Sunday, October 4, 2015

Market Capitalization in the Energy Sector

Over the past couple posts, I've discussed various characteristics of the third quarter while often turning my nose up to their stinky trends. Particularly, energy sector performance was looked at and compared using some oil and gas companies as well as the major S&P Energy index. The red numbers hadn't been that bad for some time, 7 years that is. Wasn't the period after Lehman's fall supposed to be full of gains following the recession? In fact it was in the first half of the 2015 year, the Dow Jones Industrial Average reached a very high (and perhaps exceptionally capricious) 18,000 points. If it weren't for poor statistics in the two largest world economies, bulls would be having a heyday with a sharp rebound in crude oil and exploding equities. But it wasn't. Now in October, investors and the Fed continue to clamor about the tight money conditions and low inflation, and that affects everyone differently. Today, I wanted to take one more look into Q3 and investigate some small-cap, mid-cap, and large-cap differences in the energy sector.

Using a list of 352 energy stocks compiled by NASDAQ, three stratified random samples of 20 companies will be taken. These three samples will be split up into small-cap stocks, mid-cap stocks, and large-cap stocks before the sampling process in order to establish randomization within the groupings. Large-cap stocks will have market capitalizations of $8 billion or higher, mid-cap stocks will have between $8 billion and $1 billion, and small-cap will have less than $1 billion. Looking at the mean Q3 losses, the performances of large, mid, and small market capitalization stocks can be compared and analyzed further. The third quarter time period will be measured as 65 days between June 30, 2015 and September 30, 2015. Here is the data from the stratified random sample separated into the respective groups:

2015 Q3  energy sector performance data

The first column classifies the market cap, and the second column specifies that number. The last column lists the third quarter performance of that data point in a percentage. As a list, the data provides very little insight into a comparison so let's organize it:

2015 Q3 energy sector performance boxplot

Above, a boxplot was created to organize the data and characterize its spread using one variable summaries. Looking first at the means calculated using performance data, one notices a consistent improvement with market cap. Each classification is separated by about 10 percentage points. Small-cap energy stocks performed the worst with an average of -36.40% lost in the third quarter. They also had the highest variability in their data as the standard deviation fell just below 18%. Mid-cap stocks and large-cap stocks both had similar variation with similar standard deviations, but mid-cap stocks performed 8.3% worse on average. Even without the large-cap outlier at 15.85%, the new large-cap mean at -18.72% was 8% better. Median data tells the same story. The 50th percentile from each classification differed less than 1.9% for each respective data as most of the data shows a unimodal pattern with very little skew. The only outlier was the large-cap stock Tesoro which was the sole gainer out of this sample. Except for the outlier, the showed systemic similarities on most of the observed statistics. Small-cap energy stocks had the biggest loser, and large-cap stocks had the highest gainer. A couple days ago, an article looked at the S&P Energy index for Q3 which performed at about 18%. If a portfolio were to use my random sample of large-cap energy stocks, they would have performed at about the same level (give or take 0.5%). On the other hand, no mix of energy stocks would have had a very good chance of beating the market average, unless an expert had some good luck or inside information. The losing mid-cap and small-cap Q3 averages show the risk premium of taking on undervalued stocks, especially in a time where the respective commodity price is dangerously low and global economic conditions are sluggish. At the same time, the high variability in the small-cap statistics suggests that the performance varies and growth can be found to match or beat market averages (but is very rare). Mid-cap stocks which are typically safer bets performed about 9% worse than the S&P on average, but its middle 50% beat the small-cap stocks' middle by 10. At the same time, the bottom 75% of the mid-cap sample performed worse than the S&P average. Company's between $1 billion and $8 billion have the potential to perform well in tough times if they can show the liquidity to cover revenue losses. Having solid assets and a more established portfolio could be the reason for mid- and small-cap performance differences. The onset of demand worries which appeared late into the supply glut may have been the reason why mid-cap stocks couldn't compete with large energy corporations. Establishment and assets shouldn't be what separates a large-cap stock, instead, a successful, dominant firm will show its ability to survive through large cash flow and razor-thin profit margins. As the supply glut and weak demand developed, beating the market average became increasingly harder with the accumulation of bearish sentiment surrounding the energy markets. As one can see, the top 40-45% of large-cap stocks either beat or performed at the S&P averaged. The top 25% beat it by at least 4.4. Those statistics speak to the ability of large-cap stocks to control the market and manage losses with expert leadership, but those attributes don't shield them from extreme losses. The variability in large-cap data was very similar to mid-cap losses, and the bottom 25% performed at 9 below the S&P average in the random sample taken.

2015 Q3 energy sector performance scatter plot

The last thing to look at with the given data is a scatter plot depicting a correlation between 2015 Q3 Performance and the Market Cap of the respective energy stock. The x-axis was transformed into a log scale to show the exponential connection between performance and market cap related to the third quarter. The data show a slight positive correlation between the variables with two obvious outliers. Using the lines to differentiate between small-, mid-, and large-cap, one can see the large amounts of space between market caps below $1 billion representing the variability existing in small-cap data. Above $1 billion the spreads between the data points and the line of best are smaller showing less variability. It may be cliche of me to say, but smaller companies have shown that they perform inconsistently during tight economic periods. Investors are not willing to bet on the higher probabilities of bankruptcy as a result of the premiums on borrowing that nubile firms need. Portfolio managers, may this be a warning to abandon small-cap as oil prices and demand falls because of uncertainty in that arena. On average, a randomized selection of mid-and large-cap stocks will perform better with less risk and less variability. Randomly selecting large-cap stock is safer still, although, it may be more expensive to diversify. The potential of Q4 might propose decisively different results for these groups through the next three months. Oil prices should see a net increase after a 24% decrease in Q3. Small-cap energy stocks may see a huge rebound if earnings turn out better than expected, and mid-cap energy may see that jump as well. Large-cap stocks will have a more muted rebound because of their naturally low beta, but as the cost of borrowing goes down once more, look to see expansive buying to take place as high margins free up cash for asset purchases.

I'll be looking forward to a more prosperous fourth quarter as I move away from third quarter analysis. Next up is the earnings season where oil and gas companies get to see how much the price has hurt them compared to their competitors. It is finally time to see the bottoming out of the supply glut.

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