Understanding Different Types of Risks

Harry Markowitz received the Nobel Prize in Economic Sciences in 1990 for his contributions to the body of work known as “modern portfolio theory.” Probably his greatest contribution was to turn the focus away from analyzing the risk and expected return of individual investments to considering how its addition impacts the risk and expected return of the overall portfolio.

Markowitz showed it was possible to add risky assets (with low or negative correlation) to a portfolio, increasing the expected return without increasing overall risk. He also demonstrated the importance of diversification of risk.

Today most investment advice focuses on the development of portfolios that are on the “efficient frontier.” A portfolio that is on the efficient frontier is one in which no added diversification can lower the portfolio’s risk for a given return expectation (alternately, no additional expected return can be gained without increasing the risk of the portfolio).

Working with the efficient frontier, investment advisors tailor portfolios to the individual investor’s unique situation. Unfortunately, far too many investors and/or their advisors only focus on the risks of the investments themselves.

Managing Financial, Not Just Investment, Risks

When developing an overall financial plan, there are risks—other than investment risks—that are important to consider. Not integrating the management of these risks into an overall financial plan can cause even the most carefully considered and well-thought-out investment plans to fail. Among the other risks that should be considered are human capital (wage-earning) risk, mortality risk and longevity risk. Let’s consider how these risks should be integrated into an overall financial plan.

Human Capital Risk

We can define human capital as the present value of future income derived from labor. It’s an asset that doesn’t appear on any balance sheet. It’s also an asset that is not tradable like a stock or a bond. Thus, it’s often ignored, at potentially great risk to the individual’s financial goals. How should human capital impact investment decisions?

The first point to consider is that, when we are young, human capital is at its highest point. It’s also often the largest asset young individuals have. As we age and accumulate financial assets, and our time remaining in the labor force decreases, the amount of human capital relative to financial assets shrinks. This shift over time should be considered in terms of the asset allocation decision.

The second point is that we need to not only consider the magnitude of our human capital but also its volatility. Some people (such as tenured professors, doctors and government employees) have stable jobs, and thus their labor income is almost like an inflation-indexed annuity. In other words, it acts very much like a bond. Other people (such as commissioned salespeople and construction workers) have labor income that is more volatile, and thus acts more like equities. Financial advice should incorporate these differences.

For example, for people with safer labor income, it might be appropriate to invest more aggressively—with a higher allocation to equities overall and perhaps higher allocations to riskier small and value stocks. Those with riskier labor income should consider holding less aggressive portfolios (those with higher bond allocations).

This gets to the heart of Markowitz’s work on portfolio theory: An asset shouldn’t be considered in isolation. Note there may be times when the riskiness of one’s human capital changes (after a career change, for example). If the riskiness of the human capital increases, one should consider reducing the riskiness of the other assets in the portfolio, and vice versa.

A related issue is the significance of human capital as a percentage of total assets. If human capital is a small percentage of the total portfolio (because there are large financial assets), the volatility of the human capital and its correlation to financial assets becomes less of an issue.

Correlation, Health And Mortality

The third point we need to consider involves one of the most basic principles of investing—don’t put too many eggs in one basket. Individuals should avoid investing in assets that have a high correlation with their human capital. Unfortunately, far too many people follow Peter Lynch’s advice to “buy what you know.” The result is that they invest heavily in the stocks of their employers.

This is a mistake on two fronts. The first is that it’s a highly undiversified investment. The second is that the investment is likely to have a high correlation with the person’s human capital. Employees of such companies as Enron and WorldCom found out how costly a mistake that can be.

The fourth point to consider is that human capital can be lost due to two risks that need to be addressed by means other than through investments. The first is the risk of disability. This risk can be addressed by the purchase of disability insurance. Thus, the risk of disability and how to address it should be part of the overall financial plan. The other risk is that of mortality. That issue can be addressed by the purchase of life insurance (we will discuss that in more detail).

There are still other points to consider. All else being equal, people with a high earning capability have a greater ability to take more financial risk because they can more easily recover from losses. However, they also have a lower need to take risk. All else being equal, the higher their earnings, the lower the rate of return they need from their investment portfolio to achieve their financial goals—they can choose less risky investments and still achieve them.

Risk Tolerance And Adaptability

Another factor is investors’ willingness to take risk—their risk tolerance. It’s important that investors don’t take more financial risk than their stomachs can handle. The reason is that, when the inevitable bear markets arrive, they might be more inclined to panic-sell, and the best laid plans would end up in the trash heap of emotions.

Even if they were not driven to panic, life is just too short not to enjoy it. One should be able to “sleep well” with his or her investments. Thus, a high earnings capability, or even a high need to take risk, shouldn’t necessarily result in an aggressive investment portfolio.

Yet another factor to consider is the ability to adjust your “supply” of human capital. Consider the following: You develop a financial plan that allows you to retire at age 65. However, the market’s rate of return falls below the expected return you built into your plan, or you weren’t able to save as much as you had expected. Now you will need to work longer.

Can you continue in the labor force? What level of income can you generate? Will the market allow you to sell your skills, and at what price? Younger workers typically have more ability to adjust their supply of human capital. In addition, those with a variety of skill sets also have a greater ability to adjust their supply to economic conditions.

We’ll revisit this discussion later in the week to consider additional risk factors, including mortality and longevity risk, and using “tax alpha” strategies to improve the odds of achieving your financial goals.

This commentary originally appeared April 12 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.

© 2017, The BAM ALLIANCE

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Following Up On 2017’s ‘Sure Things’

At the start of each year, I put together a list of predictions gurus make for the upcoming year—sort of a consensus of “sure things.” We keep track of the sure things with a review at the end of each quarter.

With March behind us, it’s time for our first review. As is our practice, we give a positive score for a forecast that came true, a negative score for one that was wrong and a zero for one that was basically a tie.

The Predictions

Here, then, are the first-quarter results. Each sure thing is followed by what actually happened, and the score.

1. The Federal Reserve will continue to raise interest rates in 2017. That leads many to recommend that investors limit their bond holdings to the shortest maturities. Economist Jeremy Siegel even warned that bonds are “dangerous.” On March 15, 2017, the Federal Reserve did raise interest rates by 0.25%. However, despite the prediction of rising rates actually occurring, through March 31, 2017, the Vanguard Long-Term Bond ETF (BLV) returned 1.78%, outperforming its Short-Term Bond ETF (BSV), which returned 0.56%; and its Intermediate-Term Bond ETF (BIV), which returned 1.26%. Score -1.

2. Given the large amount of fiscal and monetary stimulus we have experienced and the anticipation of a large infrastructure spending program, the inflation rate will rise significantly. On March 15, 2017, the Bureau of Labor Statistics reported that the Consumer Price Index for All Urban Consumers increased 0.1% in February on a seasonally adjusted basis. It also reported that the index for all items less food and energy rose 2.2% over the last 12 months; this was the 15th-straight month the 12-month change remained in the range of 2.1-2.3%. The February increase was the smallest one-month rise in the seasonally adjusted all-items index since July 2016. However, in January, the index rose 0.6%, and the all-items index rose 2.7% for the 12 months ending February; the 12-month increase has been trending upward since a July 2016 trough of 0.8%. We’ll call this one a draw. Score 0.

3. With the aforementioned stimulus, anticipated tax cuts and a reduction in regulatory burdens, the growth rate of real GNP will accelerate, reaching 2.2% this year. We’ll have to wait to get the first quarter figures to make a call on this one. Score 0.

4. Our fourth sure thing follows from the first two. With the Fed tightening monetary policy and our economy improving—and with the economies of European and other developed nations still struggling to generate growth and their central banks still pursuing very easy monetary policies—the dollar will strengthen. The U.S. Dollar Index (DXY) ended 2016 at 102.38. The index closed the first quarter at 100.35. Score -1.

5. With concern mounting over the potential for trade wars, emerging markets should be avoided. Despite those mounting concerns, through March 31, 2017, the Vanguard FTSE Emerging Markets ETF (VWO) returned 10.87%, outperforming the S&P 500 Index, which returned 6.07%. Score -1.

6. With the Shiller cyclically adjusted price-to-earnings ratio at 27.7 as we entered the year (66% above its long-term average), domestic stocks are overvalued. Compounding the issue with valuations is that rising interest rates make bonds more competitive with stocks. Thus, U.S. stocks are likely to have mediocre returns in 2017. A group of 15 Wall Street strategists expect the S&P 500, on average, to close the year at 2,356. That’s good for a total return of about 7%. As noted above, the S&P 500 Index returned 6.07% in the first quarter. Score -1.

7. Given their relative valuations, U.S. small-cap stocks will underperform U.S. large-cap stocks this year. Morningstar data shows that at the end of 2016, the prospective price-to-earnings (P/E) ratio of the Vanguard Small-Cap ETF (VB) stood at 21.4, while the P/E ratio of the Vanguard S&P 500 Index ETF (VOO) stood at 19.4. Through March 31, 2017, VB returned 3.74%, underperforming VOO, which returned 6.05%. Score +1.

8. With non-U.S. developed and emerging market economies generally growing at a slower pace than the U.S. economy (and with many emerging markets hurt by weak commodity prices, slower growth in China’s economy, the Federal Reserve tightening monetary policy and a rising dollar), international developed-market stocks will underperform U.S. stocks this year. Through March 31, 2017, the Vanguard FTSE Developed Markets ETF (VEA) returned 7.81%, outperforming VOO, which returned 6.05%. Score -1.

Analysis Of Results

Our final tally shows that five sure things failed to occur, while just one sure thing actually happened. We also had one draw and one too early to call. We’ll report again at the end of the second quarter.

The table shows the historical record since I began this series in 2010:

Only about 25% of sure things actually occurred. Keep these results in mind the next time you hear a guru’s forecast.

This commentary originally appeared April 10 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.

© 2017, The BAM ALLIANCE

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The Antidote To Stock Market Hysteria

Just for fun, Google the words “market pullback.” There are over 2.2 million results–most of them market predictions–and the first page of results is dominated by calls for an imminent market reversal that the simple desk calendar has already proven false.

However, despite their worthlessness, market predictions remain as predictable as market opens and closes. (And I predict no end in sight.)

But why?

First, there’s a clear profit motive. Apparent urgency leads to activity, and activity is still how most of the financial services industry makes its money.

“Bullish predictions encourage investors to pour fresh money into the markets, helping asset management companies to enjoy rising profits,” the New York Times reported, noting that the Wall Street forecaster’s consensus since 2000 has averaged a 9.5% increase each year. They accidentally got it (almost) right in 2016, but in 2008, the consensus prognostication missed the mark by 49 percentage points (an outcome that makes your local weatherman seem like a harbinger of accuracy)!

But not everyone’s positive either. My colleague and the co-author of the new book “Your Complete Guide To Factor-Based Investing,” Larry Swedroe, analyzed Marc Faber’s perpetually cataclysmic proclamations and rendered the good doctor “without a clue.”

But perhaps more surprising, despite the persistent inadequacy of market forecasts, there’s apparently a demand for such soothsaying. Market forecasters capitalize on our unquenchable desire to know the unknowable. “This irrational behavior is caused by an all-too-human need to believe that there is someone who can protect us from bad things happening,” says Swedroe.

Further, “Many individuals believe they are above average in their knowledge, overall judgments, and expertise about all types of money matters,” says Victor Ricciardi, Finance Professor at Goucher College and co-editor of the book Investor Behavior: The Psychology of Financial Planning and Investing.”

The result is that, however un-newsworthy, even venerable publications still print the crap.

What solace can I offer?

On a cosmic continuum, there absolutely is an “imminent” market collapse coming. You can’t predict it, but you can (and should) expect it. And compared even to the relative microcosm of market history, another raging bull market is likely to follow close on its heels.

The rational choice in optimal portfolio structuring, therefore, is to create a portfolio that isn’t designed solely to capitalize on the next market meltdown or spike–but to accommodate both scenarios and everything in between, with balance.

The antidote, therefore, for market hysteria is informed apathy.

Instead of acting on–or even fretting over–a single market prognostication, acknowledge that no one has demonstrated an ability to predict accurately. You’ll be in good company. Warren Buffett, in his recent letter to shareholders, gave this counsel: “[T]he years ahead will occasionally deliver major market declines–even panics–that will affect virtually all stocks. No one can tell you when these traumas will occur.”

And what does he say about those who attempt to predict? “[H]eaven help them if they act on the nonsense they peddle.”

Please recognize this proactive apathy means you likely won’t have sufficient fodder for the watercooler or cocktail party when others are regaling the group with their isolated investment wins and self-loathing losses. But that’s OK, because portfolio volatility isn’t supposed to be the most interesting thing in your life. Your portfolio is better served to simply support the most interesting things in life.

This commentary originally appeared March 18 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.

© 2017, The BAM ALLIANCE

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Turns Out the “Smart Money” Isn’t

Institutional investors are generally considered “smart money” that exploits the behavioral biases of “dumb” retail money. However, there have been some holes poked in that idea recently.

For instance, Roger Edelen, Ozgur Ince and Gregory Kadlec, authors of the study “Institutional Investors and Stock Return Anomalies,” which was published in the March 2016 issue of the Journal of Financial Economics, write that while prior research had found a positive relationship between smart money and return anomalies at shorter time horizons (three to 12 months), it turns negative for longer horizons.

Study Findings

They found: “Not only do institutional investors fail to tilt their portfolios to take advantage of anomalies, they trade contrary to anomaly prescriptions. Most notably, they have a strong propensity to buy stocks classified as ‘overvalued’ (i.e., the short leg of anomaly portfolios). For example, during the anomaly portfolio formation window (prior to anomaly returns) there is a net increase in both the number of institutional investors and fraction of shares held by institutional investors in short-leg stocks for all seven of the anomalies we consider. In four of the seven anomalies there is significantly greater institutional buying in short-leg stocks than in long-leg stocks. There is significantly greater buying in long-leg stocks in only one case.”

This surprising finding was in sharp contrast to prior research on performance, with the difference being the horizon period studied. They confirmed this horizon effect, finding a significant positive relation between quarterly changes in institutional holdings and next-quarter returns that turns significantly negative as the time horizon extends to a year or longer. Thus, the authors concluded that “while institutional trades seem to be informed when evaluated over short horizons, that assessment seems premature when evaluated over a longer horizon.”

These findings suggest that institutional investors actually fail to exploit well-known anomalies. Instead, they contribute to their persistence. Thus, the short-term outperformance may not be the result of a “smart money” effect, but instead a result of the price pressure associated with persistent institutional trading, as opposed to informed trading.

This hypothesis is consistent with the findings of George Jiang and H. Zafer Yuksel, authors of the study “What Drives the ‘Smart-Money’ Effect? Evidence from Investors’ Money Flow to Mutual Fund Classes,” which was published in the January 2017 issue of the Journal of Empirical Finance.

By studying mutual fund flows for retail (unsophisticated) and institutional (sophisticated) investors, they found that short-term persistence in performance is not due to the so-called smart money effect, but was instead caused by persistent flow. And the persistence of short-term performance then experiences a long-term reversal. In other words, institutional investors (at least institutional mutual funds) are also noise traders who can contribute to mispricings.

Who Exploits Anomalies?

Because someone owns the long leg of the anomalies, it begs the question: Who is exploiting these well-known anomalies? Mustafa Onur Caglayan and Umut Celiker provide the likely answer through their January 2017 study, “Hedge Fund vs. Non-Hedge Fund Institutional Ownership and the Book-to-Market Effect,” which covers the period July 1982 to June 2014.

The book-to-market (or value) effect is a good choice to study, as it’s well-known, and there are both risk-based and behavioral-based explanations for it in the literature. While naive investors’ overreaction could contribute to the book-to-market effect even if it’s partly explained by a risk premium, sophisticated investors—namely, institutions—should exploit this return predictability, take advantage of the anomaly and therefore mitigate the extent of overreaction. However, we saw previously that, at least in the case of mutual funds, institutions are contributing to any price overreaction, not correcting it.

Caglayan and Celiker tested whether there is a difference between hedge funds and nonhedge-fund institutional investors in terms of their ability to adjust their positions in order to take advantage of the book-to-market effect, and whether hedge funds’ decisions to invest or disinvest in a particular stock predicts future stock returns in the context of the book-to-market anomaly.

The authors focused on hedge funds and non-hedge funds’ change in stock ownership in the most recent quarter prior to the return measurement window of anomaly returns. They found evidence of a statistically significant and drastic change in hedge funds’ behavior as they adjust their preferences from growth to value stocks immediately after the book-to-market values of equities become public knowledge.

Hedge Funds Act On Information

In addition, they show that hedge funds detect overpriced growth securities and trade them to their advantage, especially when non-hedge funds move aggressively in the opposite direction. This is consistent with the idea that hedge funds are the informed investors. On the other hand, they found that non-hedge fund institutional investors do not alter their positions significantly when the information becomes public knowledge.

Specifically, the authors write: “Overvalued (growth) stocks heavily bought by non-hedge funds and simultaneously sold by hedge funds in the most recent quarter underperform significantly in the next year, generating an eye-opening three-factor alpha of -1.33% per month with a t-statistic of -5.54 and a four-factor alpha of -1.23% per month with a t-statistic of -5.21…. On the other hand, we do not find any significant negative subsequent abnormal returns for stocks sold by non-hedge funds and heavily bought by hedge funds.”

They concluded that their findings “support the notion that hedge funds detect negative information on stocks and trade them to their advantage by unloading them especially when non-hedge funds move aggressively in the opposite direction.”

They added: “It is also noteworthy to indicate that the underperformance of growth stocks heavily bought by non-hedge funds and contemporaneously sold by hedge funds lasts in all four quarters analyzed. In other words, the underperformance of these stocks in the subsequent year is not due to an underperformance in one or two quarters, but is due to underperformance in each of the four quarters, showing that our results exist in each quarter during the return measurement window of anomaly returns, and hence the impact of price pressure is completely ruled out.”

Finally, they also noted that their results held not only for the full period, but for both subperiods they studied as well.

It’s important to observe that the evidence showed a stronger ability for hedge funds to detect overpriced securities compared with underpriced securities. This aligns with the theory of limits to arbitrage and short-sale constraints, which allow mispricings to persist even after publication.

The Limits Of Arbitrage

As the authors note: “It is well known that the arbitrage of underpriced securities requires only the purchase of such stocks, while the arbitrage of overpriced securities requires the short-sale, which is much more costly for investors. Therefore, the disappearance of underpriced stocks takes much less time compared to the disappearance of overpriced securities.”

The issue of how limits to arbitrage allow anomalies to persist is discussed in detail in “Your Complete Guide to Factor-Based Investing,” my latest book, which I co-authored with Andrew Berkin.

While the evidence shows that hedge fund managers are skilled, unfortunately the historical evidence also demonstrates that they keep any “economic rent” (as you should expect, because the ability to generate alpha is the scarce resource) that results from their skill. An indication of this can be found in the fact that investors in hedge funds have not been well rewarded.

For example, for the 10-year period ending in 2016, the HFRX Global Hedge Fund Index lost 0.6%, underperforming every major equity and bond asset class. The underperformance ranged from 0.4 percentage points when compared to the MSCI EAFE Value Index to as much as 8.8 percentage points when compared to U.S small-cap stocks.

Thus, if you want to capture the premiums provided by anomalies (such as the book-to-market effect), you should invest in low-cost, passively managed funds that seek to capture the returns available in a systematic way.

This commentary originally appeared February 10 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.

© 2017, The BAM ALLIANCE

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Digging Into Active Share

The holy grail for mutual fund investors is the ability to identify in advance which of the very few active mutual funds will go on to outperform in the future. To date, the overwhelming body of academic research has demonstrated that past performance not only doesn’t guarantee future performance (as the required SEC disclaimer states), but has virtually no value whatsoever as a predictor. The only value of past performance seems to be that poor performance tends to persist—with the likely explanation being high expenses.

Believers in active management were offered hope that the holy grail had been found with the publication, in the September 2009 issue of The Review of Financial Studies, of a study by Martijn Cremers and Antti Petajisto, “How Active Is Your Fund Manager? A New Measure That Predicts Performance.” The authors concluded: “Active Share predicts fund performance: funds with the highest Active Share significantly outperform their benchmarks, both before and after expenses, and they exhibit strong performance persistence.”

Active share is a measure of how much a fund’s holdings deviate from its benchmark index, and funds with the highest active shares tend to have the best performance. Thus, while there’s no doubt that, in aggregate, active management underperforms, and that the majority of active funds underperform every year (and the percentage that underperforms increases with the time horizon studied), if an investor can identify the few future winners by using the measure of active share, active management can be the winning strategy. After publication, questions were raised about the study’s findings.

Questioning The Study

I myself raised several issues. Among them were:

  • The results could be due to a skewed distribution. A few highly concentrated funds may have enormous returns, increasing the average for the stock pickers. It would have been helpful to report the median.
  • When funds are sorted by both fund type and fund size, only the very smallest quintile of stock-picking mutual funds showed a statistically reliable abnormal return. This tells us that the only funds that generated reliable outperformance were the very smallest of the stock pickers. This reinforces the idea that skewness could be driving the results. In addition, their success would attract assets, which raises the hurdles to delivering alpha.
  • The smallest funds typically are young funds. Thus, the well-documented incubation bias could be driving the results. (Incubation bias results when a mutual fund family wishing to launch a new fund nurtures several at a time. Funds that beat their benchmarks go public, while poorly performing ones never see the light of day.) If this bias exists, the reported returns for small funds don’t mean much.

In May 2012, the Vanguard research team took a look at the issue of active share as a predictor. Their study covered the 1,461 funds available at the beginning of 2001. The final fund sample comprised 903 funds. Because the study only covered surviving funds, there’s survivorship bias in the data. Following is a summary of their conclusions:

  • Even with survivorship bias, higher levels of active share didn’t predict outperformance.
  • The higher the active-share level, the larger the dispersion of excess returns.
  • The higher the active-share level, the higher the fund costs.

The bottom line is that while active share didn’t predict performance, it did increase risks as the dispersions of returns increased. In other words, investors paid more for the privilege of experiencing greater risk without any compensation in the form of greater returns.

Updated Findings

Petajisto updated his study in 2013, adding six more years of data. Again he found: “Over my sample period until the end of 2009, the most active stock pickers have outperformed their benchmark indices even after fees and transaction costs [by 1.26% per year]. In contrast, closet indexers or funds focusing on factor bets have lost to their benchmarks after fees.” The specific recommendation was to avoid funds with active shares below 60%.

Using the same database used in the Petajisto studies, Andrea Frazzini, Jacques Friedman and Lukasz Pomorski of AQR Capital Management examined the evidence and the theoretical arguments for active share as a predictor of performance and presented their findings and conclusions in their March 2015 paper, “Deactivating Active Share,” which was published in the March/April 2016 issue of the Financial Analysts Journal. Following is a summary of the findings from the AQR paper:

  • The empirical support for the measure is weak and is entirely driven by the strong correlation between active share and the benchmark type—high active share funds and low active share funds systematically have different benchmarks. A majority of high active share funds are small-cap, and a majority of low active share funds are large-cap.
  • While active share correlates with benchmark returns, it doesn’t predict actual fund returns. Within individual benchmarks, active share is just as likely to correlate positively with performance as it is to correlate negatively.
  • Active share results are very sensitive to comparisons using benchmark-adjusted returns rather than total returns. Over this sample period, small-cap benchmarks had large, negative four-factor alphas compared with large-cap benchmarks; this was crucial to the statistical significance of the results.
  • Controlling for benchmarks, active share has no predictive power for fund returns, predicting higher fund performance within half of the benchmark indexes and lower fund performance within the other half.

New Research

A recent contribution to the debate on active share comes from Ananth Madhavan, Aleksander Sobczyk and Andrew Ang of BlackRock with their October 2016 paper, “Estimating Time-Varying Factor Exposures with Cross-Sectional Characteristics with Application to Active Mutual Fund Returns.”

Their study used cross-sectional risk characteristics (such as valuation ratios and market capitalization) to determine if active share predicted returns. Their database included 1,267 mutual funds with $3.3 trillion in assets under management, and covered the period September 2010 through June 2015. This period is out-of-sample from the period covered by Cremers and Petajisto in their 2009 paper.

They found that the measure of active share proposed by Cremers and Petajisto actually was negatively correlated (-0.75) to fund returns after controlling for factor loadings and other fund characteristics. Thus, they concluded that “it is not the case that high conviction managers outperform.” While they noted there clearly were active managers with skill, active share isn’t the way to identify them ahead of time. And they didn’t suggest another method.

There’s one other recent paper we need to review, Cremers’ December 2016 study, “Active Share and the Three Pillars of Active Management: Skill, Conviction and Opportunity.” Cremers introduced a new measure of active share that emphasizes that a fund’s active share is reduced by its overlapping holdings. His study covered the period 1990 through 2015 and is free of survivorship bias.

Using quintile sorts, comparing high and low active share funds generally meant comparing funds with an active share of 95% or greater to funds with an active share below 60%. Cremers also compared performance against two factor models, a seven-factor model (which uses the market factor, small- and midcap size factors, and small-, midcap, and large-cap value factors as well as momentum) and the standard Fama-French-Carhart four-factor model (beta, size, value and momentum). Cremers also examined the impact of turnover on performance. Following is a summary of his findings:

  • Using the seven-factor model, the quintile of funds with the highest active share had an abnormal (unexplained) return of 0.71% per year. While economically significant, the abnormal return was not statistically significant, as the t-statistic was just 1.37. Importantly, a chart in the appendix appears to show that all of the cumulative outperformance over time occurred in the brief period from 1999 through 2001 (during which the tech bubble burst, indicating that the high-active-share funds were able to sidestep the bubble). The low-active-share funds exhibited underperformance throughout the period.
  • Using the four-factor model, the high-active-share quintile’s abnormal performance was -0.36% per year, with a t-statistic of -0.49.

Factoring In Turnover

These two findings seem to make it hard to build a compelling case for active share alone being a predictor of future performance. However, Cremers also examined the impact of turnover on performance. Funds in the highest turnover quintile had average holdings of about eight months, while those in the lowest turnover quintile had average holdings of at least two years.

Using an independent 5×5 sort on active share and fund holding duration (a measure of the average holding period of the fund), the annualized seven-factor and four-factor intercepts for the high-active-share/high-duration (low turnover) portfolio are 1.88% and 1.69%, respectively. The corresponding t-statistics are 2.35 and 1.71, respectively.

As mentioned earlier, a caution is warranted insofar as the chart of the cumulative abnormal seven-factor performance over time indicates that it peaked around 2002 and has declined since then. Cremers did note that the high-active-share/low-turnover funds did outperform from 2007 through 2013, while they underperformed from 2002 through 2006, and again from 2014 through 2015.

Cremers concluded that while he believes active share matters, both in large-cap and small-cap funds, investors should use only funds with low turnover (under 50%). He noted that the evidence that high-active-share funds outperformed low-active-share funds was considerably stronger for funds with low expense ratios. Ranking funds by their expense ratio, Cremers found that the average expense ratio was 0.71% per year in the lowest quintile and 1.79% in the fifth quintile. Thus, investors should consider active funds that have high active share and low turnover.

Given that the chart in the paper seemed to indicate that the outperformance had occurred prior to 2002, I contacted Professor Cremers and asked him if he had the performance for the period 2002 through 2015. He provided me with the table below, which shows the results over that time frame for the active-share quintile portfolios (the first quintile is the lowest active share) using the seven-factor model:

The active managers in each of the quintiles produced negative alphas, with only the highest-active-share quintile not showing statistical significance. The evidence suggests that if you are going to use an active manager, you are better served by choosing one with a high active share. However, it also shows that while perhaps it was once true that active share predicted future outperformance, its time may have gone with the wind.

Elusive Alpha

This evidence is entirely consistent with the thesis of the book I co-authored with Andrew Berkin, “The Incredible Shrinking Alpha.” In our book, we provide the evidence and the explanations for why, over time, it has become persistently more difficult to generate alpha as the markets have become more efficient and the competition for alpha has gotten tougher.

You can decide for yourself whether you find the evidence on active share compelling enough to use actively managed funds. That said, Cremers makes a compelling case that if you are going to use active funds, you should avoid all funds with low active share, high turnover and high expense ratios. I would certainly agree. I would add that when it comes to picking mutual funds, investors should care less about alpha (by whatever measure) and more about actual returns.

Investors may want to own a fund that provides exposure to factors they care about, such as market beta, size, value and momentum. They should then be happy to have minimal alpha as long as they get the beta (loading on a factor they are seeking), which leads to higher returns.

In other words, such investors should rather own a low-cost, passively managed small value fund that provides high loadings on those factors and minimizes or even eliminates the negative exposure to momentum typical of value funds and has no alpha, than an active fund with less exposure to those factors even if it generates a positive alpha. The positive alpha would have to be great enough to overcome the loss of returns due to the lower loading on the factors. To illustrate this point, consider the following example.

We’ll compare the returns, loadings on factors and alphas for two funds from the same asset class (U.S. large value): the actively managed Vanguard Equity Income Fund (VEIPX) and passively managed DFA U.S. Large Cap Value III Portfolio (DFUVX). (Full disclosure: My firm, Buckingham, recommends DFA funds in constructing client portfolios.) The data is for the 15-year period from October 2001 through September 2016. The factor loadings come from Portfolio Visualizer and use the Fama-French benchmark factors and the four-factor model.

First, note that the r-squared figures are very high, indicating that the model is doing a good job of explaining returns. Second, as you can see, while VEIPX produced a positive annual alpha of 1.10% and DFUVX produced a negative alpha of -0.45%, a difference of 1.55%, DFUVX outperformed 8.8% versus 7.9%.

The reason for the outperformance is clear. DFUVX had much higher loadings on factors that delivered premiums. That allowed DFUVX to overcome the 1.55% difference in alpha. The higher loading on market beta provided about 1.4% in incremental returns, the higher loading on size provided about 0.9% in incremental returns, and the higher loading on value added about 0.1%.

While alpha is nice, you only get to spend returns. Thus, it’s important to consider all of these issues, including turnover, expense ratios and loading on factors.

This commentary originally appeared February 6 on ETF.com

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