## Demystifying Beta

It has often been said that “the market loves certainty.”  Most investors (excluding those who seek to capitalize on volatility) would love it if stocks grew in a nice, linear way that was easy to predict and explain.  Alas, stocks don’t do that. They grow in an up and down pattern that is reminiscent of an EKG readout. All that up and down movement overwhelms the brain, and makes it hard to figure out what is going on over the long run.  Since we can’t get stocks to grow in value as a nice, elegant linear function, we tend to look at trends.

On graphs, we can often use lines to show what the trend of a particular stock’s value is over time.  One particular method of doing this is a statistical technique called linear regression.  It essentially takes the average of all the ups and downs and draws a line based on those averages.   You could do the same thing with a ruler by “eyeballing it,” but the results wouldn’t be as precise as the trend line and associated equation that is mathematically generated by a computer.

That last line may have made you cringe a little; I used the words “mathematically” and “equation” in the same sentence.  If you had flashbacks to your high school algebra class, I apologize. But you needn’t be afraid; all of the math is done by computers these days.  All you need to remember from algebra class is that equations can be shown as a line on a graph. Regression analysis capitalizes on this idea in predicting the average movement of data points (stock prices) that don’t move in a nice, straight line like those homework problems from algebra class.  Regression analysis has gotten a bad reputation because of its association with math. Try to forget that; regression is a very useful tool for the investor. All the hard work is done behind the scenes. All you have to do is interpret the results. There are very easy rules of thumbs for interpreting that information.  Feel free to write those down; this isn’t algebra class, and you can’t get in trouble for cheating.

If you were to ask an economist, she would probably say something like “a particular stock’s beta is calculated by dividing the covariance the stock’s returns and the returns of a specified benchmark by the variance of the benchmark’s returns over a specified period.”  My guess is that you didn’t find that very helpful. Let me break it down for you; it’s an easy concept to grasp once we translate the statistical jargon into trader jargon. When we measure anything (such as a stock price) over time and we get different results, we call that thing a variable as opposed to a constant.  Stocks are certainly variable!

That movement of the measurement from value to value is called variation.  Statisticians measure this variability with a number called variance (closely related to standard deviation).  Simply put, variance is a particular statistic that measures the variation in something that varies, such as a stock price.  In the case of stock prices, low variability (as measured by variance) means that the stock’s price doesn’t move much. A high variance means that the stock’s price is bouncing around all over the place.  Traders don’t often use the word variability; they talk about the amount of movement in a stock’s price in terms of volatility.  It may not be precise, but you will probably be okay thinking of variance as a measure of volatility.

Enter the idea of covariance.  As you’d expect, “co” is a prefix meaning “together.”  So the idea of covariation is the idea that two measurements will vary together and, if we generate a scatterplot, the dots will form a line.  For example, we’d expect a high degree of covariance between a stock’s market price and its price to earnings ratio. If the PE ratio was the only factor in determining stock prices, then all of the dots would fall on the line perfectly.  Statisticians would refer to this is a bivariate (meaning two variables) problem, because there are only two variables being considered.

Stock prices are a multivariate (meaning many variables) problem. There are dozens of potential factors that influence stock prices, and only some of them are quantifiable (If this weren’t the case, I could come up with an equation to model future growth and have retired already).  Note that the idea of covariance is conceptually identical to the idea of correlation.

So, the big idea of regression analysis is to demonstrate as precisely as possible how two things systematically vary together.  We can apply this idea to see how much the variability (volatility) of a particular stock matches the variability (volatility) of a benchmark.  That is what Beta is. While any benchmark can be plugged into the equation, most often the variance of the S&P 500 is used with stock prices. Beta, then, is just a ratio of the volatility of a particular stock and the volatility of the S&P 500.  The math tweaks (standardizes) the results for easy interpretation. A Beta of 1.0 indicates that the particular stock you are evaluating moves precisely with the benchmark—it goes up and down exactly as does the S&P 500. A Beta less than 1.0 suggests that (at least in the past) the stock was less volatile than the S&P.  A Beta above 1.0 suggest that the stock is more volatile than the S&P.

Consider the idea that volatility is only a bad thing when it goes against the way you bet. If you are long in a stock, and it shoots past the S&P 500 average, then you picked an awesome stock! If it, however, plummets below the level of the S&P 500, then you are a much bigger loser than the overall market.  Beta assesses volatility objectively. What you ultimately decide to do with that information depends on how risk averse you are. Super conservative investors that are willing to tolerate very little risk will look for stocks with a Beta less than one, such as many utility stocks (often referred to as bond market equivalent stocks).

For example, as of this writing, the Beta for Procter & Gamble Co. (PG) is 0.6. Risk takers seeking big rewards will often look for stocks with a high Beta and the accompanying possibility of big returns—and huge losses.  Note that Beta is neutral as to evaluating great returns or terrible returns. As of this writing, the Beta for Goldman Sachs Group Inc. (GS) is 1.6. Owners of GS are springing for the good stuff this Christmas! Apple Inc. (AAPL), on the other hand, has a Beta of 1.3 and that volatility is unwelcome by investors.

To really get any useful information from Beta, there must be a correlation between the stock you are evaluating and the benchmark used in the computations.  To evaluate this, we can turn to another byproduct of regression analysis known lovingly by economists as R-squared. Think of R-squared as a percentage of covariation.  The closer to 100 you get, the more the stock traces the benchmark’s performance. The closer to zero you get, the less correlation there is between your stock and the benchmark.

More advanced measures have been developed since the advent of computer technology, such as the Sharpe Ratio. The bottom line is that Beta and other measures of volatility are useful tools (among many) that you can use to help you pick a stock that meets your investment needs and form realistic appraisals of how high it can go, and how low it can sink.

## Demystifying Market Corrections

When the market starts trending down, many investors tend to panic.  They see volatility as dangerous and formulate a belief that the market is just not for them.  These panicked investors perceive the correction as something wrong with the market as a whole and lose sight of the fact that for every stock listed, there is a company behind it.  Veteran investors have come to expect these periodic “corrections” to what can be considered inflated prices. Corrections happen all the time. After “big runs,” you should anticipate them.  It is a terrible mistake to pull out of the market when they happen.

Jim Cramer teaches that a particularly profitable strategy for dealing with corrections is to avoid the trap of being 100% invested in the market at all times.  At times when the market is tanking, cash that makes nearly nothing can look like a great investment. As Cramer put it, “Nothing feels as good as cash when the market is coming down.”  This is actually a critical element of his axiom of “selling strength and buying weakness.” When the market is surging upwards, the strategy dictates that you “trim” here and there to generate cash to be in a position to buy during the next correction.

If you don’t do this trimming and hold on, you may fall into the trap of selling your winners to subsidize your losers. This naïve practice can wreck a portfolio by filling it with junk because all of the blue chips have been sold off. When you realize that a stock is junk, then sell it and take the loss.  Use what’s left to reposition into something great. The real key to all of this is to differentiate between bad companies with deteriorating fundamentals and good companies with deteriorating stock prices.

Don’t forget that companies with good bottom lines can go bad because of larger forces that are outside of management’s control.  Geopolitics, exchange rates, fed policy, and a host of other factors can make cause a once great company to lose traction. Don’t let your emotions get in the way of making rational decisions based on shifting fundamentals.  In a slowing economy, for example, you may see a consumer shift from premium brands to store brands that can hurt the bottom line of once great premium product companies. Drug companies that have been making fortunes for years can suddenly see their bottom line drop out of sight when a family of important drugs goes off patent.  If you confuse the shifting fundamentals with a market correction and buy more while the stock is “on sale”, you can lose big.

If your portfolio is composed of great companies with great fundamentals, don’t fear the market corrections.  Those great stocks will bounce back, ready to ride the next upturn.

## Index Investing With SPDRs in Volatile Markets

Many financial experts and Titans alike recommend index investing as a strategy that will get the average investor what John Bogle calls “your fair share of the market’s return.”   A common strategy is to buy an S&P 500 mutual fund, usually through a “constant dollar investing” strategy inside a 401(k) or similar tax-sheltered retirement account. This will work over the long term if we are spared the apocalypse, but it may not be the most efficient way to go about it.   One option is to buy the market in pieces using Select Sector SPDRs.

Some people take issue with the fact that the multiple of the S&P 500 is stretched to insane levels at this late stage of a long-running bull market, and this isn’t a good time to buy. Mr. Buffett recommends index investing, but you will notice that Berkshire isn’t buying a lot of stock right now.  Mr. Buffett famously only buys cheap stocks.   An alternative to “buying the market” is to buy it a piece of the S&P 500 at a time.

The S&P 500 is designed to reflect the overall strength of the US market.   For this reason, an attempt is made to try and include every kind of business imaginable.  Many of these businesses do similar things and make money in similar ways. There are a ton of different ways to break stocks down into categories, but the most common way of doing this for the S&P is to consider sectors.  There are many mutual funds and Exchange Traded Funds (ETFs) that let you buy an index of a particular sector.

The overall S&P 500 Index may look calm, but there can be a lot of activity under the placid surface as traders rotate between sectors, trying to catch the next wave to the upside, or just trying to avoid the next correction to the downside.  These days, markets are moving toward a “defensive” posture and long out of favor sectors are making a resurgence.

The most common taxonomy is to break the market down into 11 major sectors.  At any given time, there will be sectors in the red and sectors in the green.  This happens on a day to day basis, and it happens on a much longer-term basis, consistent with the stage of the economic cycle.  A common way to track the performance of a particular sector is to watch price movements in the SPDR ETF (also known as “Spiders”) for each sector.

Many traders know the tickers for each SPDR, and this allows them to assess what a particular sector is doing. You can do the same thing with simple free tools such as Yahoo! Finance.  Once you have registered with Yahoo!, you can go to the Finance section, and there are features that let you build custom portfolios.    Once in the portfolio section, you can simply click on Create Portfolio.  Give your new portfolio a name (e.g., “Sector Sort”) and start adding the SPDR tickets.  You can use the “Create New View” feature to provide some interesting information about each fund in your list.

Note that directly comparing sectors is an apples to oranges comparison.  Obviously, we can’t expect certain sectors to perform as well as others on average.   Perhaps the best way to judge the performance of a sector is to compare the current price with the highest price during a given timeframe.  This is what you’ll see in the table below. The “52-Wk High Chg %” column tells us how much the fund is now trading below its high for the last 52 week period.  We can see that the financials and consumer discretionary sectors are trading more than 10% below their 52-week highs, as are the industrials. I’ve also added index ETFs for the Dow Jones Industrial Average (DIA), the NASDAQ (QQQ) and the S&P 500 (SPY) for comparison purposes.

 Symbol Fund Name* 52-Wk High 52-Wk High Chg % 52-Wk Low 52-Wk Low Chg % XLF Financial Select Sector SPDR ETF 30.33 -12.07% 23.79 12.11% XLP Consumer Staples Sector SPDR ETF 58.95 -11.42% 48.76 7.10% XLI Industrial Select Sector SPDR ETF 80.96 -10.87% 66.95 7.78% XLB Materials Select Sector SPDR ETF 64.17 -8.91% 53.41 9.44% IYT iShares Transportation Average ETF 206.73 -8.83% 162.38 16.07% DIA SPDR Dow Jones Industrial ETF 265.93 -8.02% 212.68 15.01% XLU Utilities Select Sector SPDR ETF 57.23 -7.09% 47.37 12.24% XLV Health Care Select Sector SPDR ETF 91.79 -6.30% 77.82 10.52% XLE Energy Select Sector SPDR ETF 79.42 -4.72% 61.8 22.44% SPY SPDR S&P 500 ETF 286.63 -3.93% 240.85 14.33% XLY Consumer Discret Sel Sect SPDR ETF 112.62 -2.06% 87.89 25.50% XLK Technology Select Sector SPDR ETF 72.43 -1.89% 54.25 30.99% QQQ Invesco QQQ Trust 177.98 -1.33% 137.5 27.72% RWR SPDR Dow Jones REIT ETF 95.73 -0.46% 81.59 16.79%

*Note that there are mutual fund counterparts to all of these funds.

The “52-Wk Low Chg %” column tells us how high the fund is now trading off if its 52-week low point.  Conventional wisdom tells us that the “out of favor” sectors are defensive sectors.  This means that these sectors tend to do well in bearish markets, but don’t have the explosive potential of other sectors in bullish markets.  Note that the REITs and technology have been flying, and they are near their highs for the past 52 weeks.

We shouldn’t take a single year perspective on long-term investment decisions, but on the face of it, it seems that the index investor would be better served by buying the “cheaper” financial and consumer staples sector right now and avoiding putting new money to work in the technology and consumer discretionary sectors. Also note that SPY (the S&P 500 ETF) is “high flying” as well, and it may not be a good idea to put new money to work directly in the index.

A more aggressive version of this strategy would be to set a cut point, say 25%, off the low for the year and rotate some of your portfolio allocation out of the most expensive sectors and into the worst performing.   In our current example, this would mean selling some XLK (Technology) and buying some XLP (consumer staples) with the proceeds. This is very similar to a portfolio reallocation strategy that most modern portfolio managers would suggest, except that my suggestion has more to do with how you put new money to work as opposed to how your existing capital should be allocated or reallocated.

Note that there are also subsector funds, such that you can invest in an even more specific area of the economy.  If, for example, you think biotechnology has a brighter future than healthcare overall, then you can buy the IBB, which is a biotechnology fund.

These days, you can also do some very dangerous things with ETFs and even mutual funds by buying inverse funds and leveraged funds.  An inverse fund does the opposite of what the index it follows does.  If you buy an inverse S&P 500 fund, it will go up when the S&P 500 goes down, and vice versa.  This is particularly dangerous because US Markets have an upward bias, and over time, you will lose money.  If you believe that you can time markets, then buying such funds when there is “irrational exuberance” can be extremely profitable, and that is the allure.

Leveraged funds use options contracts and other forms of financial wizardry to earn a multiple (such as 2 times) the return of the index. These are particularly dangerous because they are usually calculated on a daily basis, and volatility is doubled.  If you bet the wrong way, you can suffer massive losses in short order. When the biotech sector caught on fire Friday, July 6, 2018, the IBB returned a whopping +3.78%, while the leveraged ProFunds Biotechnology UltraSector Inv (BIPIX) returned +5.58%.

If you are absolutely sure that trade wars will cause a recession and that the S&P is destined to crash, you can hedge your portfolio using an inverse leveraged fund such as the Rydex Inverse S&P 500 2x Strategy A (RYTMX).  Before you do so, I encourage you to pull up a 5 or ten-year chart and see just how abysmal the performance of that fund has been in a bull market. If you get the timing or the direction wrong, you can lose some real money really fast.  The most prudent course, then, may well be to rotate out of the high flyers into the defensive names that are already down and thus “selling cheap.” As Mr. Dalio says, the risk in holding the overall market is currently “asymmetrical”–there is much more room to the downside than the upside.