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Alpha Theory Blog - News and Insights

August 11, 2022

What does it mean to be a good stock picker? Part 2

 

BOTTOM LINE UP FRONT: A slugging percentage above 1.65x demonstrates stock picking skill for an active manager.

 

In the previous post, we explained that comparing an active manager to a randomized version of an active manager was the best way to measure their stock selection skill. We discussed why constructing our random portfolio using absolute return or benchmark adjusted returns was flawed. This resulted in the use of an equal-weighted benchmark (equivalent to a monkey throwing darts in a more normalized time period*). We started with Batting Average and will continue our analysis with Slugging Percentage.

 

Slugging Percentage

 

Slugging percentage is a measurement of the average winner divided by the average loser. If I have a batting average of 50% and a slugging of 1.0x, then my fund will generate a 0.0% return (50% of stocks make +20% and 50% make -20%). Anything greater for either of those metrics and the returns turn positive. If you can find winners that go up twice as much as losers, you need only have a batting of 33.3% to result in a return of 0.0%.

 

To demonstrate skill, a manager needs a slugging percentage* of 1.65x. This may seem high, but we must understand that the market demonstrates persistent positive skew (a straightforward way to think of positive skew is that more stocks go up more than 100% than go down more than 100%). This positive skew is also one of the major reasons that the batting average of the randomized portfolio we calculated in the previous post, 37.8%, seems so low.

  

If a manager has a 40% batting average (better than 37.8%) and 1.8x slugging percentage (better than 1.65x), they are demonstrating skill in two categories: both in picking winners and picking winners that go up much more than their losers. This is a good start, but how good is 40% and 1.8x?  

 

The next step would be to Monte Carlo random portfolios and determine the probability of getting a 40% batting and 1.8x slugging portfolio. In fact, the ideal method would be to: 

  •  -  Match manager time period and industry; 
  •  -  Randomly select longs at the ratio of manager long positions; 
  •  -  Randomly select shorts at the ratio of manager short positions; 
  •  -  Randomly assign weights to Monte-Carlo’d portfolios using position
  •     size ranges; 
  •  -  Assess the gross exposure of an individual manager; and 
  •  -  Build a distribution of portfolios with those standardized
  •     characteristics to determine the probability of achieving a
  •     similar portfolio.
     

With that, you can perform significance tests and take samples from different periods to determine the persistence of skill.

  

We may explore this concept in the future, but in the interim, the simple approach above is a shortcut method to help elucidate the idea of comparing managers to random as a way of measuring manager skill. 

 

 *SLUGGING PERCENTAGE FORMULA: (if positive: average (return of stock - return of the average stock in the index) / if negative: average (return of stock – return of the average stock in the index) 

 

THE RANDOMNESS EQUATION: Equal Weighted Batting Average of ACWI * Slugging Percentage of ACWI + (1-Equal Weighted Batting Average of ACWI * Denominator of Slugging Percentage of ACWI = The Randomness Equation 

37.8% * 165% + 62.2% * -100% = 0.0% Return – The Randomness Equation 

 

July 28, 2022

What does it mean to be a good stock picker?

 

Bottom Line Up Front: A batting average above 38% demonstrates stock picking skill for an active manager. 

 

Defining skill in investing is challenging, so it is no surprise that measuring skill is even harder. While we strongly believe there is an inherent skill for many “stock pickers,” how do we measure that belief? What if we compare them to the “blindfolded monkey throwing darts?” (Sidenote: unless monkeys can read, I think the blindfold is superfluous). Said another way, can the manager beat a randomly constructed portfolio? 

 

In this experiment, we will measure skill by measuring two metrics: batting average and slugging percentage (Batting = # of positions that make money / total number of positions | Slugging = average percent gain of winning position/average percent loss of losing position).  

 

Important Note: Demonstrating skill does not mean beating the market. It also does not mean making money. I know, weird. You can be better than the monkey throwing darts and still lose to the market because indices do not equal weight the stocks in the index. 

 

Batting Average 

 

ABSOLUTE BATTING AVERAGE. Let us start by building the comparison portfolio from the ACWI Index (All-Country World Index - ~3000 global stocks - ~70% US). To demonstrate skill, a manager should be able to “beat” random. Over the past five years, the ACWI has had a 56.4% batting average (if you randomly picked 100 stocks from the ACWI, 56 of them had a return greater than 0%).

 

INDEX ADJUSTED BATTING AVERAGE. You could say that during the past five years, a manager should make money on 56% or better of their long positions. The problem with looking at it this way is that some five year periods are more positive than others. A potentially better approach is to measure the number of stocks that beat the ACWI. The ACWI was up +40.9% during this period. Once we subtract that return, only 27.1% of stocks beat the ACWI. To assess, the manager would simply measure the number of stocks up more than the ACWI during the holding period. If that was higher than 27.1%, they were demonstrating skill. 

 

EQUAL WEIGHTED BATTING AVERAGE. The problem is that the last five years have seen the largest returns from the biggest index weights (large caps outperforming the small caps).  To get a random selection of the ACWI (like a monkey throwing darts), we should equal weight the ACWI. Equal weighted, the ACWI is up 21.7%. This more reasonable version results in a batting average of 37.8%. 

 

From this, we would argue that a batting average above 37.8% demonstrates skill. The higher above, the more skill demonstrated. Of course, there is luck involved, and untangling skill and luck is difficult, but over time, we can look for persistence of skill in picking winners and losers with this metric*. 

 

So far, the only skill measured is that of picking stocks that go up. In the next post, we will measure the skill of picking the stocks that go up the most and avoiding those that go down the most (Slugging – position sizing skill).