Capitalizing On Distribution of Equity Index Returns
The market’s movements in 2020, 2022, and 2023 serve as good examples of the conceptual efficacy of an approach centered on individual securities. Such a strategy can cut poor performers while holding tightly to leaders. This can have tremendous consequence in years when there’s heavy skew in the distribution of returns, as demonstrated in the graph below, which shows the distribution of returns for each component of the SPDR S&P 500 ETF Trust (SPY).
SPY Constituents – Distribution of Return
Applying our systematic investing process at the individual stock level accounts for distribution of returns in a way that an index or ETF cannot.
2020 Case Study
While 2020 presented a wider than usual dispersion in annual returns, the historical distribution maintains a similar shape in that the index mostly consists of stocks that deliver close to the average return, but with significant outliers on both tails. For example, 2020 resulted in 54 stocks in the index delivering greater than 50% gains, while 42 stocks fell by 25% or more. The opportunity in 2020 rested in the fact that some sectors, like technology, performed excellently and were barely interrupted by the COVID-19 pandemic. Conversely, energy suffered coming into the crisis, and the negative performance was only exacerbated by the pandemic.
2022 Case Study
In a year like 2022, when the S&P 500 Index was down approximately 18%, you might expect the meat of the distribution to fall in negative territory. That assumption held true. However, it might be surprising to see that the highest frequency of stock returns fell in the < -35% bucket, as well as potentially shocking to learn more than 30 companies produced a return > 35%. Clearly this demonstrates an opportunity to be exploited by systematically moving away from outliers on the right side of the graph above and toward those on the left side.
2023 Case Study
The year 2023 is visually different and more normal in its distribution than 2022, but the opportunities are just as apparent. When companies like Lumen Technologies (LUMN) and Signature Bank (SBNY) become virtually or actually worthless, other stocks like NVIDIA (NVDA), Meta Platforms (META), and Tesla (TSLA) enjoyed an AI/tech-fueled rally. The result was that almost the same number of stocks produced a > 35% return (42) as those in the -10% to -15% or 10% to 15% buckets. One doesn’t even have to make aggressive allocations to improve their odds if they can consistently overweight the outperformers while underweighting the underperformers.
Longer-Range Perspective
The following table also illustrates the point, but over a longer time period.
You see that it is common for last year’s star sector is often the following year’s dog, and vice versa. For example, the energy sector has been either the bottom or top performer for four consecutive years.
Source: novelinvestor.comWhile the same effect could be accomplished by trading sector-based ETFs rather than individual stocks, it doesn’t account for potential distribution of returns within each sector. Said another way, trading sector ETFs would be like taking a hatchet to a problem, whereas unwrapping the index allows scalpel-like precision.
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