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Effects of stock attributes, market Effects of stock attributes, market structure, and tick size on the speed of structure, and tick size on the speed of

spread and depth adjustmentspread and depth adjustment

Kee H. ChungKee H. ChungState University of New York (SUNY) at BuffaloState University of New York (SUNY) at Buffalo

Chairat ChuwonganantChairat ChuwonganantIndiana University-Purdue University at Fort WayneIndiana University-Purdue University at Fort Wayne

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MotivationMotivation

The bid-ask spread is an important The bid-ask spread is an important measure of market quality because it measure of market quality because it represents the cost of trading in represents the cost of trading in securities markets.securities markets.

Marketmakers adjust the bid-ask Marketmakers adjust the bid-ask spread in response to new spread in response to new information embedded in order flow, information embedded in order flow, trades, and return volatility, among trades, and return volatility, among other factors.other factors.

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We know very little about the We know very little about the dynamics of the bid-ask spread. Prior dynamics of the bid-ask spread. Prior studies offer little evidence as to the studies offer little evidence as to the speed at which new information is speed at which new information is impounded into the bid-ask spread.impounded into the bid-ask spread.

There is also limited evidence There is also limited evidence regarding how market structure and regarding how market structure and trading protocol, such as tick size, trading protocol, such as tick size, affect the speed at which new affect the speed at which new information is incorporated into the information is incorporated into the bid-ask spread. In this study, we bid-ask spread. In this study, we provide such evidence.provide such evidence.

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Research Questions Research Questions How quickly do specialist/dealer quotes How quickly do specialist/dealer quotes

incorporate new information? Do incorporate new information? Do specialist quotes reflect changes in specialist quotes reflect changes in stock attributes more quickly than stock attributes more quickly than dealer quotes?dealer quotes?

How is the speed of quote adjustment How is the speed of quote adjustment related to stock attributes? Do stocks related to stock attributes? Do stocks with greater information-based trading with greater information-based trading exhibit faster quote adjustments exhibit faster quote adjustments toward optimal spreads and depths? toward optimal spreads and depths?

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Do stocks that are traded in less Do stocks that are traded in less competitive markets (e.g., fewer competitive markets (e.g., fewer dealers) exhibit slower quote dealers) exhibit slower quote adjustments?adjustments?

Do liquidity providers move more Do liquidity providers move more

quickly to optimal spreads and quickly to optimal spreads and depths for stocks with more frequent depths for stocks with more frequent

trading and higher return volatility?trading and higher return volatility?

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Do smaller tick sizes result in faster Do smaller tick sizes result in faster quote adjustments to new information?quote adjustments to new information?

What is the relation between quote What is the relation between quote

adjustment speeds and variable adjustment speeds and variable measurement intervals?measurement intervals?

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Answers to these questions would be Answers to these questions would be of significant interest to market of significant interest to market regulators because they could help regulators because they could help design better market structures.design better market structures.

Because marketmaker quotes (i.e., Because marketmaker quotes (i.e., bid-ask spreads) determine trading bid-ask spreads) determine trading costs, the speed at which specialists/ costs, the speed at which specialists/ dealers adjust quotes to new dealers adjust quotes to new information is also of concern to information is also of concern to traders.traders.

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How our study differs from How our study differs from previous studies?previous studies?

Hasbrouck (1988, 1991a, 1991b), Hasbrouck (1988, 1991a, 1991b), Hasbrouck and Sofianos (1993), Madhavan Hasbrouck and Sofianos (1993), Madhavan and Smidt (1993), Dufour and Engle and Smidt (1993), Dufour and Engle (2000) examine how marketmakers adjust (2000) examine how marketmakers adjust quote midpoints to signed trades.quote midpoints to signed trades.

Our study examines how quickly Our study examines how quickly marketmakers adjust marketmakers adjust quote widthquote width (i.e., the (i.e., the bid-ask spread) and bid-ask spread) and depthdepth (i.e., number of (i.e., number of shares at the bid and ask) to their shares at the bid and ask) to their optimaloptimal values in response to new information. values in response to new information.

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Huang and Stoll (1996), Barclay (1997), Huang and Stoll (1996), Barclay (1997), Bessembinder (1999, 2003c), and Chung, Bessembinder (1999, 2003c), and Chung, Van Ness, and Van Ness (2001) compare Van Ness, and Van Ness (2001) compare the execution costs of dealer and auction the execution costs of dealer and auction markets.markets.

Amihud and Mendelson (1987), Stoll and Amihud and Mendelson (1987), Stoll and Whaley (1990), Masulis and Ng (1995) Whaley (1990), Masulis and Ng (1995) investigate the impact of market structure investigate the impact of market structure on return volatility.on return volatility.

Affleck-Graves, Hedge, and Miller (1994) Affleck-Graves, Hedge, and Miller (1994) compare components of the bid-ask spread compare components of the bid-ask spread between auction and dealer markets.between auction and dealer markets.

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Heidle and Huang (2002) examine the Heidle and Huang (2002) examine the impact of market structure on the impact of market structure on the probability of trading with an informed probability of trading with an informed trader.trader.

Garfinkel and Nimalendran (2003) compare Garfinkel and Nimalendran (2003) compare the impact of insider trading on effective the impact of insider trading on effective spreads between NYSE and NASDAQ spreads between NYSE and NASDAQ stocks.stocks.

However, none of these studies examine However, none of these studies examine how market structure affects quote how market structure affects quote adjustment speeds on the NYSE and adjustment speeds on the NYSE and NASDAQ.NASDAQ.

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Damodaran (1993) estimates the Damodaran (1993) estimates the speed of price adjustment for a speed of price adjustment for a sample of NYSE and NASDAQ sample of NYSE and NASDAQ securities using the partial adjustment securities using the partial adjustment model of Amihud and Mendelson model of Amihud and Mendelson (1987).(1987).

Thoebald and Yallup (2004)Thoebald and Yallup (2004)

We focus on spreads and depths We focus on spreads and depths

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Some Conflicting ResultsSome Conflicting Results Jones and Lipson (1999) find that quotes in Jones and Lipson (1999) find that quotes in

NYSE- and AMEX-listed stocks adjust more NYSE- and AMEX-listed stocks adjust more quickly to the information contained in quickly to the information contained in order flow than quotes in NASDAQ-listed order flow than quotes in NASDAQ-listed stocks.stocks.

Masulis and Shivakumar (2002) show that Masulis and Shivakumar (2002) show that price adjustments are faster by as much price adjustments are faster by as much as one hour on NASDAQ using a sample of as one hour on NASDAQ using a sample of seasoned equity offering announcements seasoned equity offering announcements by NYSE/AMEX and NASDAQ companies.by NYSE/AMEX and NASDAQ companies.

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Our Main FindingsOur Main Findings

The speed of quote adjustment on the NYSE is The speed of quote adjustment on the NYSE is faster than the speed of quote adjustment on faster than the speed of quote adjustment on NASDAQ.NASDAQ.

In both markets, quote adjustment speed is In both markets, quote adjustment speed is faster for stocks with a larger number of faster for stocks with a larger number of trades, higher share prices, greater return trades, higher share prices, greater return volatility, and smaller trade sizes.volatility, and smaller trade sizes.

Stocks with greater information-based trading Stocks with greater information-based trading and in more competitive trading environments and in more competitive trading environments exhibit faster quote adjustments.exhibit faster quote adjustments.

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The speed of quote adjustment after The speed of quote adjustment after decimal pricing is significantly faster than decimal pricing is significantly faster than the corresponding figure before decimal the corresponding figure before decimal pricing in both markets.pricing in both markets.

Quote adjustment speed increases with the Quote adjustment speed increases with the length of variable measurement intervals.length of variable measurement intervals.

On the whole, our study provides evidence On the whole, our study provides evidence that stock attributes, market structure, and that stock attributes, market structure, and tick size exert a significant impact on the tick size exert a significant impact on the speed of quote adjustment.speed of quote adjustment.

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Market Structure and Speed of Market Structure and Speed of Quote AdjustmentQuote Adjustment

Masulis and Shivakumar (2002) hold that quote Masulis and Shivakumar (2002) hold that quote adjustment speed is likely to be slower on the NYSE adjustment speed is likely to be slower on the NYSE for several reasons.for several reasons.

• Limit orders on the NYSE cannot be updated Limit orders on the NYSE cannot be updated instantaneously or conditioned on public information (such instantaneously or conditioned on public information (such as the stock’s last transaction price) and this slow updating as the stock’s last transaction price) and this slow updating of limit orders can delay revisions in the specialist’s bid of limit orders can delay revisions in the specialist’s bid and ask quotes.and ask quotes.

• NYSE specialists may buy stocks when prices are falling NYSE specialists may buy stocks when prices are falling because of their affirmative obligation to stabilize prices because of their affirmative obligation to stabilize prices and this behavior can slow quote adjustment process. The and this behavior can slow quote adjustment process. The specialists’ obligation to provide price continuity can specialists’ obligation to provide price continuity can reinforce this effect because it requires them to go tick by reinforce this effect because it requires them to go tick by tick through the limit order book.tick through the limit order book.

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• Based on these observations and Based on these observations and the fact that NASDAQ is essentially the fact that NASDAQ is essentially an electronic market in which an electronic market in which dealers do not have affirmative dealers do not have affirmative obligations, Masulis and obligations, Masulis and Shivakumar conjecture that quote Shivakumar conjecture that quote adjustments on the NYSE are likely adjustments on the NYSE are likely to be slower than those on to be slower than those on NASDAQ.NASDAQ.

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Chung, Chuwonganant, and Chung, Chuwonganant, and McCormick (2004) show that a large McCormick (2004) show that a large portion of order flow on NASDAQ is portion of order flow on NASDAQ is either internalized or preferenced.either internalized or preferenced.

NASDAQ dealers do not have strong NASDAQ dealers do not have strong incentives to make quick quote incentives to make quick quote adjustments in response to adjustments in response to information shocks.information shocks.

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Specialists on the NYSE can adjust Specialists on the NYSE can adjust quotes quickly after each trade because quotes quickly after each trade because all order flow in a stock goes through all order flow in a stock goes through one specialist.one specialist.

On NASDAQ however, dealers are less On NASDAQ however, dealers are less able to make quick quote adjustments able to make quick quote adjustments to informed trading because one to informed trading because one informed trader can trade informed trader can trade simultaneously with several different simultaneously with several different dealers before the quotes are adjusted.dealers before the quotes are adjusted.

Hence, NASDAQ dealers may be slower Hence, NASDAQ dealers may be slower

in detecting information-based trading in detecting information-based trading than their counterparts on the NYSE.than their counterparts on the NYSE.

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Specialists on the NYSE may be able Specialists on the NYSE may be able to respond more quickly to changes to respond more quickly to changes in informed trading because they in informed trading because they have face-to-face contact with floor have face-to-face contact with floor brokers while such contact is not brokers while such contact is not available to NASDAQ dealers because available to NASDAQ dealers because NASDAQ operates on an electronic NASDAQ operates on an electronic screen-based system.screen-based system.

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Garfinkel and Nimalendran (2003) Garfinkel and Nimalendran (2003)

find that there is less anonymity on find that there is less anonymity on the NYSE specialist system compared the NYSE specialist system compared to the NASDAQ dealer system. They to the NASDAQ dealer system. They find that when corporate insiders find that when corporate insiders trade medium-sized quantities, NYSE-trade medium-sized quantities, NYSE-listed stocks exhibit larger changes in listed stocks exhibit larger changes in proportional effective spreads than proportional effective spreads than NASDAQ stocks. NASDAQ stocks.

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DataData

The NYSE’s Trade and Quote (TAQ) The NYSE’s Trade and Quote (TAQ) database.database.

We use the trade and quote data for We use the trade and quote data for the three-month period from the three-month period from September 2002 to November 2002.September 2002 to November 2002.

Applied various filters to minimize Applied various filters to minimize data errors data errors

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MethodologyMethodology We partition each trading day into 13 successive We partition each trading day into 13 successive

30-minute intervals. We then estimate the 30-minute intervals. We then estimate the following partial adjustment model for each stock:following partial adjustment model for each stock:

$SPREAD$SPREADi,ti,t – $SPREAD – $SPREADi,t-1i,t-1

= a= a1i1i[$SPREAD[$SPREAD**

i,ti,t – $SPREAD – $SPREADi,t-1i,t-1] + ] + 1i,t1i,t; (1); (1)

$SPREAD$SPREADi,ti,t = the mean dollar spread of stock i = the mean dollar spread of stock i during period tduring period t

$SPREAD$SPREAD**

i,ti,t = the optimal dollar spread of stock i = the optimal dollar spread of stock i during t during t

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$SPREAD$SPREAD**i,ti,t = = 0i0i + + 1i1ilog(NTRADElog(NTRADEi,ti,t) )

+ + 2i2ilog(TSIZElog(TSIZEi,ti,t) + ) + 3i3ilog(PRICElog(PRICEi,ti,t))

+ + 4i4iRISKRISKi,ti,t;; (2)(2)

NTRADENTRADEi,ti,t = the number of transactions = the number of transactions

TSIZETSIZEi,ti,t = the average trade size = the average trade size

PRICEPRICEi,ti,t = the average share price = the average share price

RISKRISKi,ti,t = the standard deviation of quote = the standard deviation of quote

midpoint returnsmidpoint returns

Optimal SpreadOptimal Spread

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Substituting Eq. (2) into Eq. (1) and after Substituting Eq. (2) into Eq. (1) and after rearrangement, we obtain rearrangement, we obtain

$SPREAD$SPREADi,ti,t – $SPREAD – $SPREADi,t-1i,t-1

= = 0i0i a a1i1i

– – aa1i1i$SPREAD$SPREADi,t-1i,t-1

+ + 1i1i a1ia1ilog(NTRADElog(NTRADEi,ti,t) )

+ + 2i2i a a1i1ilog(TSIZElog(TSIZEi,ti,t) )

+ + 3i3i a a1i1ilog(PRICElog(PRICEi,ti,t))

+ + 4i4i a a1i1iRISKi,t + RISKi,t + 1i,t1i,t. (3). (3)

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We then estimate the model for each stock We then estimate the model for each stock using time-series observations:using time-series observations:

$SPREAD$SPREADi,ti,t – $SPREAD – $SPREADi,t-1i,t-1

= = 0i0i + + 1i1i$SPREAD$SPREADi,t-1i,t-1

+ + 2i2ilog(NTRADElog(NTRADEi,ti,t) )

+ + 3i3ilog(TSIZElog(TSIZEi,ti,t) )

+ + 4i4ilog(PRICElog(PRICEi,ti,t) )

+ + 5i5iRISKRISKi,ti,t + + 1i,t1i,t. (4) . (4)

We measure the speed of quote adjustment We measure the speed of quote adjustment by the estimate of –by the estimate of –1i1i. .

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Matched SampleMatched Sample

We calculate MS for each NYSE stock against each We calculate MS for each NYSE stock against each of the 2,888 NASDAQ stocks in our study sample: of the 2,888 NASDAQ stocks in our study sample:

MS = MS = [(X[(XkkNN - X - Xkk

YY)/{(X)/{(XkkNN + X + Xkk

YY)/2}])/2}]22, ,

where Xwhere Xkk represents one of the four stock represents one of the four stock attributes and N and Y, refer to NASDAQ and NYSE, attributes and N and Y, refer to NASDAQ and NYSE, respectively.respectively.

Then, for each NYSE stock, we select the NASDAQ Then, for each NYSE stock, we select the NASDAQ stock with the smallest MS.stock with the smallest MS.

This procedure results in 539 pairs of NASDAQ and This procedure results in 539 pairs of NASDAQ and NYSE stocks with similar attributes.NYSE stocks with similar attributes.

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Speed of Quote Adjustment and Speed of Quote Adjustment and Stock AttributesStock Attributes

Hypothesis 1: The speed of quote Hypothesis 1: The speed of quote adjustment is positively related to adjustment is positively related to both the number of trades and return both the number of trades and return volatility. volatility.

Insofar as trades convey information on asset Insofar as trades convey information on asset values, liquidity providers may update quotes values, liquidity providers may update quotes more quickly for stocks that are more actively more quickly for stocks that are more actively traded and have greater return volatility. traded and have greater return volatility.

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Hypothesis 2: The speed of quote Hypothesis 2: The speed of quote adjustment is positively related to adjustment is positively related to share price.share price.

Chung and Chuwonganant (2002) show that Chung and Chuwonganant (2002) show that the minimum price variation is more likely to the minimum price variation is more likely to be a binding constraint on absolute spreads be a binding constraint on absolute spreads for low-price stocks.for low-price stocks.

Liquidity providers make slower adjustments Liquidity providers make slower adjustments toward optimal spreads for low-price stocks toward optimal spreads for low-price stocks because the binding constraint prevents because the binding constraint prevents them from making such quote revisions. them from making such quote revisions.

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Hypothesis 3: The speed of quote Hypothesis 3: The speed of quote adjustment is positively related to adjustment is positively related to both adverse-selection risks (and both adverse-selection risks (and costs) and the level of dealer costs) and the level of dealer competition.competition.

Liquidity providers are likely to make faster Liquidity providers are likely to make faster quote adjustments to new information for quote adjustments to new information for stocks with greater adverse-selection risks stocks with greater adverse-selection risks (and costs). This is because the dealer cost of (and costs). This is because the dealer cost of quoting sub-optimal spreads is probably quoting sub-optimal spreads is probably greater for such stocks.greater for such stocks.

Similarly, we hold that liquidity providers Similarly, we hold that liquidity providers make faster quote revisions to optimal make faster quote revisions to optimal spreads when competition is high spreads when competition is high

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Measurement of adverse-selection Measurement of adverse-selection costs and risks costs and risks

We use the spread component models We use the spread component models developed by Glosten and Harris (1988), developed by Glosten and Harris (1988), George, Kaul, and Nimalendran (1991), George, Kaul, and Nimalendran (1991), and Lin, Sanger, and Booth (1995) to and Lin, Sanger, and Booth (1995) to measure adverse-selection cost. measure adverse-selection cost.

We use the algorithm in Easley, Hvidkjaer, We use the algorithm in Easley, Hvidkjaer, and O’Hara (2002) to estimate adverse-and O’Hara (2002) to estimate adverse-selection risk.selection risk.

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Glosten and Harris (GH) model

George, Kaul, and Nimalendran (GKN) model

Lin, Sanger, and Booth (LSB) model

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Easley, Hvidkjaer, and O’Hara Easley, Hvidkjaer, and O’Hara (EHO)’s model (EHO)’s model

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The likelihood function:The likelihood function:

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Regression ModelRegression Model

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Regression Model Regression Model

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Does tick size affect quote Does tick size affect quote adjustment speed?adjustment speed?

The NYSE initiated a pilot decimalization The NYSE initiated a pilot decimalization program on August 28, 2000 with seven program on August 28, 2000 with seven listed issues. listed issues. The NYSE converted all 3,525 The NYSE converted all 3,525 listed issues to decimal pricing on January listed issues to decimal pricing on January 29, 2001. 29, 2001.

The NASDAQ Stock Market began its The NASDAQ Stock Market began its decimal test phase with 14 securities on decimal test phase with 14 securities on March 12, 2001. March 12, 2001. All remaining NASDAQ All remaining NASDAQ securities converted to decimal trading on securities converted to decimal trading on April 9, 2001. April 9, 2001.

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Although there is extensive literature Although there is extensive literature on the effect of tick size on market on the effect of tick size on market quality, there is little evidence as to quality, there is little evidence as to how tick size affects quote how tick size affects quote adjustment speed.adjustment speed.

In this study, we contribute to In this study, we contribute to existing literature by investigating existing literature by investigating the impact of tick size on quote the impact of tick size on quote adjustment speed using data before adjustment speed using data before and after decimal pricing. and after decimal pricing.

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Hypothesis: The speed of quote adjustment Hypothesis: The speed of quote adjustment during the post-decimal period is faster than during the post-decimal period is faster than

the speed during the pre-decimal period.the speed during the pre-decimal period. The penny tick size would be a The penny tick size would be a

binding binding constraint less frequently than constraint less frequently than the pre-the pre- decimal tick size, allowing decimal tick size, allowing liquidity liquidity providers to move toward providers to move toward optimal optimal spreads more quickly.spreads more quickly.

A smaller tick size results in greater A smaller tick size results in greater price competition because it implies a price competition because it implies a smaller cost of both front running by smaller cost of both front running by sell-side intermediaries and stepping sell-side intermediaries and stepping ahead of the existing queue by buy-ahead of the existing queue by buy-side traders. side traders.

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For NYSE stocks, we consider the For NYSE stocks, we consider the three-month period from May 28, three-month period from May 28, 2000 to August 27, 2000 as the pre-2000 to August 27, 2000 as the pre-decimal period and January 30, 2001 decimal period and January 30, 2001 to April 29, 2001 as the post-decimal to April 29, 2001 as the post-decimal period. period.

For NASDAQ stocks, we consider the For NASDAQ stocks, we consider the three-month period from December three-month period from December 12, 2000 to March 11, 2001 as the 12, 2000 to March 11, 2001 as the pre-decimal period and April 10, 2001 pre-decimal period and April 10, 2001 to July 9, 2001 as the post-decimal to July 9, 2001 as the post-decimal period. period.

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Regression ModelRegression Model

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Speed of depth quote adjustmentSpeed of depth quote adjustment

Marketmakers post both the price and the Marketmakers post both the price and the quantity of shares that they are willing to quantity of shares that they are willing to trade. trade.

The analysis of price quotes alone is likely The analysis of price quotes alone is likely to show an incomplete picture of to show an incomplete picture of marketmaker behavior.marketmaker behavior.

We analyze how adjustment speed in We analyze how adjustment speed in depth quotes varies with stock attributes depth quotes varies with stock attributes and tick size.and tick size.

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Estimating the Speed of Depth Estimating the Speed of Depth AdjustmentAdjustment

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Regression ModelRegression Model

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Regression ModelRegression Model

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5858

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SummarySummary Market structure exerts a significant impact Market structure exerts a significant impact

on the speed of quote adjustment. Liquidity on the speed of quote adjustment. Liquidity providers on the NYSE react more quickly providers on the NYSE react more quickly to new information than liquidity providers to new information than liquidity providers on NASDAQ.on NASDAQ.

Liquidity providers make faster quote Liquidity providers make faster quote adjustments new information for stocks adjustments new information for stocks with greater adverse-selection costs and with greater adverse-selection costs and quote competition.quote competition.

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Stocks with a greater number of trades, Stocks with a greater number of trades, greater return volatility, higher prices, and greater return volatility, higher prices, and smaller trade sizes exhibit faster quote smaller trade sizes exhibit faster quote adjustments to new information.adjustments to new information.

Liquidity providers on both the NYSE and Liquidity providers on both the NYSE and NASDAQ react more promptly to new NASDAQ react more promptly to new information after decimalization. Large tick information after decimalization. Large tick sizes create friction in exchange markets, sizes create friction in exchange markets, and thus delaying price discovery.and thus delaying price discovery.

Quote adjustment speed increases with Quote adjustment speed increases with variable measurement intervals.variable measurement intervals.

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Limitations and Future StudiesLimitations and Future Studies

We assume that optimal spreads and We assume that optimal spreads and depths are determined by four stock depths are determined by four stock attributes and that liquidity providers attributes and that liquidity providers make quote adjustments accordingly.make quote adjustments accordingly.

To the extent that optimal spreads To the extent that optimal spreads and depths are also functions of other and depths are also functions of other variables, our empirical models are variables, our empirical models are subject to misspecification.subject to misspecification.

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We assume that liquidity providers make We assume that liquidity providers make quote adjustments every 30 minutes quote adjustments every 30 minutes according to the target liquidity level (i.e., according to the target liquidity level (i.e., the optimal spread and depth) projected by the optimal spread and depth) projected by the value of four stock attributes during the the value of four stock attributes during the same 30-minute interval. same 30-minute interval.

It would be a fruitful area for future It would be a fruitful area for future research to estimate the speed of quote research to estimate the speed of quote adjustment using more frequent adjustment using more frequent observations (e.g., every five minutes) and observations (e.g., every five minutes) and to assess the robustness of the results. to assess the robustness of the results.