possible that the decisions made by teams were superior but making those superior decisions required higher costs. If the marginal benefits of these superior decisions failed to significantly outweigh the marginal costs incurred in making them, we would be unable to detect that teams made superior decision.

3 One concern that arises from using equally-weighted indices to examine performance with our small sample of team-managed funds is that extreme performance of one fund may unduly influence the team-managed return index. Therefore, to mitigate this potential bias, regressions were run for each of the team-managed funds against their respective index to ascertain relative performance. An examination of the 15 regressions reveals that 10 have positive α’s and 5 have negative α’s. Of the 10 positive α’s, 2 were statistically significant at the 5% level. One of the four negative α’s was statistically significant at the 10% level. Given these outcomes, we find no indication that the results reported in our previous tests are due to a small sample bias.

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Table 8 Average expense ratios for sample fundsa Investment objective

Team-managed

Individually-managed

Aggressive growth (AG) Growth (G) Growth and income (GI) Balanced (B)

1.136 0.939 0.899 1.039

1.457 1.034 0.978 0.934

a

The average expense ratios for the 156-month period are presented.

Table 9 Sharpe and Treynor measure of fund performancea Investment objective

S

TDJIA

TEW

TVW

TSP

Panel A: team-managed Aggressive growth (AG) Growth (G) Growth and income (GI) Balanced (B)

0.1079 0.1413 0.1352 0.1923

0.0055 0.0064 0.0065 0.0093

0.0055 0.0068 0.0070 0.0102

0.0050 0.0064 0.0061 0.0086

0.0052 0.0061 0.0063 0.0089

Panel B: managed by an individual Aggressive growth (AG) Growth (G) Growth and income (GI) Balanced (B)

0.0944 0.1288 0.1614 0.0072

0.0048 0.0061 0.0075 0.0090

0.0055 0.0063 0.0080 0.0094

0.0044 0.0056 0.0070 0.0083

0.0046 0.0058 0.1882 0.0086

a

The average Sharpe and Treynor measures for the 156-month period are presented.

To ascertain whether our results are driven by expenses, we examined the annual expense ratios of our sample funds. Table 8 presents the average annual expenses for our sample by management type. Results suggest that generally, the average expenses for funds managed by an individual manager are higher than those managed by a team. However, there is no statistical difference between the expense ratios. Based on this, we fail to find evidence that teams make superior decisions. This finding further supports the classical decision making theory and the EMH. Table 9 presents the Sharpe and Treynor measures for our sample. The results suggest that two trends exist. First, both the Sharpe and Treynor measures (Eqs. (4) and (5)) tend to increase with movement from the highest risk funds to the lowest risk funds. This could result from higher trading cost, due to more frequent trading, of higher risk funds. Secondly, although not significant, team-managed funds had slightly higher Sharpe and Treynor measures than did individuals. These findings are consistent with the results presented in Tables 3–6 for the Jensen measure. This suggests that benchmark selection does indeed impact the performance results.

5. Conclusion Recent studies find that mutual funds exhibit differential performance and that this differential performance persists overtime. One explanation for this differential performance

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is that it is driven by superior management. We examine whether superior management may be explained by decision making theory. Specifically, we seek to learn whether teams or individuals make better decisions on an average. This topic is vitally important since it could provide insight into why some funds may exhibit superior performance. If one type of management structure is superior, it could serve as a source of competitive advantage. The classical and behavioral decision making theories are at odds concerning expected performance outcomes. From the classical utility theory perspective, differing alternatives to the same problem should lead to the same maximizing choice and optimal performance outcome, whether the decision is made by an individual, group, or organization. Thus, we could expect that individual decision makers and group decision makers would not vary in their performance outcomes. In contrast, the behavioral decision making theory asserts that when the task is complex and completed under high levels of uncertainty, group members tend to pool and integrate their resources and correct each other’s errors, while producing qualitatively and quantitatively superior performances when compared to the average individual performance. Since the theories are at odds, the question of whether teams or individuals make superior portfolio decisions is an empirical one. To examine this issue, we use monthly continuously compounded risk-adjusted net returns of 162 open-end mutual funds over a 13-year period. To construct our sample, we first classify funds by management type and then sort them into eight investment objective groups. By creating indices of these funds for each management type within a given investment objective classification, we are able to modify the market model of Jensen (1968) to assess directly the performance outcomes of the competing theories. Empirical results suggest that there is no appreciable difference between the outcomes of team-managed and individually-managed funds. However, it is possible that decisions made by teams were superior if making these superior decisions required higher costs. If the marginal benefits of these superior decisions failed to significantly outweigh the marginal costs incurred in making them, we would be unable to find that teams made superior decision. Finally, we examined average annual expense ratios and found that expense differentials did not drive our results. In conclusion, we find no evidence that teams make better decisions. These results support the classical decision making theory and the EMH. References Arlen, J., 1998. Comment: the future of behavioral economic analysis of law. Vanderbilt Law Review 51, 1765– 1788. Arrow, K.J., 1987. Rationality of self and others in an economic system. In: Hogarth, R.M., Reder, M.W. (Eds.), Rational Choice: The Contrast between Economics and Psychology. The University of Chicago Press, Chicago, IL, pp. 201–215. Arteaga, K., Ciccotello, C., Grant, T., 1998. New equity funds: marketing and performance. Financial Analysts Journal 54, 43–50. Barney, J.B., 1996. Gaining and Sustaining Competitive Advantage. Addison-Wesley, Reading, MA. Bernthal, P.R., Insko, C.A., 1993. Cohesiveness without groupthink: the interactive effects of social and task cohesion. Group and Organization Management 18, 66–87. Bikhchandani, S., Hirshleifer, D., Welch, I., 1998. Learning from the behavior of others: conformity, fads, and informational cascades. Journal of Economic Perspectives 12, 151–170.

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Larry J. Prather received his PhD from Old Dominion University and teaches finance at East Tennessee State University. His publications appear in the Journal of Financial Markets, Journal of Economics and Finance, Journal of Research in Finance, Multinational Finance Journal and the Global Finance Journal. Karen L. Middleton received her PhD from the University of Houston and teaches management at Texas A&M University, Corpus Christi. Her publications appear in Psychology and Marketing, Journal of Global Information Technology, The Journal of Business & Entrepreneurship and The Journal of Teaching in International Business.