- Smart beta is the hottest trend in investing.
- Dividend growth investing greatly overlaps with smart beta.
- You can apply smart beta concepts to your stock picking and portfolio management.
Smart beta is hot. A recent MarketWatch article said, "As an investment product, 'smart beta' is a runaway success."
Advertised widely, smart beta has become one of the most hyped methods for generating alpha ever offered by the fund industry. Many of the newest ETFs are designed to exploit smart-beta factors.
According to the Wall Street Journal, there is an estimated $484 billion invested in over 445 smart-beta stock ETFs in the USA. That means that they hold more than 20% of all ETF assets.
It has been reported that smart beta ETFs gathered nearly $40 billion in new assets in the first half of 2015 alone, or half of the new money going into U.S.-listed exchange-traded products this year.
The purpose of this article is to present an overview and discussion of smart beta, and to demonstrate that many smart beta factors are inherently available in dividend growth stocks. In other words, many dividend growth investors already have, or may be constructing, their own smart beta portfolios.
I. What Is Smart Beta?
You may be wondering what smart beta is. Per Investopedia,
Smart beta defines a set of investment strategies that emphasize the use of alternative index construction rules to traditional market capitalization based indices. Smart beta emphasizes capturing investment factors or market inefficiencies in a rules-based and transparent way. The increased popularity of smart beta is linked to a desire for portfolio risk management and diversification along factor dimensions as well as seeking to enhance risk-adjusted returns above cap-weighted indices.
Whoa! What does that mean?
Smart beta indexes select stocks and weight them differently from conventional indexes. That is, there are two dimensions to smart beta: Stock selection and stock weighting. Both dimensions are meant to "load on" performance factors that have been identified as enhancing returns.
According to the MarketWatch article,
[I]t takes altering both pillars of index construction [stock selection and weighting] to achieve the essential purpose of smart beta - namely to present an entirely different perspective on the market and therefore how to invest in it. The payoffs are supposed to be improved returns, less risk or both.
The article proceeds to be critical of smart beta products that qualify on only one of the two dimensions. Personally, I think they are being too picky, and that smart beta exists whenever an index skews to emphasize positive performance factors. That is certainly how the investing industry is treating smart beta.
II. What Are Factors?
Basically, a factor is an attribute that both explains and produces excess returns. Factors believed to influence returns have emerged from years of academic study, the evolution of Modern Portfolio Theory [MPT], and contributions from outside academia.
Investment researchers have recognized for years that some elements of portfolio return cannot be explained merely by the broad market itself. As influencers of return have been identified, they have morphed from "anomalies" not explained by pre-existing theory to "factors" that are recognized and understood to help explain higher returns.
To understand this theoretical progression, we need to consider a brief history of MPT. I believe that, no matter what your investing predilections, it is useful to understand MPT at least at a conceptual level. That is because MPT has assumed dominance in the world of financial planning and investing. Its principles are found practically everywhere, so it is good to know about them.
III. Historical Overview of MPT
Markowitz, Risk, and Portfolio Selection
The MPT story begins in 1952, when Harry Markowitz - who would later win a Nobel Prize for his work - published "Portfolio Selection." Under Markowitz's theory, investment returns could be improved by switching focus from individual securities to diversified portfolios that overall had attractive risk-reward characteristics.
Markowitz provided mathematical justification for selecting broad portfolios instead of individual securities. This was the beginning of Modern Portfolio Theory. It was also the beginning of what has become the ubiquitous identification of "risk" with variability in returns. Markowitz said that an investor should not strive simply to maximize expected returns, but that "…the investor does (or should) consider expected return a desirable thing and variance of return an undesirable thing."
The efficient frontier of investments was derived as a map of portfolios that would optimally balance risk and reward. Under the efficient frontier, it became axiomatic that low risk leads to low expected returns and higher risk leads to higher expected returns.
Because in MPT risk and volatility are synonymous, the word "risk" was thus given a restrictive definition that has become prevalent in practically all investment literature.
According to Markowitz, optimal portfolios were those that lay on the efficient frontier. Given the choice of two portfolios with equal returns, investors will (should) choose the one with the less risk.
Capital Asset Pricing Model
The next major chapter in MPT's development came in 1964 with a paper published by William Sharpe. In the paper, Sharpe (who would later share a Nobel Prize with Markowitz and Merton Miller) developed what has become known as the Capital Asset Pricing Model [CAPM].
According to a recent article by Larry Swedroe,
CAPM looks at risk and return through a "one-factor" lens-the risk and the return of a portfolio are determined only by its exposure to market beta. This beta is the measure of the equity-type risk of a stock, mutual fund or portfolio relative to the risk of the overall market. CAPM was the financial world's operating model for about 30 years.
Under CAPM, the single factor determining returns is risk (volatility). Riskier stocks should have higher expected returns, exactly as presented in the efficient frontier picture.
Simultaneously, we began to see a rise in generalized criticism of picking individual stocks. Such criticism has become a central tenet of MPT. The reasoning goes that investors are rewarded for assuming systematic (market) risk but not for specific risk (the risk associated with a single security). This is because specific risk can be diversified away. While each asset in a portfolio carries specific risk, the investor's overall risk - the portfolio's overall volatility - is the systematic risk of the whole portfolio.
Thus risk - a portfolio's volatility - became the first factor accepted in academic circles as impacting returns.
Traditionally, beta describes a stock's volatility in relation to the market, as stated in Swedroe's paragraph above. Thus, beta is a measure of risk (volatility) representing how a security is expected to respond to general market movements.
Some stocks display more volatility than the market, others less, and some move opposite to the market. For example, suppose a stock has a beta of 0.7. That means that it tends to move 70% as much as the market and in the same direction.
Later in this article, we will see how beta is being redefined, in light of factors, to change the definition of "the market" itself.
Efficient Market Hypothesis
Professor Eugene Fama developed the efficient-market hypothesis [EMH] in the early 1960s. According to EMH, stock prices quickly reflect all publicly available information. In other words, markets are "efficient."
A corollary is that investors cannot beat the market, because the market will adjust prices instantly to account for new information.
Fama won the Nobel Prize in 2013 for EMH. According to the Nobel Prize website,
In the 1960's, Eugene Fama demonstrated that stock price movements are impossible to predict in the short term and that new information affects prices almost immediately, which means that the market is efficient…. Fama's results influenced the development of index funds.
Ironically, a co-recipient in 2013 was Robert Shiller, who believes that the market is not efficient.
In the early 1980's, however, Robert Shiller discovered that stock prices can be predicted over a longer period, such as over the course of several years. In contrast to the dominant prescription [EMH], stock prices fluctuated much more than corporate dividends. Shiller's conclusion was therefore that the market is inefficient.
It is interesting that both Fama and Shiller won Nobels at the same time for what are contradictory theories about the market's efficiency.
A debate over market efficiency continues to this day. Different flavors of market efficiency have been proposed to capture the degree of efficiency that one may expect to observe in markets. Thus, we have the Strong, Semi-Strong, and Weak theories, as explained by Investopedia. But the details behind that debate are not necessary for our overview of Modern Portfolio Theory.
Fama-French Three Factor Model
Even after CAPM, anomalies continued to show up. While some academics struggled to deny their truth, others produced studies that validated them. As Swedroe explains,
However, like all models, [CAPM] was by definition flawed or wrong. If such models were perfectly correct, they would be laws, like we have in physics. Over time, anomalies that violated the CAPM began to surface.
In 1992, Fama and fellow professor Kenneth French expanded the EMH model in their paper, "The Cross-Section of Expected Stock Returns," which added two more explanatory factors to the original single factor of risk. They found that CAPM explained only about 2/3 of the differences in returns of diversified portfolios. So they built a better model that brought the explanatory power up to 90%.
This stage in MPT's development is known as the Fama-French Three Factor Model. Per Investopedia, it is…
A factor model that expands on the capital asset pricing model…by adding size and value factors in addition to the market risk factor in CAPM. This model considers the fact that value and small cap stocks outperform [broader] markets on a regular basis.
Thus as of 1992, MPT had three academically recognized factors explaining returns:
- market volatility or risk, the original factor from CAPM,
- size, based on studies showing that smaller-cap stocks historically outperformed large-cap stocks, and
- value, based on studies showing that companies with lower price/book (P/B) ratios outperformed stocks with higher valuations.
Carhart Four Factor Model
In 1997, Mark Carhart wrote "On Persistence in Mutual Fund Performance." He added another factor to explain investment returns: Momentum. The opening lines of the paper underscore what by this time had become MPT's hard line against individual stock selection:
Persistence in mutual fund performance does not reflect superior stock-picking skill. Rather, common factors in stock returns and persistent differences in mutual fund expenses and transaction costs explain almost all of the predictability in mutual fund returns.
Stock momentum is the tendency for a stock's price to continue going in the same direction that it has been going: Stocks that have been rising tend to continue to rise, and stocks that have been falling continue to fall.
The addition of momentum brought MPT's number of explanatory factors to four.
Recent Additional Factors
There has been a veritable explosion in recent years of further studies designed to identify additional factors that explain portfolio outperformance. Not all of these studies are academic in nature, and thus they have not formally been adopted by the academic community as part of Modern Portfolio Theory. Nevertheless, they have become part of smart beta.
Here is a sampling of these factors.
In 2013, Robert Novy-Marx published "Quality Investing." In the study, he delved into "seven of the best known and most widely used notions of quality" derived from studying the work of Benjamin Graham, Jeremy Grantham, Joel Greenblatt, and others.
What he concluded is that gross profitability is the best quality indicator. He coupled this with the value factor, noting that:
[T]he real benefits of value investing accrue to investors that pay attention to both price and quality. Attention to quality, especially measured by gross profitability, helps traditional value investors distinguish bargain stocks (i.e., those that are undervalued) from value traps (i.e., those that are cheap for good reasons)….Cheap, profitable firms tend to outperform firms that are just cheap or just profitable.
A nearly simultaneous paper, "Quality Minus Junk," by Clifford S. Asness, Andrea Frazzini, and Lasse H. Pedersen (2013), defines quality by the following four factors: profitability, growing profits, safety (low volatility, leverage, and credit risk), and payout.
The latter factor is interesting, because it is the first academic recognition (that I am aware of) that dividends might be a positive factor to explain portfolio outperformance.
It is unclear whether quality has received the academic imprimatur of full acceptance into MPT. Swedroe's article says that the 4-factor model has been the standard since 1998, while the quality factors is a "recent contribution."
In the investing industry, as distinguished from academia, dividends are portrayed very positively.
The long-term benefits of dividend growth investing are well explained in "5 Simple Ways to Beat the Market, Part 4 of 5" by Ploutos, published on SA last December. In the article, the author stated that investing in companies with long track records of continuously increasing dividends has been shown to outperform on both an absolute and risk-adjusted bases over time.
This is an example of practical investing differing from academia. Academics generally maintain that dividends do not matter, and that investors should be indifferent to them. As described by Merton Miller, another Nobel Prize winner (1990, along with Markowitz and Sharpe), in "Do Dividends Really Matter?"
The academic consensus is that dividends really don't matter very much. The market does not, and should not be expected to, pay premium prices for firms adopting what are sometimes called "generous" dividend policies. If anything, generous dividends may actually cause the shares to sell at a discount because of the tax penalties on dividends as opposed to capital gains.
Miller called dividends the equivalent of an optical illusion. The opposite conclusion was expressed in "Surprise! Higher Dividends = Higher Earnings Growth" by Robert Arnott and Cliff Asness.
It is not my intention to litigate the issue here, just to note that dividend growth stocks are recognized in some quarters as generating higher total returns over time.
Perhaps surprisingly, stocks with low volatility have shown a long-term tendency to beat the market. See Ploutos' "5 Simple Ways to Beat the Market, Part 3 of 5."
The surprise, of course, is that low volatility means low risk under MPT, and as we saw earlier, risk was the very first factor identified with increased returns. Thus, the low volatility effect is still considered as an anomaly in MPT, because it runs counter to the efficient frontier discussed earlier. This quote is from Ploutos' article:
Since the groundwork behind the Modern Portfolio Theory was laid fifty years ago, it has been axiomatic that riskier portfolios should expect to be compensated with higher returns. More recent academic research has shown that this assumption holds less well at the extremes - the least risky stocks tend to outperform the most risky stocks on both a risk-adjusted and an absolute basis [emphasis in original].
Of course, readers familiar with Benjamin Graham's concept of margin of safety will have no difficulty believing that "safer" stocks may well offer better long-term returns than riskier ones.
Recently, some writers have been using beta in a different sense from the traditional definition. In the traditional definition, you will recall, beta described how a security is expected to respond to general market movements.
Recently, beta is being used to define the market itself. Under the new concept, "the market" is not necessarily a traditional collection of stocks (such as the Dow, the S&P 500, and the like). Rather, it can be an index collection built upon one or more factors. A factor-built portfolio does not convey alpha, it is said, because the portfolio itself is the market. You can't beat the market if you are the market.
The article by Larry Swedroe cited earlier is titled, "Is Outperforming the Market Alpha or Beta?" It is basically devoted to making the point that once academics have corroborated a factor, better returns resulting from that factor should no longer be called alpha. Rather, they simply result from exposure to a newly defined market. The difference between alpha and traditional beta disappears.
Why is this important? Swedroe explains:
If [active managers'] outperformance can be explained by exposure to one or more factors-also often referred to as beta, or loading on a factor-there was no actual outperformance, or alpha, on a risk-adjusted basis. If that is the case, the high fees charged by active managers can no longer be justified. Exposure to various factors can be obtained in a less expensive way through lower-cost vehicles, such as index mutual funds and exchange-traded funds.
As you can see, in that passage, Swedroe equates beta to "loading on a factor."
The drive to explain away stock-picking effectiveness has become comical. For example, it has been used to conclude that Warren Buffett is nothing special as a stock picker. He simply capitalized on known factor effects. The relevant academic study is "Buffett's Alpha," from which this quote is taken.
Berkshire Hathaway has realized a Sharpe ratio of 0.76, higher than any other stock or mutual fund with a history of more than 30 years, and Berkshire has a significant alpha to traditional risk factors. However, we find that the alpha becomes insignificant when controlling for exposures to Betting-Against-Beta and Quality-Minus-Junk factors.
In Swedroe's article, he offered this interpretation of that study.
The authors found that, in addition to benefitting from the use of cheap leverage provided by Berkshire's insurance operations, Warren Buffett bought stocks that are safe, cheap, high-quality and large….In other words, it is Warren Buffett's strategy or exposure to factors, that explains his success, not his stock-picking skills….[O]nce all the factors…are accounted for, a large part of Buffett's performance is explained and his alpha is statistically insignificant.
I will let you be the judge of that. I think it is silly, and I don't understand the need to denigrate stock picking.
Summary of MPT
Modern Portfolio Theory has evolved significantly since its academic roots in the 1950s. The evolution has largely taken place through the relentless identification of factors that help to explain returns that were formerly considered anomalous. The factors, as they have become identified, have moved ever-larger portions of observed excess returns from the realm of being unexplainable anomalies to the realm of being known and understood factors. Once that happens, excess returns based on that factor are no longer considered to be alpha.
Within academic MPT, a factor is not accepted until it has been academically verified and gained widespread approval.
Some smart beta factors have been identified by studies from outside the academic community. One illustrative factor is low volatility: Within academia, it is accepted that higher volatility leads to higher expected returns. Outside academia, the low volatility "anomaly" has been demonstrated to lead to higher expected returns.
IV. What Is the Current Status of Factors?
If we ignore the distinction between academic and non-academic origins, what do we have?
We have the fund industry, which is inventing products constantly to offer investors the chance to take advantage of smart beta factors without regard to their origin. Some products are based on single factors, while others are multi-factor stock portfolios.
The participation of fund companies injects fun into the process, because they need to market their products. To that end, they have created colorful displays to help explain the landscape.
The Evolution of Indexing
One angle to the marketing shtick is to portray smart beta as the next step forward in indexing, or Indexing 2.0. Basic indexes have been around for more than a century. Smart beta indexes are much newer, and they roam well beyond the traditional. They are not your father's indexes.
Here is how PowerShares depicts the evolution of indexing.
And here is FTSE's version:
The point is, indexes started off as ways to gauge the general market, like the Dow Jones and the S&P 500. While the traditional indexes still exist, newer indexes are a means to implement strategies based on factors. Whatever factor you want, there is an index for you. Indexes have moved far beyond the original concept of just tracking a market.
Smart Beta: Combining the Best of Active Management and Indexing
Research Affiliates was formed in 2002. More than $170 billion in assets are managed worldwide using investment strategies developed by Research Affiliates.
As with all smart beta purveyors, RA presents smart beta as combining the benefits of active management with indexing.
They state that smart beta products can help one "seek to achieve outperformance versus the market benchmark within a low-cost index chassis." Smart beta breaks the link between market prices and index weights. That is important, because:
Cap-weighted equity index funds automatically increase their exposure to stocks whose prices appreciate and reduce their exposure to stocks whose prices fall. As a result, they tend to overweight overvalued securities and underweight undervalued ones. Our research demonstrates that this built-in pattern creates a 2% return drag in developed markets and more in less efficient ones.
Morningstar analyst Mike Rawson has called strategic beta funds "index funds that make active bets."
Despite portfolio construction based upon factors, which sounds like active management, smart beta strategies are implemented via rules-based indexes, theoretically removing human judgment from the process. Of course, like fiction, this requires the willful suspension of disbelief, because humans wrote the rules in the first place.
As we saw earlier, in some hands, this process is seen as redefining the market itself.
Smart Beta Factors from the Fund Industry's Point of View
The fund industry is not constrained by academic or non-academic origins. They just want people to believe that factors are important and to buy products based upon them. So the range of smart beta factors presented by the fund industry tends to be expansive. The fund companies are competing hard to present smart beta in an understandable fashion and to convince investors to buy their products.
Here is the S&P's depiction of factors:
Morningstar, which tracks the fund industry, offers this view of what has developed:
As you can see, there is something in the smart beta index/fund world for just about everyone. By the way, Morningstar rejects the term "smart beta" on the grounds that the term wrongly conveys higher intelligence. So they call it strategic beta.
V. Smart Beta Indexes and ETFs
This is a mere overview. A complete review of the fund industry's smart beta offerings would take a book, and it would be obsolete before the ink was dry. Such is the torrid pace of smart beta developments.
So let's review several major factors through the lens of an S&P index devoted to that factor plus an ETF based upon each index. Because this article is ultimately about dividend growth investing, I will place emphasis on ETF fees and what they cost the investor in terms of income.
1. Dividend Growth
While dividend growth has not been officially adopted as a performance factor in academia, it is certainly recognized as smart beta in the investment industry. That is because companies with long track records of continuously increasing dividends have outperformed on both absolute and risk-adjusted bases over time.
An obvious example is the Dividend Aristocrats. These are stocks selected by the S&P from the S&P 500 that have increased their dividends for 25 years or more. The S&P maintains a separate index of them since 2005. The Aristocrats list has been around longer than that. Through backtesting, the S&P has determined that the index has beaten the S&P 500 by an average of more than 2% per year since 1991.
A Dividend Aristocrats ETF is approaching its 2nd birthday (NOBL). Any investor can access the Aristocrats by purchasing NOBL. But the ETF comes at a stiff expense ratio of 0.35% per year. That amount is skimmed off of your investment in a daily process. The expense ratio is scheduled to double to 0.70% at the end of September.
The ETF only yields 1.8% now, so about 16% of your potential yield is already lost to fees. After the doubling of fees, NOBL will lose more than 30% of its yield to fees, and its yield will drop below 1.5%, which is off the radar screen for many dividend growth investors.
For my own complete report on NOBL, see this article.
You will recall that size was one of the original factors in the Fama-French Three Factor Model. In Ploutos' latest article on the subject, he noted that the S&P 600 Index of small cap stocks has beaten the S&P 500 by 1.3% per year over the last 20 years, with slightly less volatility to boot.
You can access the S&P size factor through the iShares Core S&P Small-Cap ETF (IJR). It has a yield of just 1.3%, with an expense ratio of 0.12%. Again, that yield is off the radar screen for many dividend growth investors.
Value is another academically recognized Fama-French MPT factor. Value stocks have outperformed the general market, in the aggregate, over long time periods.
The S&P maintains a Pure Value Index drawn from the S&P 500. It has outperformed the S&P 500 by an average 2.9 percentage points per year since 1994, although it has done so at a "cost" of 4.2 percentage points more volatility.
The Pure Value Index identifies constituents by measures of high levels of book value, earnings, and sales to the share price. An ETF based on the Pure Value Index is Guggenheim's Pure Value S&P 500 ETF (RPV). Its expense ratio is 0.35%, and it yields about 2%, so its payout is reduced about 15% by its fees.
There are lots of ways to judge quality, and I think it is fair to say that none of them has been widely accepted academically (yet) as truly a part of MPT.
In my article "What Is Quality in a Stock?" I reviewed various factors that have been linked to quality:
- Chowder's criteria: Value Line safety ratings and credit ratings
- S&P high quality rankings
- The "new" academic quality factor discussed earlier, which covers profitability, growth, safety, and payout
The S&P has a High Quality Index, using its own proprietary methods for ranking quality. The S&P's HQ index has barely outperformed the S&P 500 itself over the past 10 years, by 0.4% per year. Other quality indexes from other companies may have better comparative records.
You can access the S&P index through the ETF (SPHQ) from PowerShares. It has a yield of 1.7% and an expense ratio of 0.29%. That means that about 14% of the yield available from the stocks is skimmed off for expenses.
5. Risk vs. Low Volatility
Despite MPT's position that risk (volatility) and reward are positively correlated, stocks with lower volatility have outperformed stocks with higher volatility. The S&P has a Low Volatility Index that tracks this anomaly/factor. Over the past 20 years, it has outperformed the S&P 500 by an average of 1.1% per year to go along with 4.7 percentage points less standard deviation.
You can access the low volatility factor by investing in the PowerShares S&P 500 Low Volatility Portfolio (SPLV). The ETF has an expense ratio of 0.25% and yield of 2.3%, so about 10% of the income from the stocks in the index is lost to fees. The relatively high yield suggests that there are many strong dividend growth stocks in the index.
6. Equal Weight and Rebalancing
The S&P 500 is cap weighted. Its equal-weight cousin, the S&P 500 Equal Weight Index, has outperformed it over the past 20 years. The cousin has averaged 1.8% better returns per year with just slightly higher volatility.
The Equal Weight Index is a smart beta index because of the weight adjustments, but not because of its components, which are the same as in the S&P 500 itself. Part of its outperformance may be based on the size factor, since the equal weight version gives more exposure to smaller companies in the S&P 500. Part of the outperformance may also come from periodic rebalancing.
If you want, you can access this factor through the Guggenheim S&P 500 Equal Weight ETF (RSP). The ETF is rebalanced quarterly to maintain the equal weights. Its yield is 1.6%. Expenses of 0.4% are skimmed off the assets, which decreases the yield of the ETF by about 25% from what is delivered to it by the stocks that it owns.
Summary of Smart Beta Index Examples
This table gives a bird's-eye view at how the factors just discussed have outperformed the S&P 500 over time, ending with the first half of 2015.
Average Outperformance per Year
Article Link (Author's Name Shown)
VI. Getting Smart Beta from Dividend Growth Stocks
Finally, I get to the destination: In my opinion, you can get many of the smart beta advantages through dividend growth stocks. You can use factors to influence your stock picking, position weighting, and portfolio management.
Let us be clear: Smart beta per se is obviously not the same as dividend growth investing.
- For one thing, smart beta factors have been developed based on studies of wide swaths of stocks. It has not been developed as the basis for picking individual stocks for small portfolios. Indeed you may not believe that smart beta is helpful in that regard.
- Furthermore, smart beta has been commercialized as the foundation for creating indexes and ETFs. A portfolio of individual stocks is not the same as an index or ETF.
- Finally, smart beta indexes are rules-based. Many dividend growth investors have guidelines for sure (here is my own business plan), but they often make judgmental decisions rather than follow rigid rules.
That said, there are ways that any dividend growth investor can apply smart beta concepts to individual stock selection, portfolio construction, and portfolio management.
First off, of course, if you are a dividend growth investor, you are by definition capitalizing on the dividend growth factor. Rather than use a commercial ETF or the Dividend Aristocrats to access this factor, I would suggest taking a look at David Fish's Dividend Champions spreadsheet [CCC].
Compared to the S&P's 52 Aristocrats, Mr. Fish identifies 106 companies that have increased dividends every year for 25+ years in a row. The longest streaks are over 60 years. He calls them Dividend Champions. The main reason that there are twice as many Champions as Aristocrats is that the Champions are not limited to stocks in the S&P 500. That means, by the way, that many more mid-cap and small-cap stocks are identified, because large size or membership in the S&P 500 is not a selection factor for CCC. (See this article for a more complete comparison between the Dividend Champions and Dividend Aristocrats.)
In addition, if one wishes to extend the factor hypothesis below 25-year streaks, the CCC also has 253 stocks with dividend increase streaks of 10-24 years (Contenders) and 375 more with streaks of 5-9 years (Challengers), for a total of 734 candidates with increased streaks of 5+ years.
You can use the CCC as well as other methods to select stocks with good values. It lists Price/Book, Price/Earnings, PEG, and Price/Sales ratios for each stock. Note that except for PEG, these are the same ratios used to construct the S&P Pure Value Index discussed earlier.
To access low volatility, see column CK on the CCC spreadsheet. It gives the 5-year beta for each stock. You can sort on that column to find stocks with the lowest betas. In my own stock grading, I have adopted beta as a scoring factor. I do not screen on it, but it is a component in how I grade dividend growth stocks. (See this article for a complete description of that portion of my grading system.)
Besides possible outperformance, there is another reason to favor stocks with lower volatility: They give you a smoother ride.
The fewer price jolts a stock gives you, the more likely you are to stick with your plan and less likely to suffer panic attacks and sell when stock prices drop. So not only have low-volatility investments performed better over long time periods than other investments, but investors are more likely to stick around and actually benefit from the superior performance.
For the individual investor, the equal weight factor tells us nothing about stock selection. But you may wish, in managing your portfolio, to equal-weight it, to take advantage of the factor. Some investors do that, and rebalance to maintain equal weights. Many other dividend growth investors neither equal-weight nor rebalance, or they follow some version of a core-and-satellite weighting system, or they weight based upon dividend amounts rather than monetary proportion of their portfolio.
I am agnostic on equal weighting and rebalancing.
Finally, beyond the CCC and similar idea sources, another way to get started on stock selection is to use the work that has gone into constructing smart beta indexes and ETFs to identify stocks as candidates for your dividend growth portfolio. You can cross-compare these index collections to see which stocks show up multiple times. They may be superior candidates.
In my opinion, dividend growth investing is already smart beta investing. That is because all dividend growth stocks display the dividend growth factor, and many are high quality and low volatility as well. In stock selection and portfolio management, any dividend growth investor may apply smart beta concepts like equal weighting, rebalancing, and the like, to further "load on" the factors.
In a future article or two, I will do some stock pickin' by applying smart beta concepts in the manners just described.
This article was written by
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