Supplemental Appendix

“Anticipatory Traders and Trading Speed”

Raymond P. H. Fishe

Richard Haynes

Esen Onur

This supplemental appendix provides additional tables and information about the sample data used in the 

study. The core data are from an order book (RAPID) sample of the WTI Crude Oil futures contract (Dec 

2011 expiration) covering 48 days of trading beginning on September 18, 2011. A follow-on sample was 

obtained for 35 days beginning on August 1, 2014 for the October 2014 expiration. Additional 

information about trades was available from the Transaction Capture Report (TCR) database that provides 

information on both sides of a trade. These samples allowed testing of 7,554 participant accounts in 2011 

and 8,087 accounts in 2014.

Analysis of the two samples showed significant attrition in customer accounts. Initially, we found 

only about 20% account matches between samples using the RAPID account identifiers. Using 

information from the TCR database these matches increased to 35%. The difference in matching 

frequencies is mainly due to the nature of account reporting in the two systems, with both databases 

together allowing additional identifiers to aid in the matching.

However, the exact size of the attrition is unknown because many new accounts may belong to 

participants who are in the 2011 sample, but are now using a different clearing firm or executing broker 

for their trades. There are several reasons that this may have occurred. First, the bankruptcy of MF Global 

on October 31, 2011 caused about 800 accounts in our sample to seek new clearing arrangements. A new 

clearing arrangement is very likely to result in a new account identifier. Second, the regulatory 

environment and economics of Futures Commission Merchants (FCMs) changed after the adoption of the 

Dodd-Frank Act.1 There were 154 FCMs operating prior to the 2008-09 financial crises. At the beginning 

of 2015, the number was down to 74. These changes alone may explain the attrition we observe in sample 

accounts. Third, the futures business has historically experienced turnover as Boyd and Kurov (2015) 

document. In their study it is the introduction of electronic trading that decreased the ranks of active 

participants. Similarly, more recent technological advances in algorithmic trading may have impacted our 

sample comparisons as trading costs continued to decrease (Fishe and Smith 2017). Fourth, participants 

do not always trade the same commodities or expirations, so there is a subset that will not match because 

they are choosing to trade other commodities or expirations. Lastly, the zero-sum nature of derivatives 

suggests that some participants will not do well if a subset earns positive profits. Depending on the nature 

of those with losses—do they hedge an underlying exposure or take directional risk—they may exhaust 

their capital and depart the business, adding to the attrition rate over time.

The following figures and tables offer additional information about our results. They are provided 

with a descriptive note to aid the reader. Some of the tables are extensions of tables in the main text.

 

1

See “Ranks of commodities brokers dwindle as U.S. futures industry evolves,” Reuters, July 2, 2015.
Month

Path 

Direction

Path 

Count

Average 

Path Ret. 

(%)

Median 

Path Ret. 

(%)

Path 

Duration 

(sec)

Path 

Volume

Number of 

Trades

Number of 

Unique 

Participants

Panel A: Statistics for All Paths

September down 611 -0.346 -0.291 481.2 296.6 195.5 27.4 

up 608 0.337 0.279 390.6 275.8 182.5 26.2 

October down 4,085 -0.145 -0.114 96.0 598.4 467.3 90.9 

up 4,090 0.148 0.113 89.6 534.5 416.8 83.6 

November down 3,207 -0.114 -0.094 70.9 584.7 470.7 97.8 

up 3,218 0.115 0.094 65.3 532.1 425.3 90.6 

Panel B: Statistics for Paths with Type E Participants

September down 196 -0.466 -0.390 859.0 504.5 324.7 1.5 

up 192 0.441 0.369 646.8 459.5 296.2 1.5 

October down 1,372 -0.185 -0.144 144.9 914.0 705.7 1.5 

up 1,259 0.201 0.154 145.2 886.5 686.7 1.5 

November down 887 -0.146 -0.123 113.9 945.7 754.2 1.3 

up 839 0.155 0.137 113.3 956.3 752.5 1.3 

Panel C: Statistics for Paths with Type R Participants

September down 359 -0.423 -0.368 627.3 400.7 259.8 2.5 

up 352 0.409 0.360 484.7 370.7 242.6 2.4 

October down 3,724 -0.149 -0.117 99.3 639.9 500.1 7.4 

up 3,665 0.154 0.118 93.6 579.4 452.0 6.9 

November down 3,057 -0.116 -0.096 72.7 604.8 486.7 7.4 

up 2,979 0.119 0.099 68.3 564.4 451.1 7.1 

Table A1

Monthly Price Path Characteristics

Summary statistics are shown for all local price paths in the December 2011 WTI crude oil contract. The paths are

computed from intraday prices on outright trades or spread trades with one side being outright. The sample covers all

trading days from September 12 to November 18, 2011. The table summarizes information by month and path direction

for all price paths, price paths with Type E participants, and price paths with Type R participants. The table shows

average and median path returns, average path duration in seconds, average path volume in contracts, and the average

number of trades. Panel A shows this information for all participants, Panel B selects paths in which a Type E

participant has trades, and Panel C selects paths in which a Type R participant trades. Type E and Type R participants

are selected using the 5% FDR critical rate. The average number of unique participants is shown for all paths, but

limited to the average count of Type E and Type R participants in Panels B and C, respectively.

Average
Volume Bins

Type E 

Participants

Type R 

Participants

All Other 

Participants

Panel A: Price Paths Trending Upward

First 10% 0.77 0.59 0.49 

10% to 50% 0.63 0.25 0.50 

50% to 90% 0.58 0.17 0.51 

Last 10% 0.65 0.08 0.50 

Panel B: Price Paths Trending Downward

First 10% 0.20 0.39 0.51 

10% to 50% 0.44 0.71 0.49 

50% to 90% 0.43 0.77 0.49 

Last 10% 0.52 0.90 0.49 

Table A2

Buy-Sell Frequencies on Local Price Paths

The table shows the fraction of all trades on the buyside by Type E, Type R,

and all other participants in four volume segments along the local price paths.

These Type E and Type R participants are those found using the FDR 5%

control rate. The four segments correspond to the first 10 pecent of path

volume, the next 40 percent to the volume midpoint, then the next 40 percent to

the 90 percent level, and finally the last 10 percent of path volume. Panel A

shows results for upward trending price paths and Panel B shows the same

results for downward trending price paths. In the first 10 percent of volume,

Type E traders are expected to disproportionately buy in upward trending paths

and sell in downward trending paths. In the last 10 percent of volume, Type R

traders are expected to sell in upward trending paths and buy in downward

trending paths, anticipating the change in local price direction in the next path.

The results confirm these hypotheses, but also suggest that buying is moderately

high (>50% in Panel A) for Type E participants in the remaining volume

segments, so they have a general buyside bias. For Type R, buying is highest in

the last 10% of volume (90% in Panel B), but seem to increase to this level in

the prior segments, suggesting a gradual strategy of searching for the reversal

point.
Up Down

Bin#10 Bin#1 Bin#2 Bin#10 Bin#1 Bin#2

Path Characteristics (1) (2) (3) (4) (5) (6) (7) (8)

No Type E in Current Path Volume Bin#1 0.095 -0.096 0.262 0.116 -0.245 -0.108 

Type E in Current Path Volume Bin#1 0.100 -0.097 0.284 0.114 -0.250 -0.188 

p-value for t-Test of Diff. (0.658) (0.948) (0.542) (0.952) (0.734) (0.274) 

No Type E_2s in Current Path Volume Bin#1 0.095 -0.096 0.261 0.118 -0.243 -0.109 

Type E_2s in Current Path Volume Bin#1 0.098 -0.097 0.272 0.098 -0.263 -0.120 

p-value for t-Test of Diff. (0.515) (0.922) (0.471) (0.232) (0.072) (0.636) 

No Type R in Previous Path Volume Bin#10 0.095 -0.956 0.149 0.267 -0.128 -0.255 

Type R in Previous Path Volume Bin#10 0.095 -0.967 0.157 0.256 -0.135 -0.232 

p-value for t-Test of Diff. (0.951) (0.608) (0.240) (0.134) (0.324) (0.001) 

No Type R_2s in Previous Path Volume Bin#10 0.095 -0.096 0.147 0.268 -(0.125) -(0.253) 

Type R_2s in Previous Path Volume Bin#10 0.095 -0.097 0.157 0.257 -(0.136) -(0.237) 

p-value for t-Test of Diff. (0.977) (0.666) (0.173) (0.117) (0.148) (0.028) 

All Bins

All paths using bins defined by Volume deciles

Table A3

Do Anticipatory Traders Bring New Information to the Market?

To test whether Type E or Type R anticipatory traders bring new price information to the market, the price change in each bin relative to the

absolute price change in the path is computed for specific bins, and for all bins combined. In the main paper, bins are defined by ten equal duration

calendar time periods across each path to avoid the endogeneity problem of volume deciles. The results here are computed using volume deciles for

comparison. Averages of these data are reported for different cases based on whether Type E or Type R participants are trading in a given time bin

in a price path. The first two columns report averages across all bins separating the averages for relative price changes by "up" and "down" trending

paths, respectively. The remaining columns also average the data by upward and downward paths, but only use specific time bins in the calculation.

The figures shown in parentheses beneath these averages are p-values for the t-test of comparing two means (unequal variances).

Means of Sample Data - Relative Price Changes by Bin

Up Price Paths Down Price Paths
Characteristics

1.246 sec -0.264

(0.001)

1.348 sec 0.069

(0.161)

1.255 sec 0.014 0.182

(0.818) (0.000)

1.275 0.285 0.051

(0.000) (0.120)

55.8% -0.052 -0.072

(0.026) (0.000)

50.2% -0.007 0.014

(0.647) (0.250)

9.8% 0.033 0.069

(0.214) (0.001)

18.3% 0.004 0.055

(0.911) (0.037)

R-Squared 2.1% 4.2%

Sample Size 6,886 7,208 

Binary Indicator for 

Algorithmic Trader

Table A4

Characteristics of Anticipatory Traders

An alternative to the regressions shown in the text is the inverse regression. This

method infers characteristics of the Type E and Type R traders relative to the entire

sample of participants. It is agruably more appropriate because the Type E and Type R

traders are known at this point in the analysis, so the results are conditional on this fact.

In the Type E (Type R) results, the dependent variable equals 1 if the trader is

identified by the FDR methods as significant in the first 10% (last 10%) of the local

price paths. The independent variables are averages by participant across all trades in

the sample. Execution speed is measured as the difference between the time the Nymex

confirms a trade execution and the time it receives the initial order from the participant

(in seconds with fractional milliseconds). The speed variable is the natural logarithm of

one plus the execution speed. The coefficients reported are transformed from initial

least squares estimates following Fishe and Smith (2012). These coefficients measure

the difference between the characteristic of the Type E (or Type R) trader and what

would be expected given all of the other characteristics in the sample. The sample is

defined to be those participants tested using the FDR method, so all traders have 30 or

more transactions. Sample sizes are reduced when participants are not observed in the

first or last 10% of path volume. The p-values of the underlying coefficient are shown

in parentheses beneath each estimate. 

Average Speed during the First 

10% of Path Volume

Average Trade Size

Average Speed during the Last 

10% of Path Volume

Average Speed over

All Trades

Sample Average 

over Participants

Type E 

Participants

Type R 

Participants

Percentage of Aggressive 

Trades

Percentage of Trades 

on the Buyside

Binary Indicator for 

Proprietary Trader
Independent Variable (I) (II) (III) (IV) (V) (VI) (VII) (VIII) (IX) (X) (XI) (XII)

0.030 0.028 0.014 0.013 -0.059 -0.053 -0.036 -0.033

(0.048) (0.063) (0.236) (0.266) (<0.001) (<0.001) (0.002) (0.006)

-0.015 -0.015 -0.008 -0.007 0.015 0.014 0.009 0.009

(<0.001) (<0.001) (0.006) (0.007) (<0.001) (<0.001) (<0.001) (<0.001)

Control Variables (Sign; p-value):

Up/Down Price Direction on Path neg (<0.001) neg (<0.001) neg (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (0.007) pos (0.004) pos (0.107)

Lag of Path Volume neg (0.254) neg (0.250) neg (0.251) neg (0.051) neg (0.050) neg (0.052) neg (0.154) neg (0.154) neg (0.161) neg (0.120) neg (0.120) neg (0.123)

Lag of Dependent Variable pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001)

Intercept pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001)

Number of Paths 15,583 15,583 15,583 15,642 15,642 15,642 15,765 15,765 15,765 15,765 15,765 15,765

R-Squared 1.4% 1.6% 1.6% 3.9% 4.0% 4.1% 4.1% 4.2% 4.3% 3.6% 3.6% 3.7%

p-value for Regression (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001)

This table shows cancellation regressions controlling for the rate of net buying behavior of Type E traders in the first 10% of path volume and Type R traders in the last 10% of path volume. The dependent variables with type E

participants are the buyside order cancellation fraction (buy cancel volume divided by total buy and sell cancel volume) for other market participants between 10% and 50% and 10% and 100% of current path volume; for Type

R participants they are the buyside order cancellation fraction for other market participants for the first 50% and first 90% of next path's volume . The independent variables are the fraction of "Anticipatory" participants net

buying volume to total volume in the specified "Percentile" of current path volume and the same fraction computed for all other non-anticipatory participants, where "Anticipatory" participants are either Type E or Type R and

"Percentile" is either the first or last 10% of volume in the path. Control variables in each rergression are a dummy variable indicating the direction of prices on the path (up/down), the lag of total path volume, and the lag of the

dependent variable. The p-values are computed using heteroscedistic-consistent standard errors and are shown in parentheses below each coefficient; for control variables the coefficient sign and p-values are shown. The

adjusted R-squared is shown at the bottom of the table.

Effects of Anticipatory Trading on the Order Cancellations of Other Market Participants

Table A5

Dependent Variables:

Buyside Order Cancellation Rates for the 

first 90% of the next path volume

Buyside Order Cancellation Rates for the 

first 50% of the next path volume

Buyside Order Cancellation Rates between 

10% and 100% of the current path volume

Buyside Order Cancellation Rates between 

10% and 50% of the current path volume

Anticipatory Participant = TYPE R

Percentile = Last 10%

Fraction of "Anticipatory" 

participants net buying volume to 

total volume in specified 

"Percentile" of current path volume

Fraction of all other, nonanticipatory participants net buying 

volume to total volume in the 

specified "Percentile" of current 

path volume

Anticipatory Participant = TYPE E

Percentile = First 10%

Anticipatory Participant = TYPE E

Percentile = First 10%

Anticipatory Participant = TYPE R

Percentile = Last 10%
Independent Variable (I) (II) (III) (IV) (V) (VI) (VII) (VIII) (IX) (X) (XI) (XII)

0.072 0.073 0.064 0.064 0.021 0.018 0.016 0.013

(0.001) (<0.001) (<0.001) (<0.001) (0.098) (0.152) (0.138) (0.214)

0.003 0.181 0.000 0.000 -0.007 -0.007 -0.007 -0.007

(0.516) (0.447) (0.888) (0.989) (0.005) (0.007) (0.002) (0.003)

Control Variables (sign; p-value):

Up/Down Price Direction on Path pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) neg (<0.001) neg (<0.001) neg (<0.001) neg (<0.001) neg (<0.001) neg (<0.001)

Lag of Path Volume neg (0.433) neg (0.490) neg (0.518) neg (0.472) neg (0.447) neg (0.472) neg (0.137) neg (0.137) neg (0.135) neg (0.143) neg (0.143) neg (0.141)

Lag of Dependent Variable pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001)

Intercept pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001) pos (<0.001)

Number of Paths 15,576 15,576 15,556 15,638 15,638 15,638 15,765 15,765 15,765 15,765 15,765 15,765

R-Squared 8.6% 8.5% 8.6% 16.9% 16.8% 17.0% 11.8% 11.8% 11.8% 18.8% 18.8% 18.8%

p-value for Regression (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001)

Table A6

Effects of Anticipatory Trading on the Order Modifications of Other Market Participants

Buyside Order Modification Rates between 

10% and 50% of the current path volume

Buyside Order Modification Rates between 

10% and 100% of the current path volume

Buyside Order Modification Rates for the 

first 50% of the next path's volume

Buyside Order Modification Rates for the 

first 90% of the next path's volume

Dependent Variables:

This table shows modification regressions controlling for the rate of net buying behavior of Type E traders in the first 10% of path volume and Type R traders in the last 10% of path volume. The dependent variables with type E

participants are the buyside order modification fraction (buy modify volume divided by total buy and sell modify volume) for other market participants between 10% and 50% and 10% and 100% of current path volume; for Type R

participants they are the buyside order modification fraction for other market participants for the first 50% and first 90% of next path's volume . The independent variables are the fraction of "Anticipatory" participants net buying

volume to total volume in the specified "Percentile" of current path volume and the same fraction computed for all other, non-anticipatory participants, where "Anticipatory" participants are either Type E or Type R and "Percentile" is

either the first or last 10% of volume in the path. Control variables in each rergression are a dummy variable indicating the direction of prices on the path (up/down), the lag of total path volume, and the lag of the dependent variable.

The p-values are computed using heteroscedistic-consistent standard errors and are shown in parentheses below each coefficient; for control variables the coefficient sign and p-values are shown. The adjusted R-squared is shown at the

bottom of the table.

Anticipatory Participant = TYPE R

Percentile = Last 10%

Fraction of "Anticipatory" 

participants net buying volume to 

total volume in specified 

"Percentile" of current path volume

Fraction of all other, nonanticipatory participants net buying 

volume to total volume in the 

specified "Percentile" of current 

path volume

Anticipatory Participant = TYPE E

Percentile = First 10%

Anticipatory Participant = TYPE E

Percentile = First 10%

Anticipatory Participant = TYPE R

Percentile = Last 10%
The two figures below show the historical behavior of inter-day and intra-day volatility in crude oil and eMini futures contracts. They tend to mirror each other over time, so that fluctuations in equities are 

similar to fluctuation in the price of crude oil.

Inter-Day Volatilities

Intra-day Volatilities