Rational Investing

TABLE 7.1 Measures of excess return (1979–2015)

However, most managers do not disclose their factor exposure and all excess return is presented as α. Over the years, we have come to realize that not all institutions use fair or representative benchmarks. Because compensation is related to outperforming a benchmark, there is much incentive by managers to choose a reference point that is more likely to be outperformed over the long run. The following situations have been observed in several organizations:

• A fixed income portfolio with almost no Treasury securities benchmarked against an index with a large allocation to Treasuries;

• A mortgage portfolio benchmarked against a fixed income index also containing a large allocation to Treasuries;

• An equity portfolio benchmarked against a combination of cash and equity without having completed a proper factor analysis to determine what is appropriate;

• A private equity portfolio benchmarked against a cash plus index in which the plus part is fairly low;

• And long biased equity hedge funds benchmarked against a cash plus index. It seems we forget common sense when we use the words “Hedge Funds.”

Investors must realize that some managers are wealthy not because of their skills but because of their benchmarks.

According Preferential Treatment to Evidence Supporting Belief

This bias occurs when we give greater weight to information that supports our beliefs, opinions, or business models than to information that runs counter to them.

Example 1—In chapter 5, we mentioned the two studies on commodities published in the same issue of the Financial Analysis Journal in 2006. One study concluded that commodities delivered a substantial risk premium between 1959 and 2004, while the other found that the risk premium was actually negative. Two studies with different conclusions—and unsurprisingly the one favoring a higher risk premium has been cited 50 percent more often in the literature.¹⁶

Example 2—We have discussed the importance of having a benchmark appropriate to the portfolio policy. However, the design of certain indices can present a more favorable view to incite potential investors to invest. For example, the Hedge Fund Research Fund Composite Index (HFRI) uses a monthly rebalanced equal weight allocation to all available hedge funds. This can contribute to better risk-adjusted return for the index due to diversification benefits. However, no investor could actually achieve the same performance because it would be impossible to implement the same allocation process. Of course, the benchmark construction methodology is disclosed to investors, but we doubt that most investors fully understand the favorable impact of the methodology on measures of past performance.

Seeing Only What One Is Looking For

Unlike the previous bias, which results from a tendency to favor specific sources of information and ignore others, here the bias occurs because we unfairly challenge the validity of information that runs counter to our beliefs and support unsubstantiated or irrelevant claims that bolster our case.

Example 1—The circumstances of the last U.S. presidential election illustrate this point fairly well. Politicians and pundits tend to criticize the accuracy of polls that do not reflect their own beliefs. According to Tetlock, it is not necessarily that these individuals are close-minded, but simply that it is easy for partisans to believe what they want to believe and for political pundits to bolster the prejudices of their audience.

Nate Silver predicted the presidential winner in all but one state as well as all thirty-five senate races in 2008. In 2012, he accurately predicted the presidential winner in all states as well as thirty-one of thirty-three senate races. Nevertheless, some of Silver’s critics attempted to discredit all of his conclusions by attacking his methodology. For example, the political columnist Brandon Gaylord wrote about Silver:¹⁷

Silver also confuses his polling averages with an opaque weighting process (although generally he is very open about his methodology) and the inclusion of all polls no matter how insignificant—or wrong—they may be. For example, the poll that recently helped put Obama ahead in Virginia was an Old Dominion poll that showed Obama up by seven points. The only problem is that the poll was in the field for 28 days—half of which were before the first Presidential debate. Granted, Silver gave it his weakest weighting, but its inclusion in his model is baffling.

However, what Silver does is simply to consider all relevant information and attribute to each component a weighting based on its strength using variables such as the accuracy of such polls in the past, sampling size, and timeliness of information. For example, the Virginia poll in question had received a low weighting in Silver’s model. His approach is based on a rigorous application of probability theory as it is being taught in most colleges.

Example 2—For many years, the Canadian mutual fund industry suffered a reputation as having one of the highest total expense ratios (TER) in the world, following a report published in a top journal.¹⁸ Answering criticisms in a letter to the president and CIO of the Investment Funds Institute of Canada, the authors confirmed their results, indicating that total expense ratios and total shareholder costs in the Canadian industry were higher than those in Australia, Austria, Belgium, Denmark, Finland, France, Germany, Italy, Japan, Luxembourg, the Netherlands, Norway, Spain, Sweden, Switzerland, the United Kingdom, the United States, and so-called offshore domiciled funds from Dublin, Luxembourg, and various offshore locations. Furthermore, the situation in Canada is even worse because, according to the Canadian Securities Administrators, individual investors have a limited understanding of how advisors are compensated. Fortunately, new regulations may now force greater disclosure.¹⁹ The industry has been attacking this inconvenient report for years.

The Canadian industry requested an “independent” study to challenge this perception.²⁰ They narrowed their intervention to the United States only because it may be difficult to challenge the observation that Canadians pay more than investors in almost all jurisdictions. The main argument of the industry is that TER in the United States do not incorporate advisory fees while in Canada they do, and by accounting for this factor, the difference with the United States may not be as significant. It has been our experience, however, that Canadian financial products are in fact more expensive than similar products in the United States. In the end, the Canadian industry has to fight the argument that TER are higher in Canada until it can restructure itself and become more competitive, no matter what reasonable evidence would show.

Own Judgment Evaluation

This bias refers to our tendency to express a higher degree of confidence than is justified by our actual performance.

Studies have shown that specialists in a wide range of fields including attorneys, physicists, physicians, psychologists, and engineers are overconfident when making professional judgments. As we mentioned in chapter 4, although investing experts are more confident in their forecasts than laypeople, they are not necessarily more accurate. Though studies show clinical diagnosis based on case statistics to be more accurate than those derived from clinical judgments, experts still have more confidence in their own judgment.²¹ One exception is professions that benefit from immediate and continuous feedback (like weather forecasters), although feedback, especially negative feedback, is not necessarily welcomed.

According to Tetlock, “One of the things I’ve discovered in my work on assessing the accuracy of probability judgment is that there is much more eagerness in participating in these exercises among people who are younger and lower in status in organizations than there is among people who are older and higher in status in organizations. This doesn’t require great psychological insight to understand this. You have a lot more to lose if you’re senior and well established and your judgment is revealed to be far less well calibrated than those of people who are far junior to you.”

This discussion helps us understand why the super forecasters in the Good Judgement Project discussed in chapter 4 have had much greater success than others. Tetlock used our understanding of human psychology and evidence concerning the superiority of algorithms over human judgment to design an experiment that identifies individuals trained in probabilistic thinking and suffer less from the effects of ego. They seek continuous feedback and are unafraid to challenge their own views. They are the closest representation of human algorithms. We recommended the use of robust investment processes in chapters 6 because most of us do not possess that which is required to become a good forecaster. Process is what protects us against our own human biases. We have to remember that the obstacles to a rational process can be overwhelming, even among scientists and specialists. For example, Newton could not accept the idea that the earth could be much older than six thousand years based on the arguments that led Archbishop Usher to place the date of creation to 4004 B.C.E. “The greatest obstacle to discovery is not ignorance—it is the illusion of knowledge” (Daniel J. Boorstin). It may be that better forecasters and better scientists are among those who have fewer illusions about their own knowledge and a smaller ego.

The Requirements of a More Rational Decision Process

How can investors implement a more rational investment process in the face of the obstacles discussed so far? In principle, while it is difficult to overcome biases, it should be easier to implement a rational process for an organization than for an individual. Decisions in most organizations are often subjected to a lengthy, complex process, while many retail investors make quick, emotional, and uninformed decisions.

A provider of low-cost investment solutions to retail clients reminded us how some of his clients will quickly move their assets to another manager if the balanced portfolio solutions he offers underperformed slightly compared to another product in their portfolio. The short-term return without concerns for risk seems to be the only consideration for some uninformed investors. Tremendous efforts at financial education are required at a younger age and much better education tools than are currently available must be designed.

This being said, organizations are certainly not immune to biases. Important decisions can be pushed through by individuals even though they may be suboptimal for the corporation. Decisions are sometimes subject to committee approval in which some members may lack necessary expertise, lowering the odds of individual accountability in the case of failure, but allowing all to claim contribution to a successful endeavor.

Investment committees with overloaded agendas may simply rush through them. For example, as a manager you may be given less than twenty to thirty minutes to explain your strategy to an investment committee in order to win an approval for a mandate that could be several hundred million dollars in size. Hence we operate in an industry in which we must often oversimplify the message to the point at which a meaningless but entertaining storyline becomes more important than understanding the investment principles.

The purpose of this last section is to provide the basis for the implementation of more rational investment processes by investors and their representatives, such as CIOs, investment committees, etc. We will use the term Management to refer to those parties in positions of authority of allocating capital to managers and strategies. On the other hand, managers, advisors, and consultants refer to those who manage assets, sell investment products, and provide asset management services.

Managers and Advisors Are Not Your Friends

An important principle to keep in mind is that fund managers and advisors are not your friends. Their interests are often not aligned with Management’s interest. Managers are selling Management a product, and they respond to their own set of incentives and are subject to biases. Alan Greenspan said the following with respect to the subprime crisis: “Those of us who have looked at the self-interest of lending institutions to protect shareholder’s equity (myself included) are in a state of shocked disbelief.”²² Well, self-interest should be expected when considering the exorbitant bonuses of some individuals in financial services. Let’s consider the statement by Greg Smith, former executive at Goldman Sachs: “I don’t know of any illegal behavior, but will people push the envelope and pitch lucrative and complicated products to clients even if they are not the simplest investments or the ones most directly aligned with the client’s goal? Absolutely. Every day, in fact.” One of the messages of agency theory, which studies the relation between principals (shareholders) and their agents (management), is that the asset owner should not be surprised that the agent’s primary concern is not for the client.

Even if you like your advisors on a personal level or are intimidated by their understanding of the subject, it does not mean that they should not be challenged. According to the field of evidence-based management, decisions should be made according to four sources of information: practitioner expertise and judgment, evidence from the local context, a critical evaluation of the best research evidence, and the perspective of people affected by the decision.²³ However, corporations will often implement processes recommended by advisors who have in fact little case evidence of the efficacy of their proposals. In the field of management, a senior consultant admitted that the restructuring processes they implemented in several firms made millions in revenues during the initial phase and then millions more from the same clients who had to fix the organizational issues created by the restructuring.²⁴ It is perplexing that some advisors require, in recommending a product to investors, a performance track record of a few years, but no such equivalent evidence for implementing new processes or strategies that they have designed themselves. In principle, the roles of advisors are to:²⁵

• Provide specialized technical and subject matter expertise;

• Validate what management wishes to do or has done;

• Present, particularly to boards, unpleasant realities;

• Serve as a catalyst, facilitator, enabler, and guide to the organization.

In most cases, advisors provide support and facilitate the decision process of Management. In this process, advisors must challenge Management while Management challenges advisors, but often advisors are not sufficiently challenged. Here are two examples.

During a recent investment conference, one presentation was given on how investors should allocate their assets between tax-free retirement accounts and taxable accounts. The presenter provided the traditional advice that fixed income securities should first be allocated to the tax-free account because tax rates on interest income are usually greater than on domestic dividends and capital gains.

As recently as February 2015, when the most prevalent bond index in Canada was trading at about a 1.75 percent yield before fees, investors were still being advised the following by a national publication:

If you have enough money to build both a registered (tax-free) and non-registered (taxable) portfolio, then investments such as bonds, GICs and high-interest savings accounts are best kept inside of an RRSP (tax-free account equivalent to an IRA), because their interest income is taxed at a higher rate. Capital gains and dividends are taxed at a lower rate, so stocks can go outside your RRSP.²⁶

This may have been the appropriate recommendation when interest rates were much higher, but in mid-2015 we operated in an environment in which yields on bonds were as low as 2 percent, dividend yields on some equity products were sometimes even higher than bond yields, and equities still had an expectation of long-term capital gains while the argument was more difficult to make for bonds. Do you prefer to avoid taxes on a bond product having a 2 percent expected return that would normally be taxed at a higher rate and be taxed at a lower rate on an equity product having a 6 percent expected return? Or do you prefer to avoid taxes on that 6 percent of expected return and be taxed at a higher rate on less than 2 percent of expected return? From a return compounding point of view, it is preferable to avoid taxes on the asset that has the higher nominal expected return.

Furthermore, foreign equity usually does not benefit from a preferential tax treatment on dividends and some specialized equity products target dividend yields that could even be higher than bond yields. Hence in a low-return environment, the argument to allocate dividends paying foreign securities to the tax-free account first is even stronger, especially in the context of exchange traded funds that charge relatively similar fees on fixed income and equity products (although there must be considerations for how withholding taxes are impacting performance in different investment vehicles). Finally, if bond portfolios are more likely to produce long-term capital losses, these capital losses would be very useful within a taxable account. The point is that this traditional advice may be valid on average, but not necessarily in the current market conditions.

The second example refers to an institutional investor that asked its advisor how they could increase the risk of their portfolio. The portfolio was mostly allocated to large market capitalization global equity and investment grade fixed income. Because the portfolio already had a relatively low allocation to fixed income, the advisor initially recommended that the portfolio be leveraged using equity futures contracts in order to maintain appropriate portfolio liquidity from the fixed income component. This advice is based on the traditional assumption that fixed income securities are more liquid than equity. But the recent decade of financial research has shown how securities are subject to liquidity risk, which is the possibility that their level of liquidity may vary through time, and especially vanish in crisis time.²⁷ The liquidity of bonds from provincial government in Canada was minimal during the worst of the financial crisis in the fall of 2008. If liquidity was required at that moment, it was easier to trade large market capitalization stocks than these bonds. The advice provided was unfortunately based on outdated knowledge.

Traditional advice should always be challenged. These two examples are not alone either. In finance, we encounter many dubious claims, for example:

• Managers with high active shares have more expertise;

• A 50 percent currency hedging on foreign exposure is a neutral position;

• Managers are more likely to outperform in an environment in which there are greater discrepancies in sector returns. The statement here refers to the probability of outperforming not to the scale of outperformance;

• Quantitative management is not as effective as, or is different from, fundamental management;

• Published funds’ ratings are always useful to identity future performers;

• Commodities (in general), gold, and real estate are good inflation hedges;

• The average forecasting expert brings benefit to the allocation process;

• Nominal corporate profits will lag in a rising inflation environment.

Each of these statements has been challenged with existing literature. Let’s consider the first four. We already mentioned in chapter 2 the study by AQR indicating that managers with a high active share may have a small-capitalization bias, which may explain some of the results. Furthermore, even if we ignore this aspect, performances could be explained by the fact that such funds would be more efficient at diversifying mispricing because the allocation processes of benchmark agnostic managers are generally insensitive to the impact of market prices on weights. We also discussed in chapter 6 how the pro- or counter-cyclical nature of a currency impacts the optimal currency hedge ratio of a portfolio. Hence a 50 percent hedge ratio is not necessarily a neutral position. Although active managers often say that greater volatility among sectors, countries, or securities is preferable to outperform, there is no evidence that greater volatility increases the probability of outperforming (although it will impact the scale of outperformance) by specific managers. Finally, there is no study that indicates that traditional value managers outperform systematic value investment processes. Again, the exposure to the value factor is a more significant determinant of long-term performance than the specific methodology used to achieve it.

The previous two examples are also good examples of the System 1 and System 2 discussion at the beginning of this chapter. When we no longer challenge traditional beliefs, answers are simply stimulated by System 1. Montier also mentions how expert advice attenuates the activity in areas of the brain that correlate with valuation and probability weighting. Thus, when Management is provided with advice from what is believed to be a reliable source, their System 2 is prone to get lazier. Therefore using advisors does not mitigate the responsibility of Management to remain diligent and Management should not be intimidated by the clout of advisory firms.

Managing Overclaiming and Disappointments

The human tendency to exaggerate success provides biased information, complicating Management’s decision process. We sometimes overrate our ability to spot deception and distinguish the relevant from the irrelevant. As discussed in chapter 4, individuals draw attention to correct forecasts while downplaying bad predictions. Additionally, there are ways of presenting numbers in a more favorable light. We mentioned in chapter 2 that some variable annuity products guarantee a minimum return of 5 percent per year during the accumulation phase, which can be ten years or more. However, in the United States the return is compounded, while in Canada it seldom is. Canadians looking at these products may be unaware of the nuance. Yet it means the U.S. minimum guarantee over ten years is greater by nearly 13 percent of the initial contract notional (62.89 percent versus 50 percent). There are many more nuances that only emerge through a careful analysis of these contracts.

The incentive to overclaim is substantial because claims made by managers, advisors, and consultants are often left unchallenged. This unfortunate state of affairs can be reduced in several ways depending on the circumstances:

• Requesting empirical evidence supporting the skill being marketed by managers, advisors, and forecasters.

In the end, when an organization considers hiring an expert who claims to have a specific forecasting ability, it should request empirical evidence over a long period before arriving at a decision and consider the sample size and the inaccurate as well as the accurate forecasts. Forecasting a recession that does not occur can be costlier (financially and professionally) than failing to forecast a recession. In marketing themselves, forecasters will emphasize only their successful forecasts. It may seem obvious, but the work of Phil Tetlock illustrates that experts with no forecasting abilities can still have very long careers.

• Establishing the rules of a pre- and post-mortem ahead of any important decision. When making an important decision, investment or otherwise, Management should attempt to determine potential issues and consequences before they occur and which metrics will be used to measure success after implementation of this decision.²⁸ The understanding by all that are involved in the decision process that there will be a defined feedback mechanism and that this information will be communicated will downplay overclaiming at the initial stage.²⁹ Furthermore, it is also important to manage the effect of surprise. It is much easier for Management to understand and communicate internally unfavorable performances in specific circumstances if Management was already aware of the specific circumstances that would trigger an unfavorable outcome.

Consider, for instance, the implementation of a plan by a pension fund to move away from an indexed equity allocation to products that extract specific factors, such as size, value, momentum, and low-beta. Moving away from an indexed allocation creates a tracking error risk not only against the index, but also possibly against the peer group. Even if Management fully understands this aspect, it is doubtful that most of the pension fund participants do. Implementing a true long-term approach for asset management is challenging.

For example, Andrew Ang mentions how the year 2008 erased all the active returns the Norwegian fund had ever accumulated.³⁰ The public understood the decline in market returns but not in active returns. The issue at hand is communication and education. The public was well informed of market risk but not of factor risks. They never understood that active management could perform so badly across the board under specific circumstances.

The quant meltdown in 2007 gives a proper example of this risk. During the week of August 6, 2007, a significant decline in the performance of strategies linked to risk factors other than the market portfolio occurred in the U.S. stock market. The cause is believed to be a large fund that closed its positions, thereby precipitating the decline of stocks bought and an increase in stocks sold short by similar funds. What is particular is that important losses were experienced in these strategies, whereas the market index was unaffected. For example, the momentum and low-beta factors ended the week about 3 percent down, while the market was up by more than 1 percent.

We also reported in chapter 6 that on a rolling ten-year basis, 2015 is one of only two periods since the early 1950s in which the market factor dominated all other risk factors. In the first seven months of 2015, the stock market performance was dominated by large market capitalization and growth firms, leaving well behind lower market capitalization and value firms that are the backbone of many smart beta strategies. Amazon alone explained one-third of the rise of the entire consumer discretionary group during this period.³¹

By definition, a risk factor is exposed to bad times. Hence although factor-based products tend to outperform traditional indices in the long run, Management must understand that they can underperform for several years. The most important aspect is to educate Management about the sensitivity of returns to risk factors and how specific products load on these factors. We must do more than manage overclaiming (i.e., that a new strategy will necessarily outperform), we must also manage overexpectations. The following section examines this recommendation further.

Raising the Bar on Benchmarking

Without proper benchmarks, we cannot easily detect or challenge overclaiming. In our industry, significant bonuses and fees are paid, while many researchers have shown the average manager of mutual funds, hedge funds, and private equity has added no value to, and even subtracted value from, fair benchmarks.

When building portfolios in chapter 6, we noted that our framework should also be used to build better benchmarks. We used our return equation to evaluate the merits of three different indices: the RAFI U.S. 1000 Index, the S&P Equal Weight Index, and the Max Diversification USA Index. We put the return of these indices on the left-hand side of our return equation, and market, size, value, and momentum as factors on the right-hand side:

R_product = R_f + α + β_marketF_market + β_sizeF_size

+ β_valueF_value + β_momentumF_momentum + ε.

Of course, factors can and should vary depending on the product being evaluated. Management should run such regressions and evaluate the product accordingly. For instance, they can prefer their active managers to have low exposures β_market and β_size to the market and size factors, but high exposures β_value and β_momentum to the value and momentum factors.

The idea is to better assess a manager’s worth. Use of multifactor-based benchmarks helps explain investment performance and better segregate excess performances due to α and exposure to risk factors. A fund manager can add value by outperforming a set of risk factors, that is, by having a high estimated α. He can also add value by appropriately timing his exposures to the different risk factors. Thus, even if managers do not report their performance to Management using a multifactor approach, Management should request the data that would allow for this analysis. Again, although a positive α is preferable, an investor can be satisfied to achieve a balanced exposure of risk factors at a reasonable cost unless it does not want to expose itself to some of these factors.

It is unusual for managers to use multifactor benchmarks, at least in their reporting to Management. First, it adds an element of complexity to the client reporting, but it also demystifies some of the reported sources of excess performance. Many traditional managers would be uncomfortable with this “transparency.” For example, we witnessed the case of an institutional manager using a combination of cash and equity to benchmark their value strategy. The rationale was that as the tilt toward value stock lowered the volatility of the portfolio, a proper benchmark was a watered-down market portfolio, say 85 percent allocated to the market portfolio and 15 percent allocated to cash. This is a badly designed benchmark for two reasons. First, weighting the market portfolio in the benchmark is not necessary. Running this regression will automatically assess the level of market risk in the portfolio through the estimated β_market. Second, the value risk factor needs to be included in the analysis, otherwise the value tilt will be misconstrued as α. We are interested in assessing whether the proposed value strategy is good at combining the market and value risk factors, is good at picking particular stocks that will outperform these two factors, or both. Using an 85/15 benchmark is like using a misspecified return equation and assuming arbitrary values for the factor exposures.

The following are examples of potential factors that could be used to design benchmarks such as:

• Private equity: Market (Equity) + Market (Bonds) + Liquidity + Credit

• Corporate bonds: Market (Bonds) + Credit

• Value equity: Market (Equity) + Value

It is unlikely that most managers would use a multifactor-based approach to report their own performance unless it is specifically requested. A first step is for Management to perform their own internal analysis and implement it among managers. It would certainly change the nature of the discussion between managers and Management and help reduce the issues of overclaiming and agency.

Learning to Be More Patient

Sir John Templeton said, “It is impossible to produce a superior performance unless you do something different from the majority.” However, it may take much longer than investors expect to increase the likelihood of outperforming an index or specific target. A three-year track record is far from sufficient to judge managerial expertise.

There are also other implications. Management tends to fire a manager after a few years of underperformance. Assuming we truly understand the investment process of the manager and are comfortable holding the strategy on a long-term basis, we should be more concerned with the performance of our portfolio of managers than with the performance of any single manager. In a study of U.S. equity managers done by a consulting firm, 90 percent of top-quartile managers over a ten-year period encountered at least one thirty-six-month period of underperformance. For 50 percent of them, it was five years.³² Therefore it would be impossible to maintain a long-term investment philosophy if we question the relevance of owning each manager that underperforms for a few years. Firing managers that underperform for a few years without consideration for other factors is akin to not properly rebalancing a portfolio or to buying high and selling low. Retail investors underperform because they buy into rising markets and sell into declining markets. Most of them do not have a process to manage their emotions. It is difficult to maintain a smart long-term investment approach without a proper understanding of the investment process of our managers.

In a 2014 interview, Seth Alexander, Joel Cohen, and Nate Chesley of MIT Investment Management company talk about their process for partnering with investment managers for what they hope will be several decades and how this can be achieved.³³ They make three statements that should resonate with our discussions in this book. First, great investors are focused more on process than outcomes. Of course, the intent is still to outperform in the long run, but they do understand that even the best managers and the best processes can have a mediocre year and three to four years of underperformance. Second, great managers do not have a cookie-cutter approach to investing or try to cater to what they think allocators want; they spend time tailoring their investment approach to what works for them. Hence both great investors and managers must have convictions in the processes. Finally, they state Jeff Bezos who often mentions how if your investment horizon is three years, you are competing against a whole lot of people, but if your time frame is longer, there is a world of other possibilities. Only with a long-term horizon and patience can we truly start emphasizing the importance of process.

Raising the Level of Knowledge in Our Industry

There is a wedge between the state of knowledge in academia and industry practices in the asset management industry. Consider two of the most important developments in the financial literature over the last seventy years: portfolio diversification by Harry Markowitz in 1952 and the capital asset pricing model (CAPM) by William Sharpe in 1964.

The first paper allowed us to understand that portfolio volatility can be reduced for a given level of expected return through diversification, hence increasing compounded returns. The second showed us why risk should be divided between its systematic component (the market), which is compensated by a market risk premium and a nonsystematic component that is unrewarded because it is diversifiable.

Many people dismiss these contributions in the industry. Mean variance investing as described in the first paper is plagued with estimation issues, and naïve implementation is a sure way to lose money. The one-factor CAPM has a poor empirical record as there are other risk factors that are remunerated.

But these papers are practically relevant, and their insights are crucial in a conversation on investments. The difficulties in measuring expected returns, volatilities, and correlations that make the Markowitz portfolios hard to implement in real life can be addressed with different solutions, as we discussed in chapter 6. But most importantly, its insight that diversification is the only free lunch in finance is crucial and underpins many existing investment solutions.

The CAPM is derived using a set of assumptions that are not entirely realistic. Similar to the case of Markowitz’s work, subsequent work relaxes these assumptions and provides better performing asset pricing models. But most importantly, the model shows each investor allocating their portfolio results in a risk factor being remunerated with positive expected return and other risks not being remunerated. Extensions of this paper similarly show the economic underpinning for other risk factors to be remunerated in financial markets. This insight allows us to understand that some factors will be remunerated and other types of risk will not.

These insights are crucial in examining the worthiness of new products. What is their source of performance? Are they properly diversified? What are the investors’ preferences and market structure that lead to this source of performance being compensated with positive returns?

Even though these papers are several decades old, their insights are often misunderstood, and we are still limited in our ability to explain simple concepts and use standard terminologies when explaining investment strategies to many investors. Continuous and relevant education is essential. Without it, we will keep repeating the same mistakes, rediscovering old investment concepts through different forms, and making investment decisions based on imperfect information. The responsibility should not rest solely on asset managers to better communicate their strategies and know-how, but also on Management to improve their general knowledge of subject matter. We hope that this book has contributed to this end.

Concluding Remarks

It is said that investing is an art and a science. The science part concerns an understanding of performance drivers and methodologies. We live in a multifactor world that requires us to identify what is relevant, what is not, and how to integrate relevant factors. We must also convince investors and their agents of the validity of our approach, and therein lies the “art.”

Many investors remain unconvinced of factor-based approaches. We often hear investors say that factor tilt portfolios have considerable risk relative to market capitalization–weighted portfolios. They have underperformed over many periods and entail greater transaction costs.

However, this reasoning is based on the assumption that we live in a single-factor world and that the market factor is always the appropriate benchmark for each of us. It also assumes that the market is the most return/risk efficient portfolio for any investor. However, which is more likely: that we live in a single-factor world or that we live in a multifactor world? That a combination of the market factor and some other riskless asset leads to the most efficient allocation for all of us or that specific combinations of factors could lead to more efficient and appropriate portfolios for some of us?

The key to making good decisions involves “understanding information, integrating information in an internally consistent manner, identifying the relevance of information in a decision process, and inhibiting impulsive responding.”³⁴ When luck plays a small role, a good process will lead to a good outcome. But when luck plays a large role, as in the investment management industry, good outcomes are difficult to predict, a poor state of affairs when patience is in short supply. This book is entirely about the process of making good investment decisions in the long term for patient investors. Implementing a good investment process requires a body of knowledge. Unfortunately, this knowledge may be tainted by biases and conflicts of interests in our industry. Even if we understood the requirements of efficient processes, there are significant implementation challenges. We hope this book provides a step in the right direction and contributes to a productive conversation on investment.