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Chief Investment Strategist
While market volatility has been well below average over the last two years, we have seen a spike in daily moves greater than 1% over the last month (as shown in Exhibit 1). In fact, if one annualized the October 1% market moves, the frequency would approach the levels of 2008 and 2009 (although the frequency of moves of greater than 2% would be lower). This pickup in volatility has led some investors to ask what this means for future market returns, and whether there are reliable signals that can help divine the next correction in the market. We recently reviewed the efficacy of various market indicators, like investor sentiment, volatility and margin debt, and found there is only modest value that can be gained from analyzing investor sentiment indicators. We also examined a more fundamental building block valuation as a timing tool, and found that while valuation has little near-term value in predicting market returns, it is valuable on a long-term basis. For this reason, we look to valuations to help forecast long-term returns. The markets that have been underperforming U.S. equities are generally trading at relatively attractive valuations increasing their long-term return potential. This bolsters the case for a strategic allocation to these markets, and cautions against throwing in the towel on underperforming regions.
EXHIBIT 1: VOLATILITY SPIKES
Sources: Northern Trust, Bloomberg. Big move trading days are market moves of more than 1%. Historical averages are from 1972 to present.
In Exhibit 1, we define big move trading days as days when the market moves by more than 1.0% (up or down). For context, this equates to a 175 point day on the Dow Jones Industrial Average at current price levels. Looking at big move days helps put the concept of standard deviation into a more understandable framework. For instance, MSCI World volatility as measured by standard deviation during the 2008-2009 time frame was 25.6%. But perhaps more relevant was the fact that markets suffered 61 and 46 days of greater-than 1.0% declines in 2008 and 2009, respectively versus the historical average of 19 days a year with losses of that magnitude. Lately, we have enjoyed a below-average number of down days with only 12 in 2013 and only 12 in 2014 (through October 31). However, looking at the monthly numbers, we clearly saw a spike in October both in terms of up days and down days (four each). Is there anything volatility and other sentiment indicators can tell us about the future path of the markets?
In Exhibit 2, we analyze the relationship between volatility and subsequent market performance by looking at the monthly Chicago Board Options Exchange S&P 500 Volatility index level (VIX) against subsequent one- and 12-month S&P 500 returns. Splitting the VIX levels into three buckets shows that no significant difference in future returns exists. The R-squared between the VIX and next 30-day returns is just 0.03, meaning that it only explains approximately 3% of S&P 500 return variability. Meanwhile, the longer-term measure is even worse with an R-squared of just 0.004, meaning the VIX explains approximately 0.4% of S&P 500 return variability. The range of realized returns does, however, increase as volatility rises.
EXHIBIT 2: NO SIGNAL IN VOLATILITY
Sources: Northern Trust, Bloomberg. Study uses monthly data from 1/31/1990 through 10/31/2014.
As we dig deeper into the details, we see further evidence that this is not a very predictive model. For example, when the VIX reaches 26, the expected return is around 11%, but ranges between -40% and 20%. Interestingly, when the VIX rises above 40, market returns have been positive 100% of the time on a 12-month basis. However, the 30-day returns illustrate that you would have to take some short-term pain before realizing this longer-term gain.
Along with the strong rise in the markets over the last five years, margin debt has been accelerating and has become a recent concern of investors both because of the elevated levels it has reached on a nominal basis (currently at $464 billion) and the way in which previous peaks in margin debt have coincided with substantial downturns in the markets. However, looking at the level of margin debt when scaled by the size of the U.S. equity markets (using the S&P 500 market cap as our proxy) paints a less dramatic picture (see right panel of Exhibit 3). By this measure, current levels of margin debt are more or less consistent with the trend line over the past 20-plus years. This secular upward trend is most likely a reflection of continually falling interest rates (and, thus, falling margin debt servicing costs). Other secular factors at play include the proliferation of hedge funds (hedge fund data is included in the margin debt metrics) and financial innovation whereby taking on margin debt today comes with substantially less friction than in the past.
EXHIBIT 3: A WORRISOME RISE IN MARGIN DEBT?
Sources: Northern Trust, Bloomberg. Monthly data through 9/30/2014. S&P 500 used to proxy U.S. market cap.
Turning to the prospect of the recent run-up in margin debt signaling an imminent downturn in the markets (as appeared to happen in 2000 and 2007), we are less convinced. Again, on a relative basis, current margin levels are fairly contained. Furthermore, we cannot be certain of the cause and effect of the recent margin debt peaks. We believe it is more likely that the market sell-offs caused the reduction in margin debt and not the other way around. Because margin debt can be used for betting against as well as betting on the market and can even be used for things that have nothing to do with the market (e.g. an individuals desire to consume) it makes sense to dive into indicators that provide a better read on investor sentiment. We discuss three in particular: investor sentiment surveys, put/call data and fund flows.
As a measure of individual investor sentiment, the American Association of Individual Investors (AAII) asks individual investors whether they are bullish, bearish or neutral on stocks over the next 6 months. Individual investors do tend to be a reasonable contrarian indicator as their moods tend to reflect what has already happened in the markets as opposed to what is about to occur. During periods where investors were excessively bearish or bullish, the surveys have been a fair market timing indicator. When theres excessive bearishness in the surveys, the median market return over the next month is 23.3% (annualized), and over the next 12 months is 16.2%. When theres excessive bullishness, the return over the next 30 days is just 3.8% (annualized), and over the next 12 months is 9.4%. Investing during periods of excessive bearishness had a 58% (one month) and 65% (12 months) probability of outperforming the markets normal-sentiment returns. This strategy has a reasonable hit rate, or probability, of success when looking at the 12-month returns.
When we use a stricter definition of excessive bearishness a two standard deviation move the S&P 500 returned 29.8% over the next 30 days (annualized, with a 66% hit rate) and 23.0% over the next year (a 69% hit rate). This is a rare event, happening only 35 weeks in the history of survey. The last time there was a two standard deviation level of bearishness was in March 2009 and the S&P 500 subsequently gained 65% over the next 12 months.
EXHIBIT 4: SENTIMENT INDICATORS LOOK MORE USEFUL THAN THEY ARE
Sources: Northern Trust, Bloomberg, Morningstar. AAII surveys: weekly since July 1987; CBOE puts and calls: daily since January 1997; Morningstar flows: monthly since February 1993. Three-week smoothing used for survey and puts and calls data.
We next turn to daily put and call volume on the Chicago Board Options Exchange (CBOE), focusing on call volume (bullish bets on the markets) as a percent of total put and call volume. To reduce the impact of professional institutional hedging activity, we excluded index option volume. Our analysis shows that analyzing put and call option volumes is a less useful indicator than individual investor sentiment surveys.
When the CBOE reported excessive call volume, the median market return over the next 12 months was about 7.7%, actually above the 7.2% return when there was excessive bearishness. The distribution was non-normal excessive bullishness only happened 4.6% of the time, while excessive bearishness happened 31.7% of the time. Markets tend to have more call writers than put writers and data dont appear to be mean-reverting as there has been a slight downtrend over the last 15 years, possibly reflecting the increasing presence of long-short equity hedge fund strategies. This study performed better on a 30-day basis as the median return during excessive call-writing was a negative 9.8% (annualized) over the next 30 days, versus 15.9% when there was excessive put-writing. The hit rate of reducing equity exposure during a bullish market was nearly 60%, which isnt bad but would require a long-term systematic program to capitalize on it.
As a final gauge of sentiment, we looked at the flow of money into and out of risk assets (e.g. stocks) and risk-control assets (e.g. bonds). We included U.S.-domiciled open-ended mutual funds, ETFs and money market funds. Fund flows can be a solid contrarian indicator on a 12-month horizon. The median market return when investors are taking risk off the table is 13.6% over the next 12 months better than the 11.4% return when risk is on, but with just a 56% hit rate. On a
30-day basis, the market has outperformed when more money is flowing to risk assets (14.7% versus 10.7%, annualized), a logical occurrence since fund flows can be a technical contributor to short-term momentum.
While sentiment can swing markets in the short-term (one year or less), over a long-term horizon (five years or more) the single best predictor of equity market return variation that we can find is valuation. Cash flow yield is our preferred valuation measure to assess valuation levels. Given its recent popularity, we also assess the cyclically adjusted price-to-earnings ratio (CAPE), which uses a 10-year rolling earnings number for earnings. Looking at the data on a one-year basis shows that valuations provide little insight with cash flow yields and CAPE explaining only 11% and 7% of return variability, respectively. However, on a five-year basis, cash flow yields and CAPE explain 43% and 37% of return variability, respectively. Coincidentally, both valuation measures currently predict a 6.4% annual return for U.S. equities over the next five years, below the 10.1% long-term historical average (data back to 1926). As we include valuations in our Capital Markets Assumptions work, this is fairly close to our forecasted 6.6% return for U.S. equities.
EXHIBIT 5: VALUATIONS MATTER LONG TERM
Sources: Northern Trust, MSCI, Bloomberg, Yale University. CAPE data used in Irrational Exuberance by Robert Shiller. U.S. market proxy: S&P 500.
In Exhibit 5, we convert the CAPE to an earnings yield figure to provide comparability with the cash flow yield data. Many investors are concerned about the heights CAPE has reached, currently standing at 26.3. Since 1881, the CAPE metric has only surpassed this level on three occasions, with peaks occurring in 1929, 1999 and 2007 all preceding major market drops. However, some (including us) question the validity of the CAPE in the current environment given the massive fall in earnings during the financial crisis (the 2008 earnings still suppress the 10-year earnings figure). Furthermore, while CAPE is currently stretched, it has been that way for some time with the metric sitting above 20 since 1995, with a brief exception during the financial market crisis. As Robert Shiller, the co-developer of the CAPE ratio, said in an August 17, 2014 NY Times article The United States stock market looks very expensive right now. The CAPE ratio, a stock-price measure I helped develop is hovering at a worrisome level. However, he went on to say The CAPE was never intended to indicate exactly when to buy and to sell. The market could remain at these valuations for years. But we should recognize that we are in an unusual period, and that its time to ask some serious questions about it. In our opinion, it seems as if Professor Shiller also believes the CAPE is a good long-term predictor of returns, but not a short-term trigger.
Valuations are one major component of our Capital Markets Assumption (five-year) return expectations, alongside earnings expectations and dividend yield assumptions. Looking at developed markets, we expect similar earnings growth across the various regions (approximately 5%). While we do expect Europe and Japan to show slower economic growth than the United States, the composition of company revenues in those regions allows better earnings potential than would be suggested by the companys country of domicile. For instance, companies in slow-growing Europe get nearly 50% of their revenues from outside of the European bloc (including nearly a quarter of their revenues from emerging markets) making those companies the most geographically diversified of all regions (by comparison 70% of U.S. company revenues come from the United States).
1Shiller, Robert J. The Mystery of Lofty Elevations. New York Times 16 Aug. 2014: BU3. Print.
EXHIBIT 6: RETURNS SUPPORTED BY GROWTH AND DIVIDENDS
Sources: Northern Trust.
While our earnings expectations across developed markets are similar, the forecasts for higher dividend yields in Europe, and slight multiple expansion, gives Europe a forecasted return advantage over the United States. Looking at emerging markets, the higher earnings growth potential (with 84% of revenues coming from domestic sources) alongside a solid dividend yield and some valuation expansion lead us to continue to expect a return premium out of those equities relative to the developed markets. The current sentiment toward many major equity markets outside the United States is soured by recent underperformance. However, the improved relative valuation increases the relative return potential and we think supports a better 5-year return expectation. We think this justifies a continued commitment to a globally diversified equity portfolio, which helps reduce dependency on any one region and reduces the risk of materially underperforming a global equity universe.
Special thanks to Raymond Luo, Investment Analyst, for data research.