Episode #86: A Quantitative Approach to Tactical Asset Allocation
Guest: Episode #86 has no guest. It’s a Mebisode.
Date Recorded: 12/11/17 | Run-Time: 34:31
Summary: It’s been 10 years since Meb wrote “A Quantitative Approach to Tactical Asset Allocation” which is the top-downloaded paper of all time on SSRN. In the coming weeks, we’re going to publish a retrospective on that paper in the Journal of Portfolio Management. So Meb thought this episode would be a good opportunity to revisit the original paper and perform his 10-year post mortem.
Here’s the abstract of the new paper, and the backbone for what you’ll hear in this episode:
“In this article, the author revisits his seminal paper on tactical asset allocation published over 10 years ago. How well did the market strategy presented in the original paper – a simple quantitative method that improves the risk-adjusted returns across various asset classes – hold up since publication? Overall, the author finds that the model has performed well in real-time, achieving equity-like returns with bond-like volatility and drawdowns. The author also examines the effects of departures from the original system, including adding more asset classes, introducing various portfolio allocations, and implementing alternative cash management strategies.”
If you’re not familiar with Meb’s original “A Quantitative Approach to Tactical Asset Allocation” don’t miss Episode 86. In many ways, this paper is foundational to the various market approaches Meb has adopted since.
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Transcript of Episode 86: Welcome Message: Welcome to “The Meb Faber Show,” where the focus is on helping you grow and preserve your wealth. Join us as we discuss the craft of investing and uncover new and profitable ideas, all to help you grow wealthier and wiser. Better investing starts here.
Disclaimer: Meb Faber is the co-founder and Chief Investment Officer at Cambria Investment Management. Due to industry regulations, he will not discuss any of Cambria’s funds on this podcast. All opinions expressed by podcast participants are solely their own opinions and do not reflect the opinion of Cambria Investment Management or its affiliates. For more information, visit cambriainvestments.com.
Sponsor: Today’s episode is brought to you by Personal Capital. Personal Capital offers insightful, free tools that help you manage all of your finances in a single location with one secure login. The tools are free, personalized, easy to set up and use, and give investors a convenient way to gain transparency into their finances. I know it because I’ve been using the online tool for years for a holistic picture of my financial life including investments and overall net worth. Today for listeners of “The Meb Faber Show,” Personal Capital is offering a special deal. Two months of free advisory services on top of the already free tools. To learn more and claim two months free, just go to personalcapital.com/meb. Again that’s personalcapital.com/meb. And now on to the show.
Meb: Hello, podcast listeners. Merry Christmas, Happy Holidays, Hanukkah, Festivus, all that good stuff. We’re getting down near the end of the year. So we’re gonna squeeze in a few different ideas. Today we have a Meb cast, which means no guests, no Jeff, just me. So if that’s your idea of a nightmare, 20, 30, 40 minutes spent alone in the car with just me, or the gym, sign off now, you’ve had your fair warning. What are we gonna do today? This is gonna be a fun retrospective.
So today, we’re looking back at my very first whitepaper wrote over 10 years ago. And this is actually a draft of a paper that’s coming out in the journal of portfolio management. It will be in their multi-asset special, which I think is either at the end of 2017 or early 2018. So you’re getting an early look. The name of this is “A Quantitative Approach to Tactical Asset Allocation,” revisited 10 years later. So I’m gonna read it, I’m gonna say “side note” if there’s some times where I wanna take a deviation or just riff as opposed to just the text. But hopefully you like it. This is a fun look back at kind of, as the Allman Brothers would say, where it all began. So let’s get started.
In 2006, we wrote a draft whitepaper titled “A Simple Approach to Market Timing,” which introduced a basic market timing strategy. I circulated it amongst various friends, professionals, but quickly found that no one was particularly interested reading it. It’s a nice way of saying no one wanted to read it whatsoever. Eventually, I changed the title to “A Quantitative Approach to Tactical Asset Allocation” and published it in 2007. I think the whitepaper got circulated maybe ’05 or ’06.
Shortly thereafter, the world was rocked by the global financial crisis. The strategy detailed in our paper performed admirably in the turmoil, likely because of the performance of this simple system, “A Quantitative Approach to Asset Allocation,” would go on to become the most downloaded paper of all time on the Social Science Research Network, which is SSRN, with approximately 200,000 downloads. I kind of smile when I say that side note because that’s kind of like Crash Davis’s famous line in “Bull Durham” where he gets the homerun record in the Minor League.
So being the most downloaded on the academic database is kind of like being the best ninth grade basketball player, I don’t know, but we’ll take it. It’s pretty cool to look back. A lot of the other authors in the top 10 are pretty famous. There’s some Nobel Laureates thrown in. What I don’t mention in the article, by the way, is the origins of the paper was that I was actually trying to avoid taking a test. I was actually going through the third level of the CMT designation, which is the Market Technicians Association, the Chartered Market Technician, which is kind of the technical analyst version of the CFA.
And back then, they announced they were doing away with the whitepaper requirement by the end of the year, so I turned in an abstract on like December 30th, snuck it in, and then had to go write a paper, which eventually became this paper. A big hat tip to one of my favorite people on the planet, Rob Arnott, who was then the “Journal of Finance,” I believe, editor…or “Financial Analyst Journal.” He actually read it and gave me some fantastic constructive criticism, harsh but fair, that probably a dozen or two dozen other people I sent it to most either didn’t read it or sent me some pretty nasty comments, and including some very, very famous authors and hedge fund managers I won’t mention, it’s the holiday season.
Anyway, let’s get back to the paper and read where we’ve been since then in the past 10 years. So 10 years have passed, so we thought it would be interesting to examine how well this timing strategy held up. What lessons have been learned? What might have been done differently with the benefit of hindsight? And what possible extensions and deviations exist for further explorations? Now, noted, this is not intended to be an expansion of the original article. We did that in Faber 2013. We published an update to the original that included a bunch of new content. Rather, this is meant to just be an accompaniment, a reflection on the performance, a timing model used in and out or sample data in the years since publication.
So what was the original system? For those of you who may not have read the original article or benefit from a little bit of a refresher, the timing strategy examined was a trend following strategy. Trend following is one of the oldest market strategies. It’s been around since at least the time of Charles Dow in the early 20th century. The most often cited long term measure trend, the probably most famous one, is the 200-day simple moving average. So therefore, we decided to use this as our starting point. I make no claims to originality. People I sometimes meet are like, “Meb thinks he invented this.” No. This has been around for forever.
So we used a monthly version of this, that’s 10-month simple moving average. The reason we did that was just there’s not that much historical data, goes back that far for daily, so we wanted to use as much data as possible. And a criterion necessary for this model to be simple enough for investors to follow, mechanical enough to remove emotion and subjective decision-making. One, we wanted to have a simple purely mechanical logic, two, the same model and parameters must be used for every asset class, and three, you wanted to just be priced-based only.
One of my favorite investing quotes is “Price is unique as an indicator, is that it can’t diverge from itself.” Any other indicator you have could diverge, whereas price is unique in that. Anyway, so what was the logic? We only had one rule, simplest system on the planet. And that is, we wanted to buy when the security is in an uptrend, sell it when it’s in a downtrend. So specifically, the buy rule is to buy something when the monthly price was above the 10-month simple moving average, you sell it and move to cash when the monthly price was less than the 10-month moving average. That’s it, takes five minutes a month. A few clarifications, all the entry and exit points are on the day of the signal at the close. It’s only updated once a month, the last day the month. Price fluctuations on the rest of month are ignored.
We use Total Return data, cache returns or 90-day T-bills, we ignore taxes, commissions, slippage, we’ll talk about that later. And if you look back in the original paper before we examine performance, I said, here’s a quote, “The attempt is not to build an optimization model (indeed, the chosen parameter is decidedly sub-optimal as evidenced later in this article), but to build a simple trading model that works in most markets. The results suggest that a market timing solution is a risk-reduction technique rather than a return enhancing one. The empirical results are equity-like returns with bond-like volatility and drawdown.”
So it’s important to understand that beating the market was never the goal of the paper. The intent was to identify a trading system with largely approximated market returns, yet it did so with less volatility and risk. The reason for this is simple. Emotions can wreak havoc on investors’ ability to follow their own state and investment plan. All too often, we fall victim to fear when markets have turned against us, and sell at nearly the worst possible time. A lot of us can think back to 2009, how hard it was to sit through that.
Although many market historians enjoy pointing towards the healthy long-term average return of most equity indices, what good are those long term averages if fear caused an investor to sell near market low? So it is with this question of mine that I sought a model that dramatically reduced volatility and drawdowns. The hope was that by implementing such a model, investors could avoid the exaggerated turbulence often results in emotion-based money losing market decisions. So did it work? Did the model work as intended?
The original article was published with data up through 2005, so we’ll examine the historical in-sample results, as well as the out-of-sample results in the 11 years since. So before delving into the data, what’s transpired in the past decade? Anything? We’ve seen the Boston Marathon bombing, the Russian invasion of Ukraine, the Affordable Care Act, continued war with Isis, Ebola, Zika, one of the most partisan periods ever in American politics culminating in the election of Donald Trump, and now the increasing threat of war with North Korea.
In the investing world, we’ve seen global financial crisis, U.S. housing meltdown followed by the steady march of this historic bull market, the end of the Fed’s quantitative easing program, historic lows and bond yields including some negative global sovereign yields, one of the biggest surprises I think in the past 10 years, and multi-decade highs and lows in oil prices, among other stories. What has this meant for asset allocation strategies in our simple model? Have any of these headlines meant this time is different? Change has always been the constant of markets, and indeed, has anything new really been seen in a world of investing? Bubbles, defaults government inventions, bear markets, fortunes made and lost, they’ve all happened before.
So to answer this question, let’s first examine the timing model on U.S. stocks before moving the perspective of an asset allocation portfolio. Now, recall all the many caveats of studies such as this. First, most of the early part of the 20th century, there was no way to trade an index fund as the S&P 500. I don’t think they really existed until the ’70s, even you could buy a basket of stocks back then. Trading based on a trend system would have been costly because of commissions and frictions, such as the bid-ask spread.
However, still useful to test the algorithm on historic data to help establish a framework for how a modern system may have performed in the past, with a nod to the future. So we show an exhibit in the paper, which you’ll have to download to look at. We’ll post in the show notes. But it shows that utilizing a timing system would have largely avoided the significant bear markets in 1930s, 2000s for example, but it wouldn’t have left the investor completely unscathed from those bear markets, nor would it have saved the investor from the sharp drop in 1987. That’s an interesting sub-note we’ll come back to it later.
And if you look at more the kind of recent 1990 to 2016 period, a few timing features stand out. First, a trend following model can underperform buy and hold during a roaring bull market such as the U.S. equity market in the ’90s. On the flip side, a timing model can avoid lengthy and protracted bear markets. So consequently, the value added by timing is only evident over the course of an entire business and market cycle. Most people wanna identify any investment approach over weeks, months, quarters, but really it’s years and almost decades.
So many people will look at the equity curves in the paper and think, “Look, they both basically end up in the same place. Is it even worth it to implement a timing model?” That’s a valid question. And I think if you’re a buy-and-hold investor that can live through bear markets down 40%, 60%, 80%, and stay the course, you may not need a trend following system. However, as we mentioned earlier, many investors cannot handle the drawdowns. And as many advisors can attest, investors of all types, stripes tempted to capitulate and sell in bear markets.
On the flip side, market timing provides its own set of challenges, namely whipsaws with false signals often that are found in choppy markets. And one of the hardest emotional challenges of investing is looking different than your peers when they are performing well. With this historical perspective in mind, let’s now turn to the results of the model since the original publication. We’ll look at in-sample versus out-of-sample.
So U.S. stocks in-sample, 1901 to 2005 did 9.65%, timing up that to 10.36%. Pretty good, right? Volatility went down. And I’m gonna start rounding because it’s too hard to do decimals. So fall at U.S. stocks 9.6%, timing, 10.3%, so better. Volatility went from 18% down to 12%. That’s a huge reduction in volatility. Sharpe ratio went up from 33% to 55%, max drawdown, big one, went from 83% down to 50%. So how’d that do? So good, across the board, right? But obviously it’s good because that was the in-sample period.
Let’s look at since publication, 2006 to 2016. U.S. stocks, still pretty good, 7.6%, timing, better, 8.5%, volatility went down from 14.6% down to 9.4%. That also goes to show how much lower volatility this period has been recently in particular. Sharpe ratio, 0.45% up to 0.8%. And the big drawdown of the 51% in the global financial crisis got reduced to 16%, so not bad. Also inflation was lower, around 1.8% versus 3.1%. So pretty darn good.
So cursory review reveals that the timing solution improved compound returns while reducing risk in drawdowns in both periods. Volatility is measured, by the way, as the annualized standard deviation of monthly returns. Drawdown, if you’re not familiar, is peak-to-trough decline in investment. So if you bought something 100 grand, went down to 50 grand, then all the way back up to 200, you lost 50% at one point. So after publication of the article, U.S. stocks got pummeled, right? Then continued this massive bull market up.
And if you look at the out-of-sample performance, timing did a monster job in 2008, actually had a slight positive year. But then the problem is it trailed after that period. And a lot of investors implement investment approaches after the fact wishing they had in the past. So a lot of investors who have implementing the timing model after the crash probably really likely struggled with staying the course of the tactical approach in the years to follow. Though most investors don’t just own U.S. stocks, but instead, allocate to a broad spectrum of assets including bonds, foreign assets, real assets. So let’s turn our attention now to the performance of the timing model as applied to a diversified global portfolio.
If you all read my favorite investing book, Dimson, Marsh, Staunton’s “Triumph of the Optimists: 101 Years of Global Investment Returns.” The book illustrates many global asset classes in 20 essentially produced spectacular gains and wealth for people that bought and held those assets for generation-long holding periods. Of course, all those assets went through regular and painful drawdowns, such as we just experienced in ’08, ’09. All the G7 countries have experienced at least one period where stocks lost three quarters of their value. Think about that, 75%. Unfortunate math of a 75% decline requires an investor a 300% gain to get back to even, that’s the equivalent of compounding at 10% for 15 years.
So what most investors do? Solution for most investors diversify across uncorrelated assets and return streams. Every bear market has a different personality, of course. 2000, 2003 was largely confined to high-flying tech stocks. And many assets and securities came nowhere near close to experiencing the major losses suffered by market cap weighted U.S. stock indices. So if you look at kind of the five main asset classes we’re going to talk about, we’re gonna talk about, and long-term followers are familiar, U.S. stocks, foreign stocks, bonds, REITs, and commodities.
And so if you look at the drawdowns during this period from…and this is the modern era of ’72 to ’05, U.S. stocks, foreign stocks both declined around 44% to 47%, bonds, 15%, REITs, almost 60%, and commodities, almost 50%. So pretty nasty declines, of course, during that period, but strong returns across the board, U.S. stocks 11%, foreign, 11%, bonds, 85, REITs, 10.5%, commodities, 12%.
And then a buy-and-hold asset allocation, 11.5%. And by the way, the Sharpe ratio is ranged for those asset classes from a low of 0.24% for U.S. bonds to a high of 0.32% for U.S. stocks. And the portfolio, you put them all together, we call it a global asset allocation. You end up with a pretty awesome 11.5% return, 8% vol, 0.6% Sharpe, and a slightly lower than 20% drawdown. So diversification 101, you put a bunch of stuff together, you end up in a better place than either of those by themselves.
So how did everything…and that’s all buy-and-hold, by the way. How did all those asset classes perform in the out-of-sample period since 2005? Well, as we all recall, the normal benefits diversification disappeared in 2008 as many what people consider to be historical non-correlated assets experienced large declines simultaneously. Although we mentioned before, every bear market has its own personality, 2003 was largely tech, market cap weighted, ’08, ’09 bear was felt almost across the board.
So if you saw that original global asset allocation portfolio, which did at 11.5% with a 20% drawdown, in the period since publication in the 10 years, it only did 3.5%, with a 12% volatility, a 0.19% Sharpe ratio, and a 46% decline. That is over double the maximum drawdown over the past 30 years prior to 1972. That’s one of the reasons it surprised people so much, is that bear market had a different personality, really reflects what happened in the 1930s. Most people managing money weren’t around doing that. So if you look at a lot of the asset classes instead of this, you know, 9%, 10%, 11%, 12% returns, they’re now down to 3%, 4%, 7%, commodities were negative. And in some cases declines were 50%, 60%, 70%, 80%. So pretty nasty. The maximum drawdown is particularly in commodities.
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Meb: Let’s now evaluate the timing model on the aggregated portfolio level. So comparing the buy-and-hold of each of those asset classes 20% each, U.S. stocks, foreign stocks, bonds, REITs, commodities. And then when the timing model looks at each asset class independently, are you in U.S. stocks, are you out? If you’re out you sit in cash. So additional advantages conferred by timing are actually pretty striking. It results in a reduction of volatility to single-digit levels, as well as single-digit maximum drawdown in both periods.
So if you look at ’72 to ’05, asset allocation did 11.5%. The timing system added a little bit of returns, 20 base points, but not the point. It took vol down from almost 9% to almost 7%. Sharpe ratio increased and drawdown was roughly in half. Out-of-sample period, again, buy-and-hold 3.5%, way lower. Timing higher, 4.8%. Volatility got cut in half, 12.8% to 6.5%, Sharpe ratio doubled from 0.19% to .59%. Most importantly drawdown went from 46% down to 9.45%. So pretty great check mark metrics across the board.
And so some people say, “Well, Meb, are these results some way skewed or influenced by the specific parameter chosen?” And again, these are out-of-sample, so we’re, like, we’re optimizing the out-of-sample period. But if you look at all sorts of different timing indicators, so everything from, say, six-month, to eight-month, to 12-month moving averages, they all end up actually…the 10-month is not superior on any metric. Whether it’s return, vol, Sharpe, or drawdown, something else did better. So that’s what we call parameter stability, which is something you wanna see when you have a timing model like this that everything tends to work.
So since the publication of this original whitepaper, super simple. We’ve written 6 books, 10 more whitepapers, 2,000 blog articles, and produced nearly 100 podcast episodes. It’s kind of insane. Some of this contest addressed, in varying degrees, how an investor might improve upon or alter that basic system. So the intent of the original paper is demonstrated simple tactical system, an investor can take significant departures to tailor a portfolio to his or her own investing temperament. We’ve had thousands of emails over the years. The people ask questions, say, “Hey, can I do this? Can I switch that?”
So we’ll talk about a few deviations that we would like to reexamine. Some of these have been published since the original paper. We can’t really consider this out-of-sample, though. Some were published in 2008, then 2009, 2010, 2011, 2012, 2013, yadda, yadda. Y’all been following the blog and papers long enough. It will be very familiar with these. But there’s three main ones to think about. The first, alternative cash management strategies, second, alternative weighting strategies, and lastly, adding more asset class and tilts.
So first one, alternative cash management. So if you think about it on average, the timing portfolio is invested in approximately 30% cash on average. Because at any time, one or more the asset classes may not be invested because of the trading rules. I mean in general, the timing model has you invested like two-thirds of the time, but that means about a third of the time, you’re sitting in cash. And it’s only like one trade per year per asset class. But that means on average, you’re sitting in a pretty significant cash chunk. And that’s a drag, right? T-bills tend to have pretty low yields, lower than certainly bonds and long-term bonds. So you own the cash portfolio using any number of funds or concepts people use as a way to optimize cash.
So if you instead say, “Hey, look, instead of bills, I’m gonna use tenure bonds,” that actually increases your return about a percent a year. Volatility comes up a little bit, Sharpe ratio increases, however, and drawdown is basically roughly the same. So you get about 200 basis point bump, two percentage points. Now, listeners say, “Well, Meb, that’s coarse because bonds been in bull market for three decades of that period.” We said, “All right, well, let’s just examine using the timing model with bills and bonds in the highly inflationary 1972 to 1981,” and it actually still helped. You’ve still got that 200 basis point bump increase in Sharpe, drawdown roughly the same, which is pretty interesting to me. So it’s worked in most periods of both inflation, as well as in disinflation and close to outright deflation. So that may be an interesting choice for you, as to use bonds instead of just bills, or some other cash management.
Two, waiting strategies. So no two investors are alike. Some optimizing wealth preservation with low volatility, above all else, some can handle a 50% loss on the path to try to generate a higher gains. So what we’ve done originally in this papers is we just looked at one set model, but we could actually tailor it to what we consider maybe three different allocations. One being conservative, one being moderate, one being aggressive. So we use the base case when we’ve been talking about this whole time is moderate.
So conservative will say, “Hey, we’ll just put a higher weighting in bonds.” So instead of 20% standard, we’ll do 40%. And then aggressive, we’ll combine two of our favorite things. So we’ll combine two close cousins, momentum and trend. So the aggressive will select the top three out of the five asset classes ranked by just whatever traditional momentum metrics, one, three, six-month total returns. And then if they’re above their long-term trend, and this was detailed in the paper we wrote called “Relative Strength Strategies for Investing,” I think back in 2010.
So what does it look like? All right. Well, the conservative, as you imagine, lowers returns, lowers volatility, same Sharpe ratio, lower drawdown. And it makes sense, the more cash you add to something, you’re gonna have lower volatility, lower drawdown, and lower returns. So for people to say, “Look, I like this timing model, but 20% in bonds isn’t enough. I’m gonna go 40%, or 60%, or 80%, I think that makes sense,” and you could…it would perform exactly as you expect. And then if you said, “Flip it, I’m gonna do the aggressive,” when you combine momentum and trend, that’s where you actually get out performance.
That is a seeking strategy, return enhancing, so that adds about 300-ish base points. Volatility of course comes up, Sharpe ratio is higher, maximum drawdown doesn’t increase that much. And partially, that’s because of the trend following filter. And we found that it is still pretty darn good portfolio. Now, you’re gonna end up being more concentrated at times. But that’s the whole point. If you’re gonna be an active investor, you need to be weird, you need to be concentrated, you need to be different.
So you can also add more asset classes and tilts. So other than simplicity, there’s no reason to focus just on these five asset classes. Technically, we believe there’s only four asset classes, by the way: stocks, bonds, commodities, currencies. Everything else kind of like REITs is sort of a combination of the other four, or, say, corporate bonds. Anyway, if you expand it to…but if you start getting this huge asset allocation with these tiny positions in 1%, or 2%, or 3%, doesn’t really make any impact because sizes less than 5% have a marginal impact on the entire portfolio risk reward characteristics.
So we face the challenge that many asset classes and index simply have not existed for a long time. So in this examination where I can include tips, junk, high-yield bonds, emerging market, foreign REITs, fundamental indexes, managed futures, currencies, other asset classes we might otherwise consider. So we came up with 13 subgroups that will cover most of the global market portfolio. They would like to allocate to while maintaining a large enough allocation for the effect to have on the overall portfolio performance. I’m not gonna detail this, but it also includes tilts to things like value, and momentum, and equities, it includes foreign bonds, it includes commodities as well as gold, and of course the real estate investment trusts.
Now, we’re adding these tilts with the full benefit of hindsight. So we know that value and momentum worked in the past, so just noted. Regardless, I still think it’s a reasonable modification to make if implemented in a thoughtful and cost-effective manner. So a lot of these what we call smart beta tilts, the investors need to be mindful of the fees you pay to express these asset class tilts in portfolio. So many funds are sold on Wall Street with 1%, 2%, 3% expense ratios, which would likely destroy of course all the potential [inaudible 00:26:43] for the funds may generate in the first place.
So if you look at the historical returns of the simple strategy on top of the normal five asset calls allocation using the smart beta and different tilt for same volatility, same drawdown, you add about another 150 basis point returns. It’s pretty cool, smart beta, right? You tilt towards value, tilt to momentum. It’s really tilting away from market cap portfolio, doesn’t really matter what you do, you end up with a higher return. In the end, of course, doing the same thing with the timing model, it kind of just elevates it another 100 basis points over the timing model using those tilts.
And then, of course, you can start to combine all of these into one. And again, we kind of smile while we’re saying this because there’s limitless extensions, so you can incorporate the tilts, as well as the granularity, as well as the aggressive approach, as well as the cash management, and put this all into one, and you end up obviously with very extremely high returns with marginal volatility, a Sharpe ratio of above one, and a reasonable drawdown.
But again, some of this is just simply saying we know what a lot of this stuff worked in the past, combining it all together. Of course you’re gonna end up with a high Sharpe ratio. But I think a lot of these tilts and deviations are based on historically sound research, and would have no problem implementing them going forward. So let’s say you wanted to implement it. There’s a lot of practical considerations to consider, tongue twister. So namely, management fees, taxes, commission, slippage.
Management fees should be identical for both buy-and-hold, as well as timing models, and varies depending on the instruments you’re using. Fair estimate, if you’re using ETFs no-load mutual funds, obviously, the lower the fee the better, but you could get as low as like three basis points today with some of these funds. As high as maybe 70 basis points if you’re using some smart beta stuff. All ETF portfolios can be found approximately. The lowest, I think, ETF portfolio in the world costs about 5 basis points, but 10, 20, 30, 40, 50, even 60, 70, totally reasonable. Make sure you wanna keep it below 1% for sure.
Commissions at this point day and age are a minimal factor, these models have low turnover. And in general, you’re making a few round-trip trades per year and less than one round-trip trade per asset. However, slippage also nearly negligible. An investor could use numerous mutual funds, which have in a day pricing, so zero slippage, as well as liquid ETFs. And there’s many brokerages today that offer very low commission trading, 5 bucks for Schwab I think. And a lot of them even have zero-cost trading. Robin Hood and others.
Taxes are a very real consideration, of course. Many institutional investors, such as endowment pension funds, enjoy tax-exempt status, although that may change with the new rules. But the obvious solution for individual is to trade the system in a tax-deferred account such as an IRA or 401(k) because it varies. Capital gains rates are different for investors, as well as varying tax rates over time, and the impact of dividends. It’s difficult to estimate the hit an investor would suffer from moving from buy-and-hold to trading this in a taxable account. But most people rebalance their holdings periodically anyway, introduce some turnover in the portfolio even if they did buy-and-hold.
I think if I remember correctly in the original paper, we assumed it was around 50 basis point impact. I can’t remember, I have to look it up. Anyway, the trend following system in general results in a higher number of short-term capital losses and a larger percentage of long-term capital gains, that’s just definition of trend following, which is a good thing. So let’s reflect. You know, the purpose of this original article was to kind of take a look at a very simple method for trying to manage risk in a single asset class an extension of portfolio of assets.
We looked at a monthly system since 1972, an investor would have been able to increase risk-adjusted returns by diversifying portfolio assets and employing a market timing solution. In addition, the investor would have been able to sidestep many of the protracted bear markets and various asset classes, and avoiding these massive losses would have resulted in equity-like returns with bond-like volatility and drawdowns.
Investors looking to tailor their portfolios could consider using alternate cash strategies, more assets in the portfolio, and alternative weighting schemes to find a portfolio that is right for them. And as I look back and reflect, we find that the timing system has performed as expected out-of-sample, which is to say performed well. Given this, it’s tempting to give ourselves a pat on the back, say, “Meb, you’re so smart.” However, what if it had done poorly? Likely, no one would be reading this article or listening to this podcast because it never would’ve become popular in the first place.
So despite the great out-of-sample performance, is it fair to judge our timing system in such a short of time? Probably not. The value of such a model is better, evaluated over the course of multiple decades rather than years, and certainly not quarters. And this might be especially important given where we stand today, looking forward to what could be in store for the markets over the next decade. Although investors have benefited from strong equity markets since the global financial crisis, the opportunity set for domestic investments is quite poor, we’ve talked a lot about this.
Evaluations for U.S. stocks, using the 10-year Shiller cyclically adjusted price-to-earnings ratio, have risen above a value of 30, which puts them well above long-term average around 17 in the long-term average of 21 for lower inflation periods. Such a reading suggests that future returns could underperform historical long-term averages. Even the father of indexing, John Bogle, recently suggested U.S. stocks might return about 4% in the coming decade. Are we gonna expect any help from bonds? Probably not. Probably the opposite is what we should expect. U.S. government bonds have climbed to all-time low yield levels, and they’re up some, but still around 2.3%.
Looking forward, we expect future total bond return to approximate their starting yield. So giving this, investors are present with the following opportunity set. We’ll assume 2% inflation going forward just to make it simple, and also round. So say U.S. stocks do 4%, nominal 2% real, bonds, 2%, nominal 0% real. So will U.S. equities significantly underperform their long-term averages? What direction will U.S. bond yields go?
Well, alternative global asset classes provide a better place for investment capital. No one can answer these questions with complete accuracy, but given that is crucial to invest thoughtfully and deliberately. Most importantly, every investor should have a plan and a process for investing in any environment regardless of how improbable, unfathomable that may. Are you prepared for all the possible outcomes such as declines of 50% in an asset class or portfolio? Are you prepared not only for currency devaluations, but also massive rallies in stocks and bonds? Can you fathom a world with interest rates at 0% as well as 10%?
The beauty of the simplistic timing model documented in this piece is that it’s dynamic, therein enabling us to answer yes to the questions above. Whether you deem it appropriate or not for your own investing style, the broader point remains. Do you have a plan or process that has prepared you for tomorrow’s market in whatever condition you may find it?
That’s it for our draft reading, guys. Hope you didn’t fall asleep, crash your car, hope the gym session was great. We’ll post a link to this paper when it comes out, and hopefully it’s end of the year, maybe first quarter, with all the various charts, updates, numbers, etc. I hope you guys liked it. I hope everyone listening has an outstanding holiday season. It’s been a lot of fun this year, we had a great time with Jeff and all the guests. We really enjoyed you guys listening. I really appreciate the time you spend on it.
So if you guys got any feedback, y’all don’t send us too much, but would love to hear some suggestions, ideas, questions, thoughts, comments, presents, feedback at the mebfabershow.com. You can always find the rest of the show notes. We’re approaching 100. And all the episodes at mebfaber.com/podcast. You can always subscribe the show on iTunes, all the other players. Thanks for listening, friends. Happy holidays, and good investing.