Ebook Economies of Scale, Lack of Skill or Misalignment of Interest?
This paper provides empirical evidence on the comparative performance of three important players in the US financial services industry: defined benefit (DB) pension funds, defined contribution (DC) pension funds, and mutual funds. Individuals, who save for their retirement, can be members of a DB or DC pension plan. DB plan managers generally decide on both strategic asset allocation and security selection on behalf of their participants. In the case of DC plans, individuals have more degrees of freedom in making asset allocation choices, but the supply of investment vehicles is mostly deter mined by DC boards. Often, negotiations with institutional asset managers result in an array of mutual funds across major asset classes. Moreover, a substantial number of private individuals directly holds mutual fund vehicles to upgrade insufficient pension accruals or simply because they are not member of a pension plan.
The three saving options potentially have different returns to investors. Mutual fund management companies are for-profit organizations, whose predominant reason for existence is to maximize the collection of fees. DB and DC pension plans are not-for-profit institutions, who provide participants with a possibility to accrue retirement benefits in a pooled, cost and tax efficient environment. The important question is whether these different objectives materialize in significant differences in returns. For instance, do professional mutual fund organizations attract more experienced and skilled portfolio managers, which consequently leads to higher returns for mutual fund investors? Or alternatively, do pension funds have a clear advantage vis-`a-vis mutual funds as a result of economies of scale, which eventually leads to lower cost levels and hence higher net returns for participants? The first question has been answered unambiguously in the mutual fund literature. Gruber (1996) and Malkiel (1995), among many others, clearly document the inability of mutual funds to beat the market. The second question has not been addressed explicitly in the finance literature, largely because of the lack of fund-specific cost and benchmark information on pension funds.
Lakonishok, Schleifer, and Vishny (1992) introduce agency costs as an additional element in the discussion on the expected performance differences between (DB) pension funds and mutual funds. They document that DB pension plans in the US consistently under-perform broad benchmarks like the S&P 500. The main argument put forward is the existence of multiple layers of agency relationships between companies, pension treasurers, money management firms and plan participants. Ambachtsheer (2005) extends this discussion by highlighting the inherent conflict that results from for-profit organizations providing management services directly to millions of faceless mutual fund investors. He argues that the combined forces of informational asymmetry between managers and clients, and the presence of pronounced principal-agent problems, logically lead to poor net investment returns.
To shed more light on this discussion, it is of vital importance to learn more about the comparative performance of DB, DC and mutual funds in a unified framework. Surprisingly little is known about this performance in the finance literature, mainly due to the absence of comprehensive pension fund data. Previous studies relied on gross returns (before fees are deducted), managed accounts of pension funds (instead of overall plan performance)and/or compared performance to broad market indices (e.g. S&P 500). In our study, we have access to an exclusive US pension fund database provided by CEM Benchmarking Inc. (CEM). This allows us to jointly address the above mentioned shortcomings in earlier research.
We contribute to the literature on the performance of financial services in five distinct ways. First, we measure the performance of equity portfolios of 716 DB plans, 238 DC plans and 4,030 mutual funds at the individual fund level in the US. Second, as we have full information on the cost structure of all individual pension- and mutual funds, we are able to present both gross and net comparative performance. The cost structure contains more components than previously documented in pension literature, including direct investment, oversight, custodial and trustee, and audit costs. Third, the CEM database allows us to employ fund-specific benchmarks in contrast to the majority of previous studies. Fourth, we test for the influence of investment style (large versus small cap), outsourcing (internally/externally managed) and risk taking (active versus passive investing) on the comparative performance of DB, DC and mutual funds. Fifth, we document evidence on the performance persistence of the three investment options. These contributions enable us to explicitly address the skill and cost arguments when assessing performance differences between pension and mutual funds.
In the empirical results section we show that (size-matched) mutual funds on average under-perform the corresponding benchmarks significantly, some times up to -300 basis points. Both DB and DC pension funds on average perform according to their fund-specific benchmarks, despite a huge difference in average size of their equity holdings. We find modest evidence of persistence in mutual fund returns and no evidence of pension fund return persistence. The deviations in performance between pension and mutual funds cannot be fully explained by differences in costs, size, risk and investment style. The performance differential might be an indication of the agency costs argument put forward by Ambachtsheer (2005).
Section 2 presents a brief overview of the existing literature on the equity performance and persistence of DB and DC pension funds and mutual funds respectively. Furthermore, we highlight the contributions of our study. In section 3 we discuss the details of the CRSP mutual fund database and the CEM pension fund database. We describe the performance measurement and risk adjustment methodology in section 4. Section 5 contains a discussion of empirical results. Finally, section 6 provides concluding comments.
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