The jump in market volatility in 2018 has provoked several concerns regarding the nature of today's electronic trading environment. The NYSE's unique hybrid market model, which features a Designated Market Maker (DMM) obligated to provide liquidity and facilitate auctions, offers unmatched stability relative to other global markets, especially during times of market turbulence.
NYSE's auctions differ from those at other equities exchanges because they combine human judgment exercised by the DMM who oversees trading activity, with automated mechanisms. This enables NYSE to conduct a manual auction if deemed necessary by market conditions. To conduct the auction, the DMM aggregates buy and sell interest and executes the maximum volume at a price intended to be reflective of market supply and demand.
The manual auction therefore gives the DMM both price and time flexibility, unlike electronic auctions which because of their automated nature are more susceptible to large price dislocations due to temporary supply and demand imbalances -- a scenario which occurred recently during an auction on the Singapore Stock Exchange (SGX). The risk of fast, automated executions has led many global exchanges, including NYSE Arca, NYSE American, and Nasdaq, to implement auction "collars" with others coming on line shortly1.
These collars set price thresholds to help to dampen auction price movement. However, the NYSE Opening Auction consistently outperforms electronic auctions, especially during volatile periods. The NYSE Closing Auction, which is more than 7.5% of total NYSE-listed trading activity, similarly outperforms other auctions.
The chart below shows the weekly count of symbols that experienced price dislocations2 above 1% for both NYSE and Nasdaq Closing Auctions, and the difference between the two measures. As the chart shows, while auction price dislocation tends to increase during periods of heightened volatility, the dislocation in the NYSE Closing Auction is far less severe compared to the dislocation that occurs in the Nasdaq Closing Auction. This is attributable to the DMM's ability to help find relevant closing prices even in challenging market conditions3.
2018 was a historic year for equity markets, with major indices reaching record highs before retreating to finish down for the year. Volatility made a comeback, but how did it compare to recent years?
Volatility returned to US markets in 2018, leading to record equity options trading volumes. At the same time, there was increased interest in options market quality, particularly as it relates to the displayed quote.1 Given NYSE’s advocacy for robust displayed markets, and the unique nature of the year’s volatility and volume, 2018 was an ideal year to study the influence of volatility on quoted spread and size. We found that adjustments for option prices can dramatically alter the interpretation of market quality metrics: after converting quoted bid-ask spread values from cents to basis points and converting quoted size from contracts to dollars, the data shows that displayed market quality was fairly consistent during some of 2018’s volatile periods.
Our analysis leveraged NBBO quote data provided from S3 and was limited to the top 15% of symbols by options ADV. We focused on options with underlying symbols that had at least 1,000,000 shares of ADV, removed any new listing and delisting, and removed symbols that changed Penny Pilot groups during the year. Finally, we excluded SPY so as to not skew the results. SPY accounts for nearly 20% of options volume, and this scale likely gives it market structure attributes not common to the rest of the tradable universe. The resulting list of symbols accounted for 71% of total equity options volume.
We calculated our normalized spread as a percent of average option price in basis points:
NBBO Spread (% bps) = Avg NBBO Spread Width ($) / Avg Option Trade Price ($)
Size was converted from contracts to dollars by calculating notional size:
NBBO Notional Size ($) = Avg NBBO Size × Avg Option Trade Price
In the charts below, we plotted monthly average spread and size for Penny Pilot (PP) and Non-Penny Pilot (NPP) symbols. In 2018, NPP names had a spread of roughly 3x the spread of PP names, accompanied by 60% greater size compared to PP names. NPP posted size fluctuated over the year, ending the year higher than it began. We also observed that spread widths for NPP symbols fluctuated more than PP symbols in nominal terms, but less so using normalized measures.
Nominal spreads followed the fluctuations of the VIX closely. We saw nominal spreads widen with the elevated VIX levels in February and March, as well as in October through the end of the year. When we adjust the spread calculation to show percent of option price in basis points, spreads still appeared wider in February, but were more muted the rest of the year. We hypothesize that the speed at which VIX spiked in February, moving from 13.47 to 37.32 in two trading days, had a greater impact on spread widths compared to the more gradual rise of VIX across months late in the year when it rose from 11.61 to 36.07 in 56 trading days. The normalization shows that although nominal spreads increased, they increased proportionally and in tandem with option prices. In our population, average option prices increased by 31.5%, whereas spread widths only increased by 25.5% in Q4.
The impact of these price normalization steps can be particularly meaningful at an individual symbol level. In nominal terms, high-priced symbols Google (GOOG) and Amazon (AMZN) had some of the worst spreads and posted size. However, in notional terms, GOOG had the tightest quoted spread of all non-penny symbols (422 bps) and moved much closer to the average posted notional size of $85,000; AMZN experienced similar results. Additionally, household names such as McDonald’s (MCD), Caterpillar (CAT), and Stanley Black & Decker (SWK) exhibited tightened quoted spreads after we converted from nominal terms to basis points. We believe that converting spread and size to notional terms may change perceptions of market quality and improve the ability to compare among symbols.
Unlike the US listed options market, many global markets of various asset classes use notional values for quote and trade increments. The above analysis details the merit of using notional values for assessing US listed options market quality, especially in highly volatile times. This perspective shows that the US listed options market handled 2018’s volatility fairly well, particularly when the elevated volatility was sustained over a period of time.
At last month's hotly debated SEC market data roundtable, there was passionate discussion over both the nature of competition and fees charged for market data and market access. Some around the table, including IEX, chimed in that exchanges should only charge transaction fees. Market data and connectivity services, they said, should be provided for free.
We disagree. Exchanges are integrated enterprises that bring broad value through trading and technology services, but not all clients use every exchange offering. Allowing investors to order a la carte empowers each to select the blend of services they need. By contrast, charging high transaction fees but "giving away" data and connectivity is like requiring all diners to order a prix fixe tasting menu. Importantly, the current competitive market structure allows investors both choices. While the blend of exchange revenues has shifted from purely transactional to a mix of execution and technology, the overall costs on NYSE markets remain a bargain. In fact, the All-in Cost to Trade on NYSE Group exchanges is lower than on IEX or on many dark pools.
Keep your eyes on the prize: the All-in Cost to Trade on NYSE Group exchanges is less than $0.0007 per share. For our members, compare that to the $0.0009 transaction fee per share that IEX charges for most of its trading, and to a typical ATS fee of $0.0010 per share.
In fact, NYSE's competitiveness is understated in the comparison above as 1) the NYSE group calculation includes some fees associated with multiple NYSE markets, including NYSE Options, and 2) there are still datacenter and telecom fees associated with trading on IEX and dark pools, even if they are earned by unregulated datacenter and telecom providers instead of the trading venue operators.
Some market participants have understandably asked for a full breakdown of our calculation. We are happy to oblige, and stand ready to discuss any market participant's specific All-in Cost to Trade upon request.
Applying separate fees for market data and connectivity allows members to choose the right bundle of products for their specific business. For example, members of multiple NYSE exchanges do not necessarily subscribe to the same market data products or use the same number of logical order ports on each venue. Some participants add and remove liquidity evenly, while others are skewed more toward one style of trading. Because market participants choose different trading, data and connectivity services, it's entirely possible that any given member's All-in Cost to Trade may be lower or higher than the average in our calculation.
What's your own All-in Cost to Trade?
Reach out to us at firstname.lastname@example.org to find out.
In today's U.S. equities markets, roughly 60% of volume executes on an exchange and 40% executes off-exchange in dark pools and other broker-dealer facilities. All activity, both on and off exchange, relies on the quoted prices from exchanges to inform transaction pricing. This makes the National Best Bid and Offer (NBBO), reflecting the best quoted prices from all exchanges, a key benchmark for all types of trading, including midpoint trading leveraged by institutional investors and price improvement offered to retail investors. We have seen that the quality and quantity of quotes contributing to the NBBO can vary dramatically between exchanges, which we can measure using a new metric called "Enhanced Quoted Spread."
The NBBO that facilitates midpoint trading and retail price improvement arises from robust competition among exchanges to provide the highest bid and lowest offers for the longest portion of the day. One standard calculation for quoting performance is the exchange's average quoted spread. Taking a simple average of quotes published by an exchange, however, can hide an important fact: many exchanges offer two-sided quotes for only a small portion of the day in many stocks. This means that an exchange's "average" quoted spread may exist for only fleeting moments of the day, and market participants looking to execute on such an exchange may frequently find the venue does not offer a competitive quote (or sometimes any quote at all).
The Enhanced Quoted Spread (EQS) measure addresses this by replacing any missing quotes with the value of the Limit Up Limit Down (LULD) band.1 If an exchange has a one-sided quote, or no quote at all, we assign that exchange the LULD band price rather than drop the observation. With this method, exchanges with occasional or periodic quotes incur a penalty for their lack of displayed liquidity rather than misrepresenting a tight but infrequent displayed market as narrow on average.
For many exchanges, the EQS calculation is similar to the average quoted spread calculation, especially in active stocks. For example, in active NYSE names, NYSE, NYSE Arca, and Nasdaq have nearly equal quoted spread and EQS calculations. However, exchanges with low market share and/or dark-oriented trading models fare worse under the EQS approach in both active and less-active stocks. For example, three venues exhibit EQS metrics of several times their standard quoted spread results, indicating they frequently have no displayed quote in the market.
As many investors have focused more attention on off-exchange trading, the exchange contribution to price formation has become frequently overlooked or even derided, even though off-exchange trades rely on exchange quotes to set prices. As the Enhanced Quoted Spread shows, contributions to price formation vary widely among exchanges. Maker/taker venues consistently outperform other venue types in both average quoted spread and in the EQS measure, suggesting that under today's market construct pricing incentives contribute positively to both the quality and reliability of displayed quotations.
1 The NYSE wishes to thank David Weisberger, who gave us the idea for this calculation.
The price of a closing auction on the primary listing exchange determines the official closing price for most liquid corporate stocks and exchange-traded products (ETPs). However, many less-liquid securities do not receive sufficient closing-price interest to generate an auction. In such cases, the official closing price is based on the consolidated last sale price before the end of trading. This presents a distinct problem for less-liquid ETPs: if the underlying index/underlying fund holdings and corresponding market maker quotes have changed, but there have not been any consolidated last sales in the closing minutes of the market, the consolidated last sale value will not reflect current market pricing. This will then cause a greater disparity between the market price and its underlying net asset value (NAV). Around two-thirds of listed ETPs fit into this category.
To address this issue, in June, NYSE Arca introduced a process for setting an official closing price for its listed ETPs that better reflects the true value for ETPs that do not end trading with a closing auction. The new Arca Official Closing Price (AOCP) methodology for NYSE Arca-listed ETPs applies when an ETP does not have a closing auction, or the closing auction is an odd lot. Previously, the AOCP for ETPs without an eligible closing auction was the consolidated last sale, regardless of when the last sale occurred - be that days, weeks or even months prior.
The new AOCP methodology uses both consolidated last sale and National Best Bid and Offer (NBBO) inputs. NYSE Arca tracks the time-weighted average midpoint price (TWAP) of the NBBO over the last five minutes of the trading day. The TWAP and the consolidated last sale are blended based on the last sale time to determine the AOCP, according to the following schedule:
AOCPs derived solely from quoting activity account for 44% of total closing prices, while AOCPs reflecting a mix of trade and quote activity account for 5% of the total.
To assess the new logic's performance, we compared the difference between the AOCP and NAV Price before the logic took effect (May 1st 2018 - June 1st 2018) and after the logic was implemented (June 4th 2018 - June 30th 2018). We measured the average difference between the AOCP and NAV Price (at close) by product for each time period for products without auctions and then compared them across the before and after periods. As expected, the average difference between the AOCP and NAV tightened dramatically with the new logic.
As expected, this performance improvement relative to NAV is most pronounced in the least-active products. For ETPs trading under 10,000 shares per day, the median difference between the AOCP and NAV decreased from 60 basis points previously to 10.6 basis points using the new logic. Because the AOCP sets the reference price for the 'Limit Up Limit Down' (LULD) mechanism on the next trading day, an AOCP more closely aligned with the NAV will reduce the potential for an LULD trading pause to be triggered. LULD is invoked during times of volatility and sets a percentage level above and below which a security can move within a five-minute period. A security is automatically halted if the price would move outside the set percentage levels. An incorrect reference price is likely to lead to more LULD trading pauses because it does not reflect the value of the security.
As these results demonstrate, the new AOCP logic produces closing prices closer to NAV for the nearly two-thirds of ETPs that close without an auction each day. In addition to helping fix hard-to-explain premiums/discounts due to stale data, a more accurate official closing price also helps provide market protection with better reference prices to LULD. Furthermore, since ETPs report market price returns versus returns on NAV, stale closing prices can inaccurately skew performance data. This is of particular concern on month-end, quarter-end, and year-end dates.