7.1 Stock diversification


This article addresses so called “within asset class” diversification for the most common investment class of stocks.  First I discuss how to diversify, and then I evaluate the degree of needed diversification within stock holdings, all from a mindful perspective.

How to diversify (individual stocks vs. stock funds)

Should you invest in individual stocks or funds that include an array of individual stocks?  For example, if you decide you need exposure to stocks from both the U.S. and foreign countries, you could buy stock in:

  • A large well-known U.S. company
  • A foreign developed country large company (like a company based in Britain, Germany, or Japan)
  • A foreign emerging countries large company (like a company based in China).

This scheme would provide some diversification of your stock portfolio across countries and markets (developed and emerging markets).  However, it would not provide any diversification across company size, given these are all large companies.  It also does not necessarily provide any diversification across the stock sectors listed in Article 7.  Even if the three companies picked are in different sectors, it still leaves your portfolio with no representation from many stock sectors.  Finally, this scheme may or may not provide diversification of style such as growth vs. value stocks.   In fact, to get some diversification across all these areas, you would have to pick many more individual company stocks.  Then you’d have to decide what proportion of your money to allocate to each of these individual stock purchases.

Alternatively, you could invest in a series of stock funds that track the performance of a basket of companies.  There are two major types of funds: mutual funds and exchange traded funds (or ETFs).  For right now, we don’t need to get into the differences between these fund types.  For an investing scheme similar to the one above, you could invest in a series of ETFs instead such as:

  • U.S. S&P 500 ETF like the one going by the ticker symbol of SPY.  This ETF tracks the performance of a basket of stocks representing all 500 companies in the S&P 500, which are all large U.S. companies.
  • A developed countries ETF like VEA, which tracks performance of mostly large companies from Japan, Britain, European countries, Australia, South Korea, and Canada (among others)
  • An emerging countries ETF like VWO, which tracks performance of mostly large companies from China, Taiwan, India, South Africa, Brazil, Mexico, Russia, and many more countries.

It might seem reasonable to assume that these two schemes would accomplish about the same investment objectives, but the actual results in terms of returns and risks would likely be extremely different.  The second scheme using ETFs produces tremendously more diversification not only by country and market, but also by sector and style, and to a lesser extent by size too.  This can all be accomplished without having to pick potentially dozens or even hundreds of individual company stocks.

Lessons learned – Outside of the hassle factor of buying individual stocks, the above comparison does not answer the question why using funds is necessarily a better approach.  My own experience in stock investing over the last 20 years provides a good example of the key differences between individual stock picking vs. fund picking.

Much of the popular investment advice around the turn of the century suggested picking individual stocks was a prudent way to invest.  In retrospect, I think this popular advice was mostly driven by the tremendous bull market of the 1990s, which as I have already noted, stimulated in many investors great overconfidence in their own stock picking prowess.  Common advice of the day was to invest in companies you know and like.  After all, if you expect to continue purchasing goods and services from those companies, millions of other people will too, and those companies will continue to grow.  Another school of thought was about picking the next technological disruption, whether it was biotechnology, internet commerce, solar power, computing and networking developments, or thousands of other ideas that looked like “sure fire” technological advances at the time.  Many other stock picking methods were also promoted.

I conducted a personal experiment starting in 2001. I had a retirement account from a past job, and I did not “roll it over” (combine it) into my new retirement account with my new job.  I decided I would use this account to pursue the best returns I could by picking individual stocks.  I kept a detailed record of each stock I bought, its performance while I held it, and its net return when I sold it.

Following the popular advice of the day, I extensively researched many individual companies from all sorts of investing angles.  I vetted, purchased, and sold individual stocks through this one account for about 15 years based on various “investment theses”.  I learned a lot about how to read a quarterly statement and assess the health and prospects of a company in general.  I steered clear of many potential stock purchases, because I often couldn’t verify the original investment thesis.  Over time I invested in what I thought were the “cream of the crop” from my various investigations.  I attempted to get some diversification along the way by varying my picks from high-flying technology growth companies to mundane value companies like utilities.  I also owned U.S., developed country (mostly Europe), and a few emerging market stocks (mostly China).

So, how did I do?  Here are some overall statistics from the period 2001 to 2015:

  • Annualized return of the S&P 500 (with regular dividend reinvestment) = 4.9%
  • Karl’s stock picks annualized return (including dividends) = 6.0%
  • Number of picks underperforming S&P 500 = about 31
  • Number of picks outperforming S&P 500 = 2
  • Number of picks going bankrupt or similar = 2

Based on the annualized returns in the first two statistics here, these results would seem to endorse individual stock picking rather than investing in something mundane like an S&P 500 index fund.  But I need to point out a few details.  For one, notice how the vast majority of my “cream of the crop” stock picks actually underperformed a simple index fund.  And two of the “growth” companies that seemed to have great potential at the time no longer even exist; one went bankrupt and the other was bought out at very low value by another company.

So, my overall return was driven mostly by one high-flying technology stock that actually fulfilled the growth prospect of the original investment thesis. At the time, I thought it was a brilliant pick.  In retrospect, I think it was mostly luck that at least one of my picks ended up paying off well.  If you take that one stock away, my total returns would have been less than the easy returns available from an S&P 500 index fund.  The main reason I profited from the high flier stock was that I held on to my shares thinking my original investment thesis was intact, and I even bought additional shares when prices seemed particularly low.  Of course, at any given time it was very challenging to confirm the ongoing validity of the thesis and decide when prices were truly “low”.

I am sure some readers will think they could have done better, or actually did better, in this period.  However, I think there are just as many individual investors out there who would have done even worse.  For example, the average individual investor only got a 2.3% annual return from 1997 to 2016, which includes 3 years of the late 1990s tech bubble market.  The graph below from JP Morgan Asset Management compares this meager average investor return to other options during the same period.


This experience taught me that there are many more things that can go wrong with individual stock picks than can go right.  Here is a just a brief list of things that went wrong with some of my stock picks including some of the “safer” ones:

  • One very large “safe” pharmaceutical company defended a class action law suit that depressed the stock by 72% at one point.
  • My very large “safe” bank stock ran right into the 2008 economic crises and still has not fully recovered.  Although I sold it some time ago, it still remains about 30% below my first purchase price.
  • My high-flying growth stock was a nauseating roller coaster:
    • Starting at $14 per share in 2001
    • Rising to $64 in 2006
    • Declining back down to $6 in 2008 (where I bought additional shares)
    • Rising back up to $50 in 2012
    • Jumping around a bit and then rising to $107 in 2014.
  • A large natural gas company that grew in value but also suffered the ups and downs of natural gas prices.  Its price growth was surpassing the S&P 500 by over 100% as recently as 2014, but it’s now substantially underperforming the same index.
  • A couple of 1990s technology juggernauts that could simply not keep pace with technological innovations by newer companies in the 2000s.

In my experience, if you buy and hold an individual stock, you should pretty much expect the stock to plunge precipitously for one reason or another at some point during your ownership.  And these company-specific plunges are often in addition to the broader market volatility that all stocks or stock funds routinely suffer.  Whether your stock ever fully recovers again is another question.  In comparison, funds help moderate these individual company disasters.  If you own a fund of 500 companies and a few of them have lawsuits, crises, or bankruptcies, the net impact on your overall fund value is relatively small.

I also found that it was actually pretty hard to exploit a technological breakthrough with a specific stock investment.  For example, I was sure in 2001 that everyone would soon be throwing out their old analog TVs in favor of new digital and high-definition TVs, and I was right.  However, the company I invested in diluted its digital TV profits with other business lines that did not perform as well.  Although this turned out to not be a loser stock, it underperformed the S&P 500.

Difficulty of stock picking – If you think my experience just reveals dumb rookie mistakes, I’d forgive you.  But my personal experience is echoed by wider evidence about the stock market and individual investor performance.  One piece of evidence I already covered is the dismal 2.3% annual return of the average individual investor from the J&P Morgan graph above, as compared to the 7.7% annual return of the S&P 500 over the same period.

Strong evidence is also provided by examining the performance of all individual stocks as compared to the overall stock market. Longboard Funds made such a comparison using the U.S. market from 1989 to 2015, which is summarized in this graph.


In this period, about 80% of all stocks collectively had a total return of 0%.  So, all the gains were a result of just 20% of the best performing stocks.  And the shape of the right end of this curve shows that just a relatively few stocks are responsible for most of the collective return.  If you look at longer periods, these statistics get even more scary.  For example, Hendrik Bessembinder conducted a study looking at stocks from 1926 to 2015 and found that 0.33% of the stocks accounted for over half the total wealth created by the stock market.  And over this period, less than half (42%) of the stocks were able to outperform simply holding short-term T-bills.  All this means that your chances of randomly picking stocks with substantial gains are likely no better 1-in-5.  And picking the very best stocks is probably more like a 1-in-10 proposition.  So, you will need truly exceptional stock picking skills to sort the wheat from all that chaff.

Other studies have similarly shown the difficulty of picking individual stocks.  Terry Odean at the University of California, Berkeley conducted studies that reviewed thousands of individual investor accounts and identified instances where the investor sold one stock and soon afterward bought another stock.  The presumption is that the individual investor was expecting the newly bought stock to perform better than the recently sold stock.  On average, the opposite was true.  Bought stocks performed 3.3 percentage points per year worse than sold stocks, as measured by price changes after the swap was made.  Some individuals did better than this average and some did much worse.  Regardless, the large majority of individual investors examined would have been much better off by simply ignoring their own stock ideas.

The utility of stock funds – By now it should be pretty apparent that it’s much easier, less risky, and generally results in better returns, when the individual invests in stock funds rather than specific stocks.  As I noted in Article 4.3, the cost of funds, which are managed by professionals for a fee, can take a substantial bite out of your investment performance.  However, this cost pales in comparison to the large performance gap between most individual investors and the simple strategy of using index funds.  The cost of most index funds is well below 0.25%, while the Morgan Stanley graph above shows the average investor annual return was 5.4% less than the S&P 500 index.  Also, because of trading fees, buying and selling individual stocks is not free either, even if you use a low-cost self-service brokerage account.  Further, as I discussed in Article 4.3, a close examination of the costs and tax burdens of prospective funds can help minimize eroding your overall return to the minimum necessary, while achieving much greater diversification.

Mutual Funds or ETFs – I mentioned above that there are two main types of funds: ETFs and mutual funds.  Mutual funds have been around for a long time and are the traditional way of investing in baskets of stocks or bonds.  ETFs are a relatively recent development and have been slowly taking over much of the mutual fund business because they are highly liquid (can usually be traded almost instantly), don’t have minimum buy-in amounts like many mutual funds, and often have lower costs (although not always).  For our purposes, I would say there are no strong advantages of either ETFs or mutual funds.  Per Article 4.3, the key is to pick a fund that meets the needed function within your portfolio and that has the lowest possible cost and tax burden.

Stock diversification within the asset class

In Article 7 I presented a table showing the major types of possible diversification within the asset class of stocks including:

  • Size
  • Geography
  • Sector
  • Style

There are two major reasons that diversification within stock holdings across these stock types is typically recommended.  One reason focuses on reducing risk in the form of volatility and the other focuses on boosting return performance.  Some people also point to specific situations where stock diversification has been shown to reduce or maintain volatility while increasing returns of your overall portfolio to a certain degree.  In the following sections I’ll cover these three rationales for stock diversification: risk reduction, return enhancement, and obtaining a better balance of risks and returns.

Stock diversification to reduce risk

The conventional wisdom is that you should diversify your stock holdings across pretty much all stock types.  This is based on the general observation that when some sizes, geographical regions, sectors, or style of stocks do well; others do poorly.  The story goes that spreading your investments across all these stock types is more feasible than attempting to pick certain stock types that will perform better or decline less in the future.  The assumption is that this diversification will decrease your stock portfolio’s risk or volatility (because when one area is up, another may be down), while at the same time providing better returns than bonds.

Stock correlations – Can we validate the proposition that stock diversification decreases stock portfolio volatility and risk?  I first want to examine the idea that when some types of stocks do well others do poorly.  The movements of various types of stocks can be compared to see when prices move together or not using “correlation” analysis.  Because I will present quite a few correlations in this article, I offer a few fundamentals about them:

  • The amount of correlation is measured by an “R-squared” value that varies in between -1 (price movements are exactly opposite of each other) to 1 (identical price movements).  A value of zero means that the price movements of the two types of stocks are completely independent of each other (essentially random).  The terms for these three situations are negative correlation, positive correlation, and uncorrelated.
  • As Rick Ferri points out  in “All About Asset Allocation” the idea of consistently negatively correlated stocks (or most other assets for that matter) is a fantasy.  Although for periods some assets may act this way, there are no real assets that increase in value and have consistent negative correlations over long periods.
  • Just like the price movements themselves, the correlations between stocks are not static.  They change over time due to varying market and economic conditions.  Here again, we need to remember that history is no guarantee of the future, and correlation relationships between asset classes that have existed for many years have been known to quickly disappear, often for unclear reasons.

The graph below from a Forbes article by Rick Ferri shows a good example, where stocks and government bonds were moderately positively correlated from about 1966 to 2000 and have been somewhat negatively correlated ever since.

As a result, Rick Ferri also points out that it’s important to look at the changes in correlations over time.  The overall R-squared value for the period in the example graph above is +0.07, but the graph shows that the correlation relationship can shift markedly in just a few years such that holding a combination of uncorrelated assets does not necessarily guarantee volatility reduction even in the short-term future.

Good long-term correlation time series graphs are a little hard to come by on the internet.  So, the correlation graphs presented here cover many different time periods and are generally biased towards more recent history.  We’ll have to take that into account as we mindfully review them.  Armed with a better understanding of correlation, let’s look at the correlations between some different stock types.

Size is measured by “capitalization” (cap for short), or how much all the company’s stock is worth on the open market.  Here is a graph comparing U.S. large, mid, and small cap stocks (as well as international stocks) from Quaker Funds.


Recent history is pretty clear that there is a very large positive correlation between the price movements of large, mid, and small cap stocks.  Since 2000, the correlation between small caps and large caps has ranged from slightly greater than +0.6 to, more recently, above +0.9.  The correlation between mid cap and large cap has been even higher.  So, the vast majority of the time, large cap stock price declines are mimicked pretty closely in timing and severity by mid and small cap stocks.  So, the hypothesis that volatility would be minimized by diversifying stocks across company size appears weak.

Geography is also often assumed to be a potential diversifier.  The Quaker Funds Graph above includes an international stock index (MSCI EAFE), which again shows a fairly consistent and high positive correlation with U.S. stocks as represented by the S&P 500.  Here is a similar graph from a presentation by Jeremy Siegel, author ofStocks for the Long Run”  that covers a longer period.


“EAFE” in this graph represents stocks from foreign developed markets, and “EM” represents stocks from emerging markets.  The correlations of these two types of stocks with the S&P 500 over the past few decades have ranged between about +0.4 to above +0.8, with the higher correlations occurring more recently.

Several researchers have warned that the correlations of global stock movements show an increasing trend.  Quinn and Voth present this graph of global stock correlations over the last 100 years, which aggregates overall stock correlations among 120 different countries.


And here is a graph showing a similar finding from J.P. Morgan that compares the correlations of Developed Markets (DM) to Emerging Markets (EM) and between different Emerging Market Countries.

All of these findings suggest that the timing and severity of price movements across geographically diverse stock holdings are relatively similar and are becoming ever more correlated.  So, you won’t necessarily dampen your stock portfolio volatility much because you hold a mixture of foreign and domestic stocks.

Sector type is the next potential area of diversification.  Here is a graph, again from J.P. Morgan, showing correlations between S&P 500 sectors and between individual S&P 500 stocks.

Again, the overall conclusion is that there’s a pretty high correlation between various stock market sectors, and if anything, those correlations have been increasing in recent history.

Stock “Style” can be characterized numerous ways, but many people focus on the dichotomy of “value” stocks vs. “growth” stocks.  The concept is that some stocks tend to be typically undervalued, have slower growing stock prices, and usually higher dividend yields.  These are so called “value” stocks.  Other stocks tend to have quick and sporadic price growth and pay little or no dividend, which are so called “growth” stocks.  Putting aside the rather difficult problem of deciding how each company’s stock falls neatly into one category or another, we can use the value vs. growth example to evaluate the potential effectiveness of diversification across stocks of different “styles”.  Unfortunately, I’ve found few good long-term analyses on the internet comparing these two particular stock styles.  Consequently, I’ll confine the evaluation of stock styles to very recent history (since about 2007), which is the subject of the next subsection.

Recent changes in stock correlations – With one exception, all of the above correlation analyses run from no earlier than 1965 to no later than 2015.  However, in 2017 I started to come across news articles reporting that stock correlations have recently and substantially declined.  Consequently, I took a closer look at the recent history of stock correlations to see if some long-term trends appear to be changing.  I posted about this in November of 2017, and therefore, the information immediately below dates to that time.

To obtain consistent information on recent correlation changes across all the above stock types, I examined 19 index ETFs using the Asset Correlation tool in Portfolio Visualizer.  This tool provides only relatively recent correlations back to 2007.   I selected the longest correlation duration available from the tool (90-day rolling correlations).  I also selected daily return correlations (instead of monthly or annual), because the tool provides a nice output graph based on daily return correlations.  Here’s a matrix of the most recent 90-day rolling correlations (November 2017) for all possible pairings of the 19 ETFs I used to present all the stock types discussed above including value and growth.  (Click on the image to enlarge.)

You’ll note a few patterns.  Specifically, correlations in the upper left portion of the matrix are still pretty high (generally above 0.5).  These high correlations are all between indices for U.S. value, growth, and size as well as between foreign and domestic markets.  However, the individual U.S. stock sectors in the bottom half of the matrix exhibit some pretty low and even negative correlations with many other indices.  The U.S. sectors with the lowest correlations are consumer staples, REIT (real estate investment trusts), telecommunications, energy, and utilities, which is a notably good outlier.

As I pointed out above, correlations are dynamic over time.  So, it’s important to see how these most recent correlations compare to recent history.  Have correlations in fact decreased recently as some news articles report?  After examining the time graphs output by the Portfolio Visualizer tool for all these comparisons, I can report that almost all recent correlations are substantially lower than they were just one or two years prior to this analysis (back to 2015).  Because there are too many correlation graphs to present here, I picked out 12 examples of this pervasive downward trend.  (For a detailed view, hover over the graphic and click on the enlarge  button.)

The downward trends seen in these examples are echoed in almost every correlation chart I generated.  In some cases, the downward trend started as early as 2012, while in others, decreases have only been observed since about 2016.  For variations in value/growth, size, or geography shown in the top two rows of graphs, the correlations are still pretty high.  Accordingly, the correlation declines have been relatively recent and small for these stock types.  However, for the U.S. stock sectors shown in the bottom two rows of graphs, the correlations have trended downward for many years and have declined substantially.  The most extreme case is shown on the bottom right graph, where the utilities and energy sectors went from a near perfect correlation in 2011 to a somewhat negative correlation in 2017.  Consequently, it appears that some of the long-term trends towards high stock correlations are starting to breakdown, at least between some stock types.

Stock volatility vs. risk – Despite some historically high correlations, past research has claimed that mixtures of stock types still reduce portfolio volatility to some degree.  As I discussed in Article 6.1, volatility is not a very good measure of actual investor risk.  Many people touting optimal mixtures of stock types focus on demonstrating relatively small decreases in standard deviations (the typical measure of volatility) over many years.  However, reduction in the upward movements of prices is not generally a logical investment strategy.  Further, reductions in small routine up and down price movements of stock prices over long periods is nice, but most individual investors are specifically seeking to reduce the risk associated with dramatic stock declines during stock market or economic crises, which often transpire in just a few days, weeks, or months.  And once those declines are over, it may take years for stock prices to recover, which may substantially undermine your long-term investing goals.

To focus on the dramatic declines question, I developed some very simple example stock portfolios and evaluated their performance during recent dramatic declines in the stock market.  My example portfolios are drawn from a large cap U.S. S&P 500 index ETF (SPY), a U.S. small cap ETF (VB), a developed markets mostly large cap ETF (VEA), and an emerging markets ETF (VWO).  Here is the percent price decline or “maximum drawdown” from 2007 to 2009 for the individual ETFs and a portfolio composed of an equal mix of the same four ETFs:

  • All SPY: -48%
  • All VB: -49%
  • All VEA: -54%
  • All VWO: -58%
  • Equal mix of all four: -52%

These statistics are provided by Portfolio Visualizer.  We don’t see any reduction in maximum drawdown with diversification across stock types.  Perhaps 2008 is not a fair test because of the dramatic and global nature of that financial crisis.  If we look at a more routine stock decline event that occurred in 2011, we see a very similar picture:

  • All SPY: -21%
  • All VB: -25%
  • All VEA: -24%
  • All VWO: -29%
  • Equal mix of all four: -24%

In both cases, stock diversification did not decrease overall declines measured over several months as compared to simply holding the S&P 500 ETF (SPY).  That’s a critical point.  In the last two most recent major stock market declines, holding an undiversified stock portfolio did better than an apparently more diversified portfolio.  Clearly, you have to be careful with the standard wisdom of diversification, because it may not be reducing risk in the way you imagined or when you most hoped it would come to your rescue.

These examples also illustrate that price declines across stock types can be of a relatively similar magnitude during any given event.  In these situations, no conceivable mixture of stocks will provide especially dampened stock portfolio declines.  While some people may be cheered by a -21% decline in their stock value as opposed to a -29% decline, personally, I don’t think I would be particularly soothed by this small “improvement”, regardless of how mindfully I viewed the situation.

Stock diversification to boost returns

The other cited benefit of stock diversification is boosting return performance without substantially raising volatility.  This benefit is often demonstrated by using past information on historical returns and volatility of different stock types to “back-test” various potential stock diversification schemes and fine-tune an overall stock mix that is intended to outperform the balance of risks and returns provided by more simple approaches like buying an S&P 500 index fund.  An example of this approach is discussed here.

We can evaluate this back-testing approach by considering some specific cases.  For example, one of the most commonly held beliefs is that, over time, small cap stocks outperform large cap stocks.  The inventor of the index fund, John Bogel, had much to say on the question of diversification to boost stock portfolio performance.  Here is a Bogel graph comparing the historical return of small cap and large cap stocks.

Taken at face value, this graph suggests that small cap stocks consistently provide better return than large cap stocks, albeit with high volatility (standard deviation in the graph).  As we have seen in previous articles small percent differences in average annual returns can cause huge differences in investment growth when projected over long periods.  So, adding some small cap stocks to your stock portfolio appears to be a sure-fire way to boost your long-term investment performance.

But Bogel points out several problems with this type of historical data mining, using small caps as an example.  For one, if you look at interim periods, the outperformance of small cap stocks is not so clear.  During certain segments of this period large cap actually outperforms small cap returns.  And from 1945 through 2001 the return provided by large cap stocks and small cap stocks have been virtually identical (12.7% vs. 13.3% annualized return). He summarizes the back and forth between large and small-cap returns using a chart like this one from Wisdom Tree.  The chart shows the cumulative returns of small cap stocks divided by large cap stocks.

At first glance, this chart would seem to further support that small caps are better, because the ratio of small cap over large cap cumulative return is almost always above one, but this is because “cumulative” returns are being compared here.  The more important point from these types of charts is that for long periods the line is going down instead of up, as indicated by green shading in the chart.  This means that during these green periods large caps are actually outperforming small caps.  Based on the historical back and forth of small and large cap performance, Bogel questions whether the evidence of consistent and long-term outperformance of one size of stock over another is real, and more importantly predictable for the future.  Bogel points out additional uncertainties that make this type of relationship difficult to validate using historical data.  Given all these uncertainties, if the alleged long-term advantage reflected in the historical data in fact materializes, your ability to capture that advantage in any given future period is questionable due to changes in correlations, changes in volatility, and costs.

We could make similar examinations across the other types of stocks as well, but much like the correlation data, the overall findings are the same: it is difficult to show consistent and predictable outperformance by one type of stock over another.  Just to back up that claim, here is another example from Capital Group based on geography showing that international stocks have historically varied between outperforming and underperforming U.S. stocks.

Here is another graph from Credit Suisse comparing emerging markets to developed markets.  It shows that neither of these markets consistently outperforms the other.


In both cases, it’s hard to argue that consistently superior returns can be gained by adding certain geographical areas to your stock portfolio.  Regardless of what stock types you consider, because diversification is about putting eggs in multiple baskets, placing only a small percentage of your stock allocation in one stock type or another will substantially dilute any superior performance provided by the stock type, if and when that better performance ever materializes.

Balance of stock risks and returns

If stock diversification cannot consistently provide reduction in the kind of risks we most care about (dramatic declines) or consistently boost returns, why is it so popular?  Part of the answer lays in general uncertainty about the future.  It’s essentially impossible to predict whether small cap, large cap, emerging market, or developed market stocks (or other types) are most likely to outperform or seesaw back and forth over the next 20 or 30 years.  Further, because correlations change over time, it’s also impossible to predict how any given mix of stock types in a portfolio might perform over the next time span.  Many point out that this unpredictability alone can be sufficient reason to diversify across stock types.  This simple argument has a mindful humility at its center.  If you don’t know which tomato variety will work well in your climate and garden soil, and you can’t easily predict whether this will be a hot or cold summer, why not plant several different varieties in hopes of a good harvest from at least a few plants?

A more complex reason for stock diversification involves balancing the risks and returns.  As noted above, if I want to boost my stock returns, I can take a gamble that small caps will continue to outperform over the long term and buy only small cap stocks.  But an important question is whether that gamble is justified by the 9% increase in volatility and whether that increased volatility will transmit meaningful additional risks to my portfolio.  As discussed in Article 6.1, to evaluate the balance of risks and returns, it helps to plot each asset (in this case stock type) on a cross plot.  This graph uses data from 1972 (except mid cap, which is from 1984) to 2015 from “Portfolio Visualizer” to plot an array of popular stock types using volatility vs. annual return (as compound annual growth rate).


At least for this relatively short period, U.S. mid cap value stocks were the clear winner, producing nearly 13% annual returns with a volatility under 20% (the second lowest volatility on the chart).  So, what if we take that U.S. mid cap value strategy and balance it with some emerging market stocks to try to boost our returns?  To answer this question we can place mixes of these stock types on our cross plot of historical return versus risk to develop the graph below, again using “Portfolio Visualizer”.

The resulting curve is typical of such evaluations and is unhelpfully termed an “Efficient Frontier” for reasons I won’t detail here.  The “Tangency Portfolio” noted in the graph is the theoretical point at which you can maximize return without taking on substantial additional volatility as compared to holding 100% mid cap value stocks.  For comparison, here are the volatilities and returns of the two 100% portfolios compared to the Tangency Portfolio:

Portfolio Volatility Return
100% Mid Cap Value (MCV) 18.07% 14.29%
100% Emerging Markets (EM) 34.40% 19.45%
Tangency (82% MCV, 18% EM) 18.59% 15.23%

Here is where we start to see some specific potential benefit from diversification.  By adding a bit of historically highly volatile and high performing emerging market stocks, we can hope to boost the return by almost 1% while hardly increasing the volatility.  As I discussed in Articles 6.1 and 6.2, small percent differences in returns, when compounded over long periods, create big differences in the total value of your portfolio.  Here is an example of the differences in growth of portfolio values over the same time period.


In this case, Portfolio 1 is the 100% mid cap value portfolio and Portfolio 2 is the Tangency Portfolio with 18% emerging market stocks added.  Assuming a $10,000 initial investment value, the ending portfolio values are $411,000 vs. $515,000, respectively.  In this particular time period, the more diversified portfolio would have provided an added $104,000 to the account value, which is 10 times the initial investment.  And the diversified portfolio created very little additional volatility along the way given that the two lines move in pretty close approximation of one another.

However, all of this analysis is still based on historical data from one window in time, and the future is unlikely to repeat this particular segment of history exactly.  Consequently, we can’t depend on getting these same relative performance results in the future with these two particular stock portfolios.  For example, using the same set up and calculator, here is the performance of these same two portfolios over the somewhat tumultuous period of time I have been actively investing (2000 to 2015).

Suddenly, the 100% mid-cap value portfolio (Portfolio 1) is looking a whole lot better, and actually outperformed the Tangency Portfolio (Portfolio 2) that was “enhanced” with some emerging market stocks.  Regardless, this analysis tells us that through diversification, we have the potential to maintain or even reduce our overall stock portfolio volatility while bumping up our rate of return moderately.  It also tells us that eventually, further optimization of stock mixes over various back test periods becomes a zero sum game.  At some point in the process the uncertainties associated with future events dwarfs the probability of potential incremental increases in performance created by very detailed optimization of portfolios.

This brings us back to the simpler more humble reason for stock diversification: general uncertainty about the future.  Having some amount of diversification helps buffer our portfolios against unpredictable changes in stock correlations, the economy, markets, and world events, up to a point.  I can buy every variety of tomato plant available at my local garden center, but planting 20 varieties does not guarantee better overall harvests as compared to planting just 5 varieties.  The weather that actually materializes each summer may be a much more important factor determining my “annual average” harvests than a highly optimized and diversified plant selection.  My diversified garden may do very well this year during a hot dry summer and may do very poorly next year during a cold wet summer.  And so it’s the same with stocks.

Conclusions on stock diversification

To summarize our mindful conclusions about stock diversification:

  • Use of stock funds, vs. individual stocks, is an easy and low-cost way to guard against catastrophic events that regularly impact individual companies and avoids trying to pick out the relatively few winners in a market composed mostly of losers.
  • At the same time, stock funds that cover large swaths of the stock market provide an easy way to gain at least some diversification across company size, geography, sector, and style.
  • All evidence suggests that stock correlations will continue to ebb and flow unpredictably.  High correlations between stock types appears to be on the ebb in the last few years, possibly creating some additional opportunities for greater diversification in stock portfolios.
  • Diversification across stock types is clearly not a silver bullet that will suddenly reduce the frequency or magnitude of future declines in your stock portfolio with any consistency.
  • At best, you should expect to see a moderate dampening of such declines in some instances as compared to holding an apparently less diversified group of stock funds.
  • Likewise, you may reap some additional returns with a more diversified stock portfolio.  But because of the ongoing seesaw return performance of various stock types, extensive stock diversification does not predictably result in consistent and substantial increased returns as compared to a less diversified group of stocks.
  • Attempts to back-test for highly optimized combinations of stock types that historically achieved better returns with less volatility are unlikely to be optimal for the future.  This is because the future will never exactly replay any past segment of history used to optimize the stock diversification in the first place.
  • As our investment plans play out, we need to monitor ongoing stock correlations and evaluate whether any of the above relationships are changing.  Our investment plans should be flexible enough to allow shifts in stock holdings should movements of certain stock types become more decoupled in the future.

Consistent with these conclusions, a moderate number of low-cost index funds that contain stocks that vary to some degree across these main stock types, is probably all the diversification most people need.  Variation across stock sectors may have the potential to provide improved stock diversification right now (November 2017), due to relatively low recent correlations.  But it’s also possible that low sector correlations will quickly change and nullify this potential benefit. 

It is highly questionable whether further stock portfolio refinements will actually ever yield better future results in term of either lower volatility or higher returns.  Unexpected “weather” can devastate even the most finely tuned diversification plan.  For this reason, some people recommend an “all market” approach, where you simply try to mimic the holdings of the entire world stock market.  For example, Vanguard has a low-cost “Total World Stock” ETF (symbol VT).  Alternatively, similar outcomes can be obtained by combining low-cost stock ETFs that focus more specifically on the U.S. market, emerging markets, and developed markets as well as different company sizes, sectors, or qualities within each of these regions.  However, your investment plans should not rely on the assumption that such diversification is going to consistently boost your performance or reduce your volatility as compared to, for example, simply holding a low-cost U.S. S&P 500 fund.  Your diversified stock portfolio may turn out to provide such advantages, but all evidence suggests that it may be just as likely to perform somewhat worse than a simpler portfolio in any given future time span.  Article 7.2 examines the same diversification questions as they apply to bonds.

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