This post is available as a PDF download here.
Summary
- Portfolio risk is often measured as the variance of returns over time. Another form of risk is the variance of terminal wealth that can arise from small variations in strategy inputs or asset returns.
- Strategies or portfolios that are more sensitive to small changes in inputs are inherently “fragile.”
- Fragile strategy design makes it difficult to rely upon backtests or historical results in setting forward expectations.
- We explore how diversification across the “what,” “how,” and “when,” axes of portfolio construction can help reduce strategy fragility.
Introduction
At Newfound, we spend a lot less time trying to figure out how to be more right than we spend trying to figure out how to be less wrong. One area of particular interest for us is the idea of unintended bets: the exposures in a portfolio we may not even be aware of. And if we knew we had the exposure, we might not even want it.
For example, consider a portfolio that invests in either broad U.S., broad international, or broad emerging market equities based upon valuations. A significant tilt towards non-U.S. assets may be a valuation-driven decision, but for U.S. investors it creates significant exposure to fluctuations in the U.S. dollar versus foreign currencies.
Of course, exposures are not limited only to assets. Exposures may be broader macro-economic, stylistic, thematic, geographic, or even political factors.
These unintended bets can go far beyond explicit and implicit exposures. In our example, the choice of how to measure value may lead to meaningfully different portfolios, despite the same overarching thesis. For example, a naïve CAPE ratio versus adjusting for differences in relative sector composition dramatically alters the view of whether international equities are significantly cheaper than U.S. equities. These potential differences capture what we like to call “model specification risk.”
Finally, we can be subject to unintended bets based upon when the portfolio is re-evaluated and reconstituted. Evaluating valuations in January, for example, may lead to a different decision versus evaluating them in July.
How can we avoid these unintended bets? At Newfound, we believe that the answer falls back to diversification: not only in the traditional sense of what we invest in, but also across how we make decisions and when we make them.
When left uncontrolled, unintended bets can make a strategy incredibly fragile.
What, precisely, does it mean for a strategy to be fragile? A strategy is fragile when small variations of strategy inputs – be it asset returns or other measures – lead to meaningful dispersion in realized results.
Now we want to distinguish between volatility and fragility. Volatility is the dispersion of strategy returns across time, while fragility is the dispersion in end-of-period wealth across variations of the strategy.
As an example, a portfolio that invests only in the S&P 500 is very volatile but not particularly fragile. Given the last ten years of returns for the S&P 500, slight variations in annual returns would not lead to significant dispersion in end-of-period wealth. On the other hand, a strategy that flips a coin every December and invests for the next year in the S&P 500 when it lands on heads or short-term U.S. Treasuries when it lands on tails would have lower expected volatility than the S&P 500 but would be much more fragile. We need simply consider a few scenarios (e.g. all heads or all tails) to understand the potential dispersion such a strategy is subject to.
In the remainder of this commentary, we will demonstrate how diversification across the what, how, and when axes can reduce strategy fragility.
The Experiment Setup
Since a large degree of our focus at Newfound is on managing trend equity mandates, we will explore fragility through the lens of the style of measuring trends. For those unfamiliar with the approach, trend equity strategies aim to capture a significant portion of equity market growth while avoiding substantial and prolonged drawdowns through the application of trend following. A naïve implementation of such an idea would be to invest in the S&P 500 when its prior 12-month return has been positive and invest in short-term U.S. Treasuries otherwise.
To learn something about the fragility of a strategy, we are going to have to inject some randomness. After all, no amount of history will tell us about the fragility of a teacup that has spent its entire life sitting on a shelf; we will need to see it fall on the floor to actually learn something.
As with our recent commentary When Simplicity Met Fragility, we will inject randomness by adding white noise to asset returns. Specifically, we will add to daily returns a draw from a random normal distribution with mean 0% and standard deviation 0.025%. Using this slightly altered history, we will then run our investment strategy.
By performing this process a large number of times (10,000 in this commentary), we can explore how the outcome of the strategy is impacted by these slight variations in return history. The greater the dispersion in results, the more fragile the strategy is.
To demonstrate how diversification across the three different axes can affect fragility, we will start with a naïve trend equity strategy – investing in broad U.S. equities using a single trend model that is rebalanced on a monthly basis – and vary the three components in isolation.
The What
The “what” axis simply asks, “what are we invested in?”
How can our choice of “what” affect fragility? Consider a slight variation to our coin-flip strategy from before. Instead of flipping a single coin, we will now flip two coins. The first coin determines whether we invest 50% of the portfolio in either the S&P 500 or short-term U.S. Treasuries, while the second coin determines whether we invest the other 50% of the portfolio in either the Russell 1000 or short-term U.S. Treasuries.
In our single coin example, each year we expected to invest in the S&P 500 50% of the time and in short-term U.S. Treasuries 50% of the time. With two coins, we now expect to be fully invested 25% of the time, partially invested 50% of the time, and divested 25% of the time.
Let’s take this notion to further limits. Consider now flipping 100 coins where each determines the allocation decision for 1% of our portfolio, where heads leads to an investment in a large-cap U.S. equity portfolio and tails means invest in short-term U.S. Treasuries. Now being fully invested or divested is an infinitesimally small probability event; in fact, for a given year there is a 95% chance that your allocation to equities falls between 40-60%.
Even though we’ve applied the exact same process to each investment, diversifying across more investments has dramatically reduced the fragility of our coin-flipping strategy.
Now let’s translate this from the theoretical to the practical. We will begin with a simple trend following strategy that invests in the underlying asset when prior 12-1 month returns have been positive or invests in the risk-free rate, re-evaluating the trend at the end of each month.
To explore the impact of diversifying our what, we will implement this strategy five different ways:
- A single in-or-out decision on broad U.S. equities.
- Applied across 5 equally-weighted U.S. equity industry groups.
- Applied across 12 equally-weighted U.S. equity industry groups.
- Applied across 30 equally-weighted U.S. equity industry groups.
- Applied across 48 equally-weighted U.S. equity industry groups.
The graph below plots the distribution of log difference in terminal wealth against the median outcome for each of these five approaches. Lines within each “violin” show the 25th, 50th, and 75thpercentiles.
The graph clearly demonstrates that by increasing our exposure across the “what” axis, the dispersion in terminal wealth is dramatically reduced.
Source: Kenneth French Data Library. Calculations by Newfound Research.
But why is reduced dispersion in terminal wealth necessarily better?
It implies a greater consistency in outcome, which is not only important for setting forward expectations, but is also important for evaluating past performance (whether backtested or live). This evidence tells us that if we are evaluating a trend equity strategy that employs a single model to make in-or-out decisions on broad U.S. equities on a monthly basis, it will be nearly impossible to tell whether the realized results are in line with reasonable expectations or overly optimistic (we can probably guess that they aren’t overly pessimistic, as those sorts of returns typically aren’t marketed).
To justify a concentration in the “what” axis, we would have to demonstrate that the worst-case scenarios would still represent a meaningful improvement in expected terminal wealth versus a more diversified approach.
It should be noted that our experiment design prohibits dispersion from every being fully reduced, as we are injecting randomness into past returns. Even if no strategy is applied, there will be some inherent dispersion in final wealth. For example, below we plot the dispersion that occurs simply from adding randomness to past returns with a buy-and-hold approach.
Increasing the number of assets in the portfolio inherently reduces dispersion for buy-and-hold because diversification helps drive the expected impact of the injected randomness towards its mean: zero. With only one asset, on the other hand, outlier events are free to wreak havoc on results.
Source: Kenneth French Data Library. Calculations by Newfound Research.
Note that adding a strategy on top of buy-and-hold can exacerbate the fragility issue, making diversification that much more important.
The How
The “how” axis asks, “how are we making investment decisions.”
Many investors are already somewhat familiar with diversification along the “how” axis, often diversifying their active exposures across multiple managers who might have similar investment mandates but slightly different processes.
We like to call this “process diversification” and think of it as akin to the parable of the blind men and the elephant. Each blind man touches a different part of the elephant and pronounces his belief in what he is touching based upon his isolated view. The blind man touching the leg, for example, might think he is touching a sturdy tree while the blind man touching the tail might believe he is grabbing a rope.
None is correct in isolation but taken together we may gain a more well-rounded picture.
Similarly, two managers may claim to invest based upon valuations, but the manner in which they do so gives them a very different picture of where value can be found.
The idea of process diversification was explored in the 1999 paper “Do You Need More than One Manager for a Given Equity Style?” by Franklin Fant and Edward O’Neal. Fant and O’Neal found that while a multi-manager approach does very little for return variability across time (i.e. portfolio volatility), it does a lot for end-of-period wealth variability. They find this to be true across almost all equity style box categories. In other words: taking a multi-manager approach can reduce fragility.
Let us return to our prior coin flip example. Instead of making a choice to invest in the S&P 500 based upon a coin-flip, however, we will combine a number of different signals. For example, we might flip a coin, roll a die, measure the weather, and look at the second hand of a clock. Each signal gives us some sort of in-or-out decision, and we average these decisions together to get our allocation. As with before, as we incorporate more signals, we decrease the probability that we end up with extreme allocations, leading to a more consistent terminal wealth distribution.
Again, we should stress here that the objective is not just outright elimination of dispersion in terminal wealth. After all, if that were our sole pursuit, we could simply stuff our money under our mattress. Rather, assuming we will be implementing some active investment strategy that we hope has a positive long-term expected return, our aim should be to reduce the dispersion in terminal wealth for that strategy.
Of course, in investing we would not expect the processes to be entirely independent. With trend following, for example, most popular models are actually mathematically linked to one another, and therefore generate signals that are highly correlated. Nevertheless, even modest diversification can have meaningful benefits with respect to strategy fragility.
To explore the impact of diversification along the how axis, we implement our trend following strategy six different ways. Each invests in broad U.S. equities and rebalances monthly but differs in the number of trend-following models employed.
The results are plotted below.
Source: Kenneth French Data Library. Calculations by Newfound Research.
Again, we can see that increased diversification across the how axis dramatically reduces dispersion in terminal wealth. Our takeaway is largely the same: without an ex-ante view as to which particular model (or group of models) is best (i.e. a view of how to be more right), diversification can lead to greater consistency in results. We will be less wrong.
A subtler conclusion of this analysis is that it should be very, very difficult to necessarily conclude that one model is better than another. We can see that if we risk selecting just one model to govern our process, seemingly minor variations in historical returns leads can lead to dramatically different terminal wealth results, as evidenced by the bulging distribution. Inverting this line of thinking, we should also be suspect of any backtest that seeks to demonstrate the superiority of a given model using a single backtest. For example, just because a 12-1 month total return model performs better than a 10-month moving average model on historical S&P 500 returns, we should be highly skeptical as to the robustness of the conclusion that the 12-1 model is best.
The When
Then “when” axis asks, “when are we making our investment decision?”
This is an oft overlooked question in public markets, but it is commonly addressed in the world of private equity and venture capital. Due to the illiquid nature of those markets, investors will often attempt to diversify their business cycle risk by establishing positions in multiple funds over time, giving them exposure to different “vintages.” The idea here is simple: the opportunity set available at different points in time can vary and if we allocate all of our earmarked capital to a particular year, we may miss out on later opportunities.
Consider our original coin-flipping example where we flipped a single coin every December to determine whether we would buy the S&P 500 or hold our capital in short-term Treasuries. But why was it necessary that we make the decision in December? Why not July? Or January? Or September?
While we would not expect there to be point-in-time risk for coin flipping, we can still consider the net effect of a vintage-based allocation methodology. Here we will assume that we flip a coin each month and rebalance 1/12thof our capital based upon the result.
Again, the probability of allocating to the extremes (100% invested or 100% divested) is dramatically reduced (each has approximately a 0.02% chance of occurring) and we reduce strategy fragility to any specific coin flip.
But just how impactful is this notion? Below we plot the rolling 1-year total return difference between two 60% S&P 500 / 40% 5-year U.S. Treasury fixed-mix portfolios, with one being rebalanced in February and one in August. Even for this highly simplified example, we can see that the total return spread between the two portfolios blows out to over 700 basis points in March 2010 due to the fact that the February portfolio rebalanced back into equities at nearly the exact bottom of the crisis.
Source: Global Financial Data. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions.
To increase diversification across the “when” axis, we want to increase the number of vintages we deploy. For our trend following example, we will assume that the portfolio allocates between broad U.S. equities and the risk-free rate based upon a single model, but with an increasing number of evenly-spaced vintages. Again, we will run 10,000 simulations that each slightly perturb historical U.S. equity market returns and compare the terminal wealth variation for approaches that employ a different number of vintages.
We can see in the graph below that, as with the other axes of diversification, as we increase the number of vintages employed, the variance decreases. While the 25thand 75thpercentiles do not decrease as dramatically as for the other axes, we can see that the extreme variations are reined in substantially when we move from 1 monthly tranche to 4 weekly tranches.
Source: Kenneth French Data Library. Calculations by Newfound Research.
Conclusion
We see two critical conclusions from this analysis:
- To develop confidence in achieving our objective we have to consider our sensitivity to unintended bets that may be included within the portfolio.
- Fragility makes it incredibly difficult to distinguish between luck and skill, particularly as strategy fragility increases. This is true for both backtested and live performance.
To conclude our analysis, below we present a graph that combines diversification across all three axes. We again run 10,000 samples, randomly perturbing returns. For each sample, we then run four variations:
- A single, randomly selected model run in broad U.S. equities that is rebalanced monthly.
- A random selection of 3 models run on 5 industry groups in 2 bi-weekly tranches.
- A random selection of 6 models run on 12 industry groups in 4 weekly tranches.
- A random selection of 9 models run on 30 industry groups in 20 daily tranches.
It should come as no surprise that as we increase the amount of diversification across all three axes, the dispersion in terminal wealth is dramatically reduced.
Source: Kenneth French Data Library. Calculations by Newfound Research.
It is also important to note that while our analysis focused on trend following strategies, this same line of thinking applies across all investment approaches. As an example, consider a quantitative value manager who buys the top five cheapest stocks, as measured by price-to-book, in the S&P 500 each December and then holds them for the next year. Questions worth pondering are:
- What does it say about our conviction when the 6thstock in the list is incredibly close to the 5thstock?
- What happens if some of our measures of book value are incorrect (or even just outdated)?
- How different would the portfolio look if we ranked on another value measure (e.g. price-to-earnings)?
- How different would the opportunity set be if we ranked every June versus every December?
While low levels of diversification across the what, how, and when axes are not necessarily an indicator that a model is inherently fragile, it should be a red flag that more effort is required to disprove that it is not fragile.
Is Multi-Manager Diversification Worth It?
By Corey Hoffstein
On January 7, 2019
In Popular, Portfolio Construction, Risk Management, Weekly Commentary
This post is available as a PDF download here.
Summary
Introduction
In their 2014 paper The Free Lunch Effect: The Value of Decoupling Diversification and Risk, Croce, Guinn, and Robinson draw a distinction between the risk reduction effects that occur due to de-risking and those that occur due to diversification benefits.
To illustrate the distinction, the authors compare the volatility of an all equity portfolio versus a balanced stock/bond mix. In the 1984-2014 sample period, they find that the all equity portfolio has an annualized volatility of 15.25% while the balanced portfolio has an annualized volatility of just 9.56%.
Over 75% of this reduction in volatility, however, is due simply to the fact that bonds were much less volatile than stocks over the period. In fact, of the 568-basis-point reduction, only 124 basis points was due to actual diversification benefits.
Why does this matter?
Because de-risking carries none of the benefits of diversification. If there is a commensurate trade-off between expected return and risk, then all we have done is reduced the expected return of our portfolio.1
It is only by combining assets of like volatility – and, it is assumed, like expected return – that should allow us to enjoy the free lunch of diversification.
Unfortunately, unless you are willing to apply leverage (e.g. risky parity), the reality of finding such free lunch opportunities across assets is limited. The classic example of inter-asset diversification, though, is taught in Finance 101: as we add more stocks to a portfolio, we drive the contribution of idiosyncratic volatility towards zero.
Yet volatility is only one way to measure risk. If we build a portfolio of 30 stocks and you build a portfolio of 30 stocks, the portfolios may have nearly identical levels of volatility, but we almost assuredly will end up with different realized results. This difference between the expected and the realized is captured by a measure known as terminal wealth dispersion, first introduced by Robert Radcliffe in his book Investment: Concepts, Analysis, Strategy.
This form of risk naturally arises when we select between investment managers. Two managers may both select securities from the same universe using the same investment thesis, but the realized results of their portfolios can be starkly different. In rare cases, the specific choice of one manager over another can even lead to catastrophic results.
The selection of a manager reflects not only an allocation to an asset class, but also reflects an allocation to a process. In this commentary, we ask: how much diversification benefit exists in process diversification?
The Theory Behind Manager Diversification
In Factors from Scratch, the research team at O’Shaughnessy Asset Management (OSAM), in partnership with anonymous blogger Jesse Livermore, digs into the driving elements behind value and momentum equity strategies.
They find that value stocks do tend to exhibit negative EPS growth, but this decay in fundamentals is offset by multiple expansion. In other words, markets do appear to correctly identify companies with contracting fundamentals, but they also exaggerate and over-extrapolate that weakness. The historical edge for the strategy has been that the re-rating – measured via multiple expansion – tends to overcompensate for the contraction in fundamentals.
For momentum, OSAM finds a somewhat opposite effect. The strategy correctly identifies companies with strengthening fundamentals, but during the holding period a valuation contraction occurs as the market recognizes that its outlook might have been too optimistic. Historically, however, the growth outweighed the contraction to create a net positive effect.
These are the true, underlying economic and behavioral effects that managers are trying to capture when they implement value and momentum strategies.
These are not, however, effects we can observe directly in the market; they are effects that we have to forecast. To do so, we have to utilize semi-noisy signals that we believe are correlated. Therefore, every manager’s strategy will be somewhat inefficient at capturing these effects.
For example, there are a number of quantitative measures we may apply in our attempt to identify value opportunities; e.g. price-to-book, price-to-earnings, and EBITDA-to-enterprise-value to name a few. Two different noisy signals might end up with different performance just due to randomness.
This noise between signals is further compounded when we consider all the other decisions that must be made in the portfolio construction process. Two managers may use the same signals and still end up with very different portfolios based upon how the signals are translated into allocations.
Consider this: Morningstar currently2 lists 1,217 large-cap value funds in its mutual fund universe and trailing 1-year returns ranged from 1.91% to -22.90%. This is not just a case of extreme outliers, either: the spread between the 10th and 90thpercentile returning funds was 871 basis points.
It bears repeating that these are funds that, in theory, are all trying to achieve the same goal: large-cap value exposure.
Yet this result is not wholly surprising to us. In Separating Ingredients and Recipe in Factor Investing we demonstrated that the performance dispersion between different momentum strategy definitions (e.g. momentum measure, look-back length, rebalance frequency, weighting scheme, et cetera) was larger than the performance dispersion between the traditional Fama-French factors themselves in 90% of rolling 1-year periods. As it turns out, intra-factor differences can cause greater dispersion than inter-factor differences.
Without an ex-ante view as to the superiority of one signal, one process, or one fund versus another, it seems prudent for a portfolio to have diversified exposure to a broad range of signals that seem plausibly related to the underlying phenomenon.
Literature Review
While foundational literature on modern portfolio diversification extends back to the 1950s, little has been written in the field of manager diversification. While it is a well-established teaching that a portfolio of 25-40 stocks is typically sufficient to reduce idiosyncratic risk, there is no matching rule for how many managers to combine together.
One of the earliest articles on the topic was written by Edward O’Neal in 1997, titled How Many Mutual Funds Constitute a Diversified Mutual Fund Portfolio?
Published in the Financial Analysts Journal, this article explores risk across two different dimensions: the volatility of returns over time and the dispersion in terminal period wealth. Again, the idea behind the latter measure is that two investors with identical horizons and different investments will achieve different terminal wealth levels, even if those investments have the same volatility.
Exploring equity mutual fund returns from 1986 to 1997, the study adopts a simulation-based approach to constructing portfolios and tracking returns. Multi-manager portfolios of varying sizes are randomly constructed and compared against other multi-manager portfolios of the same size.
O’Neal finds that while combining managers has little-to-no effect on volatility (manager returns were too homogenous), it had a significant effect upon the dispersion of terminal wealth. To quote the article,
Allocating to three managers instead of just one could reduce the dispersion in terminal wealth by nearly 50%, an effect found to be quite consistent across the different time horizons measured.
In 1999, O’Neal teamed up with L. Franklin Fant to publish Do You Need More than One Manager for a Given Equity Style? Adopting a similar simulation-based approach, Fant and O’Neal explored multi-manager equity portfolios in the context of the style-box framework.
And, as before, they find that taking a multi-manager approach has little effect upon portfolio volatility.
It does, however, again prove to have a significant effect on the deviation in terminal wealth.
To quote the paper,
In 2002, François-Serge Lhabitant and Michelle Learned pursued a similar vein of research in the realm of hedge funds in their article Hedge Fund Diversification: How Much is Enough? They employ the same simulation-based approach but evaluate diversification effects within the different hedge fund styles.
They find that while diversification does little to affect the expected return for a given style, it does appear to help reduce portfolio volatility: sometimes quite significantly so. This somewhat contradictory result to the prior research is likely due to the fact that hedge funds within a given category exhibit far more heterogeneity in process and returns than do equity managers in the same style box.
(Note that while the graphs below only show the period 1990-1993, the paper explores three time periods: 1990-1993, 1994-1997, and 1998-2001 and finds a similar conclusion in all three).
Perhaps most importantly, however, they find a rather significant reduction in risk characteristics like a portfolio’s realized maximum drawdown.
To quote the article,
Taken together, this literature paints an important picture:
But why is minimizing “the dispersion of terminal wealth” important? The answer is the same reason why we diversify in the first place: risk management.
The potential for high dispersion in terminal wealth means that we can have dramatically different outcomes based upon the choices we are making, placing significant emphasis on our skill in manager selection. Choosing just one manager is more right style thinking rather than our preferred less wrong.
But What About Dilution?
The number one response we hear when we talk about manager diversification is: “when we combine managers, won’t we just dilute our exposure back to the market?”
The answer, as with all things, is: “it depends.” For the sake of brevity, we’re just going to leave it with, “no.”
No?
No.
If we identify three managers as providing exposure to value, then it makes little logical sense that somehow a combination of them would suddenly remove that exposure. Subtraction through addition only works if there is a negative involved; i.e. one of the managers would have to provide anti-value exposure to offset the others.
Remember that an active manager’s portfolio can always be decomposed into two pieces: the benchmark and a dollar-neutral long/short portfolio that isolates the active over/under-weights that manager has made.
To “dilute back to the benchmark,” we’d have to identify managers and then weight them such that all of their over/under-weights net out to equal zero.
Candidly, we’d be impressed if you managed to do that. Especially if you combine managers within the same style who should all be, at least directionally, taking similar bets. The dilution that occurs is only across those bets which they disagree on and therefore reflect the idiosyncrasies of their specific process.
What a multi-manager implementation allows us to diversify is our selection risk, leading to a return profile more “in-line” with a given style or category. In fact, Lhabitant and Learned (2002) demonstrated this exact notion with a graph that plots the correlation of multi-manager portfolios with their broad category. While somewhat tautological, an increase in manager diversification leads to a return profile closer to the given style than to the idiosyncrasies of those managers.
We can also see this with a practical example. Below we take several available ETFs that implement quantitative value strategies and plot their rolling 52-week return relative to the S&P 500. We also construct a multi-manager index (“MM_IDX”) that is a naïve, equal-weight portfolio. The only wrinkle to this portfolio is that ETFs are not introduced immediately, but rather slowly over a 12-month period.3
Source: CSI Analytics. Calculations by Newfound Research. It is not possible to invest in an index. Returns are total returns (i.e. assume the reinvestment of all distributions) and are gross of all fees except for underlying expense ratios of ETFs. Past performance does not guarantee future results.
We can see that while the multi-manager blend is never the best performing strategy, it is also never the worst. Never the hero; never a zero.
It should be noted that while manager diversification may be able to reduce the idiosyncratic returns that result from process differences, it will not prevent losses (or relative underperformance) of the underlying style itself. In other words, we might avoid the full brunt of losses specific to the Sequoia Fund, but no amount of diversification would prevent the relative drag seen by the quantitative value style in general over the last decade.
We can see this in the graph above by the fact that all the lines generally tend to move together. 2015 was bad for value managers. 2016 was much better. But we can also see that every once in a while, a specific implementation will hit a rough patch that is idiosyncratic to that approach; e.g. IWD in 2017 and most of 2018.
Multi-manager diversification is the tool that allows us to avoid the full brunt of this risk.
Conclusion
Taken together, the research behind manager diversification suggests:
For advisors and investors, this evidence may cause a sigh of relief. Instead of having to spend time trying to identify the best manager or the best process, there may be significant advantages to simply “avoiding the brain damage”4 and allocating equally among a few. In other words, if you don’t know which low-volatility ETF to pick, just buy a couple and move on with your life.
But what are the cons?
For investment managers, a natural interpretation of this research is that approaches blending different signals and portfolio construction methods together should lead to more consistent outcomes. It should be no surprise, then, that asset managers adopting machine learning are finding significant advantages with ensemble techniques. After all, they invoke the low-hanging fruit of manager diversification.
Perhaps most interesting is that this research suggests that fund-of-funds really are not such bad ideas so long as costs can be kept under control. As the asset management business continues to be more competitive, perhaps there is an edge – and a better client result – found in cooperation.