TECH TUESDAY is a weekly content series covering all aspects of capital markets technology. TECH TUESDAY is produced in collaboration with Nasdaq.
One way we can affect change ourselves is with our own listing rules and market incentive programs. In fact, we’ve shown that our more level playing field, with incentives available to all Nasdaq members, creates a more competitive market. Our data shows that having multiple buyers and sellers both working from opposite sides to tighten spreads is better for investors and issuers.
Simple averages miss the point
However, because we spend so much time looking at trading, we know you can’t easily compare one stock to another.
Things like market capitalization drive spreads, and liquidity cause spreads to vary from less than 0.01% to well over 1.00%. It is also well known that growth and small-cap stocks have higher “beta,” which is good when markets are rising but also — by definition — means they will have higher volatility while that happens.
In short, when comparing stocks and markets – simple averages are just bad math.
Take, for an initial example, the data in Chart 1 below, which compares stock size and spreads by listing venue. Almost all stocks (dots) form along a diagonal line, showing that market capitalization matters a lot to spreads. In fact, for stocks larger than $1 trillion, the widest spread is around 9 basis points (bps) (for GOOG, before its recent split), which is cheaper than the best spread in stocks smaller than $1 billion.
Chart 1: Spreads are driven more by stock characteristics like company size than listing venue
Coloring this chart by listing venue highlights that the dots almost totally overlap. Far from there being a clear winner – when stocks are compared correctly, the differences are measured in basis points.
It would be easy to average “all stocks” by color. In fact, even though there are hardly any grey dots below $200 million in market cap in Chart 1, the grey dots that do exist confirm that the expected trend (that smaller stocks continue to have wider spreads) continues.
Clearly, the simple average would be different by a factor, not a fraction. That’s because:
- The spreads on smaller stocks are well over 100 bps — or 100x larger than for larger stocks – driven mostly by company size. So smaller stocks drag an average up pretty quickly without saying anything about relative market performance.
- To highlight this, consider larger-cap stocks also trade more because they have more value invested in them. If we calculate the trade-weighted average cost of spread, which is closer to the costs for real investors, we see that the spread is closer to 9 bps, while a simple average of all stocks, the spread is around 75 bps. That’s more than 8-times higher, just because it counts small companies that trade less the same as larger companies that trade a lot.
As all can see from the chart, using a single average without accounting for trading patterns is like comparing apples to oranges.
Understanding cross-sectional trends matter too
The composition of the stocks that make up the blue dots and the stocks that make up the grey dots are also very different.
We already know market cap matters. But within market capitalization, there are differences too. For a start, almost all of the $1 trillion-plus companies are blue. They make up around 8.5% of all the value traded each day, but when using a simple average, they count for just 0.05% of the result.
Even though Nasdaq has far more listings, it turns out the representation in institutional benchmarks, like the Russell 3000, is almost exactly equal (blue zone in Chart 2). Although, the composition of the blue zones is different.
But what makes averages even less relevant is the fact that Nasdaq has almost twice as many listings in total. That means micro-cap stocks account for 48% of the Nasdaq average but only 10% of the NYSE average.
Chart 2: Nasdaq lists far more growth companies
The fact that Nasdaq has so many more micro-cap stocks proves it is the preferred listing venue for newer growth companies. That’s clear from the data in Chart 3, which shows two things:
- The average company age is more affected by market cap than listing venue.
- But at every level of market cap, Nasdaq’s listings are still younger.
Chart 3: Nasdaq lists more younger, growth companies
Having younger, new economy stocks also affects the sector composition of each listing venue. Not surprisingly, newer companies tend to be more common in sectors like Health Care, Technology and Biotech than in Financials and Utilities.
However, those sector differences are also important to understanding how averages can be misused. Average intraday volatility is higher for newer, higher-growth sectors (Chart 4). That makes sense when we consider other research we’ve done that shows the prospects of those companies can vary widely as those companies grow into their new markets – partly because of less diversified businesses and partly because of a lack of long-term financial comparables to use for forecasting cashflows.
Understanding stock and sector-wide volatility is another important factor to account for market quality – as it is widely acknowledged that volatility also adds to spreads, even when all other things are equal.
Chart 4: New economy stocks tend to have more intraday volatility (regardless of their listing venue)
There are benefits of being a newer, high-growth company. Usually, they have higher valuation multiples and higher beta. That is all usually good when stocks are rising, but by definition, it means the stock also tends to have more volatility.
Once again, we see that the average P/E multiple changes more as market cap changes – even more than the difference we see within each grouping due to the listing venue.
Chart 5: Nasdaq lists more higher-growth companies
How do statisticians compare different populations?
The fact that many stock-specific factors affect things like spreads, trading and close volatility doesn’t mean it’s impossible to compare data across venues. Sometimes something as simple as grouping by one of the major factors (as we do with market cap above) will show a much fairer result.
However, statisticians have even better ways to do this mathematically. For example, in this research paper, academics use panel regressions to compare closing auctions between Nasdaq and NYSE. They accounted for stock characteristics like size, spread, stock prices, turnover and volatility on the day (see page 14 in the paper).
After doing that, they found that “price deviations are 1.2 bps larger for NYSE auctions.” That represents about 18% of the average price deviation (8.1 bps) – an important fraction of the average but not a multiple.
One reason, they suggest, might be the advantages that d-quote gives some participants over others. However, another paper found that NYSE auction order imbalances and indicative prices are also less accurate, which can make NYSE auctions less efficient. In short, an unlevel playing field adds to a lack of transparency of supply and demand, reducing the competition for closing liquidity.
We replicate their findings more simply, just grouping stocks by market cap. What that shows is consistent with the research above. The deviations are driven much more by size than listing venue, but across all categories, Nasdaq’s close is better.
Chart 6: Closing auction dislocation (after accounting for company size groups)
It’s harder, but not impossible, to compare AAPLs to oranges
What this all shows is that using something as simple as an average to compare all the stocks in the market is like comparing AAPLs to oranges – they might both be fruit, but everything else about them is very different.
But it’s not impossible.
There are well-known statistical ways to do this accurately, as well as easy ways to compare stocks that are more alike in the first place.
You just have to understand how the trading trends work in the first place.
Phil Mackintosh is Chief Economist at Nasdaq.