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Two Charts Show How Smart Traders Use Odd Lots to Compress Spreads in High-Priced Stocks

Two Charts Show How Smart Traders Use Odd Lots to Compress Spreads in High-Priced Stocks

For most stocks priced above $100 per share, a few things start to happen.

Firstly, as we’ve previously highlighted, almost all stocks start to trade with a bid-offer spread that is multiple ticks (cents) wide.  In addition, we see spreads in basis points also become wider on these high-priced stocks.

V is for Volume,but it also supports our proposal for intelligent ticks and the SEC’s own proposal to review round lots.

Let’s explain what we mean:

High-priced stocks almost all trade multiple ticks wide

Although high-priced stocks seem attractive to issuers these days, on many levels their tradability declines. Even hyper-liquid stocks like AMZN and GOOG trade with an average spread around 80 cents.

In fact, if we replot a chart from our “Data is Out There” post, but convert spreads to cents (instead of basis points) we see that spreads (Chart 1):

  • Increase in a reasonably tight diagonal channel as price rises
  • The diagonal channel starts at a lower price as market cap falls
  • This seems to indicate there is a consistent limit to how tight a spread can get, even for extremely liquid stocks

Chart 1: Spread in cents increase in a diagonal channel above 1 cent as stock prices increase

Avg. spread vs price

Source: Nasdaq Economic Research (data still from Sept 2018)

Why is this a problem?

We recently showed that these stocks start to have wider spreads in basis points too, shown by the U-shape in Chart 1 of our prior report. This increases investors’ costs to trade, which in theory increases the Weighted Average Cost of Capital (WACC) and should reduce prices that portfolio managers are willing to pay.

For traders, it also leads to a higher chance of quotes “flickering” as well as more adverse selection for those posting limit prices trying to capture spreads—making these stocks harder to trade.

But perhaps the biggest problem is the fact that U.S. market rules still use round lots of 100 shares for almost all stocks. High stock prices mean a round lot becomes a very large trade. For example, a round lot in GOOG costs around $120,000 while a round lot in AMZN costs almost $200,000. To put that in perspective, $200,000:

  • Qualifies as a “block size” trade<
  • Is more than 25 times the $7,600 average trade in US markets, and
  • Is larger than 99% of all retail orders[1]

Smart traders use odd lots to compress round lot constrained stocks spreads

Interestingly, when we look at the data, we see that smart traders have (again) found a more optimal solution. The higher stock prices get, the more they use odd lots at limits inside the NBBO.

Before you ask, Reg NMS does not include odd lots in the NBBO. Instead the SIP is required to post quotes based on round lots. In other words, the national best bid and offer (NBBO the public sees) is made up only of round lots. That in turn means any odd lots inside the NBBO don’t count toward the “true” spread in the market.

Odd lot limit orders also aren’t “protected”. That means that even if a small investor buying 53 shares is bid at a higher price than the SIP’s bid (NBB), sellers can “trade through,” selling at a lower price without filling the odd lot order.

Importantly, what this means is spreads are quoted artificially wide because these high-priced stocks are round lot constrained. Interestingly, the data shows another diagonal channel forming, where the probability of an odd-lot being inside the NBBO increases consistently each time stock price doubles.

As Chart 2 shows,

  • Stocks with wide spreads (blue circles) see more odd lots inside the National Best Bid or offer (NBBO). In fact stocks like GOOG and AMZN, with spreads closer to $1, see odd lots inside the quote almost 70% of the time. In contrast, stocks like APPL and MSFT, with spreads below 2c, see relatively small percentage of odd lots inside the NBBO.
  • Less liquid stocks (smaller circles) see higher odd lot rates for the same price level (circles are liquidity in $, and tend to shrink as you raise up vertically from the x-axis).

Chart 2: Percent of time Nasdaq market best quotes were odd lots for Nasdaq 100 stocks.

Percent of time true Nasdaq top of book was less than 100 shares

Source: Nasdaq Economic Research

This data is consistent with recent public comments by SEC Chair Jay Clayton and Commissioner Brett Redfearn, where they noted that an odd-lot quote is the best bid, best offer, or both approximately 75% of the day for stocks priced over $500, compared to approximately five percent of the day for stocks priced under $100. As a consequence, they proposed that SEC staff explore whether we should adjust the round lot size.

Nasdaq aggregates odd lots to round lots to be displayed on SIP in 2019

To help alleviate this problem, Nasdaq announced that beginning in Q1 2019, resting odd-lot orders are now aggregated and sent to the Securities Information Processor (SIP) for display as the Exchange’s BBO (best bid and offer).

How does this work?

Previously, if the aggregate size of all resting displayed orders at the single best price to buy and sell is less than one round lot, the orders are not included in SIP or included in the Exchange’s BBO.

After this change, if resting odd-lot sized orders at multiple price levels can be aggregated to equal at least a round lot, these odd lot sized orders, in aggregate, were sent to the SIP for display at the most aggressive price where all displayed orders at that price or better aggregate to equal at least a round lot (see example in Table 1).

Table 1: Worked example for a BID:

That should help ensure that all routers are able to find more odd lots at the best price, regardless of where they are resting.

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[1] What is Retail?, Virtu, Oct 2017

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Phil Mackintosh

Nasdaq

Phil Mackintosh, Nasdaq Chief Economist, has 28 years of experience in the Finance industry, including roles on the sell-side, buy-side and at accounting firms, which included managing trading, research and risk teams. He is an expert in index construction and ETF trading and has published extensive research on trading, ETFs and market structure.

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