It’s a mystery to me why so many investors pay brokers to pick “winning” mutual funds. But they do, and it turns out that they aren’t alone in this often fruitless quest. Pension funds pay obscene fees to “consultants” who claim the ability to select outperforming mutual funds or other types of investments.
A flawed process
The process these “experts” follow would be amusing if it didn’t cause retirement plan beneficiaries so much harm. First, they select fund managers likely to outperform, based largely on past performance. Periodically, they meet with plan sponsors to eliminate fund managers who have failed to continue to outperform. They replace them with new fund managers, again based largely on their recent track record of outperformance.
There’s only one problem. This process doesn’t work. Actually, let me clarify that. It works great for brokers and pension plan consultants. They reap commissions and fees for the dissemination of their “expertise.” It doesn’t work so well for investors and plan beneficiaries, however. They typically end up with returns that underperform risk-adjusted benchmarks, and are charged handsomely for this poor result.
A recent academic paper exposes this process for the sham that it is.
“Losers” beat “winners”
The study examined whether selecting fund managers based on recent performance was likely to lead to a favorable outcome prospectively. It looked at the performance of U.S. equity mutual funds in three-year tranches from January 1994 to December 2015. It also evaluated a number of strategies for picking outperforming mutual funds. These included a “winner” strategy (picking from top-performing funds), a “median” strategy (picking from average-performing funds) and a “loser” strategy (picking from funds not on recommended buy lists and not recommended by advisors). The authors then analyzed the performance of the very poorly performing funds (those that underperformed their benchmarks by 1 percentage point or more, as well as those that underperformed by more than 3 percentage points).
Here’s the bottom line of the findings in the study: “A heuristic of hiring recently outperforming managers and firing recently underperforming managers turns out to be 180 degrees wrong.”
The study found that hiring managers with mediocre track records led to better results than hiring past winners. And the strategy of hiring past losers had the best track record. Funds that underperformed by at least 3 percentage points (the “losers”) went on to outperform funds that had outperformed by at least 3 percentage points (the “winners”), returning 10 percent versus 8.9 percent.
The big charade
The ramifications of this study are profound. It exposes the conduct of pension plans, endowments, 401(k) plans and brokers who claim to be able to select outperforming actively managed mutual funds as a charade. The study notes that firing managers with poor recent performance and hiring those with recent positive performance is “one of the most important statistics for firing managers, and is critical in the hiring decisions as well.” It’s also consistent with the algorithm behind Morningstar’s star rating, “which gives the heaviest weight to the past three-year performance.”
The study turns this process on its head, noting, “If the results are accepted at face value, and if past performance is used at all for hiring and firing managers, it is the best-performing managers who should be replaced with those who have performed more poorly.”
Don’t ignore the data
The authors of the study note the “immense literature” finding little evidence that fund managers can consistently outperform the market on a risk-adjusted basis. The securities industry and investment consultants hope you will ignore this data. Alternatively, you could follow the results of the study to their logical conclusion and adopt a strategy of picking funds with a recent history of poor returns, but here’s a better idea:
Abandon the elusive goal of trying to “beat the market” by selecting outperforming actively managed funds. Limit your investments to a globally diversified portfolio of low-cost, passively managed funds that capture global market returns. Focus on your asset allocation (the division of your portfolio between stocks and bonds) and deferring or avoiding taxes.
As an individual investor, you can easily implement this strategy. If you are a beneficiary of a pension plan or a participant in a 401(k) plan, you have to hope your plan sponsor pays attention to this study and changes its policy for selecting the funds in your plan. If it doesn’t, maybe the threat of litigation holding them responsible for not paying attention to the “immense literature” will cause them to change their fund-selection policy.
This commentary originally appeared April 5 on HuffingtonPost.com
By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party Web sites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them.
The opinions expressed by featured authors are their own and may not accurately reflect those of the BAM ALLIANCE. This article is for general information only and is not intended to serve as specific financial, accounting or tax advice.
© 2016, The BAM ALLIANCE
I was driving with a friend recently and telling him about some projects that really excited me. I mentioned a new book I’m working on, an article I’m writing and this new hobby of adventure motorcycling in the desert.
He interrupted me and said, “How do you stay so motivated and so excited about things?”
It caught me off guard. I hadn’t really considered the “why” behind my list of activities. But as I thought about it, I realized that the one aspect each of these projects had to make me so motivated — the common thread — was the feeling of being in just a little over my head. In other words, doing things despite the fact that, as the marketing guru Seth Godin likes to say, “this might not work.”
Now, that may sound a little bit counterintuitive. It’s easy to wonder how doing stuff that makes you uncomfortable, and might not even work, is a source of motivation.
I’ve been thinking a lot about this paradox, and I could not get my friend’s question out of my head. I wondered whether I’m wired differently. But there’s something about a sink-or-swim environment that excites me.
I posted on Instagram about constantly getting in a little over my head, and my friend Dallas Hartwigtold me about this concept called hormesis, a phenomenon by which something that could significantly impair or even kill you in high doses can make you stronger in low doses. Or as the National Institutes of Health puts it, “In the fields of biology and medicine, hormesis is defined as an adaptive response of cells and organisms to a moderate (usually intermittent) stress.”
Of course, I thought. What doesn’t kill you makes you stronger. It’s not a new concept. It’s well documented that the way to grow muscle is to rip the muscle tissue, and then give it time to regrow. You give it stress, then rest, and it comes back on the other side stronger than it was before.
So what if we did the same thing in other areas of our lives? In our work, in our family life or in our recreational activities?
It makes sense that the business equivalent of building muscle is trying new things. When you throw yourself into the deep end of something new, you often face a steep learning curve. That forces you to grow, adapt, change and develop your skill set. It’s almost irrelevant if the particular project ends up succeeding. The very act of taking on something new helps you become better at your work over all.
You cannot spend your whole life in the deep end, as that is a recipe for drowning. Muscles get tired. So just like physical exercise, you have to take breaks. You have to calibrate the stress and rest cycle of any sort of entrepreneurial or creative work.
The more I thought about it, the more I began to see these experiences, of diving into the unknown, for what they really were. Some people call them work projects, but I call them adventures. After all, isn’t the definition of “adventure” to set off into the unknown, endure hardships, come back and then rest?
With this reframing, I finally had an answer to my friend’s question about how I stay motivated. It’s because I’m constantly setting off on the next adventure! How could I not?
I know that adventures are not for everyone. I know they can feel scary and intimidating. But making a habit of seeking adventures, in spite of how scary they are, may be the secret to staying motivated about the things you do.
And that, if nothing else, confers a key economic benefit onto anyone who experiences it. Even if we set aside all the tangible benefits that come from stepping outside our comfort zone, it is intuitively obvious that being more excited about your work is a surefire way to improve your performance – and turn your various ventures into adventures.
This commentary originally appeared April 11 on NYTimes.com
By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party Web sites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them.
The opinions expressed by featured authors are their own and may not accurately reflect those of the BAM ALLIANCE. This article is for general information only and is not intended to serve as specific financial, accounting or tax advice.
© 2016, The BAM ALLIANCE
Most investors believe all passively managed funds within the same asset class should have the same, or at least very similar, returns. However, while all index funds and passive structured asset class funds are similar in the way that rectangles and squares are similar, they are also very different. All squares are rectangles, but not all rectangles are squares.
Similarly, while all index funds are passively managed, not all passively managed structured asset class funds attempt to replicate the returns of popular retail indexes like the S&P 500 or the Russell 2000.
Instead, they tend to use academic definitions of asset classes and structure portfolios to minimize the inherent weaknesses of pure indexing. Those weaknesses, which result from the desire to minimize what is called “tracking error” (returns that deviate from the return of the benchmark index), include:
The Price Of Tracking Error
Another advantage that structured funds can bring, in return for an investor accepting tracking error risk, is that they can gain greater exposure to certain factors for which there is persistent and pervasive evidence of a return premium (such as market beta, size, value, momentum and profitability/quality).
For example, a small value fund could be structured to own smaller and more “valuey” stocks than a small-cap value index fund might include. It can also be structured to have more exposure to highly profitable companies. And it can screen for the momentum effect (avoiding the purchase of stocks that are exhibiting negative momentum and that delay the sale of stocks with positive momentum).
While all these attributes are benefits, they come with a “price” in the form of the aforementioned tracking error. Investors seeking the advantages of structured funds must accept the fact that it’s a virtual certainty there will be periods (even very long ones) during which they underperform an index fund in the same asset class.
Investors enjoy it when the tracking error is positive—their passively managed structured fund outperforms an index fund in the same asset class—but when the tracking error is negative, they unfortunately exhibit a tendency to make the dual mistakes of confusing strategy with outcome and losing discipline (they become impatient).
Confusing Strategy With Outcome
“Fooled by Randomness” author Nassim Nicholas Taleb had the following to say on confusing strategy and outcome: “One cannot judge a performance in any given field by the results, but by the costs of the alternative (that is, if history played out in a different way). Such substitute courses of events are called alternative histories. Clearly the quality of a decision cannot be solely judged based on its outcome, but such a point seems to be voiced only by people who fail (those who succeed attribute their success to the quality of their decision).”
In investing, there are no clear crystal balls. Thus, a strategy should be judged in terms of its quality and prudence before—not after—its outcome is known.
Compounding the problem of confusing strategy with outcome is impatience. I have learned that when contemplating investment returns, the typical individual investor considers three to five years a long time, and 10 years an eternity. When it comes to the returns of risky asset classes, however, periods as short as three or five years should be seen as nothing more than noise. Even 10 years is a relatively brief period.
To demonstrate the potential problem posed by tracking error regret, I will compare the performance of several of Vanguard’s index funds with structured, passively managed funds in the same asset class from Dimensional Fund Advisors (DFA). (In the interest of full disclosure, my firm, Buckingham, recommends DFA funds in constructing client portfolios.) However, before doing so, a brief history of capital asset pricing models will prove helpful.
Building on the work of Harry Markowitz, the trio of John Lintner, William Sharpe and Jack Treynor are generally given most of the credit for introducing the first formal asset pricing model, the capital asset pricing model (CAPM). It was developed in the early 1960s. The CAPM provided the first precise definition of risk and how it drives expected returns.
CAPM: A One-Factor Model
The CAPM looks at risk and return through a “one-factor” lens—the risk and return of a portfolio are determined only by its exposure to market beta. This beta is the measure of the equity-type risk of a stock, mutual fund or portfolio relative to the risk of the overall market.
The CAPM became the financial world’s operating model for about 30 years. But like all models, it was, by definition, flawed, or wrong. If such models were perfectly correct, they would be laws, like we have in physics. Over time, anomalies that violated the CAPM began to surface.
In 1981, Rolf Banz’s “The Relationship Between Return and Market Value of Common Stocks” found that market beta doesn’t fully explain the higher average return of small stocks. That same year, Sanjoy Basu’s “The Relationship Between Earnings’ Yield, Market Value and Return for NYSE Common Stocks” found that the positive relationship between the earnings yield (E/P) and average return is left unexplained by market beta.
And in 1985, Barr Rosenberg, Kenneth Reid and Ronald Lanstein found a positive relationship between average stock returns and book-to-market (B/M) ratio in their paper, “Persuasive Evidence of Market Inefficiency.” The last two studies provided evidence that, in addition to a size premium, there also is a value premium.
Fama-French Three-Factor Model
The 1992 paper “The Cross-Section of Expected Stock Returns,” by Eugene Fama and Kenneth French, summarized and explained these anomalies in one place. The essential conclusion from the paper was that the CAPM explained only roughly two-thirds of the differences in returns of diversified portfolios, and that a better model could be built using more than just the one factor. Fama and French proposed that, along with the market factor of beta, exposure to the factors of size and value explain the cross section of expected stock returns.
The new Fama-French model greatly improved on the explanatory power of the CAPM, accounting for more than 90% of the differences in returns between diversified portfolios. And the Fama-French three-factor model replaced the CAPM as the workhorse model in finance.
Since then, financial economists have uncovered a number of other factors (including momentum, profitability and investment) that have been shown to provide premiums that have been persistent (across time) and pervasive (across industries, countries and regions) while also improving the explanatory power of asset pricing models.
However, given that the Fama-French three-factor model does explain more than 90% of the differences in returns of diversified portfolios, to keep our analysis simple, we’ll use it to explain the differences in returns of the similar Vanguard and DFA funds.
The table below shows the 5-, 10- and 15-year annualized returns, as well as the recent weighted average market capitalizations (the size metric) and weighted average price-to-book (P/B) ratio (the value metric) for domestic Vanguard and DFA funds covering the broad market (large-cap, midcap and small-cap stocks in addition to value, core and growth stocks) plus the large value, small and small value asset classes. It also shows the factor premiums. We’ll then show how the factor models and factor returns explain the differences in performance.
As you review the data, keep in mind that while the factor models have good explanatory power, they are not perfect representations of the world. Since we are using a three-factor model, differences in returns may also be explained by exposure to other factors (such as momentum or profitability). In addition, differences in returns may be explained by a fund’s exposure to other asset classes. This is especially true in the case of the small value funds.
While the Vanguard small value fund includes exposure (currently about 11%) to REITs because REITs are included in the benchmark index, the DFA fund specifically excludes REITs, treating them as a separate asset class (if you want to own REITs, then you should own a REIT index fund). REITs have lower historical returns than small value stocks. The critical point to keep in mind is that if REITs outperform (underperform), the Vanguard small value fund will benefit (be negatively impacted).
US Broad Market
Vanguard’s Total Stock Market Index Fund (VTSMX) replicates the performance of the Center for Research in Security Prices (CRSP) U.S. Total Market Index. To do that, it basically owns all the equities in the index weighted by market capitalization. DFA’s Core Equity 2 fund (DFQTX) basically owns all the stocks in the index as well.
However, because the DFA fund is designed to provide exposure to the size and value factors, using a proprietary formula, it overweights (or has more exposure to) small and value stocks.
Note the “2” in its name reflects the fact that the fund is designed to have approximately 0.2 (20%) exposure to each of the factors. By using the regression factor tool from Portfolio Visualizer, we can see the differences in what is referred to as factor loading (how much exposure a fund has to a certain factor).
Because it is a total market fund, by definition, we should expect to see that VTSMX has no exposure to the size and value factors. That’s precisely what we find. The loadings were both 0.0. And the R-squared figure (a measure of how well the model explains the performance of the fund) was 99.9%. Given its design, we should expect DFQTX to have exposure to the two factors.
We can observe this by looking at the above table. DFQTX’s weighted average market capitalization is much smaller (only about 40% of the weighted average capitalization of VTSMX) and it is more “valuey” (its P/B ratio is about 15% lower).
In addition, the regression analysis shows that loading on both the size and value factors was about 0.2 (the R-squared was 99.5%). This means that when small and value stocks outperform (the premiums are positive), we should expect to see DFQTX outperform, and vice versa. And that’s exactly what we find.
Broad Market Results
During the most recent five-year period, in which the size premium was -2.5% and the value premium was -2.8%, VTSMX managed to outperform DFQTX by 1.1 percentage points, mainly because the DFA fund has more exposure to the underperforming small and value stocks (the fund also has a higher expense ratio, 0.22% versus 0.16%). In the 10-year period, the size premium was 0.0% and the value premium was -1.4%.
We should once again expect VTSMX to have outperformed, although by a smaller margin. And that’s what we find, as it outperformed by 0.5%. Unfortunately, DFQTX is only about 10 years old, so we don’t have returns data for the full 15-year period. However, given that the size premium was 3.1% and the value premium was 1.6% over that period, we should expect DFQTX to have outperformed.
Investors shouldn’t view DFQTX’s underperformance relative to VTSMX as “bad” nor any outperformance as “good.” Both funds did what they were designed to do. A problem arises when investors make the aforementioned mistake of confusing strategy with outcome, becoming impatient because they fail to understand that even 10 years is noise when it comes to returns of risky investments.
No more proof is required than the -1% per year return to the S&P 500 Index during the first decade of this century. Investors in stocks shouldn’t have lost faith in their belief that stocks should outperform safe Treasury bills due to the experience of that period. If they did, they missed out on one of the greatest bull markets in history.
US Large Value
Vanguard’s Value Index Fund (VIVAX) is designed to track the performance of the CRSP U.S. Large Cap Value Index. DFA’s large value fund (DFLVX) is designed to provide more exposure to the value factor, thus it has different construction rules. As you can see in the table, DFLVX’s market capitalization is about one-third smaller than that of VIVAX, and its P/B ratio is about 20% lower.
We see this in the regression as well. VIVAX has loadings on the size of -0.2 (it holds larger stocks) and 0.3 on value. The R-squared figure was 97% (demonstrating that the model explains the returns well). DFLVX’s loadings are 0.1 and 0.5, respectively (it has more exposure to both premiums). The R-squared was also 97%.
Turning to the performance analysis, we find that DFLVX underperformed by 0.4 percentage points in the most recent five-year period when both premiums were negative. It did, however, manage to outperform slightly (by 0.1 percentage point) during the most recent 10-year period when the size premium was zero and the value premium was slightly negative. But over the longer 15-year period, when both premiums were positive, DFLVX outperformed by 1.8 percentage points, just as you should have expected.
US Small Cap
Vanguard’s Small Cap Index Fund (NAESX) is designed to track the performance of the CRSP U.S. Small Cap Index. Both the DFA small-cap fund (DFSTX) and its microcap fund (DFSCX) are designed to provide greater exposure to the size factor, thus they have different construction rules.
As you can see in the table, DFSTX’s market capitalization is not much more than half that of NAESX, and DFSCX’s market capitalization is more than 70% smaller. Their P/B ratios are similar, although smaller in both cases (DFSTX by 3% and DFSCX by 7%).
We see this in the regressions as well. NAESX has a loading on the size of 0.7 and 0.2 on value. The R-squared in this case was 99%. DFSTX and DFSCX have loadings on the size of 0.8 and 0.9, respectively. The loadings on value are 0.2 and 0.3, respectively. The R-squared figures were 97% and 99%, respectively.
Turning to the performance analysis, over the most recent five-year period, both DFSTX and DFSCX were able to outperform NAESX (by 0.7 percentage points and by 0.8 percentage points, respectively) despite having somewhat higher exposures to the size factor, which was negative during the period.
This outperformance could be explained by the DFA funds’ greater exposure to the profitability factor (which, as I mentioned earlier, might result from a construction methodology that excludes certain stocks). The profitability premium was more than 3% in the five-year period, more than 3.5% in the 10-year period and 4% in the 15-year period. It might also be explained by patient trading, securities-lending activities and other factors discussed previously.
Over the most recent 10-year period, when the size premium was 0, DFSTX produced the same return as NAESX, and DFSCX underperformed by 0.9 percentage points. During the most recent 15-year period, when the size premium was 3.1%, as we should expect, we find that DFSTX outperformed NAESX by 0.6 percentage points and DFSCX, with its greater exposure to both the size factor and the value factor (which was 1.6%), outperformed by 1.2 percentage points.
US Small Value
Vanguard’s Small Cap Value Index Fund (VISVX) is designed to track the performance of the CRSP U.S. Small Cap Value Index. Both the DFA small-cap value fund (DFSVX) and the firm’s U.S. targeted value fund (DFFVX) are designed to provide greater exposure to the size and value factors. Again, they thus have different construction rules.
As you can see in the table, DFSVX’s market capitalization comes in at under half that of VISVX, and DFFVX’s market capitalization is about 20% smaller. In addition, the P/B ratios of the funds are lower, by 28% and 20%, respectively.
We see this in the regressions as well. VISVX has a loading on the size of 0.7 and 0.4 on value. The R-squared was 97%. DFSVX and DFFVX have loadings on the size of 0.9 and 0.8, respectively. The loadings on value are 0.6 and 0.5, respectively. The R-squared figures were 98% and 97%, respectively.
Turning to the performance analysis, over the most recent five-year period, when the size and value premiums were both negative, as you should expect, DFSVX underperformed VISVX by 1.9 percentage points. And DFFVX, with a lesser loading on size and value than DFSVX but a higher loading than VISVX, also underperformed (as should be expected), but by a lesser amount (1.4 percentage points).
Over the most recent 10-year period, when the size premium was 0 and the value premium was -1.4%, DFSVX underperformed VISVX by 1.2 percentage points (less than its underperformance over the five-year period, when returns to both factors were more negative) and DFFVX underperformed by a lesser amount as well, 0.5 percentage points (due to a smaller difference in exposure to the two factors).
During the most recent 15-year period, when the size premium was 3.1% and the value premium was 1.6%, as we should expect, we find that DFSVX outperformed VISVX by 1.2 percentage points. DFFVX outperformed by 1.3 percentage points.
Conclusion
The lesson I hope you take away is that you don’t want to be like investors in actively managed funds, chasing returns. Instead, the choice of the fund you use should be based on other criteria, including what factors you want exposure to, how much exposure you want to those factors, the fund construction rules, for taxable accounts whether the fund is managed for tax efficiency and, of course, a fund’s expense ratio.
And remember that just because two passively managed funds have similar names and/or are in the same asset class, it doesn’t guarantee that they’re following similar fund strategies. Thus, when making a fund decision, you want to be sure to weigh all of the criteria.
It just might be that the fund with a higher expense ratio is the better choice because it provides more exposure to the factors that determine returns and carry premiums. In other words, it’s not only cost, but cost per unit of expected return (and risk) that matters.
For example, while VISVX has an expense ratio of 0.20% (the Admiral shares version of the fund, VSMAX, costs just 0.09%), and DFSVX has an expense ratio of 0.52%, the higher costs of the DFA fund have been more than offset by greater exposure to the desired factors and a focus on adding value by minimizing the negatives of pure indexing.
Bridgeway’s Omni Small-Cap Value Fund (BOSVX)—which, in full disclosure, my firm also recommends in the construction of client portfolios—has an even higher expense ratio, but is much smaller and more “valuey” than DFSVX. Morningstar reports that the fund’s year-end 2015 weighted average market capitalization was just $627 million (about half the market capitalization of DFSVX) and its P/B was just 1.05. Thus, its expected returns are higher.
This commentary originally appeared April 13 on ETF.com
By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party Web sites.
We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them.
The opinions expressed by featured authors are their own and may not accurately reflect those of the BAM ALLIANCE. This article is for general information only and is not intended to serve as specific financial, accounting or tax advice.
© 2016, The BAM ALLIANCE