September 8, 2015

Book Review: DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth

I have always looked favorably upon do-it-yourself investing (DIY). It was a prominent feature of my own book. So I was looking forward to DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth by Wes Gray, Jack Vogel, and David Foulke (GVF), the managing members of Alpha Architect.

GVF took on an ambitious project in that they cover a broad range of subjects from the theoretical ideas of models versus experts, proper investment evaluation, and behavioral finance to the practical applications of value and momentum investing, market timing, and asset allocation.

Part 1 of the book is “Why You Can Beat the Experts.” Beginning with the Preface, GVF says it is best for investors to maintain direct control over their own accounts. They point out the misalignment of incentives and objectives between owners of capital and investment managers. Even though more concentrated portfolios usually perform better over the long run, managers prefer to hold broader portfolios that have less of a chance of deviating from benchmarks in the short run. Outperforming benchmarks gives managers little or no reward, while under performing can get one fired.
 
In Chapter 1, GVF presents evidence that model-based decision making gives better results than discretionary decision making. Simple is better than complex, yet “experts” often favor complexity so they can charge higher fees.

GVF points out that the decision making process has three components: R&D, implementation, and assessment. Experts may be useful for model R&D and assessment, but are not usually necessary for implementation. Experts’ use of qualitative information and additional data is often of little or no value. Experts can also suffer from the illusion of skill and a failure to recognize randomness.
 
In Chapter 2, GVF shows with more studies and meta-studies that models are better than experts. Models are even better than experts using the same models with discretion. Why is this? Models, unlike experts, are not subject to behavioral biases such as overconfidence, anchoring, framing, and loss aversion.

In Chapter 3, GVF goes into more detail about behavioral biases and shows just how strong they can be.  Chapter 4 explains how experts tell stories to make some approaches, such as buy-and-hold or value investing, seem like the only ones that make sense. Investors buy into these stories because they want to feel that expert opinions and judgements matter – when, in fact, they often do not.  In the course of their discussion, GVF debunks a few other investment myths, such as quality enhances value and economic growth leads to higher stock returns.

After GVF establishes the fact that we can beat the experts, Part 2 of the book moves on to explain “How You Can Beat the Experts.” Chapter 5 shows that it is difficult to identify competent and trustworthy advisors. GVF then lays out their sensible FACTS framework for investment evaluation. FACTS stand for fees, access, complexity, taxes, and search. You want fees, complexity, and taxes to be low, while accessibility and search ability should be high. Some of this may be obvious, but not everyone may realize that complexity isn’t usually desirable.

 In Chapter 6, GVF shows that Markowitz mean-variance portfolio optimization (MVO) does not hold up well in the real world due to the instability of its inputs. Research shows that equal weighting of assets does a much better job than MVO or other optimization approaches. The rest of chapter 6 is devoted to old paradigm asset allocation schemes, such as the Swensen, Bernstein, and Faber IVY 5 strategies. These aim at reducing portfolio volatility by diversifying with permanent allocations to different asset classes. Since equities have provided the highest long-run returns, permanent multi-asset allocation schemes sacrifice some potential return in pursuit of volatility reduction.[1]  Someone who appreciates momentum investing might prefer instead to hold only the top performing asset classes instead of them all.

Recognizing the value of trend-following timing overlays, in chapter 7 GFV lays out their own risk management framework that they apply to the IVY 5 portfolio. Because all five asset classes are permanently represented in the IVY portfolio, a trend-following exit from one asset class puts that part of the portfolio into Treasury bills, which can create a drag on portfolio performance.

GVF once used a 12-month moving average (MA) as a trend following filter. They show here that time series momentum (TMOM) beats out MA as a timing model in 4 out of 5 asset classes.[2]  Instead of just adopting TMOM themselves, GVF now combines 12-month absolute momentum (TMOM) with the12-month MA on a 50/50 basis to create a complicated approach that they call robust asset allocation (ROBUST). GVF says “…the evidence suggests that combining the two technical rules seems to be the strongest performer,” but results actually show that TMOM and ROBUST are at least equal in their performance and that TMOM may actually be better. Table 7.8 shows the winner (marked by an X) between MA, TMOM, and ROBUST, according to GVF.

GVF declares a tie in two cases where there is only a 1 or 2 percentage difference in Sharpe and Sortino ratios, but they say that ROBUST is the winner in two other cases (SPX and REIT) where there is the same small difference in these ratios. The only case among the 5 assets where there is a clear cut winner is bonds (LTR), where TMOM comes out ahead.

GVF presents longer out-of-sample results for the U.S., German, and Japanese stock markets. The only clear winner there is also TMOM, when applied to the Japanese market. So both in-sample and out-of-sample, TMOM shows equal or better performance to the ROBUST method before transaction costs. After transaction costs, the case is stronger for TMOM over ROBUST.[3]  It is also simpler than ROBUST.
 
GVF finishes up chapter 7 by giving a good reason, besides the usual behavioral finance ones, of why trend following works. This has to do with what GVF calls dynamic risk aversion. As prices drop, investors become more risk averse and do not want to step up to support prices. This accentuates trends as markets extend beyond their fair value. GVF does not mention it, but the same logic could apply to the upside. Investors may become less risk averse as markets rise, causing trends to overextend on the upside as well.

In Chapter 8, GVF discusses security selection using value and momentum. Their Alpha Architect website maintains an updated list of the top 100 value and momentum stocks based on the simple screening criteria that GFV presents here.

GVF’s value selection criterion is earnings before interest and taxes (EBIT) divided by total enterprise value (TEV). This is covered in greater detail in the earlier book Quantitative Value by Gray and Carlisle. GVF shows that the top decile of value stocks from 1927 through 2014 outperformed the market, but that value had much higher volatility, making the Sharpe ratios of value and the market the same. Both value and market also had high worst drawdowns of -91.7% and -84.6%, respectively.

A risk factor that GVF did not mention is the idea of a value trap. Some of the cheapest value stocks may be depressed because they deserve to be, based on poor fundamentals. These stocks may remain permanently depressed or become even more depressed because they are on the verge of bankruptcy. For this reason, it may be better to invest in more than just the cheapest value stocks.

Moving on to momentum, GVF gives behavioral explanations of why momentum works. They show that the spread between high and low momentum stocks is close to five times the spread between value and growth stocks (18.4% versus 3.7%) from 1927 through 2014. The top decile of stocks sorted by 12-month (skipping the last month) momentum and rebalanced monthly outperformed the market by 5.4% annually, after deducting 2.4% for annual transaction costs.

There is some controversy, however, about how high transaction costs might be when momentum is applied to individual stocks. Gerstein Fisher (2015) estimates that a monthly rebalanced, long-only momentum portfolio can have an annual turnover of around 300%. Lesmond et al. (2004), whom GVF cites, report that bid/ask spreads are much higher for momentum stocks. They find strong evidence that trading costs for momentum stocks are at least 1.5% per trade using conservative assumptions and a battery of trading cost estimates. If you multiply the 300% turnover by at least 1.5% per trade, you get annual trading costs of at least 4.5% annually, rather than 2.4%. Perhaps transaction costs now are less than 4.5%. But they should still be substantial, since it is unlikely that the high bid/ask spreads of momentum stocks has changed all that much.

In Chapter 9, GVF gets to the heart of DIY investing by suggesting ways for investors to implement a DIY approach other than by buying and holding a balanced stock/bond portfolio. The first way is with a basic IVY 5 portfolio of generic ETFs filtered with a trend-following 12-month moving average. The five asset classes used are U.S. and international stocks, intermediate bonds, REITs, and commodities. GVF says, “Yahoo Finance charting allows you to run a monthly 12-month MA test for each asset class and with a yearly rebalance across assets you would be in DIY heaven.”  Unfortunately, this is not possible with Yahoo finance, since it does not provide for monthly charts. Even if it did, Yahoo finance uses only price changes and not total returns. But you could use TMOM instead of an MA approach by inputting the appropriate ETFs into stockcharts.com and selecting a one year SharpChart that you could bookmark and look at monthly.[4]

GVF next presents enhancements to this strategy by applying both an MA and a TMOM timing filter (which equals their ROBUST method) and by substituting individual U.S. and international value and momentum stocks in place of generic U.S. and international stock ETFs. GVF has a risk-conscious allocation scheme that varies the percentages allocated to equities for balanced, moderate, and aggressive investors to 40%, 60%, or 80%, respectively. But since timing filters are meant to remove equities entirely from one’s portfolio during bear markets, the balanced and even the moderate allocations may be too conservative for most investors. Equities have provided the highest returns historically, and large allocations elsewhere may greatly diminish one’s accumulated wealth over a lifetime of investing.

It is not easy for most DIY investors to calculate TMOM and MA values. So to remedy this situation, GVF provides a free service on their website that applies the ROBUST filter to each asset class for balanced, moderate, and aggressive portfolios. No one needs to pay for an asset allocation or timing overlay. They are available free, along with the top 100 value and momentum stocks, to anyone who signs up on the Alpha Architect website.

GVF recommends concentrated portfolios to maximize the benefits of value and momentum investing. Modern portfolio principles tell us that a well-diversified portfolio of at least 30 stocks can eliminate most idiosyncratic risk. But this may not be true for portfolios of stocks having high bankruptcy risk. Concentrated portfolios of 30-50 of the cheapest value stocks may still be too risky.

The same issue applies to momentum stocks. Looking at the top momentum stocks according to the Alpha Architect screener, 18 of the top 25 and 31 of the top 50 stocks are in biotech/medical technology. High intra-industry covariance means that you would need to hold significantly more than the top 50 momentum stocks to have a well-diversified portfolio.

Another drawback to an individual stock momentum portfolio with only the strongest momentum stocks is that there is a good chance some of them may be takeover candidates or may have already been taken over. Takeover stocks usually do not perform as well in the future as stocks that showed less momentum but were subject to takeovers.

For all the above reasons, investors may want to hold at least 100 momentum and 100 value stocks if they are selected using simple screens such as the ones presented here. How likely is it that a DIY investor would be willing to manage 100 value and 100 momentum stocks, or even 50 of each?
 
Periodic timing model signals would add to that complexity, since you would, at times, need to exit and re-enter your entire stock portfolio. So if you do not use a timing model overlay, you should be prepared for some very large drawdowns and multi-year periods of significant benchmark under performance.[5]

Another potential problem with holding individual stocks is the issue of scalability. The identity of the top value and momentum stocks is easily available now through the use of screeners, such the one that Alpha Architect provides. Increasing amounts of capital buying the same limited number of stocks may mean diminished future returns.
 
GVF finishes up chapter 9 with their “Ultimate DIY Solution.”  Here, instead of a generic REIT allocation, they recommend a monthly rebalanced portfolio of the top one-third REITs ranked by momentum (excluding those below the 40th percentile in market capitalization).  In place of a generic allocation to commodities, GVF recommends a portfolio of commodity contracts selected on the basis of momentum and term structure.

Recognizing that DIY investors will never go about constructing all these different portfolios, GVF suggests that DIY investors hold all seven assets (U.S. value and momentum stocks, international value and momentum stocks, bonds, REITs, and commodities) in the form of exchange-traded products (ETFs or ETNs) and then apply their risk-management overlay to these funds each month. Here is a table of suggested investment vehicles:


There are currently no domestic or international momentum ETFs that hold fewer than 50 stocks, although some may be forthcoming. There are also no ETFs that hold fewer than 50 REITs or that use momentum to select REITs.

There is a just one commodity ETF or ETN with a focus on term-structure and momentum. It is the United States Commodity Index (USCI). But USCI is required to maintain positions in six different commodity sectors at all times. This means some sectors may not always have positions that have a favorable term-structure. USCI does use 12-month momentum to help select its long-only positions. According to Miffre and Rallis (2006), 12-month momentum applied to commodities is profitable, but mostly on the short side. But Geczy and Samonov (2015) report that 12-month momentum (skipping two months) works in reverse for commodities. In other words, positive 12-month momentum is a negative factor. USCI is down 43% since its high in April 2011.

In their final chapter, GVF mentions some of the reasons investors might not want to do-it-themselves. These include being able to blame someone else if things don’t go well, inertia to change, unwillingness to let go of current relationships, bias in favor of experts over models, overconfidence in one’s own abilities, models not conforming to our preferences and beliefs, and the desire to be a hero. These psychological excuses are worth thinking about. Perhaps then they will be less likely to influence us against adopting a sensible, model-based investment approach.

If I had been GVF, I might have said more about why investors who adopt DIY investing might abandon or change it into something that is no longer model-based.  DIY investing should be simple, but it is not always easy. To be successful, DIY investing requires a good understanding of the principles underlying one’s model and the requisite discipline to stick with it under varying market conditions.
Here are my main conclusions about the second half of the book:

•    The IVY 5 permanent portfolio scheme does not take advantage of relative strength momentum.
•    The ROBUST timing model trades more, is more complicated, and is no better than TMOM.
•    Value trap risk and intra-industry co-variance risk may make value and momentum portfolios with 50 or fewer stocks too volatile, while larger value and momentum portfolios may be impractical for DIY investors.
•    Both value and momentum are subject to high worst drawdowns. The use of a timing model to reduce drawdown may be difficult with large portfolios of individual stocks.
•    Turnover is high and transaction costs may offset all or most momentum profits for portfolios of individual stocks.    
•    The most practical approach is to apply a timing overlay model to asset class ETFs.  Investors can do this with TMOM and SharpCharts. Those who prefer to use ROBUST can access it for free on the Alpha Architect website.
 
The reason I can recommend this book is the good job GVF does in explaining models versus experts and behavioral biases. We can never be reminded too often of these important matters.


[1] A newer paradigm uses trend following methods to diversify among different assets on a temporal rather than a permanent basis.  This means investors can focus on equities for as long as equities remain strong and diversify into other assets when they are strongest. 
[2] GVF uses the term time-series momentum (TMOM) to mean absolute momentum. Both relative and absolute momentum are based on time series (asset returns), and absolute momentum is a better term to differentiate it from relative momentum. But we will use time-series momentum (TMOM) here to avoid confusion.
[3]GVF estimates that the MA signal has a 20% higher turnover than TMOM, while our calculations show 30% more trades. MA and ROBUST should thus have greater transaction costs and more whipsaws than TMOM.

[4] Alternatively, you could track a portfolio on ETF Screen or Morningstar that shows performance over the last year. 
[5] Timing models have a much more beneficial effect on momentum portfolios than on value portfolios.