Sometimes the simplest investing concepts we take for granted are actually a lot more complicated than we think. For example, reasonable people might rationally assume that two small cap value index funds should have identical returns since they theoretically follow the same asset. But it doesn’t really work that way because definitions matter. What does “small cap” mean? Who defines “value”? Read the fine print and two similar funds may be a lot more unique than you realize.
I’ve been thinking about that subtle complexity recently, as in the process of finalizing the most recent annual Portfolio Charts data update I refined the definition of “small cap” to better match common index fund methodologies. The tweak was simple enough, but succinctly explaining what it means and why it matters got more and more difficult the longer I thought about it. The entire process made me realize it might be a good time to have a longer discussion on how stock index fund definitions work.
So if you’ve ever looked at an assortment of large, mid, small, blend, value, and growth stocks and wondered what all of that actually means, this article is for you. It’s going to get a little technical, but if you stick with it you’ll learn something not only about how indices are constructed but also how to use that info to interpret the data you find both here and elsewhere.
An interesting recent trend I’ve noticed in portfolio discussions is a renewed debate about the resilience of factor premiums versus the good old cap-weighted stock market. It’s entirely predictable that a tough stretch for any investment has a way of bringing out both the nervous supporters on one side and the proud haters on the other. But I really can’t fault the pros for keeping an eye on performance of some of the trendier factors or the investing laypeople for wondering what everyone is even talking about.
What do I think? It’s complicated. So let’s talk about factor investing.
I’ve flown a lot over the years, and I understand first-hand how all of the little details like packing, efficiently getting through security, and getting settled on the plane become so routine for frequent travelers that they can do them without even thinking. But occasionally life throws you a curveball, just as it did on a recent flight where I was without my normal headphones. Stuck for several hours with nothing but the drone of the engines to keep me company, I can’t say I was thrilled but it turns out it was just the inspiration I needed to explain a complicated concept:
How do consistent portfolios full of volatile assets actually work?
Sure, I could go into a detailed discussion of covariance, standard deviations, and the complicated math behind efficient portfolio construction, but frankly I know I would quickly lose most people and even bore myself in the process. So inspired by the the desire for silence I normally take for granted, let’s step back and think of the problem a little differently in terms we can all relate to — noise.
When discussing historical investing data one of the more interesting points that inevitably arises is the question of just how applicable past results are to current events and future investing decisions. Some people reject all historical data as completely irrelevant because the future will never look exactly like the past, while others hold up mathematical evidence-based investing as some sort of scientific principle that one would be foolish to question. I imagine one might expect me to fall into the latter camp, but frankly I think it’s more complicated than that. Good data speaks for itself but unless you’re speaking the same language you can easily get the message completely wrong.
In the world of investing, it seems most people’s energy is focused squarely on stocks. Massive amounts of research goes into stock investing every day and even casual investors have an incredible amount of information at their fingertips. Combine that wealth of data with a long term growth pattern in the stock market since 2009 that means anyone who has been in the market less than ten years has no recollection of a single meaningful bear market, and stocks are so ingrained on our collective investing consciousness that many people don’t give other assets a second thought.
As a result, while bonds were once staples of any self-respecting asset allocation lots of investors just aren’t into them like they used to be. Some of it is definitely recency bias, and I think another factor is that many people don’t truly understand how bonds work. But I’ll concede that the bond market is also different than it was more than a decade ago, and I can understand why people might think twice about relying too much on historical data. After all, bonds sound like awesome choices when they paid 10% interest, but consistent declining rates over time surely boosted any backtested numbers while depressing yields today to something a lot less desirable. So it’s no surprise that I see questions along this line all the time:
With record low interest rates today that are even negative in some situations, what’s the point of having bonds in a portfolio at all?
That’s a very good question. And the full answer is kinda complicated and includes some advanced finance mechanics that fly under the radar even for very experienced investors. But explaining complicated concepts is kinda my thing, so let’s talk about a little thing called bond convexity.
If you’re one of the millions of people launching into the gift-buying spree this holiday season, then there’s a good chance you’re inundated with numbers right now. From
performance statistics and customer ratings to price points and discount percentages, smart shoppers are in a constant search for the best bang for the buck. Even if you’re not the type of impulse shopper that particularly enjoys the experience of browsing the aisles for just the right gift, experienced marketing professionals have learned that maximization of value is a powerful motivator that can pull even the strongest introverts into crowded stores against their better judgement. Why do you think they spend so much money on Black Friday ads touting record-breaking deals?
After a lifetime of this type of shopping conditioning, it’s no wonder that this same maximization mindset might bleed into other decisions as well. Like, for example, what asset allocation you might choose for your life savings. So the same highly intelligent shoppers often create very similar lists of investing options sorted by the most common performance metric available — average return. Just like how you might seek the best resolution for a new computer monitor or the highest customer rating for a new toy, you surely want the highest average return for your hard-earned savings. Right?
Unfortunately it’s not that simple. Contrary to your data-driven instincts, averages lie. So take a break from your holiday shopping, find a comfortable chair with your favorite drink, and let’s talk about how averages distort our thinking.
Yale University recently released their 2017 annual report for the Yale Endowment, and while normally this would pass without much notice they appear to have made a few waves by continuing an ongoing feud with Warren Buffett. In his 2016 investor letter, Buffett criticized how university endowments pursue market-beating returns through active management and suggested they might be better off investing in index funds instead. Of course the CEO of Berkshire Hathaway follows none of that advice himself, but he has consistently said that most investors including his own wife would be better off with a low-fee S&P500 index fund rather than paying expensive active managers so it’s certainly not out of character. In any case, Yale appears to have taken that a little personally and they dedicated an entire section in their annual report to dispute his claim and promote their own success.
To support their belief in active management, Yale provides data that proves their managers have exceeded stock market returns for the past two decades. For example, over the past 20 years they posted an average return of 12.1% versus 7.5% for the total US stock market which gives them confidence to say they “crush the returns produced by US stocks”. Ending with a flourish, they conclude that “not only has the model worked for the past two decades, it will work for decades to come.”
That’s bold. And it caused a bit of a tizzy in the financial blogosphere with several stories on the topic. So are they right?
Asset allocation is a obviously passion of mine, and I’m always excited when I find a new metric to tinker with. These new ideas are not only interesting in their own right, but they also allow me to go back and refine some older tools to make them even better. And it’s hard to think of a more appropriate place to start than one of my personal favorites — the Portfolio Finder.
Perhaps because of the proliferation of personal finance websites focusing on early retirement, I’ve noticed a lot of talk lately about safe withdrawal rates. I think this is absolutely terrific, as financial independence is one of the single most empowering life goals one can pursue! But greater exposure also has its downsides, as core assumptions such as the portfolio options, withdrawal method, and retirement length don’t always scale the way you might think and misconceptions can quickly propagate.
Withdrawal rates are an intellectual passion of mine, and I’m always looking for opportunities to contribute to the conversation. And with the recent boon in global portfolio data, I’m finally able to address one of the biggest questions that I’m starting to see more frequently these days.
Does the 4% rule apply outside of the United States?
I’m always on the lookout for ways to improve the tools on Portfolio Charts, and Siamond really came through with his latest update to the Simba spreadsheet. Buried in the heaps of interesting returns data is something really cool — direct calculations for safe and perpetual spending rates for a given investing period. Based on an equation from a Morningstar white paper, they are particularly elegant compared to my old method and allow me to significantly improve the speed and stability of the Withdrawal Rates calculator. And by doing so they open up a great deal of additional data that was previously too laborious to manage.
Well I’m a sucker for new data, and in the process of updating the calculator mechanics behind the scenes I took the opportunity to revisit an old question I’ve been wrestling with for a while.
How do you calculate a 40-year withdrawal rate when the worst start date for a particular portfolio was less than 40 years ago?