Indexology^® Blog

If 2016 was unremarkable, 2017 was downright sleepy…at least as far as equity markets were concerned. In 2017, the S&P 500 notched the lowest level of volatility in 27 years. Both dispersion and correlations were among the lowest levels in the same period. This is in spite of a year that was far from lacking in terms of macroeconomic catalysts—major elections took place and Catalonia threatened to separate from Spain while, domestically, the U.S. government seesawed on major legislation.

Nevertheless, as the dispersion-correlation maps reflect below, markets took it all in stride. The well below median levels of dispersion and correlation contributed to the extraordinarily muted volatility. This is true not just in the U.S. but also internationally. Dispersion-correlation maps for both the S&P Europe 350 and S&P BMI Pan Asia paint similar pictures.

The S&P 500 gained 22% in 2017 and the good times weren’t limited to just the U.S. Both Europe and Asia enjoyed a good year as well. Understandably, as we head into 2018, investors might be somewhat concerned with froth. The dispersion-correlation map won’t help us predict what 2018 will bring but it offers helpful context around turbulent times. Historically, turmoil in equity markets (such as 2000 and 2008) have been defined by dispersion levels much higher than average…and as the maps below indicate, current levels are far from there.

Dispersion-Correlation Maps

OK, that might be hyperbole.  This may not be the Top 10.  Let’s just say it’s a Top 10.  Regardless, it’s hard to deny that 2017 was a standout year for US equity markets.  To wit:

1. At the Close – despite losing 118+ points during the final trading session, the DJIA ends the year at 24,719.22, close to its all-time high, and having advanced just shy of 5000 points for an annual gain of 25.08%. That performance is the best since 2013 when the DJIA gained 26.50%.
2. Prominent Themes – Trump’s inauguration and subsequent grappling with his new administration, a major continuation of the bull market, mixed legislative results from Congress, improving corporate and economic data, a dearth of volatility and varied geopolitical issues including repeated threats from North Korea’s nuclear program.
3. New Highs – In 2017, the DJIA reached more new highs – 71 in total – than any year in history. Put another way, more than 1 of every 4 trading days closed in record territory.  By comparison, 1995 and 1925 were in the #2 and #3 slots with 69 and 65 new highs respectively. There have been 52 calendar years when the DJIA notched at least 1 new high and 70 when none were recorded.
4. Milestones – With 5 new 1000 point milestones (20k, 21k, 22k, 23k & 24k), 2017 was the most active such period on record. It took nearly twice as long for the DJIA to hit the first 1000 points (achieved on November 4, 1972) as it has for all subsequent milestones combined. Similarly, the speed with which the DJIA crossed these thresholds is notable: it took only 257 trading days to run through those 5 marks.  By comparison, it took 483 days for the DJIA to move from 18k to 19k.  Of course, there is a big caveat:  it’s important to note that as the DJIA gains in value each successive 1000 point milestone represents a smaller percentage gain.
5. Quarterly Gains – the DJIA posted a return of 10.33% in Q4, the 9^th quarterly advance in a row and the best 3 month return since Q1 2013 when the DJIA advanced 11.25%.
6. Best & Worst Days:
   • Best Day in Point Terms – November 30 (up 331.67 points), when the Dow sped through the 24k level as investors cheered progress on the GOP tax reform bill.
   • Best Day in Percent Terms – March 1 (up 1.46%), when the DJIA closed above 21k as investors were heartened by (unexpectedly?) measured remarks in Trump’s 1st address to Congress.
   • Worst Day in Point & Percent Terms – May 17 (down 372.82 or 1.78%), when markets were hammered by concerns that Trump sought to obstruct Comey’s investigation of Michael Flynn.
7. Large Moves – Or, since muted volatility was a major theme in 2017, this is more appropriately the lack of large moves. In 2017, there were only 10 trading sessions when the DJIA posted a move of 1% or greater.  This is the lowest total since 1964 when only 3 such moves were recorded for the entire year.  Since 1940, the annual average is 49 one percent moves or an experience that typically occurs in about 1 of every 5 trading sessions.  By comparison, in 2017 a 1% move occurred in only 1 of every 25 sessions.
8. High vs. Low – with a spread of over 5,100 points, the difference between the year’s high and low levels saw the biggest gap since 2008.
9. Stock Contributions – Boeing (BA) was the biggest contributor to the DJIA’s advance, adding over 955 points. Caterpillar (CAT) and UnitedHealth Group (UNH) were in the #2 and #3 spots respectively.  General Electric (GE), suffering from widely reported troubles, was the worst performer in 2017, sapping 97+ points.  IBM and Exxon Mobil were the second and third worst performers.   In all, twenty six stocks added to the DJIA while 5 detracted.  Astute observers will note that totals 31 stocks event though the DJIA only captures the performance of 30 “industrial” companies: the discrepancy arises from the newly combined DowDupont shares added in August (thus both DD and DWDP are counted in the totals).
10. Sector Contributions – the Industrials sector was the largest contributor in 2017, followed by Consumer Discretionary and Technology. All industries, save Telecom (which itself lost only ~3 points), made positive contributions in 2017.

As Asian market participants have become more aware of the importance of portfolio diversification, they have been paying more attention to the U.S. corporate bond market. The S&P 500 Bond Index seeks to measure the broad and relatively liquid U.S. corporate bond market, and it is designed to be a corporate-bond counterpart to the iconic S&P 500, widely regarded as the best single gauge of large-cap U.S. equities. In addition to the popularity of the equity index, Asian market participants should be familiar with some of the index constituents like Apple, JPMorgan Chase, Microsoft, and Wal-Mart.

Since 90% of the index is made up of investment-grade debt from blue-chip companies, it can be viewed as a proxy for the U.S. investment-grade bond market. As of Dec. 26, 2017, the index delivered a YTD total return of 5.52%, while its yield-to-maturity tightened 5 bps to 3.32%.

The S&P 500 Bond Index market value is larger than China’s and Japan’s corporate bond markets; it’s even larger than the sum of all Pan Asia local currency corporate bond markets. The S&P 500 Bond Index currently tracks over 5,000 constituents with a combined market value of USD 4.6 trillion.

Over the past decade, the S&P 500 Bond Index rose 5.66% annually; it outperformed the S&P Pan Asia Corporate Bond Index and the S&P Japan Corporate Bond Index, which gained 4.15% and 1.36%, respectively, in the same period.

Hence, the S&P 500 Bond Index could offer Asian market participants the benefits of familiarity, good returns, and stable yields.

Exhibit 1: Total Return Comparison of the S&P 500 Bond Index, the S&P Pan Asia Corporate Bond Index, and the S&P Japan Corporate Bond Index

The S&P 500^® was up 22.1% YTD as of Dec. 19, 2017 (including reinvested dividends), and international stocks were generally even more kind to USD investors (S&P Global Ex-U.S. BMI Gross Total Return [USD] was up 26.3% YTD). However, in most types of accounts we do not get to keep gross returns. Capital gains and dividends are sheltered from immediate taxation in traditional retirement accounts, such as 401(k)s, but are ultimately taxed at (presently unknown) future income tax rates.

Retirement savers must consider an extremely long horizon, encompassing career-long wealth accumulation as well as in-retirement decumulation. Diversifying account types in order to help manage future income tax liabilities may be worthwhile, especially considering that new federal income tax legislation is expected to significantly add to the nation’s outstanding debt and may also exacerbate income inequality. These two catalysts could eventually produce a political or economic reaction that could result in significantly higher income tax rates down the road.

If it were known with certainty that neither the income tax code nor one’s tax bracket would change, it would make no difference in final wealth whether one made traditional tax-deductible contributions or non-tax-deductible Roth contributions. For most people, it is a safe assumption that before-tax income is likely to be lower during retirement than when they worked. So in the context of stable tax law, traditional tax-deductible contributions make sense. But because future tax law is not known, the trade-off between making traditional or Roth contributions is about hedging one’s bets with respect to future tax liabilities.

The lower current marginal income tax rates become, the less valuable current income tax deductibility is and the more valuable locking a future tax rate of 0% through the use of Roth accounts is. Currently, rates are heading lower in the short term, but in the longer term, they may need to revert back to prevailing levels or higher. For retirement savers, this represents a significant potential future liability as they draw down traditional retirement savings. The size of the liability is not known because future tax law is not known.

Some retirement plan participants have access to Roth 401(k) accounts, while others may be able to contribute to Roth IRA accounts. The general consensus is that contributing enough into a traditional or Roth 401(k) to get a company match is a good idea. Beyond that, one may want to give some thought to diversifying account types. Because they are not taxableRothupon withdrawal, Roth accounts are not subject to required minimum distributions. That makes them ideal for providing some flexibility when managing income taxes during retirement.

In one of my previous blog posts, we demonstrated that high yield bonds exhibited a strong negative correlation with VIX and an even stronger one with VIX futures, which comes mostly from down markets. This prompted us to think that VIX futures may hold tail-risk hedging opportunities for high yield bond portfolios. In this blog, we explore allocating VIX futures to tail hedge a high yield bond portfolio, with back-tested results for the following two hedging strategies.

1. Static allocation: Allocating a static weight of x% to VIX futures in a bond portfolio.
2. Dynamic allocation: Allocating a fixed percentage (x%) of the portfolio to VIX futures according to the observed VIX spot level. At each month’s end, if the VIX spot is equal to or greater than 25, x% of the portfolio is allocated to VIX futures the next month. If the VIX spot at month’s end is less than 25, the allocation to VIX futures is 0.

Exhibit 1 shows risk, return, and drawdown figures for a high yield bond portfolio with allocation to VIX futures ranging from 0 to 100%.  On volatility alone, both static and dynamic allocation of VIX futures can help reduce portfolio volatility, as long as the VIX futures allocation is kept under 20%. Allocating more than 20% to VIX futures proved to overhedge the bond portfolio and introduce VIX as a new risk factor that can raise portfolio volatility as allocation increases.

On the return side, dynamic allocation to VIX futures can also improves portfolio returns when the allocation is kept under 20%. In comparison, static allocation to VIX futures can significantly drag down portfolio return, as rolling VIX futures tends to incur costs. Risk-adjusted return, as measured by the ratio of return over volatility, is optimized when VIX futures are dynamically allocated at 12%.

Exhibits 2 and 3 show the cumulative returns and performance statistics of a high yield bond portfolio with and without a VIX futures allocation of 12%. Dynamic allocation to VIX futures can provide downside protection and lessen portfolio drawdown—for example, during   the market turmoil of 2008, 2009, and 2011—while adding extra return to the bond portfolio. Return per unit of risk improved from 0.83 to 1.24 for the dynamic allocation strategy.

Though static allocation of VIX futures can reduce portfolio volatility and offer downside protection compared with the broad-based, unhedged S&P U.S. High Yield Corporate Bond Index, it can drag down portfolio performance significantly, due to the high cost of rolling VIX futures.

Our back-tests have confirmed the potential benefit of a dynamic allocation to VIX futures in a high yield bond portfolio. Furthermore, we found that the risk-adjusted returns of the portfolio, as measured by the ratio of return over volatility, were optimized at the VIX futures allocation of 12% with our allocation algorithm.