If not a traditional market-cap EM benchmark, what type of benchmark might be more suitable for Japanese investors? Given advances in benchmark technology, an investor can select or design, an alternative benchmark that can provide better baseline exposure in the context of their overall portfolio.
We first consider an EM benchmark that continues to group stocks by country exposures, as does a traditional EM benchmark, but rather than using market-capitalization weights, the alternative benchmark uses either ERC or EW for the benchmark’s construction. We call these “country-based alternative” benchmarks to distinguish them from a traditional EM benchmark.
As a second benchmark alternative, we group EM stocks first by sector exposure, irrespective of country. For example, an Indian technology stock and a Brazilian technology stock would be assigned to the same EM technology sector (using market-capitalization weights). Then, to construct the alternative benchmark, we again combine the various sectors using ERC or EW. We call these “sector-based alternative” benchmarks.Footnote 12
As a third alternative, we take advantage of academic research that has identified style exposures (e.g., momentum and value) as drivers of equity returns. Research suggests economic or behavioral motivations that allow stocks with these style exposures to generate positive, risk-adjusted returns.Footnote 13 To construct “style-based alternative” benchmarks, we first classify stocks from a country into style groups depending on their exposures to these styles (using market-capitalization weights).Footnote 14 Then, we combine these style groups using ERC or EW. As we show, for Japanese investors a style-based alternative EM benchmark has provided not only better returns, but also lower risk compared to a traditional EM benchmark.
For a given exposure grouping method, we construct an alternative benchmark using either ERC or EW. Both construction methodologies follow a set of rules. ERC (also known as “risk parity”) is not new and has been well studied.Footnote 15 ERC produces a risk-balanced benchmark such that each group’s contribution to the benchmark’s overall risk (volatility) is same for all groups in the benchmark. An investor considering an EM allocation may wish to adopt this approach given the higher risk involved with EM equities. Another benefit of ERC is that it does not rely on expected return assumptions. To construct a benchmark using ERC, we need only the volatilities and correlations of the various EM groups which we estimate each month using a widening window of historical data. This generates comparative results that are out-of-sample, providing an unbiased indication of how the various benchmark alternatives might perform going forward. Since the parameters are estimated each month, there is a potential for high turnover as alternative benchmarks are rebalanced. However, as shown below, by using a widening window of data, the degree of turnover is low.
The EW portfolio construction approach provides naïve diversification and does not require estimating any expected return or risk parameters. However, the portfolio has higher risk and since the EM universe is generally very volatile, it may have lower value for Japanese investors.
“Country-based alternative” benchmark approach
Exhibit 4 shows that the EM country universe is not homogeneous. There is significant performance dispersion. From February 2002 to January 2017, there was almost a 13-percentage point range in total returns (in JPY) across countries. For example, while Indonesia had almost double the performance of the overall EM market (17.8 vs. 8.7%), Taiwan performed meaningfully worse (4.9%). There was also wide variation in EM country risk (as measured by annualized standard deviation of monthly returns): Brazil had 50% greater risk compared to the overall EM market (37.0 vs. 24.1%) while Malaysia had 20% lower risk (19.7%). Most notably, the three largest EM countries had among the highest correlations to Japan and DM ex-Japan. Countries with relatively low weights in the MSCI EM index (e.g., Indonesia, Malaysia and Thailand) had lower correlations to Japan and DM ex-Japan.
These country-level performance metrics suggest the possibility of constructing a better diversified EM benchmark for Japanese investors by reducing the weights of the larger EM countries and increasing those of the smaller countries. However, moving away from market-capitalization weights raises the issue of investability. While some EM countries have free float adjusted market caps that compare favorably with mid-cap sizes in single name US stocks, other EM countries are much smaller. Accordingly, we limit the EM country list to the 11 largest countries (approximately $100 billion in market capitalization).
We weight these 11 countries to form an ERC “country-based alternative” EM benchmark. Unlike for a market-cap-weighted index, due to relative market movements in EM country groups, rebalancing of the benchmark back to the target ERC weights at the end of each month is required. The monthly rebalancing causes the average annual two-way turnover to be 75%.Footnote 16
Unlike a cap-weighted index, we do not observe significant concentration in handful of countries.
The equally weighted (EW) “country-based alternative” EM equity benchmark is constructed by allocating equally to all 11 countries. Weights are also rebalanced back to equal weights at each month end.
“Sector-based alternative” benchmark approach
We use GICS (Global Industry Classification Standard) to first group all EM stocks into 11 sectors (using market-capitalization weights).Footnote 17 As with country exposures, there is similar concentration among EM stocks in terms of their sector exposures. Almost 50% of EM stocks, by capitalization, belong to the globally oriented Finance and IT sectors (Exhibit 5). In contrast, the more defensive and arguably, more “local” sectors (e.g., Healthcare, Utilities, Telecom and Consumer-Staples)—which have lower correlation with DM ex-Japan—are less than 20%. For investors seeking to benefit from faster internal growth in emerging markets, a weighting scheme tilted toward the smaller, more local sectors might be advantageous. For February 2002–January 2017, while not as large a range as with EM country groups, we find a returns range, across EM sectors, of 7.4 percentage points.
Due to relative market movements in EM sector groups, there is a need for monthly rebalancing of benchmark back to ERC target weights. The monthly rebalancing causes average annual two-way turnover to be 66%.
ERC addresses the sector concentration issue. Instead of more than a 24.2% weight to financials, the weight in sector-based alternative benchmark is only 8.1% (as of 1/31/2017). As the volatility and correlations of healthcare sector were lower compared with the other sectors, it had the highest weight in the sector-based alternative benchmark (refer to online supplement A2). To put this in context, most recently, the healthcare sector had a 13.1% weight in the sector-based alternative benchmark but only a 2.4% weight in the traditional EM index.
The equally weighted (EW) “sector-based alternative” EM equity benchmark is constructed by allocating equally to all 11 sectors. Weights are rebalanced back to equal weights at each month end.
“Style-based alternative” benchmark approach
Finally, we consider alternative EM benchmarks based on style exposures. Market fundamentals may vary considerably across EM countries. Anecdotally, Indian equities have typically traded at relatively higher P/E multiples compared to other EM countries like China or Russia. This does not necessarily imply that the Indian equity market is too expensive and should be avoided. Relative to its historical average it may, in fact, be trading at a relatively low multiple and therefore may be of value. Therefore, we construct style groups first at the country level. To do so, we sort the stocks for each country based on a specific style (e.g., book-to-price), and the top quintile stocks within a country are combined (using market-capitalization weights) to form a country-level style group (e.g., the Brazil book-to-price style group).Footnote 18 Similarly, for size and low-vol country-level style groups, the top quintile stocks in a given country represent stocks with the lowest volatility and the lowest log(market capitalization), respectively. Each country-level style group is rebalanced every month using prior month-end data. Then, for each of the seven styles considered here, we combine the 11 country-level style groups using ERC to produce an ERC EM style group. For example, the ERC EM book-to-price value style group is defined as the ERC combination of all 11 country-level book-to-price value style groups.
Exhibit 6 shows performance metrics for the seven long-only EM style groups (“value”—book-to-price and earnings-to-priceFootnote 19; “momentum”—12-month less one-month price returns; “carry”—dividend-to-price; “profitability”—return-on-equity; “low volatility”—12-month volatility; and “size”—log(market capitalization)).Footnote 20
The range in returns among the seven ERC EM style groups was 8.0 percentage points, like the EM sector groups. We also observe that the EM size style group underperformed the traditional EM index (7.2 vs. 8.7%). This is contrary to academic findings for DM large cap equities. One might argue that the analysis period of 15 years is too short, as these robust sources of risk premia can go in and out of favor for more than a decade. Nevertheless, we exclude the EM size style group from the construction of the style-based alternative EM benchmark.Footnote 21
Finally, we combine the six EM style groups, using either ERC or EW, to form “style-based alternative” EM benchmarks. Due to relative market movement in EM style groups, the benchmark needs to be rebalanced back to ERC target weights on a monthly basis. The monthly rebalancing causes the two-way average annual turnover to be 62%.
To construct the equally weighted (EW) “style-based alternative” EM equity benchmark, we equally weight the six ERC EM style groups. The benchmark is rebalanced at each month end.