The Alpha Architect Value Proposition
Mission: Deliver Affordable Active Alpha.
Our mission is to empower investors through education. We believe an educated investor is a sustainable client, which is critical for success in the long-horizon active strategies we develop.
Our experience in academia and working with ultra-high-net-worth individuals, in the dual role of consultant and investment manager, has culminated in the development of four core beliefs that permeate everything we do:
- We believe in Systematic Decision-Making, not ad-hoc decision-making. Disciplined and repeatable processes are more reliable than discretionary judgment.
- We believe in Evidence-Based Investing, not story based-investing. Rigorous, data-driven research drives success; stories drive sales.
- We believe in Transparency, not black-boxes. We are committed to having investors understand what we are doing.
- We believe in Win-Wins, not unsustainable relationships. We are committed to a business model that prioritizes client success.
Our assessment of the competitive investment management landscape has led us to the conclusion that active managers often overcharge for the expected alpha they deliver. Net of fees/costs/taxes, investors are usually better served via low-cost passive allocations. While this is largely true today, we want to disrupt this calculus:
We seek to deliver Affordable Active Alpha.
Affordable: Active management is currently too expensive for what it delivers. This excess expense is driven by high distribution and operating costs. We minimize both of these expenses via direct marketing and technology.
Active: Our portfolios will contain enough stocks to reduce the risk associated with any individual stock, but not so many that our portfolio resembles a low-cost index. We are not a closet-indexer.
Alpha: We are focused on our sustainable active investing framework to derive expected outperformance for disciplined long-horizon investors.
Understanding the Investment Landscape:
Identifying and employing sensible approaches to asset managements is simple, but not easy. Choosing an approach is difficult because the financial services industry wants it to be difficult. Many financial service firms thrive on complexity and opaqueness to promote high-priced, low-value add products to confuse investors who are overwhelmed by financial decisions. Our hope is that the framework we outline below will allow investors to differentiate among investment management approaches based on a few key characteristics.
First, let’s define a few concepts that are central to understanding the investments industry:
- Alpha: Formally, alpha represents the intercept estimate from a regression of an investment strategy against various risk-factors. In practice, “alpha,” is a blanket term for performance above and beyond a benchmark, controlling for various risk exposures unique to a strategy.
- Tracking Error: Tracking error is defined as the standard deviation of the difference between a strategy’s returns and a benchmark’s returns. In other words, tracking error is a measure of how closely a portfolio follows, or “tracks,” an index.
- Index/Passive: By construction, index/passive portfolios are designed to have no tracking error or alpha. A good example of an index strategy is the Vanguard S&P 500 Index Fund. This fund does not attempt to add alpha, but seeks to match the performance of the S&P 500 index with minimal tracking error.
- Active Share 1: This measure quantifies the extent to which, or how “actively,” a manager reshapes a portfolio with respect to a benchmark. One can think of this as a measure of conviction, or dedication to being different from an index. Active Share is a formal calculation developed by Cremers and Petajisto (2009), but a good proxy is simply the number of securities in a portfolio. For example, a manager that holds 50 equally-weighted stocks will likely have a much higher Active Share than a manager that holds 500 market-weighted stocks.
With some basic concepts under our belt, we can now explore the asset management industry. A visual summary of our interpretation of the investment landscape is set forth in Figure 1.
We break the industry into three main categories:
Index Products: Index (“Passive”) products offer no alpha, no tracking error, and no Active Share, or conviction. These funds, typified by Vanguard’s products, have a high number of securities, low expense ratios, and low marketing costs.
“Closet Index” Products: Closet Index products, sometimes referred to as “Smart Beta,” offer little to no alpha, little to no tracking error, and little Active Share, or conviction. These funds typically have a high number of securities, low to mid-level expense ratios, and high marketing costs.
Active Products: Active products, often delivered via mutual fund or hedge fund vehicles, are characterized by high expected alpha, high tracking error, and high Active Share, or conviction. These funds have a low number of securities (< 50), high expense ratios, and high marketing costs.
Alpha Architect is helping to create a fourth category we call Affordable Active:
Affordable Active Products: Affordable Active products are characterized by high expected alpha, high tracking error, and high Active Share, or conviction. These funds have a low number of securities (< 50), mid-level expense ratios, and low marketing costs.
The 4 different categories of asset management products are summarized in Table 1.
WHAT WE DELIVER: AFFORDABLE ACTIVE ALPHA
Three words define what we seek to deliver: Affordable, Active, and Alpha. We will briefly describe what we mean by each of these terms and why they are important for the investor.
Warren Buffett obliquely references the concept of affordability in the context of stock-picking: “It’s far better to buy a wonderful company at a fair price than a fair company at a wonderful price.” 2 Most people understand the concept of affordability when they shop at the grocery store, but lose sight of it when they shop for financial services. A shopper focused on affordability weighs both the costs and the benefits of a purchase decision. By contrast, a “cheap” shopper only focuses on costs, and an “expensive” shopper only focuses on benefits. Figure 2 highlights why being an affordable-minded consumer of financial services is a good financial decision. In this hypothetical example, we have a cheap index portfolio that earns 6% annual returns and costs 0.25% a year, an affordable active portfolio that earns 7% annual returns and costs 0.75% a year, and an expensive active portfolio that earns 7% annual returns and costs 1.50% a year. The example highlights that weighing both costs (affordability) and benefits, matters for your overall investment performance.
But how can we deliver high value services at lower costs than the competition? We deliver affordability by reorganizing the way business is done in financial services. We are focused on investing in research and development, and minimizing investments in marketing and distribution costs. Our distribution model is a direct-to-consumer model, which is antithetical to the traditional industry model. Figure 3 contrasts the current structure of the financial services industry with our approach.
The traditional model relies heavily on an intermediary (top row of Figure 3). First, an asset manager develops an investment strategy (labeled “Strategy”), which is distributed by some means (labeled “Distribution”) to the investment public (labeled “Consumer’). A well-intentioned asset manager that wants to charge 75bps for a high-performance active product could be faced with a 75bp distribution fee, making the total product costs to the consumer 150bps, or 1.5%. Half of this fee goes to the strategy manager, and half of the fee goes to the salesmen sitting in the middle of the transaction. And while this example is hypothetical, it captures the spirit of the mutual fund and hedge fund industries. The consumer pays twice: once for the development of the strategy, and once for a middleman, whose role is to sell the strategy as a high-performance product.
One thing is transparent: The asset manager and the consumer would both be better off if they could cut the middle-man out of the picture. The client would save 75bps in distribution fees and the asset manager would have direct access to the client. This direct-to-consumer model is visually depicted in the bottom row of Figure 3, and we think it represents the future of the financial services industry. A disintermediated business model is our distribution model and it will help us deliver affordability.
Distribution isn’t the only way we can minimize costs. In addition to evolving the distribution model in financial services, our dedication to a low-cost infrastructure and reliance on technology, ensures our viability as a going concern and also allows us to maintain a lower overall cost structure than our competition in the active management space. We are open to having expensive office space and a personal assistant at the disposal of every portfolio manager. Box seats at every major sporting event? Sounds like a great time! But here’s the problem: these costly events don’t add value to an investment portfolio and they impose costs on a client via higher operational costs. The money would be better spent on research and development or technology to improve our offerings.
Charlie Munger, at the 2004 Berkshire Hathaway Annual Meeting, is quoted as saying, “The idea of excessive diversification is madness…almost all good investments will involve relatively low diversification.” An underlying assumption of Munger’s quote is that the investor who does not diversify must possess investment skill. Another word for Munger’s issue with diversification for a skilled manager is “diworsification.” Here is a high level summary of the diworsification problem:
- Owning more stocks can lower a portfolio’s risk profile (i.e. beneficial).
- Owning more stocks can dilute performance (i.e. costly).
But what is the optimal trade-off between performance and idiosyncratic risk?
Let’s first tackle idiosyncratic risk. Idiosyncratic risk is the risk we can eliminate via diversification. Idiosyncratic risks are risks that are uncorrelated with other risks. Examples include a building catching on fire, a hurricane, or a CEO death. But idiosyncratic risk cuts both ways: a company can benefit from, say, a favorable lawsuit outcome, or a positive FDA ruling. When investors hold a large portfolio of stocks, these idiosyncratic risks–which are unrelated–cancel each other out, on average.
Systematic risks, unlike idiosyncratic risks, are risks we can’t easily diversify away. An example might be the risk that the general economy goes into a depression. If people in the economy aren’t spending money, this affects all companies–we can’t hold a bunch of companies and eliminate this risk since they are all affected by the economy.
To get a better sense of how increasing portfolio size affects portfolio volatility in the context of stocks we simply look to the academic literature. Elton and Gruber have multiple papers and books on the subject.4
Figure 4, built with data from the original Elton and Gruber paper, highlights the relationship between portfolio size and risk. Notice that the portfolio risk goes down as the portfolio size increases; however, risk never goes to zero and the benefits to holding a bigger portfolio decline rapidly after the portfolio grows beyond 50 securities. So while we are protected by some diversification, we don’t want too much.
30 to 50 stocks is a sweet-spot where an investor eliminates portfolio idiosyncratic volatility. Additional diversification beyond this point does not help reduce volatility in any dramatic way. By diversifying beyond 30 to 50 stocks, we prevent our portfolio from concentrating on stocks we feel are “undervalued.” For example, we probably want Warren Buffett to hold at least a small portfolio of stocks–to ensure he doesn’t completely blow up–but we don’t want to force him to hold more stocks, because it is unlikely he has more than 50 good ideas. If we forced him to diversify beyond 50 positions, we would dilute his alpha.
To explore the relationship between the number of portfolio holdings and alpha, we conduct an experiment. We examine a simple high-performance value factor: EBIT/TEV (a version of enterprise multiple)5. There is a clear historical relationship between EBIT/TEV and future stock returns. High EBIT/TEV stocks earn higher returns than low EBIT/TEV stocks6. We will not argue whether this extra return earned by high EBIT/TEV stocks (value stocks) is due to mispricing or fundamental risk, but for exposition purposes, we can agree that this is an empirical fact.
First, a quick description of the study we conduct from 1964-2013. We identify firms above the NYSE 40th percentile for market capitalization that have information to calculate enterprise multiples. In 2013 terms, this equates to a universe that looks at stocks with a market capitalization of ~$2B and up. With our universe of mid to large cap firms, we split firms into three differently sized portfolios based on EBIT/TEV measures: Top third EBIT/TEV (~279 stocks), Top quintile EBIT/TEV (~167 stocks), and Top decile EBIT/TEV (~83 stocks).
In Figure 5 we display the summary statistics for the following three portfolios:
- EBIT Cheap Decile = Value-Weight, monthly re-balanced portfolio of cheapest 10% of EBIT/TEV stocks
- EBIT Cheap Quintile = Value-Weight, monthly re-balanced portfolio of cheapest 20% of EBIT/TEV stocks
- EBIT Cheap Tercile = Value-Weight, monthly re-balanced portfolio of cheapest 33% of EBIT/TEV stocks
Figure 5 highlights that more concentration increases compound annual growth rates, but also increases volatility. The increase in volatility is expected, because the smaller portfolios are less diversified. But what is the tradeoff between returns and volatility? The Sharpe ratio and Sortino ratio are two commonly used measures to calculate the tradeoff between returns and volatility. Based on Sharpe and Sortino ratios, the evidence suggests that concentration is a better risk-adjusted bet. You get a net performance boost by concentrating your holdings in strategies that earn higher risk-adjusted returns. Munger is right: to the extent you believe you have a reliable method of constructing a high alpha “active” portfolio, less diversification is desirable and often cheaper.
We believe in designing investment processes that are Built to Beat Behavioral Bias. But why do we even care about beating behavioral bias? The answer lies within the academic research underlying the field of behavioral finance. The central premise of behavioral finance is that human behavior can influence investment decisions, sometimes in ways that hurt investors. For most of you, this idea is probably not surprising. Warren Buffett is quoted as saying that investing is simple, but not easy. Similarly, integrating our understanding of human behavior into an investment process is simple, but not easy.
How, specifically, does our behavior impact stock prices? Despite our best intentions, human behavior is not always governed exclusively by rationality. In particular, our judgment and decision-making can be significantly affected by intuition, a form of abstract, automatic thinking that can override our reason. Faulty intuition and other forms of investor irrationality can cause us to do the wrong thing and can also create mispricings in the market. One example of behavioral bias effecting stock prices is representative bias, or the human tendency to extrapolate short-term patterns and ignore long-term trends. Academic research shows that representative bias likely drives the value premium, or the large spread in realized returns between value stocks and growth stocks.7
But behavioral finance is not solely focused on understanding the complex psychology behind decision-making. Bias is a necessary, but not a sufficient condition, to provide smart investors with an opportunity to profit using behavioral finance. The second building block of behavioral finance requires an understanding of market frictions, often referred to as the “Limits of Arbitrage.”8 This multi-element behavioral finance opportunity process is depicted in Figure 7.
The two building blocks of behavioral finance–understanding bias and arbitrage restrictions–combine to create opportunities for savvy long-term investors. To understand why both building blocks are important we need to understand the Efficient Market Hypothesis (the “EMH”). EMH claims that market prices reflect all publicly available information about securities. When mispricings occur in markets, they will immediately be eliminated by smart investors, who exploit these opportunities for a profit. Therefore, in the EMH view, prices will always reflect fundamental value–even when some market participants suffer from extreme behavioral bias.
We feel EMH is flawed, since it fails to account for arbitrage restrictions. In reality, exploiting investment opportunities created by irrational investors is not easy, and the ability of smart investors to “arbitrage” profitable opportunities can be limited in the short-run. Our investment strategies systematically exploit these opportunities where short-sighted professional investors are unable to exploit a profitable situation created by irrational traders. We explain our philosophy in great detail in our sustainable active investing piece.
We believe that financial services will be more competitive, transparent, and client-friendly in the future. We want to be on the forefront of this new paradigm in investment management. Our particular focus is on the active management sector and our desire is to redefine the category and make it more affordable for investors. We seek to deliver high conviction asset management at affordable costs, but more importantly, we deliver the potential for alpha along the way. In order to have confidence in our approach, one must understand not only our investment processes, but also the risk/return rationale behind active management and concentration, the inevitability of tracking error, and the behavioral finance research underpinning our entire process.
We believe our approach to the markets is effective for sophisticated, long-term focused, and disciplined investors seeking to beat passive market indexes over 5-year market cycles. The central belief behind our “edge” is that behavioral biases drive stock market anomalies, and that we can build research-driven programs that intelligently exploit these human biases to the fullest extent possible.
Below we summarize our business in one graphic:
Please join us in our quest to deliver Affordable. Active. Alpha.
- Cremers, K.J. Martijn, and Antii Petajisto, 2009, How Active Is Your Fund Manager? A New Measure That Predicts Performance, Review of Financial Studies 22, p 3329–3365.
- Buffett, W., “Chairman’s Letter,” Berkshire Hathaway Inc. Annual Report, 1989.
- SEC Investor Bulletin, http://www.sec.gov/oiea/investor-alerts-bulletins/ib_fees_expenses.pdf
- Elton, E. and Martin Gruber, 1977, Risk Reduction and Portfolio Size: An Analytical Solution, The Journal of Business 50, p 415-437.
- EBIT/TEV = EBIT (Earnings Before Interest and Taxes) divided by TEV (Total Enterprise Value – how much you would have to pay to buy a company; this includes buying all the company’s stock and debt, minus the company’s cash). EBIT/TEV measures how much a company earns (EBIT) divided by how much the company costs (TEV).
- Gray, Wesley. and Jack Vogel, 2012, Analyzing Valuation Measures: A Performance Horse Race over the Past 40 Years, The Journal of Portfolio Management 39, p. 112-121.
- Lakonishok, J., A. Shleifer, and R. Vishny. 1994. Contrarian Investment, Extrapolation, and Risk. Journal of Finance 44:1541–78.
- Schleifer, A. and R. Vishny, 1997, “The Limits of Arbitrage,” The Journal of Finance 52, p. 35-55.
Performance figures contained herein are hypothetical, unaudited and prepared by Empiritrage; hypothetical results are intended for illustrative purposes only. Past performance is not indicative of future results, which may vary. There is a risk of substantial loss associated with trading commodities, futures, options and other financial instruments. Before trading, investors should carefully consider their financial position and risk tolerance to determine if the proposed trading style is appropriate. Investors should realize that when trading futures, commodities and/or granting/writing options one could lose the full balance of their account. It is also possible to lose more than the initial deposit when trading futures and/or granting/writing options. All funds committed to such a trading strategy should be purely risk capital. Hypothetical performance results (e.g., quantitative backtests) have many inherent limitations, some of which, but not all, are described herein. No representation is being made that any fund or account will or is likely to achieve profits or losses similar to those shown herein. In fact, there are frequently sharp differences between hypothetical performance results and the actual results subsequently realized by any particular trading program. One of the limitations of hypothetical performance results is that they are generally prepared with the benefit of hindsight. In addition, hypothetical trading does not involve financial risk, and no hypothetical trading record can completely account for the impact of financial risk in actual trading. For example, the ability to withstand losses or adhere to a particular trading program in spite of trading losses are material points which can adversely affect actual trading results. The hypothetical performance results contained herein represent the application of the quantitative models as currently in effect on the date first written above and there can be no assurance that the models will remain the same in the future or that an application of the current models in the future will produce similar results because the relevant market and economic conditions that prevailed during the hypothetical performance period will not necessarily recur. There are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results, all of which can adversely affect actual trading results. Hypothetical performance results are presented for illustrative purposes only. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. There is no guarantee, express or implied, that long-term return and/or volatility targets will be achieved. Realized returns and/or volatility may come in higher or lower than expected.
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