I love finding financial wisdom in unlikely places, like in art and music. These opportunities are more abundant than you might expect. For instance, the punk-Americana outfit, The Avett Brothers, dedicated an entire tune, aptly titled “Ill With Want,” to the scourge of greed and Mumford & Sons taught us that “where you invest your love, you invest your life.”
The newest melodic metaphor to catch my ear comes from singer-songwriter Jason Isbell. He expresses his appreciation for having work in the title track of his newest album, “Something More Than Free,” but it’s the pair of questions he poses in another song, “The Life You Chose,” that really got me thinking.
“Are you living the life you chose? Are you living the life that chose you?” asks Isbell.
I fear it is the latter for many, if not most, of us. Perhaps we are stuck living a life that has grown into a web of circumstances driven more by external compulsions than autonomous impulsions. For too many, life is lived at the behest of someone else’s priorities and goals, in pursuit of someone else’s calling.
No, I’m not a Big Brother conspiracy theorist. It just seems to be the natural way of things. After all, it can be more expedient to perform the task that someone else has delegated, to attend the meeting that someone else has set, to pick up the ringing phone or to respond to that incoming email than it is to initiate in life and work.
It’s easier to consume than it is to create.
But you don’t have to be a “creative” to create. You don’t have to be an entrepreneur to lead. You don’t have to be a maverick to innovate. You don’t have to be a minister to minister, nor an advocate to advocate.
I’m not promoting self-service, but instead encouraging self-selection.
In his take on the science of motivation, Daniel Pink suggests that true motivation comes not via the carrot and stick, but through autonomy, mastery and purpose. In my book, Simple Money, I translate Pink’s observations into a practical application for goal setting, suggesting that our personal goals should have the following attributes:
Your goals should be:
Meeting these criteria helps ensure that our goals in life will stick, because we’ll be properly motivated. Then, we can get to the more practical work of applying the effort and money necessary for making them a reality.
So, which is it: Are you living the life you chose or the life that chose you?
If the latter, you may sympathize with Pearl Jam front-man, Eddie Vedder, who laments: “If I had known then what I know now….” But also keep in mind his encouragement that it “makes more sense to live in the present tense.”
This commentary originally appeared March 12 on Forbes.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
Because of the magnitude, persistence, pervasiveness and robustness of their related premiums, several factors have dominated the academic literature. Among them are market beta, size, value, momentum and profitability. However, despite their persistence, each factor has undergone even fairly long periods during which it produced negative returns.
Said another way, while investors can raise expected returns by increasing their exposure to the market, size, value and profitability premiums, over any given time period—no matter how long—the realized premiums can be negative. And that fact is what’s tempted many to find a way to “time the premiums”—to tactically allocate by shifting from stocks to bonds, small-caps to large-caps, value to growth and so on.
Asset class (or factor) rotation is a strategy that many active managers employ in an effort to enhance performance. As a strategy, it certainly has appeal, assuming you can identify (in advance) the best-performing asset class (factor). It would seem that a logical way to do this would be to rely on relative valuations.
When an asset class looks cheap relative to its historical relationships, rotate into it. For example, when the spread between the book-to-market (or price-to-earnings) ratios of value and growth stocks is wider than the historical average, then investors should load up on value stocks. On the other hand, when the ratio is relatively low, they should abandon value stocks and move to growth stocks.
This would seem to make sense, as the historical data shows that when the spread in book-to-market (BtM) ratios between value stocks and growth stocks is high, the subsequent value premium tends to be larger. The reverse is also true. Based on that information, if next year’s value premium is expected to be high, it would seem logical to own value stocks. If it were expected to be negative, then growth stocks would seem to become the logical choice.
But is it really that simple to earn abnormal returns? Does a statistical relationship always translate into a viable portfolio strategy?
A DFA Study
Wei Dai of the research team at Dimensional Fund Advisors (DFA) sought the answer to those questions in her March 2016 paper, “Premium Timing with Valuation Ratios.” Dai studied the performance of the market, size and value premiums over the period July 1926 through June 2015, as well as the performance of the profitability premium for the period July 1963 through June 2015.
Looking for trading rules that outperformed the long-only benchmarks by at least 25 basis points per year and where that outperformance was reliably different from zero, Dai examined timing strategies that trade back-and-forth between the long and the short sides of the premiums in an attempt to generate abnormal returns. The strategies rebalanced annually on June 30.
Dai considered nonparametric trading rules (a test that no assumptions regarding the specific form of the relationship between the spread and the premium, other than the direction of the relationship) that invest in the long side of a premium and then move into the short side when the valuation spread of interest is small. For example, when the book-to-market spread between value and growth is small, it suggests the subsequent value premium may be low, so the trading rule invests in the growth side.
To implement such a strategy, it is necessary to define a “small” valuation spread. Small spreads are defined as those below the 10th, 20th or 50th%ile (breakpoint) of the historical distribution. Dai also considered various strategies for when to switch back, including when the spread crosses a breakpoint, and until it crosses the 50th%ile.
She also examined data that covered all past data up to the trading day, as well as data that covered only the most recent 20-year periods. For each pair of premiums and valuation spreads, Dai ran a variety of trading rules defined by breakpoint, switchback and window. In all, he tested 200 trading rules.
Results
Following is a summary of her findings:
Dai also tested 480 parametric trading rules (rules that employ a regression approach to forecast future premiums). Since a premium is constructed by subtracting the return on the short side from the return on the long side, they reflect the relative performance of each side. Thus, the trading rule invests in the side of a premium that is predicted to do better (or the one that is more likely to do so). She found that only about 2% of simulations produced a reliably positive excess return greater than 0.25.
Given the large number of simulations, we should expect some chance of what is called a “false discovery.” For example, under a simplified multiple-testing framework in which trials are independent, about 5% of trials will appear statistically significant even if all the signals are pure noise.
Lack Of Robustness
To have confidence in an investment strategy, the evidence should be not only persistent and pervasive, but robust to various definitions. For example, we would not have the level of confidence we do in the value premium if it only existed when we measured value using the metric of price-to-book. There’s a very similar premium when we measure value using other metrics, such as price-to-cash flow, price-to-earnings, price-to-sales and (for markets outside of the United States) price-to-dividends.
Similarly, there’s a strong momentum premium whether we measure momentum by 12 months, nine months or six months. The profitability premium is strong whether it’s measured by profits-to-sales or profits-to-assets, or even cash-flow-to-assets.
Given that Dai found only about 2% of trading rules passed the test, we cannot achieve the same level of confidence in the robustness of a premium timing strategy. Dai concluded that “the percentages observed here do not constitute strong evidence that one can reliably time the subsequent year’s premiums using valuation spreads.”
Furthermore, there is other research investigating the variables that can be used to time markets.
Supporting Evidence
The 2007 study “Does Predicting the Value Premium Earn Abnormal Returns?” by Jim Davis of DFA examined style-timing strategies over the period July 1927 through June 2005. Davis found that style-timing rules did not generate high average returns, despite being able to use future information about BtM spreads.
In fact, he concluded the expected excess return of style timing is probably negative, for the same reasons that efforts to time the overall market are likely to fail. Just as ex-ante there should always be an equity risk premium, ex-ante there should always be a value risk premium. And as is the case with the equity risk premium, the value premium is so large that any trading strategy would have to be right a large majority of the time to deliver successful results.
It would be like switching from the high-speed carpool lane to the center lane on a crowded freeway. Your “freeway algorithm” might help predict when the carpool lane or center lane will move faster or slower than normal, but will it be accurate enough to justify switching into the slower lane in an effort to get to your destination quicker? The evidence suggests you are better off staying in the carpool lane.
The lesson for investors is that just because a statistical relationship exists does not necessarily imply that a profitable trading strategy based on that relationship exists as well, especially after taking into account trading and other costs.
What About Valuation?
There’s also the study “Is There Value in Valuation?” which appeared in the Winter 2013 issue of The Journal of Portfolio Management. The authors, Martin Fridson and John Finnerty, studied whether a valuation-based rotation strategy worked for high-yield bonds.
They first showed that a rotation strategy seemed to have great appeal. For example, for the period 2007 through 2011, even though mean returns for bonds rated BB, B and CCC were 9.9%, 8.3% and 15.2%, respectively, choosing the best-performing sector would have produced a return of 21.0%. Clearly, there’s a large opportunity to add value.
Before getting into their findings, it’s important to understand that, in general, you should expect the lowest-rated bonds (CCC) to outperform when the risk premium (or incremental yield over Treasurys) for high-yield bonds either remains constant or decreases and to underperform when it increases. (They can even outperform if the increase in the risk premium is small relative to the size of the premium.)
This should certainly be true if the change in risk premiums is large. (If the risk premium widens by a small amount, it’s possible that the higher yield of the lower-rated bond would provide enough of a cushion for them to still outperform.)
This is all intuitive. If a risk premium falls, the riskier asset class should outperform, and vice versa. And if this is true, then we don’t need information about relative valuations to inform us about which sector/asset class will outperform. We only need to forecast whether the premium will widen or not. (The evidence on active management demonstrates managers don’t have much success at that endeavor.) Studying the quarterly results for the period 1997 through 2011, this is exactly what Fridson and Finnerty found.
Forecasting Premium Direction
Using what is called the option-adjusted spread (OAS) to take into account the call risk inherent in corporate bonds, they found this relationship held true 76% of the time for B and BB bonds, 83% of the time for B and CCC bonds and 86% of the time for BB and CCC bonds. This finding very clearly calls into question a valuation-based trading strategy.
To know which sector to move to, all you need is to accurately forecast whether the premium for the entire asset class will rise or fall. If it’s going to narrow, then own the lowest-rated bonds. If it’s going to widen, then own the highest-rated bonds.
To test whether a valuation-based approach was likely to add value, Fridson and Finnerty built a five-factor valuation model (credit availability, capacity utilization, industrial production, speculative-grade default rate and yield to maturity on the five-year Treasury). If the OAS spread was greater than the model predicted, the asset was cheap (you should rotate into it). The asset would be expensive (you should rotate out of it) if the spread was less than fair value. The model explained a high percentage, between 74% and 80%, of the variance in performance.
Unfortunately, using various tests, the authors found no statistically significant benefit from a sector rotation strategy based on the valuation model. It’s also important to observe that high-yield bonds are an expensive asset class to trade. As a result, even if there appeared to be some theoretical advantage to a rotation strategy, the advantage could very well be ephemeral, with trading costs creating a large hurdle to overcome.
Summary
As tempting as the proposition might be, there doesn’t seem to be much, if any, evidence that a style-timing strategy can be expected to be profitable going forward. But that doesn’t mean the information contained in the size of the spreads isn’t useful. Valuations do matter. They should be used in setting longer-term return expectations (with the understanding that a wide range of potential outcomes must be expected). It’s just that using the information to time markets isn’t likely to prove successful.
This commentary originally appeared March 18 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
For almost five decades, the literature on the investment performance of mutual funds has found that very few managers possess sufficient stock-picking or market-timing talent to allow them to consistently and reliably produce positive risk-adjusted performance after considering their fees. In other words, there’s little to no evidence of outperformance beyond the randomly expected.
As my co-author Andrew Berkin and I discuss in our book, “The Incredible Shrinking Alpha,” while perhaps disheartening, this result shouldn’t be surprising given the very high skill level of active managers competing fiercely in a zero-sum game, even before expenses. Thus, investors shouldn’t expect there to be many opportunities for a free lunch.
In addition, because we should expect the scarce resource to earn any “excess returns” that occur (and the ability to generate alpha is far more scarce than investment capital), it is naive to expect that mutual fund managers won’t charge sufficient fees or attract a sufficiently large amount of assets to effectively capture any alpha they generate. Said another way, investors should not expect to be the beneficiaries of the manager’s skill.
Despite the large body of evidence demonstrating that it’s a loser’s game (one that, while possible to win, has odds so poor that it’s not prudent to try), the most common strategy used by both institutional (such as pension plans and endowments) and individual investors to select a fund manager involves hiring outperforming managers and firing underperforming ones.
Buying The Winners
Bradford Cornell, Jason Hsu and David Nanigian contribute to the literature on this strategy with their February 2016 study, “The Harm in Selecting Funds That Have Recently Outperformed.”
The authors note that research on investor behavior has found that, in defiance of the evidence, fund flows “are positively correlated with past performance. Anecdotally, investment consultants and fiduciaries acknowledge that past outperformance is ‘a’ if not ‘the’ dominant manager selection criterion, because it is intuitive and thus defensible to investors.”
To test the strategy, Cornell, Hsu and Nanigian examined whether selecting managers based on recent performance leads to outperformance for investors. Given that three years is the typical investment horizon used by institutional investors in making hiring and firing decisions, they used that horizon in their study.
To simulate the impact of actual decision-making based on track record, they compared the performance of investment policies that involve investing in a “winner strategy” (an equal-weighted investment in the top decile of funds based on benchmark-adjusted returns) to results from a “median strategy” (an equal-weighted portfolio of funds ranked between the 45th and 55th%iles) and a “loser strategy” (an equal-weighted portfolio of the bottom decile of funds).
The authors note: “Funds in the ‘Winner Strategy’ bucket would generally be the funds that are selected by wealth management platforms as part of their buy or recommended list and recommended by financial advisors to clients for consideration. Funds in the ‘Loser Strategy’ bucket would generally be funds that are not on any recommended list and are actively being eliminated from client portfolios by financial advisors.”
They also examined the investment performance produced by the strategy of investing in funds that underperformed their benchmarks by more than 1% per year, and the even more extreme case of investing in funds that underperformed their benchmarks by more than 3% per year. They wondered if doing so would eliminate future bad performance from the portfolio (or, perversely, if it would lead to future outperformance due to mean reversion).
The Winners Lose
Based on the historical evidence that there’s persistence of underperformance among the highest-cost funds, the authors eliminated the funds in the top decile of funds ranked by expense ratio from their sample. The dataset covered the period from 1994 through 2015. Portfolios were formed based on funds’ most recent 36-month performance and rebalanced monthly to maintain equal weighting across funds. At the end of the three-year period, the process was repeated. The following is a summary of their findings:
Shockingly—at least for those who believe in using past performance to make hiring and firing decisions—switching from the winner strategy to the loser strategy would have resulted in almost doubling the Sharpe ratio of the portfolio (from 0.29 to 0.51). In addition, the four-factor alphas of the winning strategy were highly negative, at -2.7%, and statistically significant at the 1% confidence level (t-stat of 3.0). In addition, it was 2.9 percentage points below the four-factor alpha of the loser strategy.
This certainly calls into question the belief that past outperformance was the result of any skill. What’s more, paying fees to consultants to help you make these hiring and firing decisions would only add to the negative impact of such a strategy.
Mind The Gap
These findings help explain the well-documented “returns gap” (what my colleague and fellow author Carl Richards called “the behavior gap”) experienced by individual investors. Due to performance chasing, the returns they earn are below the returns of the very funds in which they invest.
Providing more fuel for the fire, Cornell, Hsu and Nanigian found that the strategy of investing in the funds that underperformed their benchmarks by more than 1% actually outperformed the strategy of buying funds that beat their benchmarks by more than 1% (9.8% with a Sharpe ratio of 0.48 versus 8.7% with a Sharpe ratio of 0.37). The losers produced a four-factor alpha of -0.4% (with a t-stat of -0.7), while the winners produced an alpha of -1.7 (with a t-stat of -3.4).
The findings were the same for the strategy of investing in the funds that underperformed by at least 3%. They managed to outperform the funds that had outperformed by at least 3% (10.0% with a Sharpe ratio of 0.48 versus 8.9% with a Sharpe ratio of 0.39). The losers produced a four-factor alpha of -0.2% (with a t-stat of -0.4) while the winners produced an alpha of -1.4 (with t-stat -3.3). It doesn’t get much uglier than that.
To test the robustness of their findings, the authors also examined a 24-month horizon instead of a 36-month one. The results changed little.
Looking At Expense Ratios
In another interesting test, the authors started with a universe of the top 90% of managers ranked by lowest expense ratio and computed their annualized future outperformance from the selection date to the end of the performance reporting sample—in other words, perfect foresight. They then examined the impact of starting with this fund universe and additionally screening funds based on recent performance. Once again, they found the same results.
Equal-weighting the top 25% of winners would have returned 12.3% (with a Sharpe ratio of 0.61). However, then screening for the most recent winners would have lowered returns to 10.6% (with a Sharpe ratio of 0.43) while screening for the most recent losers would have returned 13.5% (with a Sharpe ratio of 0.68).
The authors write: “Even if investors start with a select list of good managers, using recent outperformance to further screen managers is a harmful practice.” And their findings are consistent with those of prior research on the hiring and firing decisions of pension plans and other institutional investors. Consider the following …
Hiring And Firing
Amit Goyal and Sunil Wahal, authors of a May 2005 study, “The Selection and Termination of Investment Management Firms by Plan Sponsors,” evaluated the selection and termination of investment management firms by plan sponsors (public pension plans, corporate pension plans, union pension plans, foundations and endowments).
They built a dataset with the hiring and firing choices from approximately 3,700 plan sponsors from 1994 to 2003. The data represented the allocation of more than $737 billion in mandates to hired investment managers and the withdrawal of about $117 billion from fired investment managers. The following is a summary of their findings:
It is important to note that the above results did not include any of the trading costs that would have accompanied transitioning a portfolio from one manager’s holdings to the holdings preferred by the new manager. The bottom line: All of the activity was counterproductive.
Conclusion
Cornell, Hsu and Nanigian drew two main conclusions: “First, a heuristic of hiring recent outperforming managers and firing recent underperforming managers turns out to be 180 degrees wrong …. Second, consistent with previous research, it appears superior investment performance is more a function of the systematic exposures (a persistent investment style) that managers embed into the portfolio, not some nebulous talent—elusive and unique alpha skill.”
Their findings clearly present a tremendous challenge and problem to investors who base decisions on past performance. The practical implication is that investors should clearly focus on factors other than past performance when selecting fund managers.
Thus, the logical conclusion should be that the strategy most likely to allow you to achieve the best results is to focus instead on the selection of passively managed funds that provide you with the desired amount of exposure to the well-documented factors that explain the differences in returns of diversified portfolios—factors such as beta, size, value, momentum and profitability/quality for equities and term and default for bonds—and do so in a low-cost and, for taxable investors, tax-efficient manner.
Unfortunately, the belief in active management as the winning strategy is so deeply embedded for so many investors that, faced with such evidence, the likely outcome is that they will experience cognitive dissonance. As a result, the evidence is likely to be ignored because facing up to it would be too painful an admission of belief in a false theory.
This commentary originally appeared February 19 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