Order Dynamics: Recent Evidence from the

NYSE

Andrew Ellul, Indiana UniversityCraig W. Holden, Indiana UniversityPankaj Jain, University of MemphisRobert Jennings, Indiana University

Goal and Motivation Empirically characterize traders’ order

choice under dynamic market conditions Order submission and cancellation

decisions determine liquidity-provision and liquidity-usage better understanding of the price formation

process determinants of execution quality

Analyze NYSE in 2001 All trades are in decimals Allows automatic execution (Direct+) Have all SuperDOT orders, but no orders

worked by floor brokers

Theoretical Predictions Tested

Negative Serial Correlation in Order Type -Parlour 98

Prior trader submits market buy consumes liquidity relatively more attractive to submit limit sell replenish liquidity

Cycle of consuming and replenishing liquidity negative serial correlation in order type (MB less likely to follow MB)

Puzzle: Biais, Hillion and Spatt (1995) found positive serial correlation in order type (MB more likely to follow MB) LOB will grow more imbalanced and eventually break down

Trader’s Patience – Handa and Schwartz (1996) Model trader’s timeframe for completing trade Impatient choose market orders; Patient choose limit order Extend logic highly impatient choose fast auto ex with little

regard for market conditions; More patient choose floor executions with meaningful regard for market conditions

More Literature Other Variables Suggested By Theory

Depth – Parlour (1998) Time-of-day – Harris (1998), Parlour (1998), Bloomfield,

O’Hara and Saar (2002) and others Quoted Spread – Cohen, Maier, Schwartz, Witcomb

(1981), Harris (1998), Foucault (1999) and others Volatility – Foucault (1999) Market and Own Return – Lo, Mamaysky and Wang

(2000) Related Empirical Studies

Harris (1998) Hollifield, Miller and Sandas (1999,2002) Ahn, Bae and Chan (2001) Bae, Jang and Park (2002) NYSE using 90-91 TORQ Beber and Caglio (2002) NYSE using 90-91 TORQ Hasbrouck and Saar (2002) Ranaldo (2002)

Data NYSE System Order Database (SOD)

over April – June 2001 148 stocks stratified by price and

volume Detailed info on:

order submission (time, type, size, etc.) order’s outcome: execution,

cancellation, or expiration

Hypotheses 1 Negative Serial Correlation in Order

Type for Individual Orders Hypothesis: Prior limit buy (sell) lower prob limit buy (sell)

Negative Serial Correlation in Order Type for Aggregated Order Flows Hypothesis: Higher Δ limit buys (sells) in prior 5 min

lower Δ limit buys (sells) in current 5 min

Short-term Forecasting Hypothesis: Large ask (bid) depth prob limit sell (buy)

and prob market sell (buy)

Hypotheses 2 Jump-The-Queue Hypothesis:

Large ask (bid) depth Prob inside limit sell (buy) and prob at or behind limit sell (buy)

Time of Day Hypothesis: Time Prob various order types

changes

Trader Impatience Hypothesis: Auto-ex sensitivity to market conditions

< floor sensitivity to market conditions

Methodology Analyze choices of representative trader

= weighted average over all trader types Multinomial logit with 7-way event

structure Market Buy, Market Sell, Limit Buy, Limit

Sell, Cancel Buy, Cancel Sell, No Activity (45% of limit orders are cancelled) No activity event = stock-specific amount of

clock time with no order submissions or cancellations

Median time between successive order events or 5 minutes, whichever is less

For 8 stocks = 1 second; for 50 stocks = 5 minutes

By Aggregated Order Flow Aggregate buys and sells Aggregate same 7 events by 5 min

int. Market buy, market sell, limit buy, limit

sell, cancel buy, cancel sell, no activity Estimate 7 OLS equations with the

same independent variables aggregated by 5 min int

13-way Multinomial Logit Dependent variable = 13-way event

structure

4 types of Limit Buys, 4 types of Limit Sells, Market Buy, Market Sell, Cancel Buy, Cancel Sell, No Activity Event

Limit Buy Limit Sell

Above $30.10 Marketable Worse-than-the-quote

Ask = $30.10 Marketable At-the-quote

$30.01 - $30.09

Inside-the-quote Inside-the-quote

Bid = $30.00 At-the-quote Marketable

Below $30.00 Worse-than-the-quote

Marketable

More aggressive

More aggressive

Explanatory Variables Last Event Market Buy (dummy) Last Event Market Sell (dummy) Last Event Limit Buy (dummy) Last Event Limit Sell (dummy) Last Event Cancel Buy (dummy) Last Event Cancel Sell (dummy) Relative National Best Bid size (/ shrs outstand) Relative National Best Ask size (/ shrs

outstand) Time (number of 5 min since midnight)

Control Variables Percent Spread Relative Volume (ln vol(t-5)/Shrs

outstand) Own Return (% chg in midpoint (5 min)) Own Return Squared Volatility Market Return (5 min % chg SP500 ETF) Time Squared (deviation from midday

sqr) U-shape patterns Day Return

Order by Order 13 way: positive serial correlation

Change in aggregated order flow: negative correlation

Conclusion Neg Serial Corr in Order Type by Ind Orders = Reject Neg Serial Corr in Order Type by Agg Flows=Support Resolves puzzle and supports Parlour model Short-term Forecasting = Support Jump-the-Que = Support Depth impacts in both ways Time-of-day = Support Supports Bloomfield, O’Hara, and Saar

experimental evidence Trader Impatience = Support Supports Handa & Schwartz logic applied to Auto-ex