Industry practitioners have attempted to determine the effectiveness of active management by creating models that identify and price risk factors to better model returns. The problem is the pervasiveness of correlated residuals among fund returns by managers who follow similar investment strategies, making it difficult to separate the return attributable to manager skill and that attributable to simply following common strategies. The authors attempt to solve this problem by creating an active peer group benchmark (APB) calculated from the average excess return of the active peer group to which a fund belongs. This benchmark is then added to a baseline pricing model to improve the precision of alpha.

The beauty of the authors’ approach to controlling for commonalities in mutual fund investment returns is its parsimony and simplicity. By constructing a benchmark based only on funds that make up a given fund’s peer group, the authors have a ready-made sample of funds with similar strategies that are applied to a similar subgroup of stocks. This approach obviates the uncertainty and possible regression noise associated with identifying potentially numerous risk factors and a multitude of complex strategies that could be used by funds within a peer group. In addition, their method can easily be accomplished with readily available returns and fund prospectuses.

The authors first establish that a significant amount of commonality in return residuals exists among funds within peer groups by running four-factor regressions of the APB for each peer group over three-year periods from 1980 to 2010. They then demonstrate that the APB substantially decreases the between-fund residual correlations within a peer group when it is added to standard factor models. This finding supports the APB’s effectiveness at isolating common risk-taking strategies within peer groups.

Additional tests using multiple APBs to refine alpha precision indicate that the benefits compared with using a single APB are marginal. This result shows that funds within one peer group tend not to favor strategies that are common to other peer groups. The superiority of the APB approach in identifying managers who generate alpha attributable to individual skill versus managers who simply select or leverage common strategies remains evident within several of the equity and bond fund peer groups when the authors value weight the funds in the APB (versus the base case of equal weighting), add a liquidity factor, or add market indices.

For funds in asset classes whose risk factors are less transparent (e.g., hedge funds), it is reasonable to use the APB alone in a factor model to evaluate the relative performance of fund managers. Where risk factors are unambiguous (e.g., US equities), however, it is advisable to use standard factor models augmented with the APB to properly attribute alpha.

The monthly net asset value returns, reinvested distributions, and annual expense ratios for the universe of US open-ended mutual funds are obtained from the Center for Research in Security Prices (CRSP) Mutual Fund Database for the period of January 1980–December 2010. The authors’ research focuses on US equity funds grouped by categories based on the Russell family of equity style indices (e.g., Russell 1000 Value and Russell 2000 Growth). This sampling enables the authors to leverage the abundant literature identifying priced risk factors and to facilitate the generation of APBs.

The sample includes only no-load mutual funds to minimize transaction costs and thus help investors who wish to replicate their strategy. The authors add back 1/12 of the most recently reported annual expense ratios to monthly net returns to maintain the focus on fund manager skill as opposed to cost efficiency. However, both gross and net returns are analyzed in consideration of the applicability of the APB benchmark as a passive investment alternative to institutional and retail investors.

Returns are evaluated using three different factor models: (1) the baseline (Carhart) four-factor model, (2) the augmented APB model, and (3) the APB-adjusted alpha version of the augmented model. The four base factors are exposure to market, size, book to market, and momentum. The baseline APBs are equal weighted, although a value-weighted APB is tested for robustness.

The highlight of the article is the authors’ simple yet effective approach to untangling fund managers’ alpha. Although I agree that it is possible to invest in an APB as a passive investment in theory, I think the practicalities of investing preclude it.