Research On Factors Influencing Fund Performance

The performance of active mutual funds has been of long-standing interest to financial economists. The extent to which an active fund can outperform its passive bench-mark depends not only on the fund's raw skill in identifying investment opportunities, but also on various constraints faced by the fund. One constraint discussed prominently in recent literature is decreasing returns to scale. If scale impacts performance, skill and scale interact. For example, a more skilled large fund can underperform a less skilled small fund. Therefore, to learn about skill, we must understand the effects of scale.What is the nature of returns to scale in active management? The literature has advanced two hypotheses. The first one is fund-level decreasing returns to scale:as the size of an active fund increases, the fund's ability to outperform its benchmark declines (e.g., Perold and Salomon,1991; Berk and Green,2004). The second hypothesis is industry-level decreasing returns to scale:as the size of the active mutual fund industry increases, the ability of any given fund to outperform declines (Pastor and Stambaugh,2012). Both hypotheses have been motivated by liquidity constraints. At the fund level, a larger fund's trades have a larger impact on asset prices, eroding the fund's performance. At the industry level, as more money chases opportunities to outperform, prices move, making such opportunities more elusive. Consistent with such liquidity constraints, evidence is mounting that trading by mutual funds is capable of exerting meaningful price pressure in equity markets.Both hypotheses are plausible alternatives to a null hypothesis of constant returns to scale, due to imperfect liquidity of financial markets. Moreover, these alternative hypotheses are not mutually exclusive. A fund's performance could depend on both the size of the fund and the size of the fund's competition, as proxied by industry size. If funds were to follow exactly the same investment strategy, their performance would likely depend more on their combined size than on their individual sizes, whereas the opposite would be true if the funds' strategies were completely unrelated. The reality is between the two extremes, and the relative merits of the two hypotheses must be evaluated empirically. The fund-level hypothesis has been tested in a number of recent studies, with mixed results. We provide the first evidence regarding the industry-level hypothesis, to our knowledge. We also reexamine the fund-level hypothesis by using cleaner data and econometric techniques that avoid inherent biases.One of the challenges in estimating the effect of fund size on performance is the endogeneity of fund size. If size were randomly assigned to funds, one could simply run a panel regression of funds'benchmark-adjusted returns on lagged fund size, and the ordinary least squares (OLS) slope estimate would correctly measure the effect of size on performance. Alas, size is unlikely to be randomly paired with funds. For example, larger funds could be run by managers with higher skill (e.g. Berk and Green,2004). Skill could be correlated with both size and performance, yet we cannot control for skill as it is unobservable. As a result, the simple OLS estimate of the size-performance relation is likely to suffer from an omitted-variable bias.The omitted-variable bias can be eliminated by including fund fixed effects in the regression model. These fixed effects absorb the cross-sectional variation in performance that is due to differences in skill across funds. This fixed effect approach cleanly identifies the effect of fund size on performance in the setting of Berk and Green (2004), but it applies more generally as long as fund skill is time-invariant. Unfortunately, while adding fund fixed effects removes one bias, it introduces another. This second bias results from the positive contemporaneous correlation between changes in fund size and unexpected fund returns. In general, a nonzero correlation between a regressor's innovations and the regression disturbances introduces a finite-sample bias in OLS estimates (Stambaugh,1999), and this bias extends to the fixed effects setting (Hjalmarsson,2010)To address the second bias, we develop a recursive demeaning procedure that closely builds on the methods of Moon and Phillips (2000) and Hjalmarsson (2010). This procedure runs a panel regression of forward-demeaned returns on forward-demeaned fund size, while instrumenting for the latter quantity by its backward-demeaned counterpart. The resulting estimator eliminates the bias, as proved by Hjalmarsson and confirmed in our simulation analysis. Our simulations also highlight the bias in both OLS estimators, with and without fund fixed effects. In addition to being biased, the OLS estimators heavily over-reject the null hypothesis of no returns to scale even when this hypothesis is true.Taken together, our results are consistent with the following narrative. New funds entering the industry tend to be more skilled than the incumbent funds, perhaps due to better education or greater command of new technology. As a result of their superior skill, the new funds tend to outperform their benchmarks as well as older funds. As these funds grow older, though, their performance suffers as a result of the continued growth in industry size, which is associated with steady arrival of skilled competition. Learning on the job could alleviate the negative impact of growing industry size on performance, but it does not eliminate it.