Customize Benchmarks to Fit Investors’ Investment Objectives, Not the Other Way AroundCustomizeBenchmarkstoFitInvestors’InvestmentObjectives,NottheOtherWayAround

Note: Comparative results are out-of-sample. Determination of “strong” or “weak” is based on if the value is above or below its 12-month moving average. In case of JPY/USD, if the currency is above 12 month moving average we label the months as “Weak Yen.” We use the World Economic Policy Uncertainty index from www.economicpolicyuncertainty.comopens in a new window. US and Japan recession are sourced from FRED (Federal Reserve Bank of St. Louis) based on OECD composite leading indicators following peak-to-trough marked as recession. The strong EM flows represent higher annual flows in EM than previous year (Source: IIF). Past performance is not a guarantee or a reliable indicator of future results. Chart is provided for illustrative purposes only. An investment cannot be made directly into an index.

Smart Equity Benchmark for Indian Investors

Although Indian equities are less than 3% of a market-cap-weighted global equity benchmark, Indian investors hold 99.9% of their equity in domestic equities (i.e., “home-country bias”).[2] In contrast, developed-country investors have much less home-country bias. Compounding the risk of too little country diversification, the India equity market has severe industry concentration within sectors. For example, 95% of the Indian technology sector is computer services (Figure 2). In addition, the India market has low exposure to growth industries like software, diversified retailers, transaction processing, etc. Consequently, Indian investors have high allocations to their domestic market with its high industry concentration and low exposure to growth companies.

Perhaps Indian investors may consider a “Smart India” benchmark that includes foreign exposures, but not with a simplistic “add-on” allocation to a broad international benchmark. Instead, the foreign exposure is constructed to capture those industry sectors that will improve overall industry breadth, decrease sector concentration, increase exposure to growth companies while, at the same time, reducing home-country bias.

Macroeconomic-Targeted Commodity Benchmark

In commodities, investors can select from many benchmarks. Early futures-based benchmarks such as the S&P GSCI or Bloomberg Commodity indices generally weight commodities based on their relative production. The newer generation of benchmarks control for negative roll yields (when commodities are in contango) or even control for risk by employing alternative weighting schemes like risk parity. An example of these types of newer benchmarks is the S&P GSCI Dynamic Roll Risk Weight index. However, these newer benchmarks fail to consider an investor’s investment objectives for investing in commodities, like protecting against inflation or slow growth. Consequently, a risk parity or a production-weighted benchmark, constructed without regard for the macroeconomic sensitivities of the individual commodities in the benchmark, may have an undesired sensitivity to inflation or growth.

Investors may be better served with a commodity benchmark targeted to their specific investment objective and investment horizon. To do so, investors can specify a desired inflation or growth exposure, at a given investment horizon, and then construct a customized benchmark using individual commodity futures indices. Such a customized benchmark approach not only would have delivered better risk-adjusted performance, compared to traditional commodity benchmarks, but would have been better aligned with investors’ desired macroeconomic exposures (Figure 3). Listen to our recent webinar on “Investing in Commodities” for a more detailed discussion.