All In… (Well, Almost)

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“The trend is your friend except at the end where it bends”

Jack Schwager, attributed to Ed Seykota, Technical Analysis, Wiley; 1 edition (December 1995)

The Market Compass models include macroeconomic and valuation factors, but they rely significantly on trend-following measures to identify favourable times to be exposed to the markets. Sometimes these measures provide signals that seem contrarian (for example, when the S&P 500 broke above its 200-day moving average in 2009), but sometimes, the signals, seem, well, like they’re following the trend.

Ever-richer valuations across asset classes, crisis in the Ukraine, slowdown in Japan, hawkish, then dovish, musings from Fed governors… there seem like plenty of reasons for concern. Not to mention seasonality and that other old saying, “sell in May and go away”. Nonetheless, the Market Compass models have chosen this week to signal at least moderately favourable conditions in every asset class that they track, as shown by the header graphic above. The weekly models, found on the Current Model Outputs tab, have allocated capital to every asset class except global real estate, which is squeezed out by our 5% small-position filter but nonetheless rates a positive assessment on a standalone basis (unfiltered, the models would allocate it 2-3%).

This is one of those times when trend following seems uncomfortably consensus-oriented, but perhaps it is precisely this discomfort that will make allocating to these markets profitable. Only time will tell.

Have a good week!

Monte Carlo and Moving Averages in Emerging Markets

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By and large, emerging markets have been uncomfortable places for investors in recent quarters. However, EM equities have had a run of strength in recent weeks, and this has pushed them into positive territory on a number of trend-following measures, including several that are included in the Market Compass composite trend measures.

Traditional trend-following approaches have had a challenging time in emerging markets since the financial crisis. As we can see in Figure 1, returns have basically been flat for the last four years, as occasional uptrend have reversed, leading to unprofitable “whipsaws.”

FIgure 1. Emerging Markets: Simple Moving Average System vs. Buy-and-Hold

simple return view with dates

 Data source: Yahoo! Finance

The illustration above uses data from the Vanguard emerging markets index mutual fund and a very simple trend-following approach: if the unit value of the fund finishes the week above the average for the last 45 weeks, the system “goes long” or buys into the market. If the emerging markets fund’s price is below the 45 week average, the moving average system takes a position in Vanguard’s short term bond fund.

This simple system does outperform a buy-and-hold approach, and with visibly less volatility. However, the question could be asked of whether this is a reflection of the starting and end dates chosen. If an investor had tried to apply this simple trend-following approach, starting on different dates, would the results still be favorable?

We can answer this by running a simulation that uses randomly-selected start- and end-dates. We have 900 weeks of data below, so we set up a test that allows the investor to start at any point in the first half of the data-set, and to finish any time in the second half. With 1000 random start- and endpoints, we get the following results for return (annualized) and risk (as measured by annualized standard deviation):

Figure 2. Simulation Outputs: Return and Standard Deviation, by Percentile

simulation outputs

Source: Yahoo! FInance data, 1000 simulations using Yasai Excel add-in

The simulation outputs show results for the simple trend following approach (the Moving Average results) versus a buy-and-hold for randomly chosen start- and end-dates, in percentile groups. In other words, the “5th” columns show the results for the periods that deliver the lowest 5% results; conversely, the “95th” shows results at the top end. As we can see, the moving average system delivers better returns across all percentiles, with consistently much lower standard deviation.

The Market Compass models do not use trend-following measures on their own, and we have looked in other posts at the role of other factors such as macroeconomic conditions and valuations, but this example highlights that they can offer the potential of higher returns and lower risk when compared to a simple buy-and-hold. As we have seen, even in the case of a market that is tending downwards, such as the Japanese equity market over recent decade, simple trend-following approaches can add value and reduce risk.

The outputs of the Market Compass models are available as always at the usual place.

Have a good week!

A Well-Rounded Model

Market Compass is built around a set of models that bring together macroeconomic, valuation and trend-following inputs to give a perspective on the attractiveness of different asset classes around the world, from the perspective of a Canadian or US dollar investor. The core models are updated weekly to reflect market action and economic data, although some inputs are only available monthly (sometimes with a lag) and some (such as earnings and other valuation measures) are only available quarterly.

The blog has published the model outputs weekly, and as might be expected, there has been a lot of minor fluctuation week-to-week, as trend-following, momentum and volatility measures react to fluctuations in market prices. This raises a question about how to interpret the model outputs – should an investor who updates their models weekly respond to these variations? What is the threshold of significance for changes?

There are many reasonable ways to approach this question. One would be to take a statistical approach, and to try to determine which changes have some level of statistical significance attached to them. Another would be to impose some set of rules that define when a portfolio change will be made – for example, rebalancing to the model’s outputs on some set frequency (eg, monthly or quarterly), or making a change whenever the difference between the portfolio and the model allocations exceeds a certain number of percentage points. A taxable investor could overlay a tax-loss harvesting strategy, or delay changes to defer gains. Each of these approaches has its pros and cons.

This week’s post looks at a very simple approach – what happens if the model outputs are simply rounded to reduce the number of potential trades? We use a simple momentum- and volatility-based model and a subset of the Market Compass asset classes to assess how model performance is affected by a rounding of the outputs to the nearest 5%, 10% and 20%.

The short (and somewhat surprising) example is that rounding has very little impact on model outputs. In a nutshell, reducing the models’ sensitivity to small market fluctuations – and in fact even reducing precision of the “fine tuning” applied to things like position sizing – has relatively little impact. The major value seems to come from being “generally correct and not specifically wrong”.

We can see an illustration of this in the following chart, which shows the growth from a value of 100 of a simple momentum strategy. The portfolio uses 8 asset classes (US equities through SPY, international equities (EFA), emerging markets (EEM), US real estate (IYR), global real estate (RWX), high yield bonds (HYG) and commodities (DBC)). The model assigns a basic target exposure to each based on volatility, and then allocates a greater or less multiple of that base exposure (from 2x for the top asset class, to 0x for the bottom 4) based on ranking by 12 month momentum. If momentum goes negative for any of the top 4, the model shifts that allocation into short-term Treasuries (SHY).

The “rounding” effect is applied to the volatility-based position sizing, and to the final position sizing that applies the momentum-based factor. As we can see, model results vary relatively little with rounding:

Figure 1. Model Outputs with Different Rounding Factors

model results with rounding

Data source: Yahoo Finance

The observation period only goes back to 2008, due to the availability of historical data, but the results support the idea that rounding does not have a major impact on performance. If we apply a Monte Carlo approach and sample different start and end dates (ie, we run 1000 tests with different start and end dates), we can see that this result applies generally throughout the period and is not specific to the start- and end-dates shown in Figure 1:

Figure 2. Model Results for Random Start- and End-Dates

simulation outputs

Simulated using YASAI Excel Add-in, 1000 runs for each set of parameters

As we can see, there is not a major difference in performance between the different rounding intervals. There seems to be some tendency for 10% to deliver slightly higher performance with this particular model, and 20% seems to lower risk somewhat, but at this stage, it’s hard to know whether there are general principles at work in those observations, or whether they are specific to the idiosyncrasies of this model and testing period. In keeping with the overall notion that these results broadly support the “rounding doesn’t reduce performance” thesis, Market Compass will be rounding weekly results going forward, although at this point we will use 5% (the outputs of the separate monthly model have been rounded to the nearest 5% since inception). The model results are available as always in the Current Model Outputs section of the blog.

One final note: astute readers may notice a few changes to the “Representative ETFs” listed for the Canadian dollar models. BlackRock has introduced a Canadian family of “Core” iShares with significantly reduced fees, so they get some additional representation in our sample line-up.

Have a good week!

Chasing Yield with Momentum

I suspect that for most investors, dividends and momentum seem like opposites. An investor either likes dividends, and the supposedly plodding, cash-spewing companies that pay them, or they like the go-go of momentum stocks. Wanting both seems a bit counterintuitive.

If we look at the Canadian market, though, we can see that investors may be able to increase their total returns by using momentum to pick between a dividend-focused index and the broad market – in other words, we can see what happens if, instead of being dogmatic about dividends or momentum, an investor invests in high dividend stocks when they lead the broad market in total return, and buys the broad market when dividends lag. In short, then, we are looking at the potential of a “dividend momentum” strategy – or “yield chasing” using momentum.

The experience of Canadian markets suggests that such a strategy offers some promise. If we use the S&P/TSX Composite as our measure of the broad market, and the S&P/TSX Equity Income index as our dividend index, and switch between them based on which had the strongest 12-month total return at the end of the prior month, we get the following results:

Figure 1. Total Return (Starting Value = 100)

momentum and yield - growth from 100

Data source: BlackRock

If we look at the summary statistics for the various strategies, we can see that the dividend momentum strategy outperforms, with comparable or slightly lower risk:

Table 1. Summary Statistics for “DIvidend Momentum” vs. Buy and Hold Strategies

momentum and yield - table

All three of these strategies suffered the common fate of long-only approaches in 2007-2009 – in short, they fell. We could add a further refinement to the strategies, by adding a “safe haven” asset class, in this case short term bonds, and allowing the strategies to move into the safe asset when momentum is negative. This applies a core principle of the Market Compass philosophy – don’t hold an asset class with negative momentum. This gives the following results:

Figure 2. Total Return of Strategies with Positive Momentum Rule

momentum and yield w stb - growth

And the following summary statistics:

Table 2. Summary Statistics with Positive Momentum Rule

momentum and yield w stb - table

The Dividend Momentum strategy outperforms the others by over 1.5% per year with comparable volatility.

This is not a pure analysis of the role of dividends in returns, or their interaction with momentum at a pure factor level, but it does suggest that in practice, there may be excess return available to investors who switch between dividend-focused strategies and broader approaches. This makes intuitive sense, to the extent that momentum is a tool for capturing the inefficiencies created by herd behavior – any Canadian financial advisor could certainly attest to the rush for high dividend yields that has seized investors at various times.

In short, although many commentators distinguish between yield-oriented investors and momentum chasers, there may be advantages in combining both approaches.

The Market Compass models focus on broad market indices, and do not currently consider dividend-focused alternatives. However, with the growing availability of dividend-focused ETFs, this is an area that we may return to in future. In the meantime, the current outputs of the models are found at the usual place.

Have a good week!

Interest Rates: Low Rates (Compared to Fundamentals) Suggest Low Returns Ahead

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When are interest rates “low”? The Federal Reserve has pledged to keep rates low since it stepped in to stabilize markets during the 2008-9 financial crisis, and in part this has been achieved by keeping short-term interest rates, which the Fed can control more or less directly, at levels barely above zero. Under the rubric of “quantitative easing”, longer term interest rates have been targeted through massive and deliberate bond purchases. Are there any simple indicators that would suggest whether rates are actually lower than they would otherwise have been?

One way would be to compare 10 Year Treasury yields to the nominal rate of economic growth. Historically, although the relationship can be volatile, 10 Year yields have tended to move around the nominal growth rate, as we can see in the following figure:

Figure 1. 10-Year Treasury Rates and US Nominal GDP Growth

rates vs gdp+cpi

Data source: Federal Reserve FRED database

As we can see, Treasury yields have tended to be less volatile than nominal growth. If we plot the gap between growth and yields, we get the following:

Figure 2. 10 Year Treasury Yields Minus Nominal GDP Growth

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While there are some large spikes, typically during periods of financial crisis, for the most part the difference has remained within +/- 5%. The average difference over this period is actually very close to zero, suggesting that while yields and nominal growth may move apart over the short term, they tend to track fairly closely over long periods.

We can see the tendency of this relationship to assert itself if we look at what tends to happen to 10 Year yields in the quarter following a period in which the difference is large. If the 10 Year yield is below nominal growth, it tends to rise, and vice versa, with some skew in line with the overall downward tendency of yields over the entire period:

Figure 3. 3 Month Change in 10 Year Yield Given Starting Difference Between Yield and Nominal Growth

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What has this tended to mean for fixed income returns? Not surprisingly, it means that returns have tended to be strongest when yields have been high relative to macroeconomic fundamentals. When yields outstrip growth, they tend to decline, which generates outperformance. Conversely, if yields are low relative to fundamentals, returns are muted by yields’ rise:

Figure 4. 3-Month Forward Return for Barclays Aggregate Given Starting Difference Between Yield and Nominal Growth

agg returns

Given the limited returns available in today’s interest rate environment, and the current gap between 10 Year yields (at roughly 2.60-2.70%) and nominal economic growth (about 3.0-3.5%), it is hard to be optimistic about fixed income returns in the coming quarters.

The Market Compass models do not currently use interest rates directly in setting their market stance, and in fact do not allocate to traditional fixed income. The models target a range of volatility similar to that of traditional balanced portfolios, using a broader mix of asset classes and an active tactical asset allocation approach. For those who are interested, the models’ outputs are available here.

Have a good week!

Revisiting New Highs

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We have looked before at what happens when stock market indices reach new long-term highs, as they have once again in the past week, with the S&P 500 reaching an all-time record close, and the NASDAQ reclaiming levels not seen since the heights of the dot-com bubble. We have seen generally that the average expected returns for the weeks and months following new long-term highs are slightly lower, but also somewhat less risky, than the average for all periods.

This generalization raises the question of whether this is some unique feature of new highs, or whether it is simply a reflection of the fact that, by definition, new highs occur when markets are in an uptrend. As we have seen on other occasions, uptrend periods (on a fairly broad range of definitions) tend to be less volatile than downtrend periods.

We can answer this by comparing returns from new high days to uptrending periods only. If we look at averages, we see that expectations are slightly reduced following an all-time high on the S&P 500:

Figure 1. 4-Week Forward Returns for S&P 500 Since 1950 For Uptrend / All Time High Days

avg uptrend and ath rtns

Data Source: Yahoo! Finance

If we look at the extremes, up and down, we can see that the periods following all-time highs are somewhat less extreme in both directions:

Figure 2. Maximum and Minimum 4-Week Forward Returns For Uptrend / All Time High Days

max-min uptrend and ath rtns

If we look at the distributions of returns more broadly, we can see what is driving these results: 4-week returns following long-term highs are less likely to be positive (54% vs 64%) and the extremes at both the high end (the “tails”) are more muted.

Figure 3. Distribution of 4-Week Forward S&P 500 Returns For Uptrend Days; No All Time High (Blue) and All Time Highs (Red)

4W Fwd - New Highs vs All Uptrend Periods

As we have observed before, historically, it has been mainly when all time highs have been accompanied by rising interest rates that returns are negative on average for the weeks and months immediately following an all-time high. We can see this in the data below:

Figure 4. 4-Week Forward S&P 500 Returns Following All Time Highs By 30-Year Interest Rate Change Scenario

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Rates rose last year, but seem to have settled into a range. However, good economic news is often accompanied by rising rates, so if economic recovery continues or accelerates, we might get a chance to see whether this effect will play out again.

The Market Compass weekly and monthly model updates can be found as always in the Current Model Outputs section of the blog.

Have a good week!

Bonds: What You See Is What You Get

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What returns should investors expect from bonds? The answer, generally, is that what you see is what you get, if you look at yield to maturity and look forward over the average maturity of the portfolio. This is clear when buying an individual bond – most of the return is accounted for in the stated yield to maturity at the time that the bond is purchased. There are still risks – default, embedded options, and the risk that it will not be possible to reinvest interest at the same rate along the way. For the most part, though, an investor has a pretty good idea of the nominal return that he or she will get over the life of the bond. Investors sometimes wonder whether this is true of bond funds as well – after all, in the case of funds, there is a continuous process of buying new bonds to maintain target maturity, and bonds may be sold as well as investors redeem or as a result of overbalancing or active security decisions.

We can see that yield to maturity still provides a reasonable estimate of forward return over the fund’s time to maturity. Using yields from the US Federal Reserve’s FRED database, and fund returns for related Vanguard bond funds from Yahoo! Finance, we can see that the forward-looking return for the funds follows movements in yields quite closely. In the case of short-term yields, where the portfolio goes through several complete maturity cycles over the period for which data are available, we can see this quite clearly:

1. Short Term US Treasury Yields and Subsequent Investor Returns

ST Treas

Data Sources: US Federal Reserve FRED Database, Yahoo! Finance

In the example above, we compare a yield for the 3-year spot on the yield curve to the returns of the Vanguard Short Term Treasury mutual fund. As we can see, the returns over the subsequent 3 years tend to follow the level of yields at the time of observation. The match is not perfect, in part because the average term to maturity of the fund moves around a bit over time, but the chart illustrates that investors’ expectation should be to get something close to the prevailing level of yields.

If we look at longer maturities, the relationship gets a bit looser, in part because bond prices swing further in response to interest rate movements and also because a smaller portion of the portfolio will mature over any given period (meaning that prices are not forced back to par as often). Nonetheless, using Vanguard’s Intermediate Treasury mutual fund, we can see that directionally, returns have followed yields quite closely:

2. Intermediate Term US Treasury Yields and Investor Returns

Int Treas

The question arises of what happens if we introduce credit into the equation – does the widening and narrowing of spreads have an impact? If we look at the most extreme example of this, namely high yield bonds, we can see that the answer is yes – but that credit-driven deviations from the yield-predicted return tend to revert back over fairly short periods of time:

3. High Yield Yields and Investor Returns

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For example, although returns went briefly negative due to the 2008-2009 crisis for high yield bonds purchased around 2004-2005, bonds bought shortly before and after this window delivered returns around the levels predicted by yields at the time. The most extreme case of yields being accurate in foretelling future returns occurred in 2009-2013, when the 20% level of yields was rewarded with subsequent annualized returns of 20%. High yield investors should not count on getting the full amount of return that yields seem to promise every time as defaults could eat into realized returns – but in this case, buying high yield proved to be a good bet for investors.

Yields today are low – and therefore returns over the time to maturity of bond portfolios are likely to be similarly low. Unattractive absolute returns offered by bonds are part of the reason that Market Compass was designed with a diversified, yield-oriented mix of asset classes – the Market Compass portfolio is designed to offer risk characteristics similar to those of a traditional stock-bond portfolio without using low-returning traditional fixed income. For anyone interested in the Market Compass models, the latest outputs can be found here.

Have a good week!