Articles of Interest - Amphora Wealth Management

All Fiduciaries Aren’t Created Equal

Created on Sunday, 26 June 2016 19:00

Robo-advisors have had a significant — and generally positive — impact on the financial services industry. The term typically refers to services that use models and algorithms to invest client portfolios, often in exchange-traded funds (ETFs). A benefit much touted by some of these services is that there’s no interaction with a human advisor. The entire process is done online. Betterment andWealthfront are the leading robo-advisors that fit into this category.

Lower fees

Because these robo-advisors are automated, they have significantly lower expenses than traditional investment advisors. Here’s Betterment’s fee schedule, which it offers through a wrap fee program:

0.15 percent for accounts with balances greater than $100,000
0.25 percent for accounts with balances between $10,000 and $100,000
0.35 percent for accounts with balances below $10,000

Fiduciary duty

Robo-advisors are SEC-registered investment advisors. Under both common law and federal statutes, SEC-registered investment advisors owe a “fiduciary duty” to their clients.

There is much confusion over what a “fiduciary duty” entails. According to the Institute for the Fiduciary Standard, SEC-registered investment advisors have the following obligations to their clients:

Serve the client’s best interest
Act in utmost good faith
Act prudently, with the care, skill and judgment of a professional
Avoid conflicts of interest
Disclose all material facts
Control investment expenses

A lower standard

Most brokers are not SEC-registered investment advisors. They don’t have a fiduciary duty to their clients. They are held to a much lower “suitability” standard, which permits them to sell higher-price, higher-commission products to their clients, even though the expected returns of these products may be lower than readily available lower-cost investments.

The securities industry has done a great job of obscuring the distinction between a fiduciary advisor and someone with only a “suitability” obligation. If investors understood the difference, few would elect to entrust their hard-earned money to anyone who wasn’t held to the fiduciary standard.

The DOL rule

The U.S. Department of Labor recently issued a rule requiring those who advise retirement plans to act as “fiduciaries” to their clients. While this rule has some loopholes and doesn’t apply to advice in non-retirement settings, overall it’s still a very positive development for retirement plan participants.

Sound investment advice, but…

The investment advice offered by most robo-advisors is generally sound. They advocate using a globally diversified portfolio of low-fee exchange-traded funds. They rebalance portfolios to keep their clients within their tolerance for risk and some offer tax loss harvesting to capitalize on downturns in the market.

Different levels of fiduciary advice

However, being a true “fiduciary” to clients may require more expansive services and interaction with a human advisor. A policy statement updated in April by the Massachusetts Securities Division could pose a threat to the business model of fully automated robo-advisors. William Francis Galvin is the Secretary of the Commonwealth of Massachusetts. He heads the state’s Securities Division. Mr. Galvin is generally regarded as a leader in protecting the rights of investors. Actions taken by his agency are closely followed by other state and federal regulators.

The policy statement makes the compelling point that both robo and traditional advisors are governed by the same fiduciary standard, yet robo-advisors, through disclaimers and otherwise, render advice far less expansive than traditional advisors. Specifically, fully automated robo-advisors have no personal interaction with their clients, “minimally personalize” their investment advice, may not meet the high standard of care imposed on traditional advisors for their investment decisions, and sometimes disclaim the obligation to act in their clients’ best interest.

The policy statement concludes that fully automated robo-advisors may be “inherently unable to act as fiduciaries.” Consequently, the state regulatory body will evaluate their suitability for registration, with certain guidelines in mind, on a “case-by-case” basis. The clear import is that fully automated robo-advisors may not be approved for registration in Massachusetts.

If this position is adopted by other states, it may be the death knell for fully automated robo-advisors. They may have to adjust by changing their disclaimers and adding the ability for clients to interact with qualified investment advisors. This is the approach taken by Vanguard.

The takeaway

The rise of robo-advisors has generally been a very positive development for investors who may not meet asset thresholds set by traditional advisors. They provide sound investment advice, at a low cost. They are serving primarily smaller investors who traditional advisors could not serve in a cost-effective manner. However, they may have overreached by conveying the impression the fiduciary advice they provide is equivalent to what investors receive from human advisors governed by the same legal standard.

The Massachusetts Securities Division’s policy statement is correct in highlighting this difference in service. The lesson for investors is two-fold:

Insist that all registered investment advisors are held to the same high fiduciary standard.
Don’t entrust your money to anyone who will not agree in writing to a fiduciary obligation.

This commentary originally appeared June 7 on HuffingtonPost.com

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A New Four-Factor Investing Model

Created on Sunday, 26 June 2016 19:00

For about three decades, the working asset pricing model was the capital asset pricing model (CAPM), with beta—specifically market beta—being its sole factor. Then, in 1993, the Fama-French three-factor model—which added size and value—replaced the CAPM as the workhorse model.

By eliminating two major anomalies (the outperformance of small stocks and of value stocks), it improved the model’s explanatory power from about two-thirds of the differences in returns of diversified portfolios to more than 90%. Thus, it was a major advance.

In 1997, momentum was added as a fourth factor. It too improved the explanatory power of the asset pricing model by eliminating another large anomaly. The next major advance came from Robert Novy-Marx in 2012.

In his paper, “The Other Side of Value: The Gross Profitability Premium,” he proposed a fifth factor, which also improved the model’s explanatory power while eliminating another important anomaly—the outperformance of stocks with higher profitability.

Since then, what might be called the “battle of the factor models” has occurred, with parsimony considered a major virtue—the fewer factors needed, the better. Kewei Hou, Chen Xue and Lu Zhang—authors of the October 2012 study, “Digesting Anomalies: An Investment Approach”—proposed a new four-factor model, the q-factor model. It included market beta, size, investment and profitability, and went a long way to explaining many anomalies.

In 2015, Eugene Fama and Kenneth French proposed a new five-factor model, using their original three factors and adding somewhat different definitions of investment and profitability.

Mispricing Factors
Robert Stambaugh and Yu Yuan, authors of the January 2016 paper “Mispricing Factors,” add to the literature by proposing another four-factor model that includes two “mispricing” factors in addition to the factors of market beta and size. The authors note: “Factor models can be useful whether expected returns reflect risk or mispricing.”

By incorporating these mispricing factors, they are better able to accommodate 11 well-known anomalies. These anomalies, which represent violations of the Fama-French three-factor model, are:

Net Stock Issues:Net stock issuance and stock returns are negatively correlated. It’s been shown that smart managers issue shares when sentiment-driven traders push prices to overvalued levels.
Composite Equity Issues: Issuers underperform nonissuers, with composite equity issuance defined as the growth in the firm’s total market value of equity minus the stock’s rate of return. It’s computed by subtracting the 12-month cumulative stock return from the 12-month growth in equity market capitalization.
Accruals: Firms with high accruals earn abnormally lower average returns than firms with low accruals. Investors overestimate the persistence of the accrual component of earnings when forming earnings expectations.
Net Operating Assets: The difference on a firm’s balance sheet between all operating assets and all operating liabilities, scaled by total assets, is a strong negative predictor of long-run stock returns. Investors tend to focus on accounting profitability, neglecting information about cash profitability, in which case, net operating assets (equivalently measured as the cumulative difference between operating income and free cash flow) captures such a bias.
Asset Growth: Companies that grow their total assets more earn lower subsequent returns. Investors overreact to changes in future business prospects implied by asset expansions.
Investment-to-Assets: Higher past investment predicts abnormally lower future returns.
Distress: Firms with high failure probability have lower, rather than higher, subsequent returns.
O-Score: An accounting measure of the likelihood of bankruptcy. Firms with higher O-scores have lower returns.
Momentum: High (low) recent (in the past year) past returns forecast high (low) future returns over the next several months.
Gross Profitability Premium: More profitable firms have higher returns than less profitable ones.
Return on Assets: More profitable firms have higher expected returns than less profitable firms.

The Process

Stambaugh and Yuan construct their two mispricing factors by average rankings within two clusters of anomalies whose long/short return spreads exhibit the greatest co-movement. Anomalies one through seven are in the first cluster of factors, and anomalies eight through 11 are in the second.

They then average a stock’s rankings with respect to the available anomaly measures within each of the two clusters. Thus, each month, a stock has two composite mispricing measures.

The authors constructed their mispricing factors by applying a 2×3 sorting procedure—sorting all stocks by P1 (and then P2) and assigning them to three groups, using as breakpoints the 20th and 80^thpercentiles of the combined NYSE, AMEX and Nasdaq universe.

They chose 20th and 80^th percentile breakpoints rather than at the 30th and 70^th percentiles because mispricings tend to occur more in the extremes of the deciles. They then created value-weighted portfolios. Combining these two factors (P1 and P2) with the market and size factors creates a four-factor model.

Stambaugh and Yuan’s approach was motivated by the fact that “anomalies in part reflect mispricing and that mispricing has common components across stocks, often characterized as sentiment. A mispricing interpretation is consistent with evidence that anomalies are stronger among stocks for which price-correcting arbitrage is deterred by greater risks and impediments.” This is often referred to as limits to arbitrage.

Results
Their study covers the period 1967 through 2013. Following is a summary of their findings:

A four-factor model with two “mispricing” factors, in addition to market and size, accommodates a large set of anomalies better than notable four- and five-factor alternative models.
Their four-factor model’s overall ability to accommodate a wide range of anomalies exceeds that of both the four-factor q-model from Hou, Xue and Zhang and the five-factor model from Fama and French. The Fama-French five-factor model leaves all but one of the 11 anomalies with economically and statistically significant alphas. The q-factor model does somewhat better, leaving seven anomalies with significant alphas. Of the nine positive alphas in the four-factor anomaly model, all but one are lower than any of the corresponding alphas for the other models. The sole exception is the return on assets anomaly, for which the q-model produces a smaller alpha. That is unsurprising, given that the q-model includes a profitability factor. In addition, only two of the four-factor anomaly model t-statistics exceed 2.0 (a third has a t-statistic of 1.90) and the alphas for the asset growth and distress anomalies flip to negative values (with t-statistics of -1.96 and -1.03).
The relative performance of the models was similar when they expanded the universe of anomalies to the larger set of 73 anomalies examined previously by Hou, Xue and Zhang.
Both the q-model and the four-factor anomaly model do a good job of pricing the factors in the Fama-French five-factor model. In contrast, the Fama-French model doesn’t perform well in explaining the returns of the anomaly-based model, and doesn’t fare as well in explaining the returns of the q-model.
Short-leg betas (loadings on the anomalies) are generally larger in absolute magnitude than their long-leg counterparts. This is consistent with a limits-to-arbitrage argument for persistent mispricing—there is more uncorrected overpricing than uncorrected underpricing. Given that many investors are less willing or able to short stocks than to buy them, overpricing resulting from high investor sentiment gets corrected less by arbitrage than underpricing resulting from low sentiment.
Arbitrage asymmetry is consistent with the relationship between investor sentiment and anomaly returns. The short leg of the long/short anomaly spread is significantly more profitable following high investor sentiment, whereas the long-leg profits are less sensitive to sentiment.
Replacing book-to-market with a single composite mispricing factor (anomalies one through 11), rather than by clusters, produces a better-performing three-factor model (superior to the Fama-French three-factor model).
Their size factor reveals a small-firm premium of 46 basis points per month, nearly twice the premium of 25 basis points implied by the familiar size factor in the Fama-French three-factor model. This result is consistent with the findings from the 2015 study “Size Matters, If You Control Your Junk.” The study’s authors found that the size premium becomes substantially greater when controlling for other stock characteristics potentially associated with mispricing.
In a test of robustness, their results were basically the same when they split the time period into two basically equal subperiods.

Implications
Stambaugh and Yuan note that because higher idiosyncratic volatility (IVOL) implies greater arbitrage risk, mispricings should get corrected less among stocks with high IVOL. That’s exactly what they found, providing further support for their results.

For investors, it’s important to note that the authors’ finding that there is more uncorrected overpricing than uncorrected underpricing doesn’t mean a mutual fund would have to short a stock that’s overpriced to benefit. It can benefit by avoiding purchasing the overpriced stocks, creating a filter to screen out stocks with the characteristic that creates the mispricing.

Thus, passively managed long-only mutual funds can put this knowledge to work. Dimensional Fund Advisors (DFA) is likely the most well-known firm that has long used screens to eliminate certain stocks from its eligible buy list. (Full disclosure: My firm, Buckingham, recommends DFA funds in constructing client portfolios.)

Summary

Through their research, financial economists continue to advance our understanding of how financial markets work and how prices are set. The Fama-French three-factor model was a significant improvement on the CAPM. Mark Carhart moved the needle further by adding momentum as a fourth factor. And the creators of the q-factor model made further significant advancements, which in turn motivated the development of the competing Fama-French five-factor model.

Now we have a new four-factor model that incorporates anomalies and appears to have greater ability to explain the differences in returns of diversified portfolios than some prominent alternatives.

The competition to find superior models is what helps advance our understanding not only of the markets, but of our understanding about which factors to focus on when selecting the most appropriate investment vehicles and developing portfolios.

This commentary originally appeared May 11 on ETF.com

The Paranoid Survive, but They Burn Out. Take a Break.

Created on Sunday, 26 June 2016 19:00

Perhaps you’ve heard the expression, “Only the paranoid survive.” Ring a bell? If so, it’s probably because that’s the title of a book by Andrew S.Grove, the former chairman and chief executive of Intel.

When I read this book in late 1999, I bought into the need to always be looking for opportunities and to live my life at full throttle. I was afraid that if I stopped for even a moment just to rest, relax and recover, I wouldn’t “make it” (whatever that means).

I was paranoid, and I was surviving — but just barely.

Nonstop meetings and mile-long to-do lists were the norm. Any time I had a block of free space in my schedule, I rushed to fill it. I might be missing out if I didn’t. At the time, I didn’t realize this was a stressful way to live. As the stress added up, I played hard as well to help me deal with it.

I started cycling and brought the same, full-throttle mentality with me. Even though I knew, on an academic level, the importance of rest and recovery, I never appreciated either. I rode hard every time I went out. I kept going until I got sick or injured and had no choice but to take time off.

I repeated this process for years. Go super hard. Crash. Be forced to rest. Feel better. Repeat.

Only recently did I start to understand that the problem wasn’t that I needed something to distract me from my stress. I needed rest. But first, I had to face an underlying problem. I’d bought into being paranoid, into never letting up, and as a result, I was uncomfortable with feeling like there was not any slack in the system.

If I had anything extra — time, energy or money — I felt like a fool if I didn’t spend it.

I’m not alone. I have a friend who makes a lot of money, but he always talks about how broke he is. He’ll even pull out the lining of his pockets to demonstrate that they’re empty. Because I know how much he makes and how much he saves, I asked him why he keeps claiming poverty.

He told me that he and his wife purposely keep their checking account balance low so they always worry about overdraft fees and avoid spending money. It’s their way of dealing with the slack in their system. They cannot handle the thought of extra money sitting in their account.

This is a great approach if you want to save money and be miserable. But there’s a better way to deal with the extra resources in our lives. It starts with thinking about slack a little differently. Instead of spending every extra ounce of energy, maximizing every minute and using every cent, look for ways to reinvest it.

Think of the compounding power of reinvesting interest and stock dividends over time. The same is true for time and energy, and the returns are exponential.

We often think we need to consume everything — kind of a “use it or lose it” mind-set. Maybe you’re filling some free time with a new work project. Maybe you’re using that last ounce of energy to go on just one more ride. Or maybe you’re squirreling away yet another dollar for the future and beating yourself up with the threat of a big, bad overdraft fee.

Whatever that thing you’re doing, it’s time to stop. Instead of going on yet another hard ride, take an easy walk at sunset. Instead of running around with empty pockets, keep a twenty handy and see how long you can go without spending it. Get used to the idea of having a little bit of excess in your system. Better yet, don’t be afraid to do nothing.

For a long time, we’ve been told to squeeze every last drop out of life, but in the process, we’ve gotten close to the redline of flaming out. So cut yourself a little slack and remember that more than just the paranoid survive.

This commentary originally appeared May 31 on NYTimes.com

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