Performance Attribution for Passive Strategies (Digest Summary)

Despite passive investing now representing the majority of US equity investments, attribution models have not kept pace. The traditional, single-factor attribution models ubiquitous in active investing have limited application in passive investment strategies.

Passive investing, while intended to mirror the benchmark, does not perfectly replicate it. The tracking error is relatively small, and it thus demands a more detailed performance attribution analysis than that for actively managed portfolios, which takes on added significance in the passive world because these strategies represent the majority of US equity investments. The author lists and discusses 12 specific factors that should be included in an effective passive management performance attribution model and explains the impact of each factor on performance.

This research highlights cash drag as one of the largest drivers of tracking error. Because a portion of the assets are not invested and are thus not exposed to market variations, cash drag may produce portfolio returns that are higher in down markets and lower in up markets. Although managers may use futures to equitize cash and overcome this effect, doing so is potentially another source of error owing to a futures mismatch. Fund expenses, transaction execution, and trade costs also play a significant role in determining the excess returns of passive strategies. A fund’s trade execution timing also matters. Trades are typically executed at market close to better track index performance; however, that is not always possible and can lead to tracking error.

In addition, each fund has specific tax rates based on domicile, which could create an advantage over the benchmark return. “Swing pricing” and customization of benchmarks are recent attempts to mitigate these influences. Swing pricing, common in the mutual fund industry, applies a factor to transaction costs that helps reduce the impact of large redemptions on long-term investors. Custom benchmarks attempt to reduce tracking error through customization, but this approach can unintentionally inhibit investment managers’ marketability.

Finally, the implementation of attribution solutions requires a significant amount of data and transparency across both the fund and the benchmark. The author favors transaction-based attribution over a holdings-based approach, because the former considers all trades and aligns more closely with the performance book of record. The author concludes that the addition of factors that help explain excess returns is becoming critical in the current regulatory environment, where managers are under increased scrutiny to provide more transparency for fund performance drivers.

The author draws on his extensive experience to discuss the importance of performance attribution and the critical factors that should be included in any effective solution. No formal data-based research is provided.

This study would have benefited from the inclusion of more practical guidance on the application and construction of the performance attribution model. The successful application of all factors may be a challenge given the small tracking error, which makes the variance attributable to each factor not always statistically significant at certain confidence levels.

Providing a comprehensive approach to performance attribution for passive strategies, this well-structured research should be essential reading for fund managers, portfolio managers, researchers, and investors. Using this framework, researchers and practitioners may wish to further investigate and build performance attribution models designed for passive strategies.