Indexology^® Blog

Commodities are a direct way to protect against inflation since they are the natural resources that build society.  The same food and energy that is in CPI (Consumer Price Index from the Bureau of Labor Statistics,) is in the commodity indices like the S&P GSCI and DJCI, and more energy has provided more inflation protection since energy is the most volatile component of CPI.

In this video featuring Bob Greer and Boris Shrayer, the link between pension liabilities, inflation and commodities is discussed.  They say commodities can be a nearly perfect hedge for real lifestyle costs when buying food and gasoline. However, pension assets need to accumulate more if they will pay out more in liabilities.  The conversation extends beyond pensions to endowments and foundations where if levels of gift giving and supporting of staff at universities are maintained, then purchasing power needs to be preserved as inflation rates rise.  The link between inflation and liabilities can be complex, where commodities are an imperfect hedge since some inflation from commodities may not move exactly with all types of inflation, like wage inflation for example.

One of the important things that Boris points out is that this inflation doesn’t just apply to institution but it impacts individuals “or anybody.”  Without knowing for sure how inflation and interest rates might move in the future, strategies have evolved to minimize uncertainty around how much an account balance is worth as an income stream.  For example, the conversation has changed from “how big should your 401K be at retirement?” to “what percentage of your current income do you need to replace annually for retirement (inflation protected)?”  The latter style is akin to the defined benefit/pension conversation and much more relatable for retail clients.  Why worry about hitting a certain savings number/account size when you’re not clear what the withdrawal rate will be or how much impact inflation may have during your retirement?

Two main applications enable this switch, and are implemented in the S&P STRIDE (Shift To Retirement Income and Decumulation) indices.  The first uses a process that is analogous to dollar cost averaging into income producing assets.  It is a three-phased glide path that is innovative because it not only de-risks the allocation automatically as investors pass TO and THROUGH retirement, but it also addresses inflation risk.

The three phases are:

1. Accumulation. This consists of a conventional target date glide-path using stock and nominal bond indices.
2. Transition. The index weight is gradually shifted from the target date glide-path to a pool of constituents that secure real future income.
3. Decumulation. The income producing constituents are divested, modelling retirement income cash flows.

The table below shows what allocations look like at different ages with the other significant breakthrough that is the addition of TIPS (Treasury Inflation-Protected Securities.)  Using a glide path approach with TIPS may help financial planners to provide clients a higher degree of stability when predicting inflation adjusted income during retirement.

The index series builds in a liability driven investment (LDI) strategy by shifting from equities and fixed income to TIPS, that is implemented during the transition phase. Notice there is a 75% allocation to TIPS by age 65, which is at the age when many individuals retire, and the TIPS allocation increases for another 25 years.

Interestingly, the S&P Target Tuition Inflation Index, that aims to grow with tuition inflation over the long term, has a similar allocation to TIPS as in the latter part of S&P STRIDE.

Although the tuition inflation has risen much more than general CPI, a large component of the tuition inflation increase is correlated with general CPI. However, the time horizon for college tuition is shorter than for the whole three-stage retirement life-cycle, so there is a constant 80% allocation to TIPS in the S&P Target Tuition Inflation Index to help preserve the capital while the remainder of the index allocates between stocks and nominal bonds, resulting in an index that has grown with tuition inflation.

Whether saving for retirement or college, TIPS help change the framework from addressing the question of how big savings are at a moment in time to how much of current income needs to be saved for the goal.  Preserving purchasing power in the face of unknown interest rates and inflation can be better managed with TIPS, and can now be measured with indices using them.

I would like to acknowledge my colleague, Travis Robinson, Senior Director of Financial Advisor Channel Management at S&P Dow Jones Indices, for his contribution to this article regarding key points on S&P STRIDE.  For more information on STRIDE, register for our upcoming webinar, “Strategies for Managing Retirement Income Risk” on October 24 at 2PM ET.  

As discussed in my previous blog, dividend investing has gained popularity across regions, driven by the low interest rate environment and aging demographics in different high income regions.  A similar trend is observed in China and Hong Kong.  Facilitated by the Hong Kong-Mainland Stock Connect programs, mainland market participants can more easily invest in the Hong Kong equity market.  In December 2016, we launched the S&P Access Hong Kong Index, which is designed to reflect the universe of the Hong Kong-listed stocks available to Chinese mainland market participants through the Southbound Trading Segments of the Stock Connect programs (Stock Connect Southbound).  In response to increasing demand for dividend investing, we also launched the S&P Access Hong Kong Low Volatility High Dividend Index on Feb. 20, 2017.

The S&P Access Hong Kong Low Volatility High Dividend Index is designed to measure the performance of the 50 least volatile, high-dividend-yielding stocks in the Stock Connect Southbound universe, with construction as shown in Exhibit 1.

Compared to the parent index, the S&P Access Hong Kong Low Volatility High Dividend Index was consistently overweight in industrials and consumer discretionary and underweight in financials and consumer staples.  Between Jan. 31, 2011, and July 31, 2017, the index had a high dividend yield, ranging from 4.5% to 8.4%, as of the rebalance date.

Historically, the S&P Access Hong Kong Low Volatility High Dividend Index has outperformed its benchmark on an absolute and risk-adjusted basis.  It is important to highlight that, historically, the simple high dividend yield portfolio, without any low volatility screen,[2] underperformed the benchmark, with a more volatile return.  However, after adding the low volatility screen, the S&P Access Hong Kong Low Volatility High Dividend Index recorded a higher absolute return with lower return volatilit  y and drawdown over the back-tested period.  This showed the benefit of using the low volatility screen as a quality measure to exclude those high-yielding stocks with depressed stock prices (see Exhibit 2).

Exhibit 3 shows the performance of the S&P Access Hong Kong Low Volatility High Dividend Index versus the hypothetical simple high dividend yield portfolio across different Hong Kong equity market cycle phases, defined with respect to the Hang Seng Composite Index’s (HSCI’s) performance trends (three bearish, three recovery, and four bullish cycle phases).  The S&P Access Hong Kong Low Volatility High Dividend Index and the simple high dividend yield portfolio outperformed the HSCI in seven and six out of ten of these market cycle phases, respectively.  With the aid of the low volatility screen, the S&P Access Hong Kong Low Volatility High Dividend Index exhibited more defensive characteristics with reduced return drawdown during bear market phases compared with the simple high dividend yield portfolio.

[1]   For detailed index methodology, please see http://spindices.com/indices/strategy/sp-access-hong-kong-low-volatility-high-dividend-index-cny.

[2]   The simple high dividend yield portfolio consists of 75 high-dividend-yielding stocks before the low volatility screening, with eligible criteria, weighting method, and rebalancing schedule following the S&P Access Hong Kong Low Volatility High Dividend Index methodology.

The release of the SPIVA U.S. Mid-Year 2017 Scorecard provided a welcoming dose of optimism for proponents of active management.  One statistic, which was picked up heavily by financial media (Financial Times Article) and market participants, is that 52.49% of All Domestic Funds managed to beat the S&P Composite 1500^® (the designated benchmark).  The conversation thereafter sounds something similar to the following: “Active management is back…”

A single SPIVA statistic should not be seen as a full representation of the report.  The SPIVA Scorecard is meant to be a tool viewed in its entirety rather than cherry picking the (very first) data point in the report.  For example, the All Domestic Funds category comprises individual funds investing against their respective cap ranges, and it is then aggregated and compared against the S&P Composite 1500.  This means that funds investing in domestic small-cap equities are being compared against an index with a large-cap tilt.  The effective difference in benchmark performances was 22.47% (S&P SmallCap 600^®) versus 18.09% (S&P Composite 1500), certainly a much lower bar to clear.  With that in mind, the focus should be on the relative performance of managers across various capitalization segments or the style against their respective benchmarks.

Despite this clarification, a fair amount of optimism was provided by the results in the domestic large-cap equity space.  With only 56.56% of large-cap active managers underperforming the benchmark for the one-year period ending June 30, 2017, it marked a substantial reversal of fortune from the results seen at mid-year and year-end 2016, with 66% and 84.62% of the managers underperforming as of Dec. 31, 2016, and June 30, 2016, respectively.

In addition to improvement in relative performance figures, large-cap equity managers also appeared to benefit from better security selection skills (see Exhibit 1).  The dark blue bar represents the portion of the excess return above the benchmark coming from security selection within each GICS sector.  The light blue bar measures the portion of excess returns coming from sector allocation decisions.  They both represent active returns stemming from managers’ decisions.  The numbers below and above the chart represent the total return of the S&P 500^® over the past 12-month period.

While both allocation and security selection effects are active decisions that contribute to active returns, we can observe that a cumulative negative 12 months of returns in the market is typically followed by managers adding value with allocation.  This may be a consequence of managers avoiding certain volatile sectors.  One of the perceived benefits of active management is that it provides cushion during market downturns.  To confirm this, we examined the six-month periods between Dec. 31, 2000, and June 30, 2017, and separated the excess returns of large-cap managers during up and down markets.  What we can see from Exhibit 2 is that managers, on average, do poorly in regard to stock selection decisions during down markets.  The value that is added during up market periods (average annual active returns of 19 bps) is negated by the -54 bps that they subtract when the market is down.  This is primarily driven by the poor stock selection (-70 bps) during down periods.

Based on the recent SPIVA U.S. Scorecards, actively managed domestic equity funds appear to have had a positive trend over the past three semiannual periods.  While the results are a welcome change and demonstrate that active allocation and security selection decisions have been adding positive value, market participants may want to consider beyond short-term results and examine managers’ performance during various market environments.  As we have demonstrated, actively managed funds, on average, have not provided positive excess returns during down markets, driven primarily by poor security selection.

Today, the Dow Jones Industrial Average closed above 23,000 for the first time – offered here are a few factoids associated with that milestone:

• A Record – there have been four (4) 1000 point thresholds crossed in 2017, the most of any year since the DJIA’s inception in 1896.
• Less Impact Per Milestone – of course, as the Average gains in value each 1000 points represents a smaller percentage movement. In this case, the move from 22,000 to 23,000 results in a 5.18% return.
• A Steady Advance – there have been 51 new highs achieved year to date, the most since 2013 when the DJIA struck a new high 52 times. Prior to that, the most new highs were marked in 1995 (69 new highs).

Style consistency analysis is an essential element of the SPIVA^® India Scorecard that helps evaluate the percentage of funds that retained the same investment style classification throughout the investment horizon.  Other than performance and survivorship, the style consistency of funds is an important metric for market participant that can influence their asset allocation decisions.  In the SPIVA India Mid-Year 2017 Scorecard, we observed low style consistency for the five-year period across most fund categories, except the Indian ELSS (see Exhibit 1).

As of June 2012, there were 161 Indian Equity Large-Cap funds available for investment.  Out of these 161 funds, 34 funds were either merged or liquidated over the five-year period ending June 2017, which led to a survivorship rate of 78.88%.  Of the remaining 127 funds, another 68 drifted in style during the five-year period, resulting in a low style consistency of 36.65%.

Similarly, a low style consistency was observed for the Indian Equity Mid-/Small-Cap and Indian Composite Bond categories, which were 49.25% and 41.00%, respectively, over the five-year period. Over the same horizon, only Indian ELSS funds were style consistent.

Source: S&P Dow Jones Indices LLC, Morningstar, and Association of Mutual Funds in India.  Data as of June 30, 2017.  Past performance is no guarantee of future results.  Table is provided for illustrative purposes.

Exhibits 2 and 3 investigate further into Indian Equity Large-Cap and Indian Equity Mid-/Small-Cap funds that survived the five-year investment horizon but were style inconsistent during this period.  In the Indian Equity Large-Cap category, 68 funds changed their Morningstar category definitions[1] during the five-year investment horizon. Of these funds, 43 were reclassified to India Fund Flexicap, whereas 15 others were reclassified as India Fund Sector – Infrastructure, as per Morningstar categories as of June 2017.[2] In the case of Indian Equity Mid-/Small-Cap, 24 of the 67 funds were style inconsistent; 22 of these funds were reclassified as India Fund Flexicap.

Consistency in fund style is important because a change in investment style alters the risk/return characteristics of the fund.  Funds with inconsistent style may reduce the effectiveness of asset allocation decisions.

Source: S&P Dow Jones Indices LLC and Morningstar.  Figures based on SPIVA India Mid-Year 2017 Scorecard.  Table is provided for illustrative purposes.

Source: S&P Dow Jones Indices LLC and Morningstar.  Figures based on SPIVA India Mid-Year 2017 Scorecard.  Table is provided for illustrative purposes.

[1]   http://advisor.morningstar.com/Enterprise/VTC/CategoryDefinitions_India_April_2016.pdf

[2]   Please refer the SPIVA India Mid-Year 2017 Scorecard for current and historical mapping of SPIVA and Morningstar categories.