This blog post is available as a PDF here.
Summary
- Recently a paper was published by AQR where the authors advocate for an integrated approach to multi-factor portfolios, preferring securities that exhibit strong characteristics across all desired factors instead of a mixed approach, where securities are selected based upon extreme exposure to a single characteristic.
- We believe the integrated approach fails to acknowledge the impact of the varying lengths over which different factors mature, ultimately leading to a portfolio more heavily influenced by higher turnover factors.
The Importance of Factor Maturity
Cliff Asness, founder of AQR, recently published a paper titled My Factor Philippic. This paper was written in response to the recently popularized article How Can “Smart Beta” Go Horribly Wrong? which was co-authored by Robert Arnott, co-founder of Research Affiliates.
Arnott argues that many popular factors are currently historically overvalued and, furthermore, that the historical excess return offered by some recently popularized factors can be entirely explained by rising valuation trends in the last 30 years.
Caveat emptor, warns Arnott: valuations always matter.
Much to our delight (after all, who doesn’t like to see two titans of industry go at it?), Asness disagrees.
One of the primary arguments laid out by Asness is that valuation is a meaningless predictor for factors with high turnover.
The intuition behind this argument is simple: while valuations may be a decent predictor of forward annualized returns for broad markets over the next 5-to-10 years, the approach only works because the basket of securities stays mostly constant. For example, valuations for U.S. equities may be a good predictor because we expect the vast majority of the basket of U.S. equities to stay constant over the next 5-to-10 years.
The same is not true for many factors. For example, let’s consider a high turnover factor like momentum.
Valuations of a momentum basket today are a poor predictor of annualized returns of a momentum strategy over the next 5-to-10 years because the basket of securities held could be 100% different three months from now.
Unless the same securities are held in the basket, valuation headwinds or tailwinds will not necessarily be realized.
For the same reason, valuation is also poor as an explanatory variable of factor returns. Asness argues that Arnott’s warning of valuation being the secret driver of factor returns is unwarranted in high turnover factors.
Multi-Factor: Mix or Integrate?
On July 2nd, Fitzgibbons, Friedman, Pomorski, and Serban (FFPS) – again from AQR – published a paper titled Long-Only Style Investing: Don’t Just Mix, Integrate.
The paper attempts to conclude the current debate about the best way to build multi-factor portfolios. The first approach is to mix, where a portfolio is built by combining stand-alone factor portfolios. The second approach is to integrate, where a portfolio is built by selecting securities that have simultaneously strong exposure to multiple factors at once.
A figure from the paper does a good job of illustrating the difference. Below, a hypothetical set of stocks is plotted based upon their current valuation and momentum characteristics.
In the top left, a portfolio of deep value stocks is selected. In the top right, the mix approach is demonstrated, where the deepest value and the highest momentum stocks are selected.
In the bottom left, the integrated approach is demonstrated, where the securities simultaneously exhibiting strong valuation and momentum characteristics are selected.
Finally, in the bottom right we can see how these two approaches differ: with yellow securities being those only found in the mix portfolio and blue securities being found only in the integrated portfolio.
It is worth noting that the ETF industry has yet to make up its mind on the right approach.
GlobalX and Goldman Sachs prefer the mix approach in their ETFs (SCIU / GSLC) while JPMorgan and iShares prefer the integrate approach (JPUS / LRGF).
The argument made by those taking the integrated approach is that they are looking for securities with well-rounded exposures rather than those with extreme singular exposures. Integrators argue that this approach helps them avoid holding securities that might cancel each other out. If we look back towards the mix example above (top right), we can see that many securities selected due to strength in one factor are actually quite poor in the other.
Integrators claim that this inefficiency can create a drag in the mix portfolio. Why hold something with strong momentum if it has a very poor valuation score that is only going to offset it?
We find it somewhat ironic that FFPS and Asness both publish for AQR, because we would argue that Asness’s argument points out the fundamental flaw in the theory outlined by integrators. Namely: the horizons over which the premia mature differ.
Therefore, a strong positive loading in a factor like momentum is not necessarily offset by a strong negative loading in a factor like value. Furthermore, by integrating we run the risk of the highest turnover factor actually dominating the integrated selection process.
Data
In the rest of this commentary, we will be using industry data from the Kenneth French data library. For momentum scores, we calculate 12 one-month total return and calculate cross-sector z-scores[1]. For valuation scores, we calculate a normalized 5-year dividend yield score and then calculate cross-sector z-scores.[2]
Do Factor Premia Actually Mature at Different Time Periods?
In his paper, Asness referenced the turnover of a factor portfolio as an important variable. We prefer to think of high turnover factors as factors whose premium matures more quickly.
For example, if we buy a stock because it has high relative momentum, our expectation is that we will likely hold it for longer than a day, but likely much shorter than a year. Therefore, a strategy built off relative momentum will likely have high turnover because the premium matures quickly.
On the other hand, if we buy a value stock, our expectation is that we will have to hold it for up to several years for valuations to adequately reverse. This means that the value premium takes longer to mature – and the strategy will likely have lower turnover.
We can see this difference in action by looking at how valuation and momentum scores change over time.
We see similar pictures for other industries. Yet, looks can be deceiving and the human brain is excellent at finding patterns where there are none (especially when we want to see those patterns). Can we actually quantify this difference?
One way is to try to build a model that incorporates both the randomness of movement and how fast these scores mean-revert. Fitting our data to this model would tell us about how quickly each premium matures.
One such model is called an Ornstein-Uhlenbeck process (“OU process”). An OU process follows the following stochastic differential equation:
To translate this into English using an example: the change in value z-score from one period to the next can be estimated as a “magnetism” back to fair value plus some randomness. In the equation, theta tells us how strong this magnetism is, mu tells us what fair value is, and sigma tells us how much influence the randomness has.
For our momentum and valuation z-scores, we would expect mu to be near-zero, as over the long-run we would not expect a given sector to exhibit significantly more or less relative momentum or relative cheapness/richness than peer sectors.
Given that we also believe that the momentum premium is realized over a shorter horizon, we would also expect that theta – the strength of the magnetism, also called the speed of mean reversion – will be higher. Since that strength of magnetism is higher, we will also need sigma – the influence of randomness – to be larger to overcome it.
So how to the numbers play out?[3]
For the momentum z-scores:
| Theta | Mu | Sigma | |
| NoDur | 0.97 | 0.02 | 1.00 |
| Durbl | 1.00 | 0.03 | 1.63 |
| Manuf | 1.22 | -0.03 | 0.96 |
| Enrgy | 0.98 | 0.06 | 1.69 |
| HiTec | 1.04 | 0.03 | 1.49 |
| Telcm | 1.15 | -0.07 | 1.52 |
| Shops | 1.22 | 0.03 | 1.24 |
| Hlth | 0.84 | 0.11 | 1.39 |
| Utils | 1.48 | -0.09 | 1.61 |
| Other | 1.18 | -0.09 | 1.13 |
| Average | 1.10 | 0.00 | 1.36 |
For the valuation z-scores:
| Theta | Mu | Sigma | |
| NoDur | 0.11 | -0.20 | 0.34 |
| Durbl | 0.08 | 0.58 | 0.49 |
| Manuf | 0.13 | 0.01 | 0.37 |
| Enrgy | 0.07 | 0.19 | 0.40 |
| HiTec | 0.09 | 0.23 | 0.33 |
| Telcm | 0.07 | 0.03 | 0.38 |
| Shops | 0.11 | -0.15 | 0.36 |
| Hlth | 0.05 | -0.47 | 0.36 |
| Utils | 0.06 | -0.35 | 0.40 |
| Other | 0.11 | -0.01 | 0.37 |
| Average | 0.08 | -0.01 | 0.38 |
We can see results that echo our expectations: the speed of mean-reversion is significantly lower for value than momentum. In fact, the average theta is less than 1/10th.
The math behind an OU-process also lets us calculate the half-life of the mean-reversion, allowing us to translate the speed of mean reversion to a more interpretable measure: time.
The half-life for momentum z-scores is 0.27 years, or about 3.28 months. The half-life for valuation z-scores is 3.76 years, or about 45 months. These values more or less line up with our intuition about turnover in momentum versus value portfolios: we expect to hold momentum stocks for a few months but value stocks for a few years.
Another way to analyze this data is by looking at how long the relative ranking of a given industry group stays consistent in its valuation or momentum metric. Based upon our data, we find that valuation ranks stayed constant for an average of approximately 120 trading days, while the average length of time an industry group held a consistent momentum rank was only just over 50 days.
Maturity’s Influence on Integration
The scatter plots drawn by FFPS are deceiving because they only show a single point in time. What they fail to show is how the locations of the dots change over time.
With the expectation that momentum scores will change more rapidly than valuation scores, we would expect to see points move more rapidly up and down along the Y-axis than we would see them move left and right along the X-axis.
Given this, our hypothesis is that changes in our inclusion score are driven more significantly by changes in our momentum score.
To explore this, we create an integration score, which is simply the sum of the valuation and momentum z-scores. Those industries in the top 30% of integration scores at any time are held by the integrated portfolio.
To distill the overall impact of momentum score changes versus valuation score changes, we need to examine the absolute value of these changes. For example, if the momentum score change was +0.5 and the valuation score change was -0.5, the overall integration score change is 0. Both momentum and value, in this case, contributed equally (or, contributed 50% each), to the overall score change.
So a simple formula for measuring the relative percentage contribution to score change is:
If value and momentum score changes contributed equally, we would expect the average contribution to equal 50%.
The average contribution based upon our analysis is 72.18% (with a standard error of 0.24%). The interquartile range is 59.02% to 91.19% and the median value is 79.47%.
Put simply: momentum score changes are a much more significant contributor to integration score changes than valuation score changes are.
We find that this effect is increased when we examine only periods when an industry is added or deleted from the integrated portfolio. In these periods, the average contribution climbs to 78.46% (with a standard error of 0.69%), with an interquartile range of 70.28% to 94.46% and a median value of 85.57%.
Changes in the momentum score contribute much more significantly than value score changes.
Integration: More Screen than Tilt?
The objective of the integrated portfolio approach is to find securities with the best blend of characteristics.
In reality, because one set of characteristics changes much more slowly, certain securities can be sidelined for prolonged periods of time.
Let’s consider a simplified example. Every year, the 10 industry groups are assigned a random, but unique, value score between 1 and 10.
Similarly, every month, the 10 industry groups are assigned a random, but unique, momentum score between 1 and 10.
The integration score for each industry group is calculated as the sum of these two scores. Each month, the top 3 scoring industry groups are held in the integrated portfolio.
What is the probability of an industry group being in the integrated portfolio, in any given month, if it has a value score of 1? What about 2? What about 10?
Numerical simulation gives us the following probabilities:
So if these are the probabilities of an industry group being selected in a given month given a certain value score, what is the probability of an industry group not being selected into the integrated portfolio at all during the year it has a given value score?
If an industry group starts the year with a value score of 1, there is 99.1% probability it will never being selected into the integrated portfolio all year.
Conclusion
While we believe this topic deserves a significantly deeper dive (one which we plan to perform over the coming months), we believe the cursory analysis highlights a very important point: an integrated approach runs a significant risk of being more heavily influenced by higher turnover factors. While FFPS believe there are first order benefits to the integrated approach, we think the jury is still out and that those first order effects may actually be simply due to an increased exposure to higher turnover factors. Until more a more substantial understanding of the integrated approach is established, we continue to believe that a mixed approach is prudent. After all, if we don’t understand how a portfolio is built and the source of the returns it generates, how can we expect to manage risk?
Navigating Municipal Bonds With Factors
By Justin Sibears
On May 15, 2017
In Carry, Defensive, Momentum, Popular, Risk & Style Premia, Value, Weekly Commentary
This post is available as a PDF download here.
Summary
Recently, we’ve been working on building a simple, ETF-based municipal bond strategy. Probably to the surprise of nobody who regularly reads our research, we are coming at the problem from a systematic, multi-factor perspective.
For this exercise, our universe consists of six municipal bond indices:
These indices, all of which are tracked by VanEck Vectors ETFs, offer access to municipal bonds across a range of durations and credit qualities.
Source: VanEck
Before we get started, why are we writing another multi-factor piece after addressing factors in the context of a multi-asset universe just two weeks ago?
The simple answer is that we find the topic to be that pressing for today’s investors. In a world of depressed expected returns and elevated correlations, we believe that factor-based strategies have a role as both return generators and risk mitigators.
Our confidence in what we view as the premier factors (value, momentum, low volatility, carry, and trend) stems largely from their robustness in out-of-sample tests across asset classes, geographies, and timeframes. The results in this case study not only suggest that a factor-based approach is feasible in muni investing, but also in our opinion strengthens the case for factor investing in other contexts (e.g. equities, taxable fixed income, commodities, currencies, etc.).
Constructing Long/Short Factor Portfolios
For the municipal bond portfolio, we consider four factors:
As a first step, we construct long/short single factor portfolios. The weight on index i at time t in long/short factor portfolio f is equal to:
In this formula, c is a scaling coefficient, S is index i’s time t score on factor f, and N is the number of indices in the universe at time t.
We measure each factor with the following metrics:
For the value, momentum, and carry factors, the scaling coefficient is set so that the portfolio is dollar neutral (i.e. we are long and short the same dollar amount of securities). For the low volatility factor, the scaling coefficient is set so that the volatilities of the long and short portfolios are approximately equal. This is necessary since a dollar neutral construction would be perpetually short “beta” to the overall municipal bond market.
Data Source: Blooomberg. Calculations by Newfound Research. All returns are hypothetical and backtested. Returns reflect the reinvestment of all distributions and are gross of all fees (including any management fees and transaction costs). The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results.
All four factors are profitable over the period from June 1998 to April 2017. The value factor is the top performer both from an absolute return and risk-adjusted return perspective.
Data Source: Blooomberg. Calculations by Newfound Research. All returns are hypothetical and backtested. Returns reflect the reinvestment of all distributions and are gross of all fees (including any management fees and transaction costs). The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. The benchmark is an equal-weight portfolio of all indices in the universe adjusted for the indices that are calibrated and included in each long/short factor index based on data availability.
There is significant variation in performance over time. All four factors have years where they are the best performing factor and years where they are the worst performing factor. The average annual spread between the best performing factor and the worst performing factor is 11.3%.
Data Source: Blooomberg. Calculations by Newfound Research. All returns are hypothetical and backtested. Returns reflect the reinvestment of all distributions and are gross of all fees (including any management fees and transaction costs). The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. The benchmark is an equal-weight portfolio of all indices in the universe adjusted for the indices that are calibrated and included in each long/short factor index based on data availability. 1998 is a partial year beginning in June 1998 and 2017 is a partial year ending in April 2017.
The individual long/short factor portfolios are diversified to both each other (average pairwise correlation of -0.11) and to the broad municipal bond market.
Data Source: Blooomberg. Calculations by Newfound Research. All returns are hypothetical and backtested. Returns reflect the reinvestment of all distributions and are gross of all fees (including any management fees and transaction costs). The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results.
Data Source: Blooomberg. Calculations by Newfound Research. All returns are hypothetical and backtested. Returns reflect the reinvestment of all distributions and are gross of all fees (including any management fees and transaction costs). The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results.
Moving From Single Factor to Multi-Factor Portfolios
The diversified nature of the long/short return streams makes a multi-factor approach hard to beat in terms of risk-adjusted returns. This is another example of the type of strategy diversification that we have long lobbied for.
As evidence of these benefits, we have built two versions of a portfolio combining the low volatility, value, carry, and momentum factors. The first version targets an equal dollar allocation to each factor. The second version uses a naïve risk parity approach to target an approximately equal risk contribution from each factor.
Both approaches outperform all four individual factors on a risk-adjusted basis, delivering Sharpe Ratios of 1.19 and 1.23, respectively, compared to 0.96 for the top single factor (value).
Data Source: Bloomberg. Calculations by Newfound Research. All returns are hypothetical and backtested. Returns reflect the reinvestment of all distributions and are gross of all fees (including any management fees and transaction costs). The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. The benchmark is an equal-weight portfolio of all indices in the universe adjusted for the indices that are calibrated and included in each long/short factor index based on data availability. The factor risk parity construction uses a simple inverse volatility methodology. Volatility estimates are shrunk in the early periods when less data is available.
To stress this point, diversification is so plentiful across the factors that even the simplest portfolio construction methodologies outperforms an investor who was able to identify the best performing factor with perfect foresight. For additional context, we constructed a “Look Ahead Mean-Variance Optimization (“MVO”) Portfolio” by calculating the Sharpe optimal weights using actual realized returns, volatilities, and correlations. The Look Ahead MVO Portfolio has a Sharpe Ratio of 1.43, not too far ahead of our two multi-factor portfolios. The approximate weights in the Look Ahead MVO Portfolio are 49% to Low Volatility, 25% to Value, 15% to Carry, and 10% to Momentum. While the higher Sharpe Ratio factors (Low Volatility and Value) do get larger allocations, Momentum and Carry are still well represented due to their diversification benefits.
Data Source: Bloomberg. Calculations by Newfound Research. All returns are hypothetical and backtested. Returns reflect the reinvestment of all distributions and are gross of all fees (including any management fees and transaction costs). The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. The benchmark is an equal-weight portfolio of all indices in the universe adjusted for the indices that are calibrated and included in each long/short factor index based on data availability. The factor risk parity construction uses a simple inverse volatility methodology. Volatility estimates are shrunk in the early periods when less data is available.
From a risk perspective, both multi-factor portfolios have lower volatility than any of the individual factors and a maximum drawdown that is within 1% of the individual factor with the least amount of historical downside risk. It’s also worth pointing out that the risk parity construction leads to a return stream that is very close to normally distributed (skew of 0.1 and kurtosis of 3.0).
Data Source: Blooomberg. Calculations by Newfound Research. All returns are hypothetical and backtested. Returns reflect the reinvestment of all distributions and are gross of all fees (including any management fees and transaction costs). The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. The benchmark is an equal-weight portfolio of all indices in the universe adjusted for the indices that are calibrated and included in each long/short factor index based on data availability. The factor risk parity construction uses a simple inverse volatility methodology. Volatility estimates are shrunk in the early periods when less data is available.
In the graph on the next page, we present another lens through which we can view the tremendous amount of diversification that can be harvested between factors. Here we plot how the allocation to a specific factor, using MVO, will change as we vary that factor’s Sharpe Ratio. We perform this analysis for each factor individually, holding all other parameters fixed at their historical levels.
As an example, to estimate the allocation to the Low Volatility factor at a Sharpe Ratio of 0.1, we:
Data Source: Blooomberg. Calculations by Newfound Research. All returns are hypothetical and backtested. Returns reflect the reinvestment of all distributions and are gross of all fees (including any management fees and transaction costs). The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. The benchmark is an equal-weight portfolio of all indices in the universe adjusted for the indices that are calibrated and included in each long/short factor index based on data availability. The factor risk parity construction uses a simple inverse volatility methodology. Volatility estimates are shrunk in the early periods when less data is available.
As expected, Sharpe Ratios and allocation sizes are positively correlated. Higher Sharpe Ratios lead to higher allocations.
That being said, three of the factors (Low Volatility, Carry, and Momentum) would receive allocations even if their Sharpe Ratios were slightly negative.
The allocations to carry and momentum are particularly insensitive to Sharpe Ratio level. Momentum would receive an allocation of 4% with a 0.00 Sharpe, 9% with a 0.25 Sharpe, 13% with a 0.50 Sharpe, 17% with a 0.75 Sharpe, and 20% with a 1.00 Sharpe. For the same Sharpe Ratios, the allocations to Carry would be 10%, 15%, 19%, 22%, and 24%, respectively.
Holding these factors provides a strong ballast within the multi-factor portfolio.
Moving From Long/Short to Long Only
Most investors have neither the space in their portfolio for a long/short muni strategy nor sufficient access to enough affordable leverage to get the strategy to an attractive level of volatility (and hence return). A more realistic approach would be to layer our factor bets on top of a long only strategic allocation to muni bonds.
In a perfect world, we could slap one of our multi-factor long/short portfolios right on top of a strategic municipal bond portfolio. The results of this approach (labeled “Benchmark + Equal Weight Factor Long/Short” in the graphics below) are impressive (Sharpe Ratio of 1.17 vs. 0.93 for the strategic benchmark and return to maximum drawdown of 0.72 vs. 0.46 for the strategic benchmark). Unfortunately, this approach still requires just a bit of shorting. The size of the total short ranges from 0% to 19% with an average of 5%.
We can create a true long only portfolio (“Long Only Factor”) by removing all shorts and normalizing so that our weights sum to one. Doing so modestly reduces risk, return, and risk-adjusted return, but still leads to outperformance vs. the benchmark.
Data Source: Blooomberg. Calculations by Newfound Research. All returns are hypothetical and backtested. Returns reflect the reinvestment of all distributions and are gross of all fees (including any management fees and transaction costs). The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. The benchmark is an equal-weight portfolio of all indices in the universe adjusted for the indices that are calibrated and included in each long/short factor index based on data availability.
Data Source: Blooomberg. Calculations by Newfound Research. All returns are hypothetical and backtested. Returns reflect the reinvestment of all distributions and are gross of all fees (including any management fees and transaction costs). The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. The benchmark is an equal-weight portfolio of all indices in the universe adjusted for the indices that are calibrated and included in each long/short factor index based on data availability.
Below we plot both the historical and current allocations for the long only factor portfolio. Currently, the portfolio would have approximately 25% in each short-term investment grade, pre-refunded, and short-term high yield with the remaining 25% split roughly 80/20 between high yield and intermediate-term investment grade. There is currently no allocation to long-term investment grade.
Data Source: Blooomberg. Calculations by Newfound Research. All allocations are backtested and hypothetical. The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results.
Data Source: Blooomberg. Calculations by Newfound Research. All allocations are backtested and hypothetical. The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results.
A few interesting observations relating to the long only portfolio and muni factor investing in general:
Data source: Calculations by Newfound Research. All allocations are backtested and hypothetical. The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. Weighted average durations are estimated using current constituent durations.
Data source: Calculations by Newfound Research. All allocations are backtested and hypothetical. The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. Weighted average credit scores are estimated using current constituent credit scores. Credit scores use S&P’s methodology to aggregate scores based on the distribution of credit scores of individual bonds.
Currently, the portfolio is near an all-time low in terms of duration and is slightly titled towards lower credit quality sectors relative to the benchmark. Historically, the factor portfolio was most often overweight both duration and credit, having this positioning in 53.7% of the months in the sample. The second and third most common tilts were underweight duration / underweight credit (22.0% of sample months) and underweight duration / overweight credit (21.6% of sample months). The portfolio was overweight duration / underweight credit in only 2.6% of sample months.
Data source: Calculations by Newfound Research. Analysis over the period from June 1998 to April 2017.
The average beta to the low volatility factor, ignoring non-statistically significant values, is -0.23. This is most likely a function of category since the category consists of funds with both investment grade credit quality and durations ranging between 4.5 and 7.0 years. In contrast, our low volatility factor on average has short exposure to the intermediate and long-term investment grade sectors.
Data source: Calculations by Newfound Research. Analysis over the period from June 1998 to April 2017.
Only 14 of the 33 funds in the universe have statistically significant exposure to the value factor with an average beta of -0.03.
Data source: Calculations by Newfound Research. Analysis over the period from June 1998 to April 2017.
The average beta to the carry factor, ignoring non-statistically significant values, is -0.23. As described above with respect to low volatility, this is most likely function of category as our carry factor favors the long-term investment grade and high yield sectors.
Data source: Calculations by Newfound Research. Analysis over the period from June 1998 to April 2017.
Only 9 of the 33 funds in the universe have statistically significant exposure to the momentum factor with an average beta of 0.02.
Conclusion
Multi-factor investing has generated significant press in the equity space due to the (poorly named) “smart beta” movement. The popular factors in the equity space have historically performed well both within other asset classes (rates, commodities, currencies, etc.) and across asset classes. The municipal bond market is no different. A simple, systematic multi-factor process has the potential to improve risk-adjusted performance relative to static benchmarks. The portfolio can be implemented with liquid, low cost ETFs.
Moving beyond active strategies, factors can also be valuable tools when setting strategic sector allocations within a municipal bond sleeve and when evaluating and blending municipal bond managers.
Perhaps more importantly, the out-of-sample evidence for the premier factors (momentum, value, low volatility, carry, and trend) across asset classes, geographies, and timeframes continues to mount. In our view, this evidence can be crucial in getting investors comfortable to introducing systematic active premia into their portfolios as both return generators and risk mitigators.
[1] Computed using yield-to-worst. Inflation estimates are based on 1-year and 10-year survey-based expected inflation. We average the value score over the last 2.5 years, allowing the portfolio to realize a greater degree of valuation mean reversion before closing out a position.
[2] We use a rolling 5-year (60-month) window to calculate standard deviation. We require at least 3 years of data for an index to be included in the low volatility portfolio. The standard deviation is multiplied by -1 so that higher values are better across all four factor scores.