“We are prone to overestimate how much we understand about the world and to underestimate the role of chance in events.”
—Dan Kahneman, Thinking, Fast and Slow
We hope that Part One left you with a central message: We can beat the experts. Experts are biased and overconfident. Many years of experience are not something to be worshipped but something to be questioned. A long history of success does not guarantee future success; instead, it is a red flag for bias and overconfidence. We don't need an expert to make good decisions; we need a psychology coach. We know overconfidence is an overwhelming influence in decision-making, and this effect is often magnified for experts—unless it is rigorously contained. Many successful experts aren't even aware they are overconfident, which means they almost certainly have not put systematic processes in place to counteract overconfidence. We can do better and beat the experts. In order to beat the experts what we need is an evidence-based systematic decision-making process.
Yet the experts themselves can create additional barriers to adopting such processes. After all, the experts don't want you to follow an evidence-based systematic process—it would cut them out of the picture and eliminate their rationale for charging you a fee. We need to rigorously defend against their influential tactics. Financial service professionals utilize the following techniques to influence us into hiring them to make decisions on our behalf:
Before you get snared in a trap set by a financial “expert,” reread Part One of this book. Next, identify if the expert is pitching you on her services by leveraging fear, greed, complexity, or relationship tactics. If you sense a trap, run the other way as fast as you can (and don't forget your wallet!). And if you simply don't have the time to deal with your portfolio, focus on experts that utilize systematic evidence-based approaches in their investment portfolio.
But let's say you are ready to be the chief investment officer of your family office, whether it has assets of $10,000 or $5,000,000,000. You want to learn; you are willing to work hard; and you have eaten a healthy dose of humble pie. The next question is the following: “How do I manage my portfolio?” The process to becoming a DIY family office is straightforward:
The remainder of this chapter is dedicated to outlining each of these steps in greater detail.
When it comes to investing, there is a simple question we often ask: What would Charlie Munger do? Mr. Munger is Warren Buffett's better half at Berkshire Hathaway, a multibillionaire and a master of human psychology and decision-making. As we mentioned earlier, one of Charlie's favorite approaches to decision-making is to “invert, always invert.” What Charlie means by this is that when evaluating a solution, instead of asking, “Why is X a good solution?” we should turn the question around, and work backward from the inverted question. For example, we might also consider, “Why is X not a good solution?” Inverting the question, and turning it over in different ways, helps us understand X in a new way and may give use reasons to reconsider our acceptance of X. This inversion of perspective illuminates the opposite of what others are looking at and provides a deeper understanding of potential solutions.
When Charlie talks, we listen. So instead of identifying all the reasons why investors can benefit from investment advisors (which everyone can cite chapter and verse), we decided to come up with a list of 10 things people don't like about their financial advisors. Here is the list of 10 things that destroy the potential value of the financial experts we hire:
Not surprisingly, a do-it-yourself investment approach addresses many of the shortcomings of financial experts.
Financial advisors are ubiquitous in the United States, where many investors hire them to assist with investing, financial planning, and other types of financial decision-making. Advisors can be helpful when people lack the expertise or the time to devote to addressing esoteric or complex financial questions. If you seek advice in areas where information is ambiguous, however, and it's hard to judge the quality of the advice based on your own knowledge, you can get stuck with a second-order problem:
What is your basis for relying on the advisor in the first place?
Especially under conditions of uncertainty, what gives you confidence that you should trust the advisor and that the advice is any good? As we discussed in Part One of this book, there are many reasons why we shouldn't put faith in an expert.
Periodically, it comes time to review these relationships. So how do people assess the quality of advice they get from their advisors?
Certainly, you can consider an advisor's decision-making track record. How good has the advice been in the past? For example, do you get a monthly statement comparing your returns to a benchmark? Has following the advice led to good outcomes? But this hindsight approach has its challenges.
Some decisions are rare. A home mortgage, for instance, a change in marital status, a windfall, or selection of a fund for a retirement account, may be one-time events, where it may take many years to see whether the advice was good. Additionally, adverse economic shocks, like the financial crisis, or idiosyncratic risks, like a huge drawdown in, say, a gold position, may also cloud your ability to judge the quality of the advice. So you may have to fall back on other means to draw your conclusions.
Perhaps the advisor has similar values to yours. They exhibit a lot of professionalism. These presumably have something to do with their ability to provide advice, don't they? More generally, the most fundamental question one must answer is do you trust the advisor? Of course, as the great Ernest Hemingway quipped, “The best way to find out if you can trust somebody is to trust them.” But now we are faced with a chicken-and-egg problem. On the one hand, we want to work with someone trustworthy, but on the other hand, maybe we need to work with someone first to ascertain if they are trustworthy. What should we do?
We're not sure we agree with Mr. Hemingway, at least in this context. Blindly following someone's advice and then evaluating them after the fact might not be an ideal approach, but is one that many follow. Identifying who is trustworthy, without having worked with them before, is a highly subjective activity that allows experts an opportunity to exploit our vulnerabilities. And even if we have worked with them before, establishing trust in them is sometimes simply a matter of feelings or personal opinion. Yet as we move from observing tangible, performance-related outcomes, to more subjective approaches involving “trust,” we can get into trouble. The problem is that we sometimes trust advisors based on criteria that have nothing to do with their competence. Our trust can be misplaced. That is, we may consider an advisor to be trustworthy, and thereby view the person with approval, when in fact we are getting bad advice. As it turns out, this happens all the time.
In Individual Judgment and Trust Formation: An Experimental Investigation of Online Financial Advice, a working paper by Julie R. Agnew et al., the authors explore how trust is related to the assessment of advisor performance. The research highlights some surprising factors that influence our evaluations of an advisor's quality. In what should not be a surprise at this point, many individuals' evaluation metrics are entirely unrelated to the quality of advice given. As a side note, while the research was conducted in Australia, we have few doubts that it reflects realities here in the United States as well.
The paper illuminates many of the factors that lead people to trust advisors. For instance, the authors find that people prefer younger advisors. Is this a reliable criterion for assessing trustworthiness? They also prefer people who have professional certifications. However, given the blizzard of existing certifications in the United States, understanding which credentials matter is nontrivial. People also are “sticky” with their advisors, perhaps exhibiting a form of endowment effect or familiarity bias, in which we underestimate the riskiness of something simply because we are familiar with it. Just because you've been with an advisor for 20 years does not mean you are getting good advice. And yet, this is exactly what many believe.
These factors may have little or nothing to do with providing good financial advice, and yet we rely on them. Ambiguous situations give rise to additional opportunities for the clever advisor to game the system, and manipulate and exploit clients. For instance, the paper describes the psychological ploy of catering, which occurs when an advisor agrees with an investor's suggested approach to gain trust. Once trust is established, they can then disagree with the approach and offer bad advice.
A particularly interesting finding was that people were heavily affected by advice related to easy financial topics, offered early on in an advisory relationship. The paper describes an experiment in which advisors gave advice on “easy” topics, as well as on “hard” topics. The researchers find that if an adviser gives correct advice on an “easy” topic, the respondent can judge the quality easily, allowing them to form a firm opinion of the adviser. However, if this same adviser then follows with advice on a “hard” topic, judging the advice quality is difficult and the respondent starts making bad decisions. The respondent, who really can't judge the advice on the “hard” topic, relies on their prior judgement of the “easy” advice to determine if the “hard” topic advice is good or bad. As a result, a pre-existing good opinion of the adviser will be reinforced by “ambiguous” advice on a hard topic, regardless of whether the advice is really correct or not. This explains why advisers who can establish their trustworthiness early on in a relationship continue to be trusted, even if they give the wrong advice on hard topics.
The research on trust and relationships is scary. People can naively trust people in fancy suits bearing advice on simple financial matters. As the research shows, all you need to do is show some familiarity with the simple stuff. If the investor trusts you based on the simple advice, you can say whatever you want about the hard stuff!
An example illustrates how this might work in practice. Consider two scenarios:
Easy Scenario
Advisor: “You have credit card debt and a large cash position. The interest you're earning on the cash is less than the interest expense on the credit card debt. You should pay off your credit card debt.”
Hard Scenario
Advisor: “So you're interested in investing in an index fund? All these funds will match the index returns, but some have been around for a long time and have managers who have established a reputation in the industry, and that kind of quality is really important for any investment product. You should be skeptical of funds with the lowest management fees, since these are going to be funds you've never heard of, with managers who aren't well established.”
What's that, you say? This example is unrealistic? Think no financial advisor would ever give advice this obviously bad?
Consider the MainStay S&P 500 Index Fund Class A mutual fund (ticker: MSXAX), for which the expense ratio is 60 basis points (as well as a 3 percent front-end load). Let's look at Morningstar's analysis of the past 10 years of its performance versus its index, the S&P 500 total return index, shown in Table 5.1.
Table 5.1 MainStay S&P 500 Index Fund Performance as of December 5, 2014
| MSXAX Performance | YTD | 1 Yr. | 3 Yr. | 5 Yr. | 10 Yr. |
| Fund | 13.81 | 18.00 | 20.06 | 15.16 | 7.33 |
| S&P 500 TR USD | 14.45 | 18.67 | 20.76 | 15.83 | 7.95 |
| +/– S&P 500 TR USD | −0.64 | −0.67 | −0.70 | −0.67 | −0.61 |
As you would expect, the MSXAX trails its benchmark by the margin of its expense ratio (60 basis points). Now, why would you invest in this fund, when you can invest in a tax-efficient ETF index for essentially nothing? We can't really think of a good reason. In fact, we can't think of a reason why this fund should even exist—but the fund manages over $2 billion! It stands to reason that some financial advisor, somewhere, somehow, must be recommending it. And if so, this strikes us as objectively bad advice.
While an index fund is a fairly straightforward example for many, it illustrates the point that there are investors out there who are not thinking about index investing in the right way. And so what happens when we move on to additional “hard” areas in finance? Questions about diversification, asset allocation, risk management, exotic hedge fund strategies, and so on; how do we assess decisions made by advisors in these areas? What about our favorite complicated subject—taxes? The same concept applies. Just because an advisor can give you good advice on “easy” topics does not mean you should necessarily rely on advice in other areas.
Various studies have shown that we systematically overestimate the quality of advice we receive. For example, the working paper referenced above describes how the financial services regulator in Australia undertook a study of how consumers perceived the quality of advice they were getting. They evaluated the objective quality of advice given to a small sample of Australians seeking retirement advice. They found only 3 percent of the advice could be considered good quality, while the majority of the advice (58 percent) was adequate and the remaining advice was poor. Despite these low evaluations from the Australian regulator, most participants (86 percent) ranked the quality of the advice they were receiving as “high.” In addition, 81 percent trusted the advice they received from their advisor “a lot.” This simple study suggests that people can have difficulty objectively assessing the quality of advice given. And why should we expect they could accurately assess such advice? The investing skills they lack, which are why they are seeking advice in the first place, are the same skills they would need to differentiate good from bad advice. Often, what we perceive as high-quality advice is, in fact, terrible advice.
At the end of the day, a trustworthy and competent financial advisor can be an exceptional ally for a struggling investor. But how we find these wonderful advisors is a challenge: It is difficult to assess the quality of advisors and their financial advice. As consumers of financial advice, we can at least be aware of common behavioral pitfalls to which we may be vulnerable. We should also be aware that much of the advice from financial experts is poisoned with bias and overconfidence. All of this suggests that managing our own portfolios is not such a radical concept.
If we can't trust advisors, on average, and we can't trust ourselves because of our innate bias, who can we trust? Not surprisingly, we suggest that investors stick to a systematic framework to assess investment opportunities. Our framework is called the FACTS, which is a mnemonic acronym we will explain shortly. And while there is no “one-size fits all” strategy assessment and allocation model, a systematic framework for decision-making can help simplify the process and maximize returns.
For every investment strategy that needs to be assessed, the FACTS framework (consisting of Fees, Access, Complexity, Taxes, and Search) can be employed to clarify important considerations for the prospective investor. Our experience suggests that the vast majority of taxable family offices and high-net-worth individuals should focus on strategies with lower costs, higher accessibility and liquidity, easily understood investment processes, higher tax-efficiency, and limited due diligence requirements. For example, the FACTS would suggest, in general, that investors make use of more managed accounts and low-cost passively managed 1940 Act products (ETFs and mutual funds), and fewer private hedge funds and private equity vehicles. Using the FACTS framework can help assess cost/benefit trade-offs across strategy characteristics, which, in turn, improves portfolio results net of taxes, fees, and overall brain damage.
“I can't figure out why anyone invests in active management, so asking me about hedge funds is just an extreme version of the same question.”
—Eugene Fama, at the Investment Management Consultants Association 2013 Conference
Traditional “two and twenty” hedge fund compensation, referring to fees of 2 percent on assets and 20 percent of the gains, has been an industry standard for years. These days, 2/20 has been reduced to something more like 1/20, and with an ounce of negotiation, fees may drop to 1/10. To ascertain where fees have been and where they might be going, we highlight some tables from Ken French's work on the “Cost of Active Investing.”1 Table 5.2 shows the time series of expenses charged by mutual funds from 1980 through 2006 (CEF stands for closed-end fund). All-in expense ratios went from over 200bps to around 100bps.
Table 5.2 Fund Fees over Time (1980 to 2006)
| Open-end Mutual Funds | Expense Ratio | |||||
| Expense Ratio | Annuitized Load | Total | Percent Passive | CEFs | ETFs | |
| 1980 | 70 | 149 | 219 | |||
| 1981 | 71 | 167 | 237 | |||
| 1982 | 75 | 128 | 203 | |||
| 1983 | 76 | 113 | 190 | |||
| 1984 | 82 | 114 | 196 | 1.0 | ||
| 1985 | 80 | 105 | 185 | 1.1 | ||
| 1986 | 81 | 101 | 183 | 0.8 | ||
| 1987 | 86 | 96 | 182 | 0.9 | ||
| 1988 | 96 | 97 | 193 | 1.2 | ||
| 1989 | 94 | 84 | 178 | 1.6 | ||
| 1990 | 93 | 76 | 169 | 2.3 | ||
| 1991 | 90 | 65 | 155 | 2.9 | ||
| 1992 | 96 | 50 | 146 | 3.9 | ||
| 1993 | 98 | 47 | 145 | 3.9 | ||
| 1994 | 98 | 42 | 139 | 4.7 | ||
| 1995 | 96 | 42 | 139 | 4.7 | ||
| 1996 | 93 | 40 | 134 | 5.8 | ||
| 1997 | 92 | 35 | 126 | 7.3 | ||
| 1998 | 90 | 30 | 120 | 9.0 | ||
| 1999 | 91 | 27 | 117 | 9.7 | ||
| 2000 | 96 | 24 | 119 | 9.8 | 96 | |
| 2001 | 97 | 19 | 116 | 10.9 | 92 | 20 |
| 2002 | 98 | 18 | 116 | 12.9 | 101 | 17 |
| 2003 | 96 | 17 | 113 | 12.4 | 98 | 18 |
| 2004 | 91 | 18 | 108 | 12.7 | 104 | 19 |
| 2005 | 87 | 16 | 103 | 12.5 | 103 | 20 |
| 2006 | 85 | 15 | 100 | 12.6 | 109 | 21 |
Table 5.3 highlights hedge fund fees from 1996 to 2007. As compared with mutual fund fees, hedge fund fees have stayed fairly steady over time. Although hedge fund fees still have room to fall, and mutual fund fees represent a step in the right direction, with serious negotiation and persistence, it may be increasingly the case that fees can be “whatever you think is fair, since we'd love an allocation.” Certainly we would advocate for negotiating fees lower generally, but in addition, investors can apply the FACTS to give them context for thinking about these fees.
Table 5.3 Hedge Fund Fees over Time (1996 to 2007)
| US Equity-related Hedge Funds | ||||
| Performance Fee | Total | |||
| Mgmt. Fee | Quoted Performance Fee | Realized Performance Fee | Mgmt. Fee + Realized Performance Fee | |
| 1996 | 0.92 | 18.24 | 3.79 | 4.71 |
| 1997 | 1.05 | 18.4 | 5.4 | 6.45 |
| 1998 | 0.98 | 18.25 | 2.56 | 3.54 |
| 1999 | 1.03 | 18.24 | 5.91 | 6.94 |
| 2000 | 1.09 | 18.42 | 2.35 | 3.44 |
| 2001 | 1.2 | 18.93 | 1.51 | 2.71 |
| 2002 | 1.24 | 19.09 | 1.38 | 2.62 |
| 2003 | 1.25 | 19.12 | 3.4 | 4.65 |
| 2004 | 1.23 | 19.03 | 2.28 | 3.51 |
| 2005 | 1.26 | 19.03 | 2.18 | 3.44 |
| 2006 | 1.27 | 18.95 | 3.25 | 4.52 |
| 2007 | 1.28 | 19.15 | 3.35 | 4.63 |
A steady reduction in fees is becoming the new norm for the investment management industry. Lower fees are a good thing for investors, less so for investment managers. Yet, the achievement of low fees, per se, is not a panacea for the investor; the appropriate level of manager compensation depends on the situation. For instance, international markets are inherently more expensive to trade than domestic markets due to the high costs of maintaining overseas brokerage and custody operations. Another example might be a highly active strategy with high expected performance that is not available elsewhere in the marketplace and that cannot be replicated at a lower cost. Nonetheless, leaving such nuances aside, here are some basic questions one must ponder when thinking about fees.
Here are some questions you might want to ask. Who pays for service providers? What about compensation using so-called soft dollars, or the manager's ability to push operating costs onto the investors? Does the strategy push the client into a higher-price commission regime so the manager can benefit? How about other trading and execution costs? Is order flow being sold to a third party? Are there any 12b-1 fees? What about fund administration, fund accounting, or board fees for mutual funds? Are there any out-of-pocket reimbursements? How is performance calculated when an investor withdraws, in whole or in part? Are there withdrawal penalties? Any discretionary mark-to-market accounting or suspect third-party valuations? Beware of the fine print! Remember that fees compound over time, so scrutinize them carefully.
Charging a performance fee can make sense, but is there a clawback provision if the manager loses all your money after a great year? What is the hurdle? For example, a 0 percent hurdle for a product with generic market exposure doesn't make sense, because an investor can buy market exposure for next to nothing! Why should you pay more simply because the market happened to go up? Is there a high-water mark provision? Do managers “eat their own cooking,” and invest in their own funds, which can align incentives and address conflicts of interest? If a manager doesn't own what he recommends you should own, think twice about placing your own money with the manager.
Many funds are “closet” indexers. Can we clone the manager's exposure via relatively low-cost financial engineering? In other words, is my long/short equity manager more than 95 percent correlated with a strategy that simply goes long the Russell 2000 Value Index and short the S&P 500 index? If a cloning technology costs 50 bps and gets 95 percent of the exposure, the manager needs to either: (1) drop his fees; or (2) not get an allocation. So-called smart beta products are another area where investors can often reproduce smart beta exposure, but more cheaply, by financial engineering smart beta products into their constituent components.
“The possibility that liquidity might disappear from a market, and so not be available when it is needed, is a big source of risk to an investor.”
—The Economist, September 23, 19992
In The Wizard of Oz, Dorothy's traveling party sings, “Lions, and tigers, and bears, oh my!” But in the investment management world, it's more like, “Lock-ups, gates, and redemption fees, oh my!” Dorothy and her friends would be justifiably terrified of today's managers and the many access-reducing strategies they employ. If there exists a way for a manager to maintain a positive lock on your capital, you can bet he will be happy to attempt it. Nonetheless, maintaining flexibility and access to one's capital is critical in an uncertain world where unforeseen liquidity needs may occur, opportunities can arise unexpectedly, and managers can go from “hero” to “zero” in the blink of an eye. In some specialized situations, giving up access might make sense, but all too often investors do not fully assess the cost/benefit of access. It should be noted that most mutual funds and ETFs do not have restrictions on capital; however, some do, so keep reading even if you never plan to invest in a hedge fund or private equity.
The empirical evidence on investor access suggests there is a wide range of access provisions in the marketplace.3 Access rights are especially important in private vehicles, such as real estate funds, hedge funds, or private equity funds. George Aragon analyzes a database of hedge funds and reveals some surprising results. In Table 5.4, Aragon highlights that over 16 percent of hedge funds lock up investor capital in the fund for one year or longer.
Table 5.4 Hedge Fund Lockup Periods
| Lockup (months) | 0 | 1 | 3 | 6 | 9 | 12 | 18 | 22 | 24 | 30 | 90 |
| Fund | 2,380 | 15 | 15 | 38 | 1 | 404 | 1 | 1 | 12 | 3 | 1 |
Does this make sense?
Perhaps, but here are some questions to consider.
Monthly redemption with 90-day notice and a promise to send funds within 90 days of liquidation is really six-month+ redemption. There can be side pockets, notice requirements, redemption suspensions, and other provisions that limit, or restrict, redemption rights. And if you don't know what these terms are, be wary of such investments—make sure you know what you are buying! Watch for semantic shenanigans in subscription agreements. The devil is in the details.
Do the access privileges line up with the liquidity of the underlying assets? Monthly liquidity for a manager trading illiquid pink-sheets, or five-year locks for a manager trading mega-caps, probably don't make sense. The former may subject you to drawdowns during redemptions, while the latter is simply unjustifiable. Be on the lookout for mismatches between portfolio asset liquidity and portfolio manager liquidity provisions. There are other hazards, including in-kind distributions and preferential liquidity rights.
Limited partnerships are theoretically interesting, but what happens when another LP sues the fund and everyone's capital is locked up? Also, consider that, technically, a general partner of an LP can have access to client funds (think Bernie Madoff). Go for separately managed accounts (SMAs) or an exchange-traded fund (ETF) vehicle, when possible. These vehicles ensure your capital is available when you want it. That way, you can go to cash and wire the proceeds to a bank account on the same day, if you like.
After dropping 20 percent of your capital into private equity, venture capital, and hedge funds, all with five-year+ lockups, you suddenly realize that the markets have blown up and your “alternatives” exposure is now 60 percent of your capital—you'd love to scale back, and perhaps reallocate to areas where dislocations have created opportunity in liquid markets, but you cannot. Yikes. Illiquid alternatives can have dramatic effects on overall allocation flexibility in adverse markets.
“Any intelligent fool can make things bigger, more complex, and more violent. It takes a touch of genius, and a lot of courage, to move in the opposite direction.”
—E.F. Schumacher
Jason Zweig's book, Your Money and Your Brain, highlights an interesting conversation with Harry Markowitz, who won a Nobel Prize in 1990 for his groundbreaking work in portfolio selection. In their conversation, Zweig asks Markowitz how he invests his own money. Markowitz responds with the following:
“I should have computed the historical covariance of the asset classes and drawn an efficient frontier…I split my contributions 50/50 between bonds and equities.”4
The intellectual leader behind modern portfolio management is an equal-weight asset allocator? We'll come back to the quote in a moment, but first let's review some general observations on Markowitz's mathematically sophisticated approach to asset allocation—the one he chose to disregard when it came to his own portfolio.
The mean-variance story is compelling. Mean-variance optimization involves calculating the returns of each asset and correlations for each pair of assets, and solving for the efficient frontier that maximizes returns while minimizing risk. It's an approach that would make any math teacher smile. But there is an important assumption embedded in the approach: the inputs, which are based on history, amount to a forecast of returns, correlations, and volatility for the future.
Although Markowitz did win a Nobel Prize, at least partly based on his elegant mathematical solution to identifying mean-variance efficient portfolios, a funny thing happened when his ideas were applied in the real world: mean-variance performed poorly.
The reason is that the historical returns, and correlations, which informed the input for the optimization algorithm, were different from what investors actually experienced in the real world in the future. That is, the estimates were “unstable,” in the sense that they turned out to be guesses that simply weren't any good.
Academics began to see the basic flaw in the approach: Estimates for any covariance matrix are going to be unstable, and thus the “optimized” weights spit out from a model influenced by an unstable covariance matrix will also end up being unstable and unreliable. And so this highly engineered, mathematically beautiful, and Nobel Prize–winning approach translated into a no-value-added-situation for investors.
This is something to keep in mind when considering any optimization method for asset allocation. It also emphasizes a critical point related to asset allocation that every investor should be aware of: Complexity does not equal value.
Recall Charlie Munger's admonition to “invert, always invert.” So if complexity does not equal value, is it perhaps simplicity that equals value? Later in Chapter 6, we will discuss how simple equal-weight allocations do indeed seem to reliably beat complex allocation approaches.
Markowitz himself may have realized the value of simplicity intuitively. Consider the irony: The founder of modern portfolio theory uses an equal-weight allocation. And one of the central assumptions underlying mean-variance optimization is that investors care about risk and return trade-offs. Yet, as Markowitz highlights, his decision-making framework has little to do with risk and return trade-offs. In 2014, now that we have a long enough data trail, we can show that Markowitz's model doesn't outperform a simple equal-weight allocation. The reason for this underperformance is not a critique of the model, which is clearly an incredible intellectual achievement, but has everything to do with the practical realities of accurately estimating a covariance matrix. We will come back to this issue later in the chapter and in the next, when we explore asset allocation in more detail.
The point, however, is that academic insights and good models don't necessary translate into good practical ideas. This insight is helpful for a do-it-yourself investor, because it implies that we don't need a PhD in finance to implement a reasonable asset-allocation strategy.
Is the complexity a front for the manager, or does the complexity of the strategy drive the alpha? There is seldom a connection between a strategy's complexity and its effectiveness. If you are having trouble understanding why the complexity exists, it may be intentional on the part of the manager. If the manager cannot justify the complexity relative to a simpler model than the complexity is not necessary.
Complexity is often correlated with data fitting, for example, when managers identify a very specific allocation scheme that has worked in a small sample. If the complex system is slightly changed, do results completely dry up? If they do, then the system is not robust. Reversion to the mean, and mediocre performance, or much worse, is in your future.
Clarity and simplicity help facilitate communication and education, which breeds trust and confidence. Outsourcing investment activity to managers with highly complex, expensive, and opaque investment strategies does not facilitate clear communication. And while it might not matter if the portfolio is making money, what happens when it starts losing? Suddenly, understanding becomes very important, as people start asking difficult questions. Defending a losing investment in a Vanguard index fund is easier than defending a loss in credit default swaps.
Any banker who lived through the 2008 financial crisis understands how complication can create risk-management problems. How does one risk manage a machine-learning algorithm that is trading leveraged exotic derivatives with a jump-diffusion model, infused with a touch of fractal mathematics and string theory? Black boxes of this sort can be problematic for risk managers. In short, always ask yourself if the complexity of a strategy creates a risk management blind spot.
“Another very simple effect I very seldom see discussed either by investment managers or anybody else is the effect of taxes… If you sit back for long, long stretches in great companies, you can get a huge edge from nothing but the way that income taxes work.”
—Charlie Munger, On the Art of Stock Picking
Tax minimization strategies are—and always will be—a critical aspect of long-term wealth creation. Tax efficiency is an important investment consideration that is often overlooked, even by experienced allocators. Indeed, tax considerations often predominate over performance considerations. This cruel investment calculus often seems lost on mutual fund investors. Over long periods of time, a tax drag of 50 percent or more a year on a short-term trading strategy has a tough time outlasting an inferior investment strategy that defers taxes for many years into the future.
Some scary numbers: Starting in 2013, the marginal investor will pay 23.8 percent on long-term capital gains (20 percent plus 3.8 percent health care tax) and 43.4 percent (39.6 percent plus 3.8 percent health-care tax) on short-term capital gains (see Table 5.5). These figures do not include city, state, or local taxes, which can boost these figures much higher (e.g., California) (see Table 5.5).
Table 5.5 Tax Rate Increases from 2012 to 2013
| 2012 Tax Rate | 2013 Tax Rate | Health Care Tax | 2013 Total | % Increase | |
| Tax-exempt Interest | 0% | 0% | 0% | 0% | 0% |
| Qualified Dividends | 15% | 20% | 4% | 24% | 59% |
| Long-term Gains | 15% | 20% | 4% | 24% | 59% |
| Nonqualified Dividends | 35% | 40% | 4% | 43% | 24% |
| Short-term Gains | 35% | 40% | 4% | 43% | 24% |
| Taxable Interest | 35% | 40% | 4% | 43% | 24% |
Source: Internal Revenue Service Documents.
The tax bite matters: Consider a manager returning 30 percent a year. Yes, it's an amazing return, but the manager churns the portfolio consistently to generate these returns. After a 43.4 percent tax, this yields only 16.98 percent to his investors. Another manager makes 22.28 percent a year—quite an accomplishment—but this manager trades less frequently to lock in long-term capital gains. After tax, this managers' return is 16.98 percent. Finally, consider a buy and hold manager that generates 16.98 percent a year, but never sells or rebalances the portfolio. After tax, this manager still makes 16.98 percent. All three managers generate 16.98 percent after-tax returns, but the three managers have very different pretax results, and therefore offer vastly different security-selection skill levels. But that doesn't matter. In these cases, alpha is irrelevant—it is tax efficiency that matters.
Consider the following when it comes to taxes.
How are returns taxed? Can this strategy be more tax-efficient? Why or why not? Can it ever be tax-efficient?
For any actively managed equity strategies, avoid mutual funds. These vehicles generate 1099s with unpredictable tax liabilities. Avoid limited partnership hedge fund structures with hidden “basis.” Investing is tough enough—avoid embedded tax liabilities at all costs.
Alpha of 5 percent a year, with a 10 percent tax drag, nets out to a –5 percent liability. Not good. A good question to ask is what your after-tax alpha would have been when viewing a great backtest.
Maybe this strategy is too aggressive. Are the tax benefits of this strategy too good to be true? Are the managers “pushing the envelope” with untested tax approaches? Could it trigger an unwanted audit? Are there tax and legislative risks that can be assessed? IRS rules can change; lawsuits can occur; lawyers are expensive, and you need to pay them with after-tax dollars.
“There are about 8,000 planes in the air and 100 really good pilots.”
—Ray Dalio in 2020 Vision
We've finally got the report from our consultant telling us that we need 40 percent of our assets in hedge funds. Great, let's go hire the best hedge fund managers! Wait a second, now we have to compile a list of candidates, fly around the world interviewing people, conduct due diligence, and after all that maybe we need to hire yet another consultant who specializes in understanding hedge fund strategies. Suddenly, manager search isn't as easy as we previously expected. How can we be confident in our ability to identify the very best? Even once we have the managers, things don't get easier. We have to monitor them each year, do on-site visits, watch for style drift, make sure their growth will not impact their returns, etc. Search costs are a real cost associated with particular portfolio allocations. One needs to weigh the benefits of more exotic allocations, such as hedge funds, private equity, and venture capital, against these costs. Different questions to address regarding search costs:
If we lower portfolio risk via an exotic allocation, but incur an annualized 200bps in search costs to attain the allocation, does it still make sense?
Compared to a 1940 Act product, exotic allocations require more hands-on monitoring and periodic reassessment. Now we need a staff with specialized expertise. Or maybe we outsource to a consultant. How much is this going to cost?
As a one-time expense, manager search costs might not be too prohibitive. However, consider the fact that managers can fail or leave the business, and the need to maintain a stable of the top managers is an on-going process. Additionally, there can be significant switching costs associated with moving to new managers, including commissions, market impact, and the opportunity costs of being out of the market during a transition.
Staring at performance charts is only the first step when deciding on an investment strategy. A range of additional factors must be considered. The world is infinitely complicated, and these factors need to be assessed critically and individually when investing. The FACTS framework can help identify the right questions, which will help investors make better decisions. The FACTS framework consists of Fees, Access, Complexity, Taxes, and Search. By no means does the FACTS framework cover every aspect of strategy selection or investment decision-making, but we hope it will provide an important tool for investors as they seek to deploy their hard-earned capital most effectively.
We have established some FACTS to consider using the framework just outlined. Where do we go from here? Next, we outline the building blocks of portfolio management, which consist of the following three concepts:
The father of modern portfolio theory, whom we alluded to earlier in this chapter, was Harry Markowitz, who developed techniques to maximize returns while minimizing risk through portfolio diversification (he won a Nobel Prize for his work in 1990). Modern portfolio theory, as formulated by Markowitz, states that investors should seek to maximize expected returns while minimizing the expected variance of those returns (the tangency portfolio described above). This is achieved through diversification, since investing in asset classes with low covariance results in a lower overall portfolio variance than investing in individual asset classes. Markowitz's ideas have been extensively developed within academia and the world of finance over the past 50 years.
We begin the portfolio investing process by identifying the basic building blocks which can provide diversification. We divide asset classes into three broad categories: (1) equities, (2) real assets, and (3) fixed income investments.
Equities are ownership interests in operating businesses, and provide exposure to earnings growth and dividend income. Real assets are tangible, physical assets whose intrinsic value is related to their utility, which enables them to be exchanged for other products or services. These are real estate and commodities. Real assets may generate cash flow, and have traditionally been viewed as a means of providing protection against inflation. Fixed income relates to investments that generate a return based on stable periodic payments at regular intervals with a return of principal at maturity. Fixed income investments, or bonds, typically provide income and have low historical volatility.
While Markowitz's pioneering research in the 1950s highlighted the benefits of diversifying across multiple asset classes, there has been substantial debate over the ensuing decades on the best way to achieve this. Since investors can generate varying rates of return and assume alternative levels of risk by combining quantities of asset classes in different ways, this debate has focused on the development of theoretical models that have provided insight into an array of weighting methodologies.
These methodologies have offered competing approaches to optimizing portfolio performance over time, but they are all based on attempts to simultaneously maximize return while assuming the least amount of risk, also referred to as mean-variance optimization.
Despite the diversity and sophistication of these models, there are two criticisms common to all of them: (1) the length of the sample periods tested, which are short, and (2) the estimates of return and volatility used to calculate the allocation rules, which can change in the future. Fundamentally, these models are derived from uniformly small samples whose results often do not hold up when implemented over long, out-of-sample, time frames, and their return and volatility estimates, also when applied out-of-sample, tend to be noisy and prone to error over time, which often results in the models' diverging, sometimes radically, from predicted outcomes.
As it turns out, this problem is not unique to financial markets.
As Daniel Kahneman points out in his 2011 book, Thinking Fast and Slow:
…The dominant statistical practice in the social sciences is to assign weights to the different predictors by following an algorithm, called multiple regression…The logic of multiple regression is unassailable: it finds the optimal formula for putting together a weighted combination of the predictors…One can do just as well by selecting a set of scores that have some validity for predicting the outcomes and adjusting the values to make them comparable (by using standard scores or ranks). A formula that combines these predictors with equal weights is likely to be just as accurate in predicting new cases as the multiple-regression formula that was optimal in the original sample…The important conclusion…is that an algorithm that is constructed on the back of an envelope is often good enough to compete with an optimally weighted formula.5
Is it possible that a simplified weighting regime for asset classes will offer performance comparable, or perhaps superior to, the rich body of portfolio research that has emerged over the past half century? We explore this question in Chapter 6.
In general, while efforts to time the market should be viewed with skepticism, certain risk-management, or “market-timing” strategies, which have been explored in academia, appear to reduce risk, without significantly impacting long-run returns. In particular, the application of simple moving average rules has been demonstrated to protect investors from large market drawdowns, which is defined as the peak-to-trough decline experienced by an investor.
Jeremy Siegel, in his book, Stocks for the Long Run, explores the effect on performance on the Dow Jones Industrial Average from 1886 to 2006, when applying a 200-day moving average rule. Siegel found that this simple technical rule outperforms a buy-and-hold approach, both in absolute terms and on a risk-adjusted basis.6
This simple strategy seems to protect investors from large drawdowns, which are difficult to recover from. Research indicates that the rule can be used effectively across asset classes. We will explore more ideas and concepts in Chapter 7.
There are literally hundreds of academic papers written on “anomalies,” or strategies that purport to beat the market on a risk-adjusted basis. Anomaly chasing has become so popular that academics have written papers making fun of other academics. John Cochrane calls the literature on anomalies a “zoo of new factors.”7 For example, Harvey Campbell, Yan Liu, and Heqing Zhu examine 316 papers that study seemingly anomalous patterns published in a selection of academic journals. The authors highlight that there is a bias toward publishing findings that have strong statistical significance, but disregard (1) that some of these may be empirical tests unrelated to any theory and thus possibly data mined, and (2) in finance, unlike in science, no one publishes replication studies, and so there is little verification of discovered factors. Campbell et al. argue “that most claimed research findings in financial economics are likely false.”8 The authors also find that there are some anomalies that still appear anomalous, even after accounting for data-snooping. Of all the anomalies documented in academic literature, the two most durable strategies identified are value and momentum strategies.
The value investing philosophy, established by Benjamin Graham in the 1930s, is based on buying securities at prices that are low relative to their intrinsic value. While Graham intuitively grasped the effectiveness of buying stocks at low prices, academics began to explore the concept with rigorous analysis, beginning in the 1970s, with some noting that portfolios constructed with firms with high earnings-to-price or book-to-market ratios, high dividend yields, or a high ratio of cash flow to price seemed to earn abnormal returns above what was expected. In the 1990s, Eugene Fama and Kenneth French famously identified the value premium, and demonstrated that value stocks have delivered higher returns with lower volatility over long periods of time.9 A growing body of work further refined the value anomaly, which today is among most firmly established effects in financial markets. More recently, academics have begun to explore how the value anomaly can be applied to additional asset classes beyond equities. There are a number of academic papers and studies describing how investors can apply the value investing approach in new and creative ways.10
Researchers have also begun to explore the question of why these strategies produce superior returns. Today, some of the literature suggests that innate human behavioral bias plays a role in the value effect, as investors overreact to stocks or assets that have performed poorly and, underestimating their future growth prospects, oversell them. We suggest that investors should seek out approaches to exploiting the value anomaly across various asset classes, and in particular the benefits of individual security selection available within equity markets.
Momentum in finance refers to the tendency of investments that have done well in the recent past to continue doing well, and of investments that have done poorly to continue doing poorly. As with value, the concept of momentum in securities markets has been around a long time. As far back as the 1920s, famed stock picker Jessie Livermore, immortalized in the book Reminiscences of a Stock Operator, was using momentum-like strategies to beat the market. With the passage of time, academics inevitably began to study the effect in earnest, beginning in roughly the 1990s, and hundreds of papers on the subject have since appeared in the literature.
Recently, Christopher Geczy and Mikhail Samanov explored the momentum effect extensively over a long time frame, in their paper 212 Years of Price Momentum (The World's Longest Backtest: 1801–2012), which clearly establishes and documents the persistence of momentum over two full centuries in US equity markets.11 Academics have now established that momentum works across a range of asset classes, and in markets outside of the United States.
Once again, as with value, it appears that innate human behavioral bias may explain the existence of the effect. New research is exploring specific behavioral traits that give rise to the momentum effect, including how investors are slow to incorporate news into their forecasts, and use recent performance of securities as a signal. We investigate value and momentum in Chapter 8.
Chapter 5 questions the trust we put in our experts when it comes to investing. We then nudge the reader to consider a do-it-yourself approach. We also reviewed our “FACTS” strategy evaluation framework. Each investment decision should involve a discussion of fees, access, complexity, taxes, and search costs. Finally, we outlined the three core elements of portfolio management: asset allocation, risk-management, and security selection. We explore these three elements in the next three chapters.