Benjamin Graham, who first established the idea of purchasing stocks at a discount to their intrinsic value more than 80 years ago, is known today as the father of value investing. Since Graham’s time, academic research has shown that low price to fundamentals stocks have historically outperformed the market. In the investing world, Graham’s most famous student, Warren Buffett, has inspired legions of investors to adopt the value philosophy. Despite widespread knowledge that value investing generates higher returns over the long-haul, value-based strategies have continued to beat the market. How is this possible? The answer relates to a fundamental truth: human beings behave irrationally. We follow an evolutionary mindset that focuses on surviving in the jungle, not optimizing our 401k portfolio. While we will never eliminate our survival instincts, we can minimize their impact by employing quantitative tools.

“Quantitative,” is often considered to be an opaque mathematical black art, only practiced by Ivory Tower academics and supercomputers. Nothing could be further from the truth. Quantitative, or systematic, processes are merely tools that value investors can use to minimize their “survival” instincts when investing. Quantitative tools serve two purposes: 1) protect us from our own behavioral errors, and 2) exploit the behavioral errors of others. These tools do not need to be complex, but they do need to be systematic. The research overwhelmingly demonstrates that simple, systematic processes outperform human “experts.” The inability of human beings to robustly outperform simple systematic processes also holds true for investing, just as it holds true for most other fields. 1

Much of the analysis conducted by value investors—reading financial statements, interpreting past trends, and assessing relative valuations—can be done faster, more effectively, and across a wider swath of securities via an automated process. Gut-instinct value investors argue that experience adds value in the stock-selection process, but the evidence doesn’t support this interpretation.(2) Why? When value investors respond to non-quantitative signals (e.g., the latest headlines on MSNBC, their expert friend’s opinion at the cocktail party, etc.), they unconsciously introduce cognitive biases into their investment process. These biases lead to predictable underperformance.  Alpha Architect’s Quantitative Value (QV) philosophy is best suited for value investors who can acknowledge their own fallibility. Granted, our approach is not infallible, and should always be questioned; however, the approach seeks to deliver the following: a systematic, evidence-based, value-focused investment strategy that is built to beat behavioral bias.

Note: For those who want to dive right into the specifics of the Quantitative Value Indexes, information is available below:

An Introduction to the Quantitative Value Index

When we set out to develop our Quantitative Value (QV) approach we had one mission in mind:

  • Identify the most effective way to systematically capture the value premium.

Our mission involved two core beliefs:

  • Value investing works over the long haul because the strategy is highly volatile
  • There is a mispricing component of the value premium that is caused by an overreaction to negative fundamentals.
  • To extract the highest expectation from the value premium, the portfolio needs to be focused (i.e., 50 stocks or less) and not a closet index.

After a decade of value investing research, rewrites, and regressions, our comprehensive findings on systematic value investing were published in our book, Quantitative Value.

The book has been well received by the investment community, for example: 2

This book is an excellent primer to quantitative investing…

Alex Edmans, Ph.D., Associate Professor of Finance, The Wharton School, University of Pennsylvania

Quantitative Value is a must read for those with a love of value investing and a desire to make the investment process less ad-hoc.

Tony Tang, Ph.D., Global Macro Researcher and Portfolio Manager, AQR Capital Management

Gray and Carlisle take systematic value-based investing to the next level.

Raife Giovinazzo, Ph.D., CFA, Research Analyst in Scientific Active Equity, Blackrock

What resulted from our research is a reasonable, evidence-based approach to systematic value investing. Others agreed with us. In 2012, Alpha Architect partnered with a multi-billion dollar family office and sophisticated investors to turn our theoretical QV approach into reality. We spent several years building the operational infrastructure needed to ensure a smooth transition from academic theory to real-time performance. In the end, we distilled our entire process into an index that reflects five core steps (depicted in the figure below):

  1. Identify Universe: Our universe generally consists of mid- to large-capitalization U.S. exchange-traded stocks.
  2. Remove Outliers: We conduct financial statement analysis with statistical models to avoid firms at risk for financial distress or financial statement manipulation.
  3. Screen for Value: We screen for stocks with low enterprise values relative to operating earnings.
  4. Screen for Quality: We rank the cheapest stocks on their long-term business fundamentals and current financial strength.
  5. Investment with Conviction: We seek to invest in a concentrated portfolio of the cheapest, highest quality value stocks. This form of investing requires disciplined commitment, as well as a willingness to deviate from standard benchmarks.

Step 1–Identify the Investable Universe: Mid- and Large-Caps

The first step in the QV investing process involves setting boundaries on the universe for further screening.  There are several reasons we place such limits around the stocks to consider.  A critical aspect involves liquidity, which is related to the size of the stocks under consideration.  In general, if we include stocks that are too small, the possibility of large price movements on small volume is a real risk. Ignoring liquidity leads to overstated backtests relative to actual returns.  In other words, if we include small stocks in our universe, the back-tested results may generate phenomenal returns, but these returns are likely unobtainable in the real world, even when operating with small amounts of capital.

In order to honestly assess and implement the QV approach, we eliminate all stocks below the 40th percentile breakpoint of the NYSE by market capitalization. As of December 31, 2013, the 40th percentile corresponded to a market capitalization of approximately $2 billion.  Our universe also excludes ADRs, REITS, ETFs, financial firms, and others that present various data challenges incompatible with the QV approach. 3 Another requirement is that the firms we analyze have an adequate number of years of data to draw from, as some of the QV metrics require that we analyze financial data over the past eight years.

In summary, our investment universe contains liquid, non-financial companies with at least eight years of public operating history.

Step 2–Remove Outliers: Look for Red Flags

As noted value investor Seth Klarman has advised, “Loss avoidance must be the cornerstone of your investment philosophy.” This is an important concept, and underlies the first phase of our approach. As an initial criterion for making a successful investment, we seek to eliminate those firms that risk causing permanent loss of capital.  

Permanent loss of capital can come in many forms, but we bucket these risks into two basic categories: manipulation/fraud and financial distress (e.g., bankruptcy).

We leverage some tools that can help us identify “Red-Flag” firms:

  1. Accrual red flags
  2. Predictive models

Accrual Red Flags

Our first set of tools calculate measures related to accruals. Bernstein succinctly states the problem with accruals: 4

CFO (cash flow from operations), as a measure of performance, is less subject to distortion than is the net income figure. This is so because the accrual system, which produces the income number, relies on accruals, deferrals, allocations and valuations, all of which involve higher degrees of subjectivity than what enters the determination of CFO. That is why analysts prefer to relate CFO to reported net income as a check on the quality of that income. Some analysts believe that the higher the ratio of CFO to net income, the higher the quality of that income. Put another way, a company with a high level of net income and a low cash flow may be using income recognition or expense accrual criteria that are suspect.

As Bernstein states, the problem with accruals is that they open the door for potential financial statement manipulation. A range of academic research has tested the hypothesis that investors fail to appreciate the importance of accrual measures and their impact on stock returns. 5  We have leveraged this research to develop our own forensic accounting tools that use various accrual metrics to identify potential manipulation and subsequently eliminate these firms from our investment set. We look at extreme accruals and balance sheet bloat to capture red flags that might be associated with accruals.

We specifically target the following measures:

  • STA = (net income – cash flow from operations) / Total Assets (see Sloan 1996)
  • SNOA = (Operating Assets – Operating Liabilities) / Total Assets (see Hirshleifer et al. 2004)

Predictive Models

Another set of tools we use involves statistical prediction techniques. Implementation of these models is highly technical, but the mechanism is intuitive. An example helps illuminate the process. Consider the case of financial statement manipulation: We hypothesize that high accruals, lots of leverage, rapidly changing financial statement ratios, and rapid sales growth might be related to manipulation. The problem is understanding how these variables are related.  To build our solution, we need take two steps: 1) Identify a group of firms that manipulated their financial statements in the past, and 2) use statistical techniques to identify the relationship between the manipulator and the variables we think matter. Finally, we test our model on another sample of manipulator firms and examine if the model has any “out-of-sample” prediction ability. If the model works, it will predict, with a success rate better than chance, if a firm has manipulated financial statements. This process is essentially a simple version of “machine learning.” While this process sounds complicated, the procedure outlined is followed by academic researchers who have identified effective ways to pinpoint manipulation and financial distress. 6 We leverage these studies, and our own internal research, to develop prediction models that identify problematic firms and eliminate them.

The models we use are as follows:

Minimize Garbage

The final step is to remove all firms in the universe that sit in the bottom 5% percentile ranks on any of the measures mentioned above (for accruals, we eliminate the bottom 5% based on the average percentile rank of the two measures). The graphic below depicts the high-level process:

Step 3–Value Screens: What Value Metric Performs the Best?

Step 1 and Step 2 identifies a universe that we can analyze. On average, we are left with a universe of 800 publicly traded common stocks (US) that are large and liquid. Most importantly, they  do not reveal statistical “Red Flags” imply questionable accounting or impending loss of capital.

In Step 3, we screen for the cheapest stocks. Ben Graham long ago recognized the importance of paying a low price for stocks. Graham’s “value anomaly,” or the significant outperformance of low price-to-fundamental stocks relative to high price-to-fundamentals, is now well-established in the academic and practitioner communities alike. However, practitioners continually tinker with this conclusion in order to create a better mousetrap. Typically, these ad-hoc adjustments include measures such as low price-to-earnings, low price-to-book value, dividends, etc. Not a week goes by when we aren’t solicited with a hot, new metric to test against our approach. We sought to provide a comprehensive answer to this debate. In the Journal of Portfolio Management, we published a peer-reviewed assessment of the best valuation metric(s) available. Simply stated, which measure of value works best for identifying stocks most likely to outperform? 7

We reviewed historical stock market returns against a myriad of value strategies. More importantly, we directly tested them against one another in a quantitative horse race. The “horses” in our race were the following valuation metrics:

  • E/M – Earnings to Market Capitalization: The E/M ratio is simply a firm’s earnings divided by its total market capitalization. 8
  • EBITDA/TEV – Enterprise Multiple: Employed extensively in private equity, this is simply a firm’s earnings before interest, taxes, depreciation and amortization (EBITDA) divided by its total enterprise value (TEV). 9
  • FCF/TEV – Free Cash Flow Yield: The numerator for this metric is Free Cash Flow, which is net income + depreciation and amortization – working capital changes – capital expenditures. Once again, total enterprise value (TEV) is in the denominator.
  • GP/TEV – Gross Profits Yield: Revenue – cost of goods sold in the numerator (GP), and total enterprise value (TEV)in the denominator.
  • B/M – Book-to-Market: The book value of a firm divided by the firm’s market value (an academic favorite).

Our conclusion? Enterprise multiples are arguably the most effective metric to capture the so-called value premium. But don’t take our word for it.

Loughran and Wellman (2009) make the following claim regarding enterprise multiples:

…the enterprise multiple is a strong determinant of stock returns

Walkshäusl and Lobe (2015)  conduct the analysis of enterprise multiples on international markets and conclude the following:

return predictability is pronounced in developed and emerging markets…

Based on the evidence, it would appear that EBITDA/TEV is the best-performing price metric in terms of both raw returns as well as on a risk-adjusted basis. Now, we aren’t necessarily wedded to EBITDA/TEV.. In fact, all the valuation-based metrics beat the benchmark; however, we like enterprise multiples because they represent the valuation metric that a private company buyer would use to assess an investment opportunity. As Benjamin Graham, the intellectual founder of the value investment philosophy, states in his classic text, The Intelligent Investor, “Investment is most intelligent when it is most businesslike.” 10

Moreover, we have conducted our own formal investigation into why enterprise multiples “work the best,” at least historically.

To ascertain whether the EBIT/TEV value factor is attributable to risk or mispricing we set up the following experiment: 11

  • Break the cheapest and most expensive EBIT/TEV portfolio into different buckets based on their predicted mispricing (using a variety of measures).
  • We create two portfolios:
    • High Predicted Mispricing: Long a portfolio of cheap high mispricing and expensive high mispricing
    • Low Predicted Mispricing: Long a portfolio of cheap low mispricing and expensive low mispricing

If EBIT/TEV is a risk-based measure, the difference in the performance of the predicted “high mispricing” and “low mispricing” portfolios should be insignificant because mispricing doesn’t drive performance, risk does. However, if there is a difference in these two portfolios, the results suggest that mispricing arguably drives the premium.

Our research suggests that the Enterprise Multiple (EM) effect can be attributed to mispricing, and not due to higher systematic risk. Although we will not deny that higher risk likely plays some role in the higher expected returns. Here is a figure highlighting the core conclusion when it comes to the enterprise multiple effect: 12

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

To summarize, the enterprise multiple metric seems to capture a higher degree of systematic mispricing then the multiples similar cousins, book-to-market, earnings-to-market, and so forth.

In our index methodology, we use a variation on the enterprise multiple as part our valuation screening technology (EBIT/TEV), and screen our universe from Step 1 and Step 2 down to the ten percent cheapest stocks based on EBIT/TEV. This screen ensures we are dealing with a subset of firms that are sitting in the “bargain bin” at our universe.

Step 4–Quality Screens: Quality Differentiates Cheap Stocks

After “cleaning” our liquid universe (Step 2) and zeroing in on the “bargain bin” of the cheapest stocks (Step 3), we move onto Step 4 of our investment process. Step 4 addresses a simple concern: How do we separate cheap stocks that may be cheap for a good reason (junk) from cheap stocks that are fundamentally mispriced (good value)?

Academic research highlights fundamental analysis (often referred to as “quality” metric analysis) can help differentiate among the winners and losers when sifting through the cheap stock bargain bin. For example, Piotroski and So (2012), make the following statement:

…a simple financial statement analysis-based approach can identify mispricing embedded in the prices of value firms.

Here is an annotated figure from their research that highlights their key point:

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

The black bars reflects a portfolio that captures the generic value premium: long cheap stuff; short expensive stuff. The solid black line represents the portfolio that is long cheap quality and short expensive junk; the dotted line is long cheap junk and short expensive quality. Under the risk-based explanation for the value premium, all three strategies should be roughly the same. However, the evidence suggests that fundamental analysis, or “quality” metrics, can help a value investor improve their results. With this knowledge, we add two quality screens to our systematic value process:

1) Long-Term Business Strength
2) Current Financial Strength

Long-Term Business Strength

In thinking about Long-Term Business Strength, or “economic moat,” we turn to the Sage of Omaha for guidance. Warren Buffett looks for businesses with enduring competitive advantage and sustainable earnings power (above and beyond their competitors). What does that competitive advantage look like? A firm might manufacture goods at a lower cost, provide a product for which there are no direct substitutes, or represent a trusted brand that keeps customers coming back. These types of advantages, and others like them, are the collective “moat” that allow companies to raise the drawbridge and defend market share from the competition.

As quantitative investors, we are not focused on understanding the details of any particular moat. Instead, we want to objectively identify which metrics are appropriate for assessing an economic moat’s strength. One key feature of economic moats is that they enhance the profitability of investments, which allows the firm to generate above-average returns on invested capital. Any business with a wide moat, therefore, requires lower rates of reinvestment to maintain or grow existing production capacity, leaving additional capital that can be distributed to owners without affecting the company’s future growth. Thus, investment profitability can be used to identify companies with economic moats.

In assessing an economic moat, we are particularly interested in high returns that are sustained over a full business cycle. To do so, we use eight years for our long-term average calculation, as this captures a typical boom-bust business cycle. We use three metrics that help us identify statistical evidence for an economic moat: Long-term free cash flow generation; long-term returns on capital; and long-term margin characteristics.

Granted, an Economic moat is a valuable quality signal, but it only represents one leg of our fundamental analysis. We must also be certain that the cheap stocks under consideration have some level of current financial strength

Current Financial Strength

We introduce the notion of financial strength with an analogy. Suppose you had to sail across the Atlantic and were given a choice between making the crossing in either an eight foot sailing dinghy, or a 60 foot yacht.  Which would you choose?  Obviously, you would want the safety and security afforded by the larger, more seaworthy yacht.  The same concept holds when deciding upon the stocks to include in your portfolio: all things being equal, an investor should seek out those stocks that are less vulnerable to downturns or other macroeconomic shocks.

We know intuitively why a durable 60-foot yacht protects sailors better than a fragile dinghy: its heavy keel keeps it stable, it won’t roll violently in heavy winds, and it can take a pounding by waves.  What are the financial characteristics that enable a firm to protect capital during a stormy business climate or from unanticipated developments?  Several years ago, Joseph Piotroski, a specialist in accounting-based fundamental analysis, and currently a professor at Stanford, did some interesting analysis relating to this subject. He used a nine-point scale, utilizing common accounting ratios and measurements, to evaluate the financial strength of companies and eliminated those most at risk of financial distress.  This scale, which he called the “F_SCORE,” involved financial statement metrics across several areas: profitability, leverage, liquidity and source of funds, and operating efficiency.  The results were nothing short of astonishing: Piotroski found that a value investment strategy that bought expected winners and shorted expected losers generated a 23 percent annual return between 1976 and 1996—a record of which even Buffett would be proud. 13

As Sir Isaac Newton noted, “If I have seen further, it is by standing on the shoulders of giants.” We also believe in standing on the shoulders of giants whenever possible since, as Newton observed, you can see so much farther. We therefore use Piotroski’s F-SCORE as a basis for our approach to measuring current financial strength, but with some improvements. Here is a simple outline of our current financial strength 10-point checklist:

  1. Current profitability (3 items)
  2. Stability (3 items)
  3. Recent operational improvements (4 items)

The current financial strength score reduces the overall financial health of a firm to a single number between 0 and 10, which can be used as a basis for comparing a firm’s overall financial strength versus that for other firms.

Integrating Price with Quality

For both aspects of quality–Long-Term Business Strength and Current Financial Strength–we tabulate thousands of data points based on the principles discussed above and derive quality scores for all firms in our cheap universe identified in Step 3.

Here is a breakdown of the metrics that go into our quality assessment and how they are weighted:

We sort our cheap universe on our composite quality score to identify a universe of what we believe are the cheapest, highest-quality value firms.

Step 5–Invest with Conviction: Focused Value Factor Exposure

Steps 1 through 4 systematically seeks to identify the cheapest, highest quality value stocks. We believe that this portfolio of stocks has the highest probability of capturing the value premium over the long-term.

But one question remains: How do we construct our final QV portfolio?

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.” Another word for Munger’s issue with diversification for a skilled manager is “diworsification.” Elton and Gruber, professors with multiple papers and books on the subject of diversification, highlight that the benefits to holding a bigger portfolio of securities decline rapidly after a portfolio grows beyond 50 securities. 14

So while we are protected by diversification, we don’t want too much. Jack has a nice post that talks directly to the Elton and Gruber findings in the context of value investing. Moreover, Charlie Munger is correct: to the extent you believe you have a reliable method of constructing a high alpha “active” portfolio, less diversification is desirable.

In the spirit of less diversification (aka “high conviction”), we construct our index to have around 40-50 securities. Consider a hypothetical illustration of our screening process, which roughly reflects our experience managing our index in the real-world:

  1. Identify Investable Universe: We typically generate 900 names in this step of the process.
  2. Forensic Accounting Screens: We usually eliminate 100 names, bringing the total to 800 stocks.
  3. Valuation Screens: Here we screen on the cheapest 10% of the universe, or 80 stocks.
  4. Quality Screens: We calculate a composite quality score and eliminate the bottom half, leaving 40 stocks.
  5. Invest with Conviction: We invest in our basket of 40 stocks that are the cheapest, highest quality value stocks.

Our index has the following construction details:

  • Equal-weight
  • Quarterly rebalanced (international is semi-annually rebalanced)
  • 25% sector/industry constraint
  • No financials
  • Pre-trade liquidity requirements

We don’t like to emphasize historical performance, because we believe the process is paramount. However, if you’d like to see the hypothetical performance we suggest you review our index educational materials, which are available here.

Why Isn’t Everyone a Concentrated Systematic Value Investor?

We feel we have identified a reasonable systematic value investing approach that will capture a large value premium over time. But while all of this may sound promising, one must consider a simple question:

If this is so easy, why isn’t everyone doing it?

The easy answer is that most investors aren’t insane. Value investing works because it is risky and painful. There is no way around this basic fact. Investors who follow our index must buy stocks that probably make them uneasy, and almost all of our portfolio holdings have business problems that are lamented by the Wall Street Journal and CNBC day in and day out. Some of these problems will actually play out in the future and the index will lose money on these positions. However, on average, we believe these lamentations will never be as bad as initially advertised and the index will benefit, in the aggregate, when expectations revert to normal.

Nevertheless, the road will be bumpy, full of volatility, and is not for everyone.

Consider the experience of a systematic value investor who simply buys low-priced stocks. Our approach, while not exactly the same as a simple low-price value strategy, shares many of the same characteristics—both good and bad—so this thought experiment serves as a nice case study to contextualize the costs and benefits of contrarian investment programs.

Using data on portfolios sorted by book-to-market ratios, we examine time periods where it was painful to be a value investor.

One such period is during the run-up to the internet bubble. We examine the gross total returns (including dividends and cash distributions) from 1/1/1994-12/31/1999 for a Value portfolio (High book-to-market decile, market-weighted returns, FF_VAL), and a Growth portfolio (Low book-to-market decile, market-weighted returns, FF_GROWTH), the S&P 500 total return index (SP500), and the 10-Year Treasury Total Return index (10-Year). 15

The figure below highlights the extreme underperformance of the simple value portfolio relative to a simple growth portfolio and the broader market. From 1994 to 1999, value underperformed growth by almost 7 percentage points a year. Now that’s pain! When one compounds that spread over 5 years it translates into a serious spread in cumulative performance.

Summary Statistics FF_VAL FF_GROWTH SP500
CAGR 19.68% 26.51% 23.83%
Standard Deviation 14.60% 16.17% 13.63%
Downside Deviation (MAR = 5%) 12.52% 11.02% 10.50%
Sharpe Ratio (RF=T-Bills) 0.98 1.25 1.30

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

The figure below makes the point even more clear. The value strategy underperforms the broad market for 5 out of 6 years.

1994 -4.88% 1.62% 1.35% -4.02%
1995 40.77% 36.32% 37.64% 22.97%
1996 19.54% 20.84% 23.23% 2.08%
1997 31.09% 31.74% 33.60% 10.29%
1998 28.29% 41.76% 29.32% 11.55%
1999 9.12% 31.05% 21.35% -4.20%

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Would  you retain your financial advisor if they underperformed for 6 years straight? Most would not. Even the most disciplined and hardened value investor would have a hard time staying disciplined to a philosophy that lost to the market for almost 6 years in a row. Warren Buffett, arguably the greatest investor of all-time, was criticized in the media for “losing his magic touch” at the tail-end of the late ‘90s bull market.[ref][/ref]

Of course, looking back, we now realize that in 1999 the internet bubble was about to burst. Value investors got the last laugh over the next 6 years. From 2000 to 2006 value stocks earned 13.00 percent a year relative to the market’s paltry -4.57 percent/year performance. Here are the annuals:

2000 6.67% -21.03% -8.34% 3.14%
2001 10.98% -18.69% -11.88% -10.80%
2002 -17.75% -24.24% -21.78% -5.14%
2003 59.58% 22.64% 28.72% 26.68%
2004 16.66% 6.38% 10.98% 15.70%
2005 7.39% 3.92% 5.23% 10.24%
2006 20.79% 9.27% 15.69% 14.82%

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Over the full cycle from 1994 to 2006, value came through: Value earned 16.03 percent a year, while the market earned 8.69 percent a year. An investor compounding at a 2.03 percent spread over the market return over nearly twenty years will generate a substantially different wealth profile over time. The figure below shows the performance of the simple low-price value strategy relative to the market from 1994 to 2006:

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Since 2006, value has entered the pain trade. Now for a 10 year stretch (the figures below are from 2007 to 2016).

Summary Statistics* FF_VAL FF_GROWTH SP500
CAGR 3.30% 8.98% 7.09%
Standard Deviation 28.50% 15.80% 15.22%
Downside Deviation (MAR = 5%) 20.36% 12.01% 11.77%
Sharpe Ratio (RF=T-Bills) 0.23 0.58 0.48

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Will value ever come back? Who really knows. But history suggests that the value premium is often captured by those with the ability to take on the most pain.

Conclusions Regarding the Quantitative Value Process

In the short-run, most of us simply cannot endure the pain that value investing strategies impose on our portfolios and our minds. For those in the investment advisory business, providing a strategy with the potential for multi-year underperformance is akin to career suicide. And yet, at Alpha Architect, we explicitly focus on building our Quantitative Value Indexes based on our systematic value investing philosophy. Clearly, these indexes are not for everyone. However, our hope is that we can educate investors with the appropriate temperament on what it takes to achieve long-term investment success as a value-investor. The single most important factor is sticking to a value investment philosophy through thick and thin. Our systematic value investment process facilitates our ability as investors to simply “follow the model” and avoid behavioral biases that can poison even the most professional and independent fundamental value investors.

We believe value investing works over the long-haul. Benjamin Graham distilled the secret of sound value investment into three words: “margin of safety.” We’ve focused on the behavioral aspects that drive value investing and taken Graham’s original motto a bit further. Our enhanced process can be distilled into the following:

We seek to buy the cheapest, highest quality value stocks.

— Wesley R. Gray and Jack R. Vogel, co-CIOs Alpha Architect

Information on our Quantitative Value Indexes is available here.

Here are some specific research/educational materials:

The Quantitative Value book, co-written with Toby Carlisle, outlines the details associated with steps 2, 3, and 4 if you’d like to learn more about the process.

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  1. Grove, W., Zald, D., Lebow, B., and B. Nelson, 2000, “Clinical Versus Mechanical Prediction: A Meta-Analysis,” Psychological Assessment 12, p. 19-30.
  2. The recommendations are directed towards the quality of the book and are not an endorsement of advisory services provided by Alpha Architect, LLC or affiliates. Alpha Architect does not know if the recommenders approve or disapprove of its services. The recommendations were chosen from a list of formal recommendations based on if the author had a PhD or not.

  3. The elimination of financial firms is due to Step 2 of the Quantitative Value process, mainly due to the leverage of financial firms
  4. Bernstein, L. 1993. Financial Statement Analysis. 5th ed. Homewood, IL: Irwin.
  5. Examples include Sloan, 1996, “Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings?” Accounting Review 71, p. 289-315 and Hirshleifer, Hou, Teoh, and Zhang, 2004, “Do Investors Overvalue Firms with Bloated Balance Sheets?” Journal of Accounting and Economics 38, p. 297-331.
  6. Beneish, M. D, 1999, The detection of earnings manipulation, Financial Analysts Journal, 55(5), 24-36 and Campbell, Hilscher, Szilagyi, 2011, Predicting Financial Distress and the Performance of Distressed Stocks, Journal of Investment Management 9, p. 14-34.
  7. Jack Vogel and I have a formal paper on this subject, “Analyzing Valuation Measures: A Performance Horse Race over the Past 40 Years,” published in The Journal of Portfolio Management 39, p 112-121.
  8. Note the E/M is the inverse of the more commonly referenced P/E ratio.
  9. Total Enterprise Value (TEV) can be thought of as the price an outside buyer would need to pay to buy the entire firm — the buyer would need to buy all the equity and the debt, but would receive back any cash the company has on hand. Formally, we measure TEV as follows: TEV = Market Capitalization + Short-term Debt + Long-term Debt + Preferred Stock Value – Cash and Short-term Investments.
  10. Graham, B. 1993. The Intelligent Investor. 4th Revised Edition. New York, NY: Harper & Row Publishers.
  11. here is a summary of the paper
  12. The image below is a visualization of the results from Table 2 of a working research paper by Crawford, Gray, Vogel, and Xu (accessed 6/1/17)
  13. Piostroski, J., 2000, “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers,” Journal of Accounting Research 38, p. 1-41.
  14. Elton, E. and Martin Gruber, 1977, Risk Reduction and Portfolio Size: An Analytical Solution, The Journal of Business 50, p 415-437.
  15. Bloomberg and Ken French Website: