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Alpha Theory Blog - News and Insights

May 03, 2018

Positive Skew…Part 2 – Maybe It’s Not So Bad for Active Managers After All

In my last post, I discussed the negative impact of positive skew for active managers. Basically, that more than 50% of all stocks in a given market underperform the average because there are stocks that go up more than 100% but no stocks that go down more than 100%. This means that if you pick a random portfolio of stocks from the market, you have a greater than 50% chance of underperforming the market because most portfolios will not hold those few stocks that went up more than 100%.

 

Because of the popularity of the last post and TV appearance, we spent time digging further into the data to answer questions posed by readers and viewers. We noticed that there was a tendency for the returns between the average stock return and the index return to be different.

 

And that is the problem with using the average stock return as the hurdle for funds. Investors are not measured against the average stock return, they’re measured against the benchmark, typically the S&P 500. Most indexes are market cap weighted, meaning that the index return and the average stock return are generally different.

 

In the example below, we’ve taken the current S&P 500 constituents and calculated their return since the beginning of 2012 and compared that to an average return (Equal Weighted) and the actual return of the S&P 500. The S&P 500 over that period was up 136% vs 175% for the average stock (this isn’t a perfect analysis because the constituents in the portfolio changed over that time but it is an approximation).

 

Positive Skew-part2

 

The graph above shows the distribution of individual stock returns over that period. You can see the outliers that pull the average stock return (red line) up to a point where 63% of individual securities underperform the average of 176%. But the S&P 500 was up 136% (green line) over that period so only 51% of stocks underperformed the benchmark. Pretty much a coin flip.

 

We brought positive skew up with Andrew Wellington at Lyrical Asset Management. They have done some great analysis comparing the top 1000 stocks by market cap in the US to the S&P 500 each year going back to 1998.

 

Chart2

Source: FactSet and Lyrical Asset Management

 

As you can see in the chart above, the average stock beating the S&P 500 index is a coin flip. For the past 20 years, the likelihood of any individual stock beating the S&P 500 in any given year is 50.2%. If I build random portfolios using the Top 1000 stocks in the US, there is a high likelihood that the portfolio return will be close to the S&P 500 return.

 

Some years are clearly better than others. ’98 and ’99 were horrible stock picking years. If you didn’t own the few stocks that had meteoric rises, you had a high likelihood of underperforming the S&P 500. ’01 and ’02 were good stock picking years. Over 60% of stocks beat the index.

 

What this means, is that any given fund’s batting average should be compared to the batting average of the universe of stocks compared to the benchmark. A 54% batting average in ’98 is heroic, in ’03, 54% is just inline. Take a look at 2017. It was the 3rd hardest stock picking environment in the last 20 years using this metric.

 

But what about other indices? Thankfully, our friend Julien Messias from Quantology Capital Management has done the analysis (1999-2014) comparing the S&P 500 and Russell 2000. Below are thoughts from Julien on the topic:

 

The Russell 2000 components returns exhibit a much more leptokurtic distribution (fat-tailed) than S&P 500, meaning that you have a huge part of the index’s components suffering from huge loss (or even bankruptcies), with an average of more than 60% of the components underperforming the index performance and 2% of the components with huge performance (more than 500% per year). The performance of the index is therefore pulled up by those latter 2%.

Assuming a stock-picker operates at random to choose its investment within the index universe, this means that his performance should be closer to the median performance of the components, than to the index performance itself. Therefore, given that the median performance is almost always lower than the index performance (see chart below), an investor in Russell 2000 securities is very likely to underperform and very unlikely to outperform.

The S&P 500 distribution is much more mean-centered, with very shallow/thin tails, meaning that the average stock picker is much more likely to generate a performance close to the index performance (graph from Lyrical AM) and less likely to underperform.

 

Chart3

Source: Quantology CM

 

The Russell 2000 index more apparently displays the impacts of positive skew because it is less impacted by a contribution of a few very large companies. AAPL, MSFT, GOOG, AMZN make up 12.2% of the S&P 500 while the Russell 2000’s top 4 positions make up 1.7% of the index. The result is that the average of all stocks in the Russell 2000 is much closer to the Russell 2000 index return than the average of all stocks in the S&P 500 (recall the large difference from the 2012 to 2018 analysis that showed the S&P 500 return was 136% vs 175% average of all stocks).

 

This means that the index chosen as the benchmark for your fund has a profound impact on your ability to beat it. More specifically, the probability of beating the S&P 500 with a random portfolio is 50%, for the Russell 2000, it’s 42%.

 

There has been quite a bit of press regarding positive skew. It’s a great conversation but, for the average fund that is measured against the S&P 500, the impact is overblown. Almost every investor is compared against a benchmark. I recommend that you dig a layer into your benchmark and measure its positive skew, the likelihood of beating the average stock return, the likelihood of beating the index return, and compare your hit rate against the hit rate each year to know how difficult or easy it was for you on any given year.

 

Quantology Capital Management Russell 2000 and S&P 500 Analysis:

 ­ Screen Shot 2018-05-03 at 10.03.58 AM Screen Shot 2018-05-03 at 10.05.52 AM

Does not include management fees

Data is cleaned from index turnover, with updates every year

April 06, 2018

Positive Skew is Negative for Active Managers

 

Let’s play a game. In this game, there are 10 random poker chips in a bag. 9 of these chips will give you a return between -8% and +8% on the money that you bet. The 10th coin will give you a 100% return. The distribution of returns for this game has a positive skew.

 

Screen Shot 2018-04-06 at 9.29.11 AM
 

If offered to put money down on this proposition you would take it because you would expect a 10% return if you could play the game over and over.

 

Now let’s add a wrinkle. Your goal isn’t just to make a positive return, you have to beat the bag. The bag puts 10% of their money on each chip and pulls them all. Voila, a 10% return. One last wrinkle, you can only pick one chip at a time.

 

How many times out of 10 would you beat the bag? Only 1 in 10. 90% of the time you would lose to the bag. It doesn’t matter if we expand the number of chips as long as the bag maintains the same positive skew (we could increase the to 100 chips and you get to pick 10, 100 chips and you pick 1000, etc.)

 

By now, you’ve probably guessed that the bag is the market, the chips are stocks, and you are, well, you. This is the game we play when trying to beat an index. True, you can be better than the market at figuring out the good chips but given that initial conditions for a random game means you lose 9 out of 10 times, it’s really hard to beat the market. Add fees and the likelihood of beating the market goes down even further.

 

Positive Skewness has gotten a decent amount of press over the past year because of the championing of JB Heaton who wrote a paper1 researching the impacts of positive skew on manager underperformance. Heaton’s paper is similar to research from Dr. Richard Shockley in 19982. See below for an article written by Bloomberg News on the topic.

 

Picture1

Source: Bloomberg News (“Lopsided Stocks and the Math Explaining Active Manager Futility” by Oliver Renick)

 

Given that many of the conversations active managers have today revolve around active versus passive, “positive skew” should be top of mind. This is my push to increase awareness.

 

Given that active managers can’t change market skew, what should we do? We could measure skill in a different way. Let’s say I want to measure a manager skill. If I take all of the stocks of the markets they’re investing in and then randomly build 100,000 portfolios with the same number of securities as the manager. I can then plot where that manager falls on the distribution and give them a Z-Score for how far away from the norm they are. I could do the same thing for hedge funds by randomly buying and selling securities in the same universe as the investor.

 

I’m not saying that this excuses active managers from underperforming passive strategies, but it should at least be a more realistic assessment of their skill. My hope is that positive skew becomes just as common an explanation as fees when discussing active manager underperformance. Only by knowing the causes, will we be able to make changes that allow active managers to outperform.

 

1 Nicholas Polson and Jan Hendrik Witte; Hendrik Bessembinder of Arizona State University

2“Why Active Managers Underperform the S&P 500: The Impact of Size and Skewness,” published in the inaugural issue of the Journal of Private Portfolio Management. One of the original authors of the study is Richard Shockley.