Stock Funds

 Watch Our Video on Constancy Stock Portfolios

Stock funds form the core of your investment portfolio, as they have the potential to deliver the highest returns over the long term.  As an owner of the companies in your stock portfolios—companies providing the goods and services we all rely on and enjoy each and every day—you participate in the fruits of their success.

I include three strategies to serve together as the core of your stock portfolio: US Large Cap Value, US Small Cap Value and International (non-US) Value.  Holding this mix of stock strategies provides exposure to the small cap premium, while achieving asset allocation and portfolio diversification benefits.  The stock funds may advance and decline at different times, helping to reduce overall volatility.  International stocks, in particular, can offer exposures to different economic conditions.  In times of extreme advances and declines, the strategies may nevertheless move very much hand-in-hand; however, even in such instances, holding multiple funds serves portfolio diversification by increasing the total number of stocks held across your entire portfolio.

Note that for all three strategies, I suggest rules-based, automated implementations of a value-profitability discipline, given the premium available over time in doing so (and with no additional volatility).  The value, profitability and small cap premiums available in international markets have been particularly strong historically, thus a single international portfolio across all capitalizations.  I provide the names and tickers of specific stock funds that fit the bill in the box at the bottom of this page.  Note that most of these funds integrate additional evidence-based disciplines, like momentum and low-volatility, as well.

Given the unpredictability of market returns from year to year, as the time frame for your goals shortens, the probability of realizing the return premiums targeted by these funds decreases, though they remain good with over five years.  You can see this in the table immediately below, which looks at all the rolling periods (1-year, 5-year, 10-year, 15-year, 20-year) from 1928 to 2016 (profitability premium starts in 1964).

Table 3: “Batting Average” of Premiums over Different Time Periods
20 Years 15 Years 10 Years 5 Years 1 Year
Size (Small Cap) Premium 86% 76% 70% 65% 54%
Value (Low Multiple) Premium 99% 92% 85% 74% 61%
 Profitability (High RoC) Premium 100% 95% 86% 78% 68%

Source: Historical Benchmark Returns; Data Library of Kenneth R. French, Professor of Finance at the Tuck School of Business at Dartmouth College.

Accordingly, for the Constancy Model Portfolios designed for shorter time frames, I pare back allocations to these value strategies, small cap in particular, in favor of a low cost, large cap fund that closely mirrors the S&P 500, the index most commonly identified with the US stock market.

Overview of Stock Investment Process

Advances in financial technology allow fund managers to apply classic analytical principles and techniques in order to select stocks more effectively and at lower cost.  This is a fairly straightforward undertaking, but because it’s a fairly new approach, I’ve found people can have a hard time understanding it.  So, as an illustration, I present below a diagram of a systematic method I developed .  I hope this helps you invision this approach better.  Fund companies use different terminology to describe this investment approach: quantitative, factor-based, style-factor, “quantamental”, systematic, rules-based and (my least favorite) “smart beta”.  And they will all have their own unique algorithms (procedure or steps), which will vary from what I present below.  But they all get at the same thing: a methodology to systematically construct and manage a portfolio of stocks meeting proven investment criteria—maximizing the probability of capturing available return premiums over time.


At the Start

Excluding Real Estate Investment Trusts (REITs), which I regard as a separate asset class, there are approximately 3,000 publicly-traded companies with a primary listing in the United States and sufficient trading liquidity—trade more than $500,000 per day on average.  About 750 of these are large-cap companies (account for 90% of the total US market capitalization, similar to Russell 1000).  And about 2,200 are small-cap companies (account for the remaining 10% of the total US market capitalization, similar to Russell 2000).  To give you a quick sense for the international stock portfolio, there are over 8,000 companies with primary listings on overseas exchanges and $1mm per day average trading liquidity.

Step 1 – Inherent Stability Filter

My method filters out companies with short historical financial records, messy financial results or that have too much debt.  This limits the investible universe to high quality businesses, with the most stable earnings streams.  This filter has the added benefit of cleaning out “noise” in the data that cause problems with rules-based approaches.  Note that while nearly half of large-cap stocks make it through this filter, only about 20% of small-cap stocks do.  This is not surprising as larger companies have greater resources and are often more diversified in their products, services and end markets.

Step 2 – Profitability and Value Criteria

From the Inherent Stability Universe, my method then select the stocks that have the best combination of solid profitability and low valuation.  The profitability criteria, which measure the rate at which a company creates value, indicate companies with competitive “moats” and smart capital allocation.  Profitability is another measure indicative of companies that are inherently stable, as well.  The valuation criteria give weight to various measures of earnings and cash flow, relative to the value ascribed to the company by the market.  The valuation criteria are the most important, as valuation discipline is the most powerful driver of excess return over time.  My method measures profitability and value criteria using multi-year averages, in keeping with the idea that longer time frames provide greater insight.

My method uses the intersection of profitability and valuation, because I don’t think you can look at price in a vacuum.  To use an analogy, price alone is insufficient to think about whether you are getting a good deal on a new car.  To do so, you need to think about price relative to the make and model and features of the car.  A bargain price on a baseline Ford Focus is going to look much different than a bargain price on a fully-loaded Mercedes S-Class.  The ultimate attribute we care about for companies we are buying is the profits they generate and how efficiently and dependably they generate them.  So, in thinking about whether we are getting a good deal on an investment in a company’s stock, I think it is sensible to look at valuation relative to profitability.  Statistical studies have demonstrated that doing so makes the value premium more robust.

At the End

At the end of the process, there is portfolio of stocks that best capture the attributes sought by this system.  With my method, both Large Cap Value and Small Cap Value portfolios typically each have about 150 stocks in them.  Running the same process on the larger starting universe, an International Value portfolio typically has nearly 500 stocks in it.  So, as you can see, while the process is highly-selective, the portfolios are very well-diversified.  And to further ensure diversification, my method imposes in each portfolio a 33% limit on exposure to any one industry sector.


Managers of systematic strategies run this process continuously or at set intervals (monthly, quarterly, yearly) to ensure that the portfolios consistently reflect the characteristics targeted.   To do this, they sell companies that no longer meet all criteria (on valuation grounds, most typically), and buy companies that now do.  In my model, turnover (change in the portfolio) is typically 20-30% each year.

In short, taking advantage of advances in information technology, systematic fund managers offer a 21st-century application of the principles and techniques of Security Analysis, the original and still greatest text on the fundamental analysis of companies for purposes of investment selection. This allows for a more efficient selection process, which allows for lower costs for to them and thus lower fees to you.  In addition, it enables them to apply these principles and techniques more comprehensively and with greater discipline. Here’s how.


Let’s return to the core teachings of Security Analysis, which I discussed in the section, A Sensible Approach.

Quantitative Factors Are the Most Telling

The authors of the book, Benjamin Graham and David Dodd, emphasize that while analysis entails studying both quantitative and qualitative factors, the quantitative factors hold primacy, the company’s historical financial record and current financial position above all.

Broadly speaking, the quantitative factors lend themselves far better to thoroughgoing analysis than do the qualitative factors. The former are fewer in number, more easily obtainable, and much better suited to the forming of definite and dependable conclusions. Furthermore the financial results will themselves epitomize many of the qualitative elements, so that a detailed study of the latter may not add much of importance to the picture.

Benjamin Graham and David L. Dodd, Security Analysis, Sixth Edition, 82.

They refer to this as compiling the “statistical exhibit.” For decades this was a laborious and time-consuming undertaking. Over the last two decades, however, information technology has given rise to robust databases of historical financial results. As a result, “statistical exhibits” of all companies are now available at the touch of a button. This allows investment professionals to survey the entire market efficiently and comprehensively, identifying those companies whose financial profiles best meet targeted investment criteria.

Stick to the Historical Financial Record

In addition to this broad efficacy, automation of this analytical activity reinforces discipline. Mr. Graham and Mr. Dodd were adamant that for the true analyst, the historical financial record is the only reasonable and proper basis for investment, as opposed to forecasts of future results.

Analysis is concerned primarily with values which are supported by the facts and not with those which depend largely upon expectations. In this respect the analyst’s approach is diametrically opposed to that of the speculator, meaning thereby one whose success turns upon his ability to forecast or to guess future developments. Needless to say, the analyst must take possible future changes into account, but his primary aim is not so much to profit from them as to guard against them.

Benjamin Graham and David L. Dodd, Security Analysis, Sixth Edition, 86.

Much of active stock investing today has come to rest on forecasting. By contrast, an automated approach to surveying historical financial results requires systematic managers to adhere, by definition, to their original advice of sticking to the facts. Sentiments and biases—by which all forecasts are colored—can play no part. As is the case with index investing, stock selection becomes more objective—though based on proven criteria as opposed to third party indices. If the process and criteria designed up-front is sound, investors in such funds should have no concern about skill uncertainty in its implementation.

Valuation Discipline Above All

With their “intrinsic value” concept, Mr. Graham and Mr. Dodd laid the foundation for the valuation discipline.  Its simple brilliance is to preach caution: we should not take for granted that the market price at which a security is currently trading reflects, with any degree of accuracy, what it may actually be worth.  It provides a measure against which to assess whether the current price may or may not be a good deal.  Critically, as soon as they introduced the concept of intrinsic value, Mr. Graham and Mr. Dodd were quick to make sure people didn’t fall in love with it.  That is, to recognize that our assessment of intrinsic value may be off base, just as the stock price is.

In all of these instances he appears to be concerned with the intrinsic value of the security and more particularly with the discovery of discrepancies between the intrinsic value and the market price.  We must recognize, however, that intrinsic value is an elusive concept.  In general terms it is understood to be that value which is justified by the facts, e.g., the assets, earnings, dividends, definite prospects, as distinct, let us say, from market quotations established by artificial manipulation or distorted by psychological excesses….

The essential point is that security analysis does not seek to determine exactly what is the intrinsic value of a given security.  It needs only to establish either that the value is adequate—e.g., to protect a bond or justify a stock purchaseor else that the value is considerably higher or considerably lower than the market price.  For such purposes an indefinite and approximate measure of the intrinsic value may be sufficient.  To use a homely simile, it is quite possible to decide by inspection that a woman is old enough to vote without knowing her age or that a man is heavier than he should be without knowing his exact weight.

Benjamin Graham and David L. Dodd, Security Analysis, Sixth Edition, 64, 66.

Benjamin Graham came to see this as the most essential aid to successful investing, such that he came to coin the motto, “margin of safety,” which we quoted and discussed in the section, A Sensible Approach.  And so, selecting stocks trading at market prices adequately below what the shares are likely worth, i.e. with a margin of safety, lies at the core of the criteria and process used by systematic, factor-based managers.  Given “intrinsic value” is an “elusive concept,” applying this approach is never easy, but a systematic approach with valuation multiples as the defining criteria sets a clear bar: limiting selections to stocks trading at the lowest earnings multiples across the market, despite the fact that these same companies have produced the cleanest, most consistent financial results and generated the highest levels of profitability.

Evidence of Inherent Stability

Given their emphasis on the historical record, Mr. Graham and Mr. Dodd stressed the importance of identifying businesses that were likely to endure. That is, businesses with “inherent stability”, as I discussed in the section, A Sensible Approach.

It is manifest…that future changes are largely unpredictable, and that security analysis must ordinarily proceed on the assumption that the past record affords at least a rough guide to the future. The more questionable this assumption, the less valuable is the analysis. Hence this technique is more useful when applied to…a business of inherently stable character than to one subject to wide variations…

Benjamin Graham and David L. Dodd, Security Analysis, Sixth Edition, 68-69

It is impossible to guard completely against future changes in companies’ prospects. But quantitative criteria can again be applied systematically.  For example, my model includes filters that exclude companies whose historical financial results are subject to wide variations. This has the added benefit of addressing a shortcoming of automated approaches that rely on surveying databases—namely, that such approaches have trouble dealing with unusual or “outlying” results (a problem known as “garbage in, garbage out”). Filters and rankings can limit the investible universe of companies to those who have had stable, clean and reliable financial results.

Suggested Stock Funds

Fund managers first began to offer systematic, factor-based strategies over two decades.  One of the earliest to do so is Dimensional Fund Advisors (DFA).  Dimensional pioneered factor-based investing (dimensions, in their parlance), putting into practice key ideas developed in academic finance.  Eugene Fama and several other Nobel Prize winners continue to advise Dimensional, which is now one of the largest mutual fund companies in the United States.

Today, many other fund companies offer their own versions.  Interest in these strategies has picked up in recent years, so the offerings are growing.

Systematic, Factor-Based Stock Funds: Name, Ticker and Net Expense Ratio
Large Cap Small Cap International
AQR (Applied Quantitative Research) Large Cap Multi-Style Fund, QCELX, 0.45% Small Cap Multi-Style Fund, QSMLX, 0.66% International Multi-Style Fund, QICLX, 0.60%
BlackRock iShares ETFs iShares Edge MSCI Multifactor USA ETF, LRGF, 0.20% iShares Edge MSCI Multifactor USA Small-Cap ETF, SMLF, 0.30% iShares Edge MSCI Multifactor Intl ETF, INTF, 0.30%
Dimensional Fund Advisors (DFA) US Large Cap Value, DFLVX, 0.27% US Small Cap Value, DFSVX, 0.52% World ex-US Value, DFWVX, 0.52%
Glenmede Quantitative US Large Cap Core, GTLIX, 0.65% Quantitative US Small Cap, GQSCX, 0.85% Quantitative International, GTCIX, 1.00%
Goldman Sachs ActiveBeta ETFs US Large Cap Equity, GSLC, 0.09% US Small Cap Equity, GSSC, 0.20% International Equity, GSIE, 0.25%
QS Investors US Large Cap Equity, LMISX, 0.70% US Small Cap Equity, LMSIX, 0.92% International Equity, LMGEX, 0.95%

Above, in alphabetical order, are five fund providers (three mutual funds, two ETFs) whose overall philosophy and approach is in keeping with what I think works best in investing, as described throughout this website.  And here I will provide some quick color on each of the funds in the table.

AQR is a thought-leader in factor-based investing.  One of its founders, Cliff Asness, studied under Eugene Fama at the University of Chicago, and the firm today employs approximately 70 PhDs.  AQR’s offering includes both single style portfolios, notably momentum which was the subject of Mr. Asness’ early academic work, as well as multi-style portfolios.  As I advocate strategies that integrate factors to identify companies with the best mix of attributes, I show AQR’s multi-style funds in the table above.  AQR’s site is rich with papers on style factors and other quantitative investment topicsoften published in leading academic journals.  Among these is an excellent study of the benefits of integrating factors.  In addition to value and quality (similar to what I call inherent stability and profitability), AQR’s multi-style portfolio integrates momentum and low-volatility attributes, which academic studies have likewise shown as sources of return premia.  AQR offers its strategies in mutual fund format.  It offers tax-managed versions of these funds for taxable accounts.  AQR funds are available directly to individual investors but only with very high minimum investments; as a result, practically speaking, you likely need to access them through an adviser.

BlackRock’s entry into factor-based investing resulted from its acquisition of Barclays Global Investors, an early developer of quantitative strategies.  A few years ago BlackRock hired Andrew Ang, a next-generation academic in the factor field, to further and champion its efforts in this area.  BlackRock offers a number of single-factor strategies (value, quality, momentum and size).  Again, as I believe in integrating factors to identify companies with the best mix of attributes, I show BlackRock’s multifactor offerings in the table above.  They and all BlackRock’s factor portfolios are part of its iShares “smart beta” line-up of ETFs.

Dimensional is a pioneer in systematic, factor-based investing.  It approaches investment management with a bedrock belief in efficient markets.  It targets size, valuation and profitability as factors that account for the return premiums observed in stocks.  DFA continues to divide the market into value and growth stocks first and then tilt each group toward profitability.  I thus suggest Dimensional’s value strategies, which result in an intersection of value and profitability criteria.  DFA offers a range of funds with varying exposures to these criteria, from full exposure, which I show in the table above, to pure index funds that closely track the S&P 500 benchmark.  You must work with an adviser that uses Dimensional funds to have access to them.  Such advisers tend to share the investment principles and values I introduce throughout this website, including serving clients to a fiduciary standard as Registered Investment Advisors.  Dimensional offers its strategies in mutual fund format.  It offers tax-managed versions of these funds for taxable accounts.

Glenmede has a long history in developing and managing systematic strategies, starting with its Quantitative International strategy which dates back to 1988.  Its strategies are purely quantitative implementations of classic, common-sense fundamental criteria, as well as technical factors like price momentum.  Its integration of multiple factors is a positive, and I favor its core strategies which incorporate both value and profitability.  Setting Glenmede’s strategies apart, they employ fundamental criteria that vary by industry, depending on which are most relevant to each industry.  And it also utilizes a second model specifically geared toward risk, which guides its selling discipline.  There has been good continuity in the investment team, dating back to each strategies inception; the small cap strategy is a relatively recent addition.  Glenmede offers its strategies in mutual fund format.

In 2015, Goldman Sachs Asset Management introduced its “ActiveBeta” ETFs, the US large and small cap and international versions I show in the table above.  These are multi-factor stock portfoliosvalue, quality, momentum and low-volatility.  Unlike the multi-factor approaches used by AQR and BlackRock, Goldman’s methodology does not integrate the factors.  It first sorts companies on each single factor independently and then mixes these portfolios together in equal parts (25% each) into the overall “ActiveBeta” portfolio.  This can miss out on important interaction effects (a stock that is not the cheapest or the highest quality (most profitable), but is the cheapest relative to its level of profitability).

QS Investors is an independent firm under the larger Legg Mason umbrella.  Similar to the ideas I’ve conveyed here, QS offers strategies that draw important ideas from both quantitative and behavioral finance.  It also implements portfolios based on the intersection of value and fundamental factors (cash flow generation and growth, which together relate very strongly to the profitability concept I’ve described).  And it combines this with a sentiment factor, which it measures in a number of ways.  QS distinguishes itself by varying the emphasis it places on either the value or fundamental factors based on the current market environment in terms of sentiment.  It gives greater weight to those factors that on average have performed better in sentiment environments (positive or negative, i.e. greed or fear) most closely approximating the environment then prevailing.