Once upon a time, there were two professors, a paper, a three-factor model, and ultimately a Nobel Prize.  1992 was a good year.  The story was simple and powerful, the effects observable to the naked eye and remarkably persistent, if you could stand painful intervals such as 95-99 when the tech boom raged, and large growth dominated. Yes, there was math involved, but even the poets could understand the fundamentals.  Returns of a portfolio (assuming broad diversification) would be driven by three factors, straightforward to measure and understand:

  1. Market – how much equity did you have?
  2. Size – how much of the equity was in small companies?
  3. Value – how much of the equity was in companies with low prices?

Dimensions of Expected Returns Chart

1. Relative price as measured by the price-to-book ratio; value stocks are those with lower price-to-book ratios.
​ 2. Profitability is a measure of current profitability, based on information from individual companies’ income statements.

A discussion of company size was easy enough.  Discussing value measures and explaining why book value was used instead of earnings – p/b instead of p/e – was not rocket science either.  And for those adventurous enough to take the next step into the math, understand how you could measure the explanatory power of each variable was not too daunting.  Even T stats can be made simple enough, with a little patience.  And isn’t it interesting to see the explanatory power of the other variables disappear as the most important variables are introduced into the equation?  Like magic.

The end of understanding and happiness

Well, DFA just could not leave well enough alone.  First, they started discovering a variety of what we might call ancillary factors, things which were not fundamental to portfolio construction, but which did affect returns.  For example:

  • Trading costs – as a passive manager, DFA obviously sought to minimize turnover and trading costs, but they also learned how to “make markets” in stocks where a significant bid/ask spread could be captured with minimal tracking error introduced.
  • Momentum – this pattern of trending in the prices of individual securities affected the outcomes of trading activity, as securities sometimes continued the movement that triggered the need to trade. Trading in a rigid manner could cost you return, but patience could be rewarded with better pricing in buying and selling.
  • Signals from securities lending – it appeared that short sellers sometimes had useful information, those stocks seem to decline, on average, when the premium paid to borrow them rises to a high level, indicating major interest in short selling.

These factors can be exploited by passive[1] strategies.  Not having to track a rigid benchmark means being able to buy or sell particular stocks when the other party is willing to pay more or accept less than the current price, and it means not having to immediately buy or sell a security whose size or value metrics cross a rigidly defined boundary.  And when a stock goes “on special” because the short sellers are selling it, it means refraining from buying more (rather than dumping the shares held).  For all these factors, it appears that active trading costs more than the value that can be added but using the information more passively can create enhanced returns.

But it got worse – they discovered profitability, the fourth factor

The coup de grace for the poets came when DFA announced they were adding another factor to their portfolio construction toolkit.  No longer satisfied with tilting the equities towards small company stocks and stocks with low prices (defined by book value compared to stock price), they now wanted to overweight a factor called profitability. At first blush, this seemed sensible.  In fact, it seemed obvious.  More profitable companies were better to invest in, more profits mean better returns, right?  This was even called “growth” a few times, and that is where the train went off the rails.  Growth?  Wait, we thought unloved companies with low prices outperformed growth stocks.  Aren’t growth stocks the high-flying companies with fast-growing earnings we have always heard about?  How does this make any sense? The answer, unfortunately for the poets among us, lies in the math, and the basic statistics which illustrate how this factor interacts with the others.  Seems like there is no way around it now, time to dig into the formulas and try to get the meaning of the statistical proofs we struggle to understand… but wait!

Maybe there is an easier way to think about this…

We were using just price and book value, two variables used to assess how cheap or expensive a stock is.  Now we see that a third variable has useful information; earnings data also tells us something, but how best to extract that information?

The Simple Answer

For starters, think of these two factors, value (based on p/b) and profitability, as height and weight.  If you have been evaluating health just using weight, you failed to take account of the obvious fact that taller people can weigh more and still be healthy.  Stronger earnings (height) can support a higher valuation (weight) and vice versa.  Pretty simple! To measure profitability, we chose “earnings to book value” as the most useful ratio to use.  We add earnings to enhance the “explanatory power” of the model, and the reasons make sense if you think about it.  It turns out that earnings tend to be persistent, companies with stronger earnings today tend to continue to have stronger earnings in the future, and to outperform similar companies with less robust earnings. But price always matters, if you overpay you will underperform.  And so, you might expect that the best measure would be price to earnings, which much of the market follows, but that is not the case.  It is close, but book value works better.  Why? First, recognize that we have captured book and price (factors) already, so adding earnings to the mix either way will capture the same information.  But price is more volatile, contains more statistical noise, whereas book value tends to be more stable and reflects current book value plus net new investment.  It is easier to see strong, sustained profitability when using book value, you see a fairly stable ratio without all the fluctuations driven by price changes.

Summary of the two factors:

  1. Earnings to book value tells us about what we might call the “core profitability” of the business, completely independent of the price of the stock. A stock with more earnings (relative to its book value) is generally a better business and is worth more.
  2. Price to book value tells us whether a stock price is high or low.

When we put these two together, we see why we are willing to pay a higher price (as measured by p/b) for a stock that is more valuable (based on earnings to book), and vice versa.  This is more and better information than we had before, when we simply bought low priced stocks as if they were all the same.

In the end, you have two things to consider:

  • how is the stock priced as a multiple of book value? Is it cheap or expensive?
  • how good is its profitability as a function of book value?

A low-priced stock with good profitability is best, obviously.  And you can consider the tradeoffs more easily now, better profitability can justify a somewhat higher price (a tall person can weigh more).  And the opposite is also true, poor profitability calls for an even lower price to make a stock attractive. The model now creates a group of value stocks which is adjusted for profitability, and the statistical shape of that group is moved in the direction of somewhat higher price to book overall, with better expected returns.

The Big Picture – three variables require only two ratios:

Book Value Chart

So, what does this all mean to me?

Funny you should ask at this moment in time.  As of April 2020, we have seen a long period of underperformance by the value and size factors, as large growth has dominated the headlines and the returns.  As we have discussed many times, the five largest publicly-traded companies in the S&P 500 and the world are now Microsoft, Apple, Google, Amazon and Facebook.  A lot has happened during this period, big things. As you can see in the accompanying charts, value and size are factors that have added value over the long term, but not for every period.  Even for some 10-year periods, they can underperform.  Profitability is a different factor, and often behaves independently, thus not only providing incremental return but incremental diversification.  And during this period of value and size underperformance, profitability has performed well.

Historical Observations of 10 Year Period

Disclosure: Information provided by Dimensional Fund Advisors LP. Past performance is no guarantee of future results. Actual returns may be lower. In USD. Indices are not available for direct investment. Index returns are not representative of actual portfolios and do not reflect costs and fees associated with an actual investment. 10-year premiums are calculated as the difference in annualized 10-year returns between the two indices described. High Prof minus Low Prof: Fama/French US High Profitability Index minus the Fama/French US Low Profitability Index. See “Index Descriptions” in the appendix for descriptions of Fama/French index data.

Bottom line is that a globally diversified equity portfolio with extra exposure to value, size and profitability factors has outperformed the broad market in the long run and mitigated the risk of underperformance over shorter timeframes. The benefits of intelligent, patient trading make it even better. We believe this constitutes best practice for the long-term investor, a strategy you can live with, in good times and bad. Discipline and consistency are the keys to success, when all is said and done.

 

Team Hewins, LLC (“Team Hewins”) is an SEC registered investment adviser; however, such registration does not imply a certain level of skill or training and no inference to the contrary should be made. The information contained within this letter is for informational purposes only and should not be considered investment advice or a recommendation to buy or sell any types of securities. Past performance is not a guarantee of future returns. It should not be assumed that diversification protects a portfolio from loss or that the diversification in a portfolio will produce profitable results. The opinions stated herein are as of the date of this letter and are subject to change. The information contained within this letter is compiled from sources Team Hewins believes to be reliable, but we cannot guarantee accuracy. We provide this information with the understanding that we are not engaged in rendering legal, accounting, or tax services. We recommend that all investors seek out the services of competent professionals in any of the aforementioned areas. For detailed information about our services and fees, please read our Form ADV Part 2A, which can be found at https://www.advisorinfo.sec.gov or you can call us and request a copy at (650) 620-3040.

 

[1] “Passive” in this context refers to equity management strategies that seek broad market exposures without attempting to time the market or select individual stocks.  In contrast to that, active managers generally seek to outperform based on the belief that they have better knowledge than the market and can exploit “inefficiencies.”  Index funds are one type of passive strategy, but there are others as well.

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