Indexology^® Blog

Considering commodities were on pace to set the worst January since 1975 at one point, down 14.3% by Jan. 20, the final monthly loss of just 5.2% is impressive. The S&P GSCI Total Return rebounded 10.6% with nine of the twenty four commodities posting gains for the month.

Does this mean commodities hit the bottom or that this is just a bounce in a much darker scenario? That probably depends on the oil supply decisions from Saudi Arabia, Russia and Iran, in addition to Chinese demand growth, the strength of the dollar and the weather. However, an examination of the historical annual performance of the S&P GSCI based on the direction of returns in January for single commodities and sectors gives hope that 2016 may be a positive year.

Again, nine of the twenty four commodities in the S&P GSCI were positive in January. Historically, there is a higher chance the year will end positively than negatively based on the first month’s performance for seven of those commodities. The most interesting statistic of the positive single commodities is that when gold has gained in January, the S&P GSCI has gained for the year almost 3 of every 4 times, or in 72% of the time. Gold gained 5.3% in January 2016, so there may be a 72% chance of a positive year in 2016 for the S&P GSCI, based on that historical data point.

What is more compelling is that of the fifteen negative commodities, only three have had the majority of years ending negatively based on their losing January months. For most of petroleum, the first month’s direction is only as good as a coin flip, but when Brent Crude loses, that has been a good thing in 63% of years for the S&P GSCI. Another namesake commodity, copper, that many watch as an indicator of economic health, lost 3.1% in January, but based on history, the S&P GSCI has gained in 68% of years where copper lost in the first month.

On average, there is a 59% chance of a positive year in 2016 for the S&P GSCI  based on the number of times in history positive years have followed the direction of commodities this past January. The sectors tell the same story with a slightly higher chance, 64%, of a positive commodity year in 2016. Still, the outcome for the year is uncertain, especially since the direction of the January performance of the S&P GSCI itself, doesn’t say much. According to the historical data, there is only a slightly greater chance, 53%, of a negative year than a positive one in 2016.

Technicians frequently compare the performance of the S&P Consumer Discretionary sector with the Consumer Staples sector for guidance on the market’s “risk-on” or “risk-off” trend. When the more cyclical Consumer Discretionary sector, which contains such sub-industries as autos, homebuilding and retail, outperforms the more defensive Consumer Staples group, which includes food, beverage and tobacco firms, investors look upon that as a sign that the S&P 500 is entering into, or maintaining, an upward trend, and vice versa. History tells us, however, that the S&P High Beta (SPHB) and Low Volatility (SPLV) Indices offer a better market-timing mousetrap.

As the name implies, the S&P 500 High Beta Index measures the performance of 100 stocks within the S&P 500 that have the highest trailing 12-month standard deviations. On the other hand, the S&P 500 Low Volatility Index consists of the 100 stocks with the lowest volatility.

One of the drawbacks of monitoring the Consumer Discretionary/Consumer Staples (CD/CS) relative strength is that nearly 40% of the sub-industries in the Consumer Discretionary sector (and 44% of the cap-weighting) have betas that are equal to or lower than the market itself. A glaring example is Restaurants, which is dominated by the fast-food giant McDonalds, a company more frequently associated with defensiveness than cyclicality. This group, which represents 11.5% of the Consumer Discretionary sector, has a beta of 0.6. As a result, an investor seeking to compare apples with oranges through the CD/CS relative strength, actually ends up comparing something closer to oranges with tangerines.

To see which indicator – CD versus CS or HB vs. LV – was better at offering “risk on/risk off” guidance, I looked at monthly price performances over the past 20 years, capturing the S&P 500’s price return whenever the cyclical index (CD or HB) beat the defensive index (CS or LV), but applying no change when the defensive index beat the cyclical one. Obviously, I had no way of knowing in advance if CD or HB would beat their respective defensive counterparts in the month ahead. I was merely interested in seeing which pair delivered superior results whenever their market timing signals were either “on” or “off.”

The S&P 500’s price-only compound annual growth rate (CAGR) from 12/31/95 through 12/31/15 was 6.2%. However, by owning the S&P 500 when CD beat CS, and then switching into cash when CS beat CD, the 20-year CAGR jumped to 14.9%. However, owning the S&P 500 when HB beat LV, and in cash during those months when LV beat HB, the CAGR soared to 20.9%.

So there you have it. There is now an alternative for those who chart the relative performance of the S&P 500 Consumer Discretionary sector against the S&P 500 Consumer Staples sector, but have been a bit disappointed with recent signals. A new and better mousetrap, in my opinion, is available by monitoring the relative strength of the S&P 500 High Beta and Low Volatility Indices. When HB is beating LV, its best to stay in stocks. Yet when LV outshines HB, it has usually been wise to take a powder. Of course there’s no guarantee that what worked in the past will work again in the future.

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Question One: How big is Japan’s local currency bond market?  How does it compare to China’s bond market?

The size of the local currency bond market in Japan (tracked by the S&P Japan Bond Index) stood at JPY 1,154 trillion as of Jan. 27, 2016, which is equivalent to USD 9.7 trillion.  It is 1.7 times the size of the local currency bond market in China, as measured by the S&P China Bond Index.  Japanese sovereign bonds represent over 70% of market exposure; the current yield-to-maturity of the S&P Japan Sovereign Bond Index is 0.22%, compared with 2.83% for the S&P China Sovereign Bond Index.

Question Two: How did bonds perform in 2015?

The S&P Japan Bond Index gained 0.87% in 2015, benefiting from a rally in the last quarter (see Exhibit 1).  The index’s yield-to-maturity tightened by 15 bps to 0.24% in the same period.  In government bonds, the S&P Japan Sovereign Bond Index outperformed and increased 1.04%.  Within the corporate sectors, the S&P Japan Utilities Bond Index was up 0.95%, beating other sectors like financials, services, and industries.

Question Three: What about inflation-linked bonds?

Riding on weakening inflation expectations, the S&P Japan Sovereign Inflation-Linked Bond Index dropped 0.63% for the year.  The index’s yield-to-maturity has remained in negative territory since the second half of 2015 and stood at -1.15% as of Jan. 27, 2016.

The Bank of Japan (BoJ) announced on Jan 29 that it will apply a rate of negative 0.1% to excess reserves that financial institutional place at the bank, with the goal to push down borrowing costs to stimulate inflation. The BoJ aims to meet its 2% inflation target; reaching that target has been delayed due to weak consumer spending and the deflationary impact of the drop in oil prices.

Exhibit 1: Total Return Performance of the S&P Japan Bond Index

Historically, factor-based strategies have generated significant risk-adjusted returns in the long run, but they can also exhibit a high amount of cyclicality in the short run.  Based on our studies of factor performance under different financial regimes—the market cycle, the business cycle, and the investor sentiment regime—we found that factor strategies historically have been most responsive to market cycle analysis, while business cycle and investor sentiment analysis have served as good complements to market cycle analysis (see Exhibit 1).

Overall, value and high-dividend stocks generally produced the highest outperformance during both bull markets and market recovery periods.  However, they underperformed the market during the 1994-2000 market rally, partly because of their low exposure to information technology stocks.  On the other hand, growth stocks displayed strong performance after the market had bottomed out at the beginning of 2003, and their streak continued in the ensuing bull market—but they vastly lagged the S&P 500^® in bear markets.

Quality stocks generally produced positive performance in rising markets, but their defensive characteristics came to the foreground in bearish markets and recovery periods.  Nevertheless, the defensiveness of quality stocks did not match that of low-volatility stocks, which beat the S&P 500 by the highest margin in falling markets.  Low volatility underperformed the most when the market rebounded from its troughs.

Conversely, momentum stocks delivered consistent and material excess return during bull markets, but they underperformed in recovery periods because of large price trend reversals.  Small-cap stocks outperformed the market in both bearish periods, and although they beat the market during recovery periods, they did not deliver consistent or significant excess return in bullish periods

In sum, factor strategies had distinct cyclicality and performed differently in various market and economic conditions.  Apart from strategic implementation to achieve targeted portfolio risk exposure, single-factor smart beta indices can also be implemented tactically to express macro-investment views.

For more information on how smart beta strategies performed in different financial regimes, download the research piece or watch our smart beta video series.

Defined contribution (DC) plans, such as 401(k)s, place challenging demands on individuals, the highlights of which are to save diligently (which implies having adequate income, as well as budgeting abilities), to rationally select from among numerous investment vehicles, and to judiciously transition from wealth accumulation to income generation as retirement approaches. Innovations in plan design, such as auto enrollment and auto escalation, have helped with the first challenge. The introduction of target date funds, and other diversified investment alternatives, has helped with the second. But the retirement services industry and its clients—plan sponsors—continue to grapple with the last. The majority of 401(k) participants still do not have access to in-plan income solutions or, perhaps more importantly, to in-plan investments that integrate well with out-of-plan income solutions.

S&P DJI believes there is room in the target date industry for a new category, because it is possible to combine a glide path with a risk management framework, where the risk that one seeks to manage is the uncertainty of future income. Most people think of risk mitigation as managing the volatility of investment returns, or wealth level. However, investment drawdowns are not the only risk DC participants face as they seek to fund retirement consumption. A given amount of capital can fund some level of future income, but that level varies greatly, mainly as a function of prevailing interest rates, even though wealth may remain stable. In other words, a portfolio of T-Bills and high-quality, short-term bonds may provide stability of wealth, but may fail to provide stability of income purchasing power.

There are probably many plan participants and retirement savers for whom mitigating investment drawdowns, as retirement approaches, is an appropriate investment policy. However, there also may be many savers who have a need to integrate their investments with the funding of sustainable, inflation-adjusted future income. Often, this need goes unmet. With S&P DJI’s recent launch of the S&P Shift To Retirement Income and Decumulation (STRIDE) Indices, we hope to create a beachhead for new territory in the field of retirement investing. Nothing would please us more than to see popular fund rating shops like Morningstar and Lipper create a new target date fund category in the coming years that recognizes the risk management framework represented by the S&P STRIDE Index Series.