A wide array of investment professionals use benchmarks to assess the success of their investment process or asset optimization analysis. Such criteria as objectivity, available data, and market coverage, among others, are used in the selection process. Surprisingly, costs are usually not taken into account. Given that costs have risen over the past few years and are not clearly understood by investors, there is a need for more transparency. In this article, the authors focus on US and global equity markets, noting that future research could include different geographies and different asset classes.

Benchmarks are commonly used in the investment world to represent a target market, for marketing purposes, or as part of an optimization process. Nevertheless, access restrictions may apply, including licenses, the sharing of data, and time lags. As the number of indexes has grown over time, acquiring the correct data has become more complex. To increase the transparency of costs, the Spaulding Group has developed guidelines that include a benchmark cost analysis.

The authors show that capitalization, sector exposure, and security selection are the main drivers of performance. Nevertheless, the indexes turn out to be very similar owing to capitalization weighting; the largest stocks are given the highest weights. Although there can be differences in how vendors identify the country of multinationals, these effects are offset by capitalization weighting. The same is also true for the number of securities in a benchmark because the smaller companies have lower weights. Using capitalization also results in similar sector weights between indexes. The authors observe that the benchmarks have similar sector weightings because of similar weighting and selection methodologies.

The authors advocate an increase in cost transparency. Their research could affect the work of benchmark vendors: Although a current cost might be hidden, an increase in transparency could force vendors to provide a breakdown of their services.

The authors consider vendors of six US equity indexes and four global equity indexes for their analysis. When certain vendors offer several indexes, the most common index is used. Furthermore, no indexes composed entirely of small-cap stocks are included. Lastly, only developed and emerging markets—no frontier markets—are considered. The authors end up with 10 years (2005–2014) of monthly data for global indexes and 7 years (2008–2014) of monthly data for US (domestic) indexes.

After finishing the index selection process, the authors apply three criteria to assess the differences between the indexes. They use an analysis of variance to assess the difference in monthly returns. But they deem this test inappropriate because the monthly return does not answer the question whether an index represents a certain market. Thereafter, the authors use a correlation matrix because this method is also used for asset allocation and monitoring. The authors also use a principal component analysis (PCA), which is a good complement to the correlation matrix because it shows the relative influence of underlying factors.

Both the correlation matrix and the PCA show that the global and US indexes are strongly correlated. The PCA also shows that the first component explains almost all the variance, and the correlation matrix shows correlations close to 1 for all indexes.

Benchmarking is used by many investment professionals, either as a standard to beat or to test whether a passive portfolio moves in line with the overall market. the spotlight very often, so this research is most welcome. The authors set up a clear method whereby benchmarks of different vendors are compared, using a correlation matrix and a PCA as test methods. Their results clearly show little difference between the benchmark returns. Although the cost differences have not been specifically highlighted in this research, the authors make a strong case for increased transparency.