July 2023
IN THIS ISSUE
The meme stocks are back…
The use of transitory has not *entirely* been transitory
When regulation is good
How to underperform
Virtually all traditional active managers claim expertise in investment performance. But what does that mean?
Perhaps the most common performance benchmark is the S&P 500 Index. This index makes particular sense for managers who stock-pick among large cap publicly traded U.S. companies. So managers “skillfully” buy the subset of S&P 500 companies that will do better, avoid or short those companies that will do worse, and collect sizable fees for their efforts.
But as they say, “diversification is the only free lunch”, and this strategy sacrifices diversification for the chance at outperformance.
So why does this stock-picking strategy systematically underperform the S&P 500 Index benchmark? Skewness, for one. Here’s Craig Lazzara of S&P Dow Jones Indices:
If stock returns were normally distributed, stock selection would be no harder than a coin flip; a randomly chosen stock would have an even chance of delivering above-average performance. When the distribution is skewed, selection becomes much harder. Of the 1010 stocks that were part of the S&P 500 between 2000 and 2019, only 267 were above average. The probability that a randomly-chosen stock would deliver above-average performance, in other words, was 26%, not 50%. When fewer stocks outperform, active management is harder.
The compounding effect of this skewness over many years reveals a grim reality for the traditional active management industry. S&P Dow Jones Indices also produces a semi-annual SPIVA (S&P Index Versus Active) Report, that notably tracks the percentage of all large cap funds that outperform the S&P 500 Index benchmark. If collective stock-picking skill existed across the industry, we would see that edge reveal itself in greater than 50% outperformance over the long term. Well, here are the actual data of outperforming funds over 1, 5, 10, and 15 years:
1 year 48.92%
5 years 13.49%
10 years 8.59%
15 years 6.60%
Stock-picking skill is clearly not evident in these numbers, this is the opposite of what traditional active managers claim will happen.
Persistence is another way to measure managerial skill. If top-performing managers continue to be top-performing over long time horizons, then it is more likely that their top-performing returns can be explained by skill rather than luck. But if top-performing managers regress to the mean (or worse) then that early outperformance is better attributed to luck than skill.
Well, here is SPIVA’s U.S. Persistence Scorecard:
“we consider the above-median managers in each fund category for the first five years, and then ask what fraction of the initial set of top managers repeated their above-median performance in the second five years. If performance were completely random, we would expect 50% of the winners in the first five years also to win in the second five years; if substantially more than 50% of the winners repeated in the second interval, that might be evidence of consistent skill. Results, however, fell well short of this mark.”
Only 37% of top-half funds repeated their top-half performance in the second 5 year span.
There are plenty more statistics making this same case against skill in traditional active management, so let’s move on.
Let’s assume that most traditional active managers typically hold between 10-30 companies selected strategically “to effectively remove their unsystematic risk exposure” (What Is the Ideal Number of Stocks to Have in a Portfolio?). With conviction in their stock-picking skill, these managers typically hold a very large majority of their AUM in their top 10 positions. After all, why would managers allocate capital in their 11th-500th best ideas when they could invest in their 1st-10th best ideas?
So for our purposes, let’s assume traditional active stock-pickers pare down the 500 S&P companies to 10-30 companies, sacrificing broad-based diversification for only 6.60% of their funds to outperform the S&P 500 Index benchmark over 15 years.
How, then, can traditional active managers reduce this systematic underperformance? One way is to not sacrifice as much diversification via stock-picking in exchange for not as much underperformance.
That’s my assessment of This Must-Know ETF Beats the Market Over the Long Term. As readers of this newsletter know, I take great interest in - and apply great scrutiny to - claims of long-term S&P 500 Index outperformance. This “must-know ETF” is the Invesco S&P 500 Quality ETF (SPHQ), which currently has 101 holdings and its top 10 positions make up 48.5% of the fund. So there’s a strong case that SPHQ is more diversified than traditional active stock pickers holding 10-30 positions.
As for performance, SPHQ is one of the rare funds that over the past 10 years has beaten VOO (Vanguard’s S&P 500 Index Fund ETF). Beaten by how much, you ask? This much:
Over time, SPHQ’s results have been comparable to those of VOO. Over the past three years, VOO has beaten SPHQ by a narrow margin with a total annualized return of 12.8%. But over the past five years, SPHQ’s annualized return of 11.4% is actually slightly better than VOO’s 11% return. Over the past 10 years, SPHQ also leads VOO by a narrow margin of 12.1% to 11.9%, making SPHQ one of the rare ETFs that can say it has “beaten the market” over the long term, albeit by a very narrow margin.
12.1% versus 11.9%, for a very slim 0.2% edge that is sufficiently slim for which random variation is Likely the best explanation. Across an entire industry there will be a handful of funds that outperform through random variation alone, just like a perfectly fair coin flipped 100,000 times will very Likely land heads (or tails) slightly more (or less) than 50,000 times - but we would not say the coin is biased unless the outcome is significantly different from 50% heads (or tails).
The other question of course is whether SPHQ’s 0.2% “edge” will persist over the next 10 years from now. If randomness ebbs just a little bit in the other direction than the past 10 years, that 0.2% “edge” could vanish. Additionally, while both VOO and SPHQ have small expense ratios (0.15% for SPHQ versus 0.03% for VOO), SPHQ’s performance “edge” is likewise small, and SPHQ’s 0.12% higher expenses will continue to eat away at whatever portion of the 0.2% “edge” that remains.
So here’s my assessment: Stock-picking funds that want to reduce systematic causes of Index underperformance can increase diversification to narrow that performance gap, in effect becoming more like the Index benchmarks against which their performance is measured but not too close to the Index to be labeled the derogatory “closet indexer”. Then through random variation alone, some of those funds will experience performance swings that close the rest of that performance gap and a slim bit more, leading to headlines like This Must-Know ETF Beats the Market Over the Long Term.
Thinking in Likelyhoods, unless there is significant outperformance beyond what random variation can explain, there is no basis for attributing a slim performance edge to “skill”. Q.E.D.
ONE MORE THING…
The meme stocks are back… are they “gonna be in trouble”? The meme stocks are back and that could be a red flag for the stock market, says strategist
The use of transitory has not *entirely* been transitory. Inflation has been 'much more transitory than persistent', says Ed Yardeni
When regulation is good. Strong regulations promote healthy financial markets. This Ripple decision may be exactly what the Crypto industry needs. Ripple Labs notches landmark win in SEC case over XRP cryptocurrency
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