SQM Research Newsletter and Ratings Update - April 2023
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Market-Cap v. Equal Weight 

by Rob da Silva, Head of Research 

One of SQM's research analysts recently shared an interesting commentary piece from S&P Global. 

In reviewing the first quarter, it said: 

"The S&P 500 increased 7.5% during the first quarter of 2023. Though it was led by a few big outperformers, more than half of the stocks on the index closed above their end-of-December prices." (S&P DJI Daily Dashboard - Monday, April 3) 

The sentence that really caught my eye was: 

"Most of the S&P 500's Q1 gains were concentrated in just a handful of large names: the top 10 contributors were responsible for 90% of the benchmark's gains, with the triumvirate of Apple, Microsoft and Nvidia contributing over 50%." 

This seems pretty spectacular and uncommon. Or is it? So down the rabbit hole I went to take a look at the Australian market performance for Q1. 

The S&P ASX 200 increased by 3.46% during the first quarter of 2023. After some deft number crunching, the following table spat out: 
 

The concentration of returns seen in the US played out in a similar fashion in Australia. Over 50% of returns came from 3 tech giants in the US. In Australia, our three titans delivered over 40% of returns. Tech behemoths? No! Mining behemoths – BHP, Rio and Fortescue. It couldn't be a better example of the structural differences between the US and Australian markets. Especially the concentrations in sector weights in those markets. 

To take another step further, the top 10 stocks in the US brought 90% of the gains. In Australia, it was over 77%, so not hugely different. 

What drives such outcomes is the combination of market-weighted indexes combined with a long run of outperformance from certain sectors (tech) or styles (momentum and growth). 

Index returns become massively dominated by the combination of popular, trending stocks that are outperforming the rest of the pack to the point where they are a huge part of the index. 

Big Weight times Big Performance =  All (well, most) of the Returns     FANG anyone? 

This works in reverse as well. When the bubble pops, or the air goes out of these balloon stocks, they are the anchor that drags the market down. 

So this leads us to a time-honoured debate – market-weighted or equal-weighted, which is better? 

Let's examine the pros and cons and then check some empirical data. 

Market-cap-weighted indexes are often dominated by a handful of mega-caps. These stocks have achieved that status over a long period of evolution which includes super-normal business growth and heavy stock price outperformance of the 'rest of the pack'. That's great while all is going well. The success of these elites "pulls up" the performance of a market cap index due to the virtuous circle of a heavy core of outperforming stocks that are big and growing in weight. But the same can happen in reverse. When the bubble pops, the downfall can be swifter and more vicious. And the market-cap index plummets right along with the previously celebrated darlings. 

Take Netflix as an example. Its stock peaked at around US$690 on 29th Oct 2021. It was owned by just about everyone – celebrated for its rapid subscriber growth and worldwide domination of the streaming industry. Then came the shocking statement that subscriber growth had pivoted to reverse gear – cataclysmic! When the smoke cleared, the stock had tanked by 72%, bottoming at US$190 on 29 Apr 2022 (it's now in the low $300s). It took a mere 6 months to lose over 70% of its value. Prior to the 2021 peak, the last time the stock had traded at around US$190 was 17th Nov 2017, 4 years earlier. 

So 4 years of relentlessly crawling up the stairs to US$690 was completely undone in 6 months by jumping in the elevator and hitting the down button. 

When the stock market hits a cyclical peak, the market cap-weighted indices are almost always overweighted to overvalued stocks. It's a natural consequence of more money chasing the latest and greatest winners. It also skews the perception of 'value' in the rest of the market as the mega-caps do not necessarily represent the whole market. There can be huge sector biases, as our example at the start demonstrated. The US is dominated by big tech, and Australia is dominated by big mining. Not the best way to get true diversification. 

As for equally-weighted indexes, they tend to have less sector bias but arguably more size bias. Each stock in the index has the same weight, regardless of its market capitalisation. So the mega-caps concentrated in an industry group don't dominate – thus, less sector bias. 

Consequently, equal weighting provides more exposure to smaller companies that may have higher growth potential. That's great for performance, but there is somewhat of an offset. Liquidity. Small stocks are harder to buy and sell, seen as more 'risky' and may be more sensitive to market fluctuations. The overall impact of this might be to increase the volatility of equally weighted indexes and increase the size of drawdowns when they occur. 

On the other hand - smaller companies may be better positioned to capitalise on niche markets or emerging economic or industry trends. This means that equally weighted indexes may offer better downside protection in certain market conditions. So it's not entirely clear-cut! 

Let's look at some empirical data to see if it might provide some clarity. 

The charts and tables below are constructed from the following data set: 

• The universe of stocks making up the S&P/ASX 200 Index 

• Total performance for each of those stocks for Q1 2023 (i.e. 3 months) 

• Portfolios of varying concentrations (10, 25 and 50 stocks) drawn at random from the ASX200 

• 5,000 random portfolios are equally weighted (designated as EQ.Weight) 

• 5,000 random portfolios are weighted in proportion to the top "n" weights in the ASX200 Index, depending on the concentration of the portfolio set (designated as Gross.Mkt.Weight) 

The next three charts show the distribution of returns from the 5,000 random portfolios: 

(Note: each chart has the same axes scales so they can easily be compared visually) 

In looking at these three charts, the following observations stand out: 

• The probability of getting the average (i.e.' market') return increases as the number of stocks increases. 

• The probability of getting the market return portfolio is always higher for the equally weighted portfolios than for market-cap weighted: 

'Market Return' Probability 

• The spread of outcomes is tighter for the equally-weighted portfolios in all cases. 

• Consequently, the 'tail risks' are higher for market-cap-weighted portfolios, i.e. they have a greater risk of underperforming and a greater risk of outperforming. 

• The negative (LHS) and positive (RHS) market-cap tail risks are not symmetrical. There is a higher probability of market cap outperforming equal weights than there is of them underperforming. You can see this by looking at the size of the gap between the blue and red lines on each side of the distribution. 

So the 50 stock left-hand side (below average returns) looks like this: 

While the right-hand side (above average) has a bigger area under the curve (i.e. probability) 

The table below re-affirms the observations above and prompts a couple of additional comments, namely: 

• The 'average' return is higher for the market-cap portfolios, but… 

• The % of portfolios with negative returns is considerably higher for the market-cap portfolios. So unsurprisingly, the extra return comes with extra risk. 

To wrap up, here is the table in pictures, as visualisation is often easier to digest: 

(Note: This is for a 3-month period, these observations may not all hold for longer timelines)