Anticipatory Traders and Trading Speed

Raymond P. H. Fishe

Richard Haynes

and

Esen Onur*

This version: May 2017

ABSTRACT

We examine whether speed is an important characteristic of traders who anticipate local 

price trends. These anticipatory participants correctly trade prior to the overall market and 

systematically act before other participants. They use manual and algorithmic order entry 

methods, but most are not fast enough to be high frequency traders (HFTs). Those 

anticipating price reversals appear informed while those trading early appear skilled. The 

case for them affecting the volume of other traders is weak. A follow-up sample three 

years later shows significant attrition in accounts and a meaningful difficulty maintaining 

the anticipatory strategy. To identify these traders, we devise novel methods to isolate 

local price trends using order book data from the WTI crude oil futures market.

Keywords: High frequency traders (HFT), Algorithmic traders, Manual traders, 

Anticipatory traders, WTI crude oil futures

JEL classification: G10, G13

*Fishe: Patricia A. and George W. Wellde, Jr. Distinguished Professor of Finance, Department of Finance, Robins School of 

Business, University of Richmond, Richmond, VA 23173. Tel: (+1) 804-287-1269. Email: pfishe@richmond.edu. Haynes: 

U.S. Commodity Futures Trading Commission, Washington, D.C. 20581. Tel: (+1) 202-418-5000. Email: rhaynes@cftc.gov. 

Onur: U.S. Commodity Futures Trading Commission, Washington, D.C. 20581. Tel: (+1) 202-418-5000. Email: 

eonur@cftc.gov. Tel: (+1) 202-418-5000. We thank Jennifer Conrad (Co-Editor), an anonymous referee, as well as Jonathan 

Brogaard, Joel Hasbrouck, Steve Kane, Albert Menkveld, Scott Mixon, David Reiffen, Michel Robe, John Roberts, Sayee 

Srinivasan, and participants at the AFA meetings in San Francisco (High Frequency trading session) for comments on earlier 

versions of this research. The co-authors of this paper prepared this research under the direction of the Office of the Chief 

Economist at the CFTC, where economists produce original research on a broad range of topics relevant to the CFTC’s 

mandate to regulate commodity future markets, commodity options markets, and the expanded mandate to regulate the swaps 

markets pursuant to the Dodd-Frank Wall Street Reform and Consumer Protection Act. These papers are often presented at 

conferences and many of these papers are later published by peer-review and other scholarly outlets. The analyses and 

conclusions expressed in this paper are those of the authors and do not reflect the views of other members of the Office of 

Chief Economist, other Commission staff, or the Commission itself. All errors and omissions, if any, are the authors’ own 

responsibility. Corresponding author: Raymond P. H. Fishe.
Anticipatory Traders and Trading Speed

I. INTRODUCTION

The growth of high frequency trading (HFT) in financial markets has led to concerns that such 

traders may use processing speed to disadvantage other participants. Theoretical models 

suggest that HFT traders may gain by entering new markets to compete with slower 

participants (Hoffmann 2014; Biais, Foucault, and Moinas 2015).1 Consistent with theory, fast

traders have an advantage if they process news or intraday order information quickly in order 

to anticipate future order flows and prices. Liquidity costs may increase if these anticipatory 

traders discover an underlying parent order from the child order flow (O’Hara 2015). In this 

light, anticipatory trading may reduce market efficiency by temporarily raising (or lowering) 

price from what would occur without such participants (Brunnermeier and Pedersen 2005). 

However, if parent orders are informed then anticipatory trading may increase efficiency by 

faster price discovery, which is made even more efficient when HFTs are anticipatory traders.

Brogaard, Hendershott, and Riordan (2014), Hirschey (2016), and Jiang, Lo, and Valente 

(2013) provide support for anticipatory trading by some HFTs. These analyses show that 

HFTs appear to identify and trade before short-horizon price movements in equity and fixed 

income markets. However, the correlation between non-HFT activity and subsequent price 

changes is often positive in these results, so the relationship between the presumed 

comparative advantage of HFTs and the ability to anticipate price changes is unclear.

 

1

Some researchers also suggest that anticipatory trading generates negative externalities, such as reducing 

liquidity provision, vitiating behavior as with quote stuffing, or inducing slower traders to depart (e.g., Ye, Yao, 

and Gai, 2013; Biais, Foucault, and Moinas, 2014; Foucault, Kozhan and Tham, 2014; Han, Khapko, and Kyle, 

2014; Menkveld, and Zoican, 2014).
2

This study seeks to determine whether the ability to trade at high speeds is necessary for 

anticipatory trading. Our approach is different from the previous HFT literature because we 

identify all sample traders—not just HFTs—who can repeatedly execute trades that anticipate 

subsequent intraday price changes (“anticipatory traders”). We do not pre-condition the 

analysis on an already specified group of HFTs or use filters designed to isolate such traders 

from other participants.2 Once we have identified the group of anticipatory traders, we 

examine whether speed is a determining characteristic to confirm whether anticipatory 

strategies are a HFT phenomenon.

To find anticipatory traders we develop a novel method to identify temporary, directional 

changes in intraday prices, which we call price paths. Conditional on these short-term price 

movements, we identify who is successful at trading in the beginning (or end) of such paths. 

We identify two types of anticipatory traders: those that act early during a directional price 

change (“Type E”), and those that forecast an up-coming price reversal (“Type R”). Early 

refers to trades in the first 10% of path volume and to act before a price reversal means to 

trade correctly in the last 10% of the previous path’s volume. 

Our sample provides evidence that a relatively small fraction of traders—4.1% (11.1%) 

out of 7,554 tested with 2.5% (5%) control rates—anticipate short-term directional price 

changes and/or price reversals. We find more Type R than Type E participants, with both 

groups containing algorithmic- and manual-entry traders. An investigation of Sharpe ratios for 

these traders confirms the relative success of these anticipatory strategies. However, most of 

 

2 Biais and Foucault (2014) and U.S. Securities and Exchange Commission (2014) offer a detailed discussion of 

the conditioning methods used in recent HFT research. Such methods may vitiate inferences about the overall 

population behavior.
3

these traders do not appear to be HFTs and accordingly our tests support the view that speed is 

not a significant determinant of anticipatory trading.3 

The data examined are a subset of the regulatory information collected from exchanges by 

the Commodity Futures Trading Commission (CFTC). Our primary analysis uses anonymous 

account-level information from both order book and trade files for the WTI crude oil futures 

contract traded on the CME/Nymex exchange. We use two different samples for analysis. The 

initial sample is for the December 2011 expiration and contains 48 trading days beginning on 

September 12, 2011. The follow-up sample is for the October 2014 expiration and covers 35 

days beginning on August 1, 2014. The WTI contract is one of the most active futures 

contracts with high volumes and daily participation by 1,000s of accounts. 

Although most HFT research uses equity market data, futures markets involve many HFTs 

and exhibit many features found in equity markets.4 Both equity and futures exchanges offer 

subscribers real-time, low-latency access to order book information, trading volumes are 

correlated through time and market quality patterns are similar. Specifically, crude oil and 

equity price volatility are highly correlated, bid-ask spreads decreased as volumes increased, 

and execution speeds have increased with the growth of HFT participants.

5 One important 

difference is that equity markets offer multiple execution venues for the same security. Thus, 

 

3 Note that our results do not imply that speed is unimportant in a general sense as increases in participant speeds 

are connected to market quality improvements. For example, HFTs are associated with spread-decreasing limit 

orders (Carrion 2013; Hagströmer, Nordén, and Zhang 2014), providing liquidity against temporary shocks 

(Brogaard, Hendershott, and Riodan 2014), and spread decreases in general (Hendershott, Jones, and Menkveld 

2011) and when they are the new entrants (Brogaard and Garriott 2015). See Menkveld (2016) for a recent 

review of this literature.

4

See Fishe and Smith (2017) for a discussion of HFT research in commodity markets and the findings in 

Kirilenko, Kyle, Samadi and Tuzun (2015) regarding HFT activity during the Flash crash.

5 Recent equity statistics are summarized in Angel, Harris, and Spratt (2015) and related references. The market 

quality and volatility characteristics of WTI crude oil futures are found in Brunetti, Büyükşahin, and Harris 

(2016), Raman, Robe and Yadav (2016), Easley, Prado, and O’Hara (2016), and Haynes and Roberts (2015, 

2017), the latter discusses speed characteristics for several commodities.
4

a caveat to our findings is that while speed may be less important for anticipatory strategies in 

consolidated futures markets, the gains from quoting on multiple platforms may generate a 

comparative advantage for HFTs to use such strategies in equity markets. 

To make inferences about the speed of Type E and Type R traders, we measure order book 

latencies; specifically, the time between order entry and either cancellation or execution

messages. We make speed inferences from all account orders as well as from a bootstrap 

simulation that gives equal weight to each participant account, thereby reducing the impact of 

skewness from high volume accounts (Fishe and Smith 2017). In these results, both Type E 

and Type R participants appear slower, on average, than other participants in both cancellation 

and execution speeds. Algorithmic traders are faster, but being algorithmic and either Type E 

or Type R does not further increase speed. Also, the average proprietary trader is faster than 

when trades are done on behalf of customers, and those who modify orders are, on average, 

much slower to both cancel and execute trades. Finally, Type E and Type R groups do not act 

faster when they are trading in the first or last 10% of path volume, respectively, indicating 

that they do not seem to be strategically using speed for their anticipatory strategy.

To find anticipatory traders, our methods examine thousands of trader histories and test 

statistics. We limit the possibility of false positives by controlling for the false discovery rate

(FDR) as defined by Benjamini and Hochberg (1995) and implemented by Storey (2002) and 

Fishe and Smith (2012). Our results use both 2.5% and 5% control rates for the FDR. 

We identify local price paths by considering how a trader with near perfect foresight 

might act. This trader would try to initiate trades in the correct direction at or around price 

reversals, and seek confirmation that price changes were directional and not merely random 
5

noise.6The sequential probability ratio test (SPRT) by Wald (1945) identifies such directional 

price paths. This approach provides a test for non-random, trending behavior and allows us to 

pinpoint turning points between up and down price trends.7The price path method uses 

information that traders do not have at the time of their trades, so it is a conservative approach 

and biased against finding such systematically successful participants.

Given that some participants are anticipatory traders the question arises as to whether they 

are advantaged because they bring new information to the market or because they make better 

predictions relative to information derived from market activity. Theoretical models suggest 

that HFTs may benefit from faster access to information or news trading, which may also be 

the case for anticipatory traders.8 Thus, we investigate price behavior in paths both with and 

without Type E and Type R participants. We use clock time deciles in these paths to control 

for the endogeneity generated from selecting Type E and Type R groups based on volume 

deciles. We find no significant differences in price changes when Type E traders are in the 

first (or second) time decile of a price path. However, Type R traders appear informed as the 

first time decile in a path shows significant additional price movement when they trade in the 

last time decile of the previous path. This deviation occurs in the correct direction for both 

increasing and decreasing price paths. 

We also examine whether Type E and Type R traders affect the trading volume of other 

participants. For example, they may incite other participants to trade—a so-called 

 

6 Our approach makes results conditional on local price paths in the same way that research on the “Flash Crash” 

or other market events is conditional on trade paths around those events (e.g., Kirilenko, Kyle, Samadi, and 

Tuzun, 2015; Menkveld and Yueshen, 2013).

7 An alternative is to use a preset condition to define whether prices have changed sufficiently to identify turning 

points (cf., Hautsch, 2012, p.36).

8 Better predictions may involve statistical representations of dynamic limit-order book models (e.g., Cont, 

Stoikov and Talreja, 2010; Huang and Kercheval, 2012) and/or access and ability to act on information that 

affects prices before others act (Foucault, Hombert and Rosu, 2016).
6

“momentum-ignition” effect—and thereby are not exactly anticipatory because they have 

caused others to trade.9 Our results show no effects for Type R traders, but possibly some 

weak effects for Type E traders, although not aligned with their first decile trades.

Finally, we examine whether these traders retain their skills using the 2014 sample. We 

find substantial attrition among accounts, so that only 35% of the 2011 accounts are found 

trading three years later.10 Of those that remain we find that 9% of the Type E group continues

with an anticipatory strategy, while 20% of the Type R group continues with the reversal 

strategy. In total, the 2014 sample shows a 24% (11%) reduction in the total size of these 

Type E and Type R groups at the 2.5% (5%) FDR control rate. Analogous to the difficulty of 

fund managers maintaining a “Hot Hand”, these findings suggest that anticipatory trading 

became more difficult to implement after 2011 and that the Type E or early trading strategy 

was the relatively more ephemeral strategy (Barras, Scaillet, and Wermers 2010).

This paper proceeds as follows. Section II discusses the related literature. Section III 

describes the methods used to identify anticipatory traders, along with a general discussion of 

the price path approach. Section IV discusses the data and how we measure speed. Section V 

provides our analyses and results. Finally, Section VI offers several conclusions.

II. RELATED LITERATURE

Several researchers offer evidence on anticipatory trading. Jiang, Lo and Valente (2013) 

analyze how often transactions are in the right direction compared to subsequent price 

 

9 Momentum ignition and other follower-inducing strategies require that other participants believe there is a 

positive probability of very recent informed trades (Allen and Gale, 1992). These strategies are among those 

thought to be used by HFT firms (U. S. Securities and Exchange Commission, 2014). 

10 Boyd and Kurov (2012) also find significant attrition by participants in energy futures after side-by-side 

electronic trading began in 2006. Their estimates imply a 50% loss of traders after 2.5 years. 
7

changes around macroeconomic announcements. Frino, Ibikunle, Mollica, and Steffen (2016) 

follow a similar approach for trading in Brent oil futures around the London fixing period. 

Bae, Dixon, and Lee (2017) study the front-running of large orders for the KOSPI 200 futures 

contract. All of these studies report evidence of anticipatory trading, but do not resolve the 

question of whether speed is necessary. Importantly, Jiang, Lo and Valente (2013) find that 

non-HFT limit orders are more informative for future prices than HFT limit orders. These 

latter results suggest that anticipatory traders are not singly defined by an HFT label.

11

Other more general studies by Brogaard, Hendershott, and Riordan (2014) and Hirschey 

(2016) use a Nasdaq sample with HFT participation noted in the trade data. Brogaard et al. 

find that the correlation between net order flow for all sample HFTs and subsequent returns is 

positive, but it is short lived and quite low—less than 4% at one second and near zero at two 

seconds. Interestingly, they find that non-HFTs demanding liquidity show higher, longer lived 

correlations with subsequent returns than HFTs demanding liquidity, implying that sub-groups 

excluding HFTs also appear informed. Hirschey (2016) also finds that liquidity-demanding 

trades by HFTs precede liquidity-demanding trades by non-HFTs. These studies leave 

unanswered the question of why some non-HFTs are predictive of returns if HFTs have 

anticipated their net orders. A possible explanation arising from our results is that successful 

anticipatory strategies are found among both HFT and non-HFT groups, so the Nasdaq HFT 

filter does not sufficiently separate anticipatory from non-anticipatory behavior.

Anticipatory trading may be similar to momentum trading strategies. Bikhchandani, 

Hirshleifer, and Welch (1992) and Allen and Gale (1992) are momentum studies that may 

 

11 Clark-Joseph (2012) suggests that aggressive HFTs execute multiple, generally unprofitable smaller size 

orders for the purpose of obtaining order book information. When this information indicates a high propensity 

for a directional price movement, the firm then profits from well-timed larger orders.
8

include anticipatory behavior.

12 Bikhchandani et al. develop a model in which informational 

cascades cause momentum reactions. Those who react first are like anticipatory traders. Allen 

and Gale show that traders are affected by big traders because of the likelihood that they may 

be informed. Thus, smaller traders have an incentive to trade in the same direction as a big 

trader, making the big trader appear anticipatory. These environments may create “following 

trades” by investors, which imply that some traders may be misclassified as anticipatory. To 

sort out the degree to which such traders vitiate our results, we examine the overall volume 

behavior following trades by those identified as anticipatory.

Lastly our study addresses the use of filters to identify HFTs for analysis. Others have 

observed that sample filters—inventory turnover, trading volume, cancellations, etc.—pose a

problem for population inference. Biais and Foucault (2014) and the U.S. Securities and 

Exchange Commission (2014) discuss several of the filter methods used to classify data as

algorithmic- or HFT-related. Biais and Foucault warn that “One problem with this approach is 

that it may select HFTs with a specific trading style...while excluding others (p. 10).” Thus, 

rather than pre-condition the analysis on a subset of trader characteristics, we seek to infer 

those population characteristics by identifying all traders associated with a specific strategy. 

III.METHODS

Our analysis involves statistical methods to identify intraday price trends. The discussion 

below explains how such paths are identified and how the FDR method is used to find

participants who can systematically execute trades during selected segments on these paths. 

 

12 De Long, Shleifer, Summers, and Waldman (1990), Hirshleifer, Subrahmanyam, and Titman (1994), Chan, 

Jegadeesh, and Lakonishok (1996), and Dong, Polk and Skouras (2014) offer examples of general momentum 

behavior. 
9

a. Local Price Paths

Our approach to identify price paths uses statistical methods to locate sequences where 

price changes exhibit some degree of short-term predictability. We use the SPRT to define the 

local neighborhood size and to test for non-randomness. In the resulting sequences, we then 

search for local price extrema.

The process starts with the transaction price series from which we remove Ptif Pt = Pt−1, 

keeping the first price of each such sequence to preserve unique price levels. Then, we remove 

all sequences of contiguous bid-ask bounce. Specifically, all cases in which ∆Pt = −∆Pt−1 are 

excluded. We retain the prices into and out of such sequences, but remove the intermediate 

implied bid-ask trades. For example, consider the price sequence, Pt ≡ {3,4,5,5,4,4,5,4,5,6}. 

After the first filter, the sequence is Pt

∗ ≡ {3,4,5,4,5,4,5,6}, from which we retain 

P1

∗

, P2

∗

, P7

∗

, and P8

∗

after the second filter. The purpose behind removing bid-ask bounce 

sequences is to exclude periods in which liquidity replenishment is sufficient to satisfy 

liquidity demand. These sequences may provide information, but arguably provide little help 

identifying a price trend. Note that after we have identified the price trends, we restore all

observations to the dataset for identification of Type E and R participants.

The above procedure produces a sequence in which ∆Pt ≠ 0 ∀t. We then define a set of 

candidate prices based on the SPRT test results. Within a group of K prices, the price Pt

∗ is a 

candidate for a local minimum at trade t

∗

if 

(i) the count of previous price changes, n

− ≥ δK, where n− = 1(∆Pt < 0) for 

t ∈ {t

∗

,t

∗ − 1,t∗ − 2, ... ,t∗ − K}, and
10

(ii) the count of subsequent price changes, n

+ ≥ δK, where n+ = 1(∆Pt > 0) for 

t ∈ {t

∗ + 1,t∗ + 2, ... ,t∗ + K}.

The parameter δ ∈ [0,1] creates a consistency condition and is used to assign confidence 

to our selection mechanism based on the power of the SPRT. The parameter K defines the 

local neighborhood of trades. For example, if K is large and δ = 1, then every price change 

before the candidate local minimum will decrement the previous price towards the minimum 

and every price change after the local minimum will increment the previous price away from 

the minimum. We use the same approach, but reverse the inequalities to define a candidate 

local maximum price.13

The basic statistical properties of error rates guide the selection of the consistency 

parameter and the size of the local neighborhood. Consider the null hypothesis that the binary 

variable tracking the sign of any price change is binomial with null parameter, Ho: q = 0.5,

which normalizes the null distribution of sign changes to a random sequence. A participant 

attempting to detect a price change is most concerned about rejecting this null in the 

neighborhood of a candidate price extrema. Thus, it is useful to establish confidence that the 

null is rejected. The SPRT provides a means of identifying the appropriate size of the local 

neighborhood necessary to reject the null as this test is uniformly most powerful against any 

other test in its expected stopping time (Wald, 1948).

The SPRT computes the likelihood ratio for each successive observation in the trade 

sequence given a null and alternative hypothesis. It uses type I (α) and type II (β) errors rates 

for these hypotheses to establish bounds for rejecting one hypothesis versus another. In our 

 

13 This method may produce cases in which multiple minimums or maximums are contiguous on a price path. 

We remove such cases by selecting a global maximum or minimum in such sequences. The final price paths 

alternate in the sign of ∆Pt, where this price change is from the beginning to the end of a path. 
11

calculations we set both of these error rates equal to 10 percent, which then feeds back to the 

neighborhood size and consistency parameter. 

To determine the neighborhood size, we simulate the number of trades necessary to reject 

the null (Ho: q = 0.5) against the alternative (Ha: q = 0.8). We use a strongly convincing 

alternative versus one closer to the null as participants would not rely on a testing method for 

local trends if it required a large number of trades, perhaps more than might be observed in a 

local trend. Using small differences between the null and alternative hypotheses creates longer 

required sampling sequences. With 1,500 simulations, we found that if participants selected 

17 observations, then in only 10 percent of the cases would they require more observations 

before rejecting the null. As this choice equals the required Type II error rate (β = 10%), we 

use 17 observations on both sides of a candidate price to define the local neighborhood.

To define the consistency parameter, we use a choice that follows from the Type I error 

rate (α = 10%). Under the null hypothesis, this error rate is Pr[∑ x ≥ x

′

| K = K

∗

] ≤ 0.10, 

where x ∈ {0,1} to indicate either a negative or positive price change. As the neighborhood 

size is set by the SPRT such that K

∗ = 17, using the binomial distribution, the cutoff for 10 

percent arises when 10 < x

′ < 11. We experimented on randomly chosen days with both 

choices and found that x

′ = 10 gave somewhat more paths, but the overlap was near 100% 

with x

′ = 11. As more paths are expected to make it more difficult to consistently trade in 

the correct direction, we used x

′ = 10 in our analysis. Thus if at least 10 out of 17 price 

changes are observed with the appropriate sign—positive (negative) before a candidate price 

maximum (minimum) and negative (positive) after the candidate price—then we define that 

price as a valid “candidate” for a local extrema. This approach produces a consistency 
12

parameter δ approximately equal to 60%.14 To choose among the set of valid candidate prices 

within the same neighborhood, we select based on the conditions:

(iii) Pt∗

min = min{Pt

∗, Pt

} ∀t≠t

∗ , where t ∈ {t

∗ − K,t∗ − K − 1, ... ,t∗ + K} for a 

minimum, and 

(iv) Pt∗

max = max{Pt

∗, Pt

} ∀t≠t

∗ , where t ∈ {t

∗ − K,t∗ − K − 1, ... ,t∗ + K} for a 

maximum. 

Figure 1 illustrates how to conceptualize the working of the price path algorithm and the 

relative location of anticipatory traders. The figure shows a sequence of trades (the “x’s”) for 

a portion of the day’s trading. There are periods with market-making activity in which nonprice moving liquidity-based trading creates a bid-ask bounce sequence (shaded areas). When 

new information about the value of the asset arrives or liquidity demand changes, the market 

price reacts until the information is impounded in the price or new liquidity arrives to resolve 

the imbalance. Local price reversals occur at the specified price extrema (the circled trades). 

In the figure, Type E traders possess skills to process order flow and information to 

systematically forecast the short-term direction of prices. These participants react quickly 

after a price reversal occurs. Type R traders may use strategies that analyze order book 

liquidity or new information to place limit order prices near upcoming price reversals. The 

trades of these participants occur before but close to the local price extrema. 

 

14 We also simulated our results with Type I and Type II error rates equal to 5%. These simulations gave a 

neighborhood size of 24 observations to maintain the type II error rate and a consistency parameter of 

approximately 63%.
13

b. Finding Anticipatory Traders

To identify which participants may be making use of an anticipatory strategy, we use the 

False Discovery Rate (FDR) method of Benjamini and Hochberg (1995) as applied by Storey 

(2002) and Fishe and Smith (2012), which adjusts for multiple testing problems.15 The FDR 

controls for the expected proportion of false discoveries in our sample. By effectively 

adjusting test statistic critical values, the FDR method limits these expected mistakes to a prespecified proportion of successful statistics. We use both a 2.5 and 5 percent control rates, so 

we often report two sets of results. The FDR method gives greater confidence that the 

participants we identify as anticipatory traders are truly either Type E or Type R traders.

We use a volume metric to determine whether a trader may be classified as participating 

early or late in a price path. Let xi,t

j

∈ Xj be the quantity traded by participant i at time t in the 

vector of all trades (Xj) on price path j. If participant i is a buyer (seller) at time t in path j then 

xi,t

j > 0 (xi,tj < 0). If participant i does not trade at time t in path j then xi,tj = 0. The 

heaviside function, si,t

j = h(xi,tj

∈ Xj; d), defines whether trade xi,t

j

arises in the first d

th

percentile of path j’s volume. The heaviside function equals 1 if the trade is in the first d

th

percentile and equals zero otherwise. The price direction along path j is defined to be 

increasing (decreasing) if ∆Pj > 0 (∆Pj < 0), where the first and last trade prices on the path 

are used to compute this difference. 

Participants are identified as successful Type E traders on a given path if their trades occur 

in the first d

th percentile of path volume and their trades are on the correct side for the path’s 

 

15 Recent applications of FDR include Barras, Scaillet, and Wermers (2010) who sought to identify fund 

managers with positive alpha performance, Bajgrowicz and Scaillet (2012) who examine the success of technical 

trading rules, Fishe and Smith (2012) who identified the number of informed traders in several futures contracts, 

and Harvey, Liu, and Zhu (2014) who examine threshold critical values necessary to claim a new risk factor after 

hundreds of asset pricing tests by previous researchers. 
14

price change. For a given value of d, we compute the sample frequency of successes for each 

participant:

Fi

Type E =

∑ ∑ 1(xi,t

j >0, ∆Pj>0 | s

i,t

j

= 1 )+1(xi,t

j < 0, ∆Pj< 0 | s

i,t

j

= 1 )

Tj

t=1

J

j=1

∑ ∑ 1(xi,t

j

≠ 0)

Tj

t

J

j=1

, (1)

where 1( ) is the binary indicator function based on the given expression, Tj is the number of 

trades on path j, and J is the number of price paths in the sample. 

To determine the null hypothesis, consider what may arise for traders who are not 

attempting to compute turning points for intraday prices. If a trader is randomly placing both 

buys and sells during the day in small sizes, then across all paths we might expect to find 

about 10% of these trades in the first 10% of path volume with d = 10. But how many of these 

are expected to be successful, meaning that they are aligned with the price path direction? The 

answer depends on how volume is distributed across up and down paths as well as how a 

trader mixes order size and side (e.g., buy or sell sides). If volume is approximately equally 

distributed between up and down paths, order sizes are small, and order sides are about equal 

in number, then a null of 5% may be appropriate for these tests. However, volume is on 

average higher in down paths, traders often vary order sizes, and many traders end up with an 

unequal number of buys and sells. Such differences will alter the relevant null hypothesis. 

Rather than seek a general solution for such nuances, we back up a step and impose a 

more restrictive condition in our tests. The measure in equation (1) is a statistic indicating the 

proportion of participant i’s trades that were executed in the first d

th percentile of volume and

were in the correct price direction. This proportion is conditional on our perfect foresight 

calculation of local price trends. If d = 10, then it is clear from how the price paths are created 

that a trader has a 10% chance of executing (a buy or sell) within the first 10% of path volume 
15

assuming trades are randomly placed during the day. Appropriate adjustments for order size 

or order side will lower this fraction. Thus, to make it more difficult to find successful 

anticipatory traders we use 10% as the null hypothesis. For each trader we test the null 

hypothesis, H0: Fi

type E =

d

100

. This is a binomial test and will have statistical power if a 

participant trades a sufficient number of times. 

For our empirical work, we set d = 10 to identify Type E traders. To identify Type R 

traders, we consider the last 10th percentile of trading volume to be indicative of whether a 

trader uses information or foresight to anticipate the coming reversal of the price path. To 

measure success for Type R traders, we compute the proportion analogous to equation (1) 

using d = 90 to define the heaviside function: 

Fi

type R =

∑ ∑ 1(xi,t

j >0, ∆Pj<0 | s

i,t

j

= 0 )+1(xi,t

j < 0, ∆Pj> 0 | s

i,t

j

= 0 )

Tj

t=1

J

j=1

∑ ∑ 1(xi,t

j

≠ 0)

Tj

t

J

j=1

. (2)

The null hypothesis that we test to identify Type R traders is H0: Fi

type R =

100−d

100

. Note 

that to ensure statistical power, we confine our investigation to participants with more than 30 

trades in our sample.

IV.DATA

The data are derived from audit trail files for the CME/Nymex WTI light sweet crude oil 

futures contract. The WTI contract is traded worldwide on the Globex electronic platform. For 

the WTI contract each one cent move in price represents a $10 change in contract value, 

which provides leveraged returns even for relatively small changes in price when weighed 

against the relatively low margin requirements.
16

Our initial sample covers the period from September 12, 2011 to contract expiration (48 

days). This period is selected based on the trading and open interest activity in the December 

2011 contract, which is the first or second most active month in the year. A follow-up sample 

was available from August 1, 2014 to September 19, 2014 (35 days) for the October 2014 

expiration, which was the nearby contract for most of this period. These data are used to 

examine whether anticipatory strategies survive through time. Both samples contain all trades 

and orders posted, modified, and/or cancelled on the CME/Nymex exchange. We test for 

anticipatory trading in both samples, using participant accounts which have 30 or more trades. 

This cutoff is imposed to provide power to the FDR method. 

In order to determine price paths using the SPRT described above, we remove non-price 

forming trades from the sample, which are mainly transfers and offsets. We filter out spread 

trades where both sides are holding the spread as these trades provide only relative price 

information. However, if the initial side of a spread trade is an outright, we keep that side’s 

price if it is for the correct expiration. 

Table 1 provides statistics on the trading volume, number of participants based on manualand algorithmic entry, and order book data calculated across days in both samples.

16

 The WTI 

contract is quite active in the two samples with an average of 178,447 (225,723) contracts 

traded each day in 2011 (2014). There is a daily average of 2,285 (4,173) accounts active in 

2011 (2014). The majority of these accounts use manual entry methods to place orders. 

Algorithmic accounts increased from a daily average of 421 to 704 between samples.

Participants act to modify on average 62-67% of new orders and eventually cancel an average 

 

16 A trader is manual or algorithmic based on a specific variable (Tag 1028) found in the exchange-created 

dataset. See the CME Globex reference guide for additional details on this tag and reporting requirements 

(http://www.cmegroup.com/globex/files/GlobexRefGd.pdf). 
17

between 82-86% of those orders. Both samples show that the WTI crude oil contract is traded 

in a quote-driven market, with market orders on average less than 2.5% of daily orders. In this

analysis, we do not examine stop orders, offsets, transfer messages, or special order types, 

such as TAS (Trade at settlement) trades.

V. ANALYSIS

The first task is to estimate local price paths in the 2011 data. Then we use the FDR method to 

assess whether any participants systematically trade in the correct direction on a path. After 

identifying such traders, we examine whether anticipatory traders are rewarded by computing 

Sharpe ratios by accounts. We then examine their characteristics relative to other traders, 

specifically cancelation and execution speeds. Next, the question of whether such traders are 

informed is addressed as well as whether the volume of other traders is affected by

anticipatory traders. Finally, we compare the 2011 and 2014 samples to investigate whether 

the Type E and Type R groups retain their skills three years later.

a. Local Price Paths

We apply the SPRT method to find local price paths for each day in the sample. Table 2 

reports summary statistics derived from all price paths as well as paths that contain 

anticipatory traders (see below). Path information is summarized by price direction. The data 

show the average and median path returns in percent, average path duration in seconds, 

average path volume, average number of trades, and average number of participants in a price 

path. Average and median returns are nearly equal between up and down paths, with up paths 

about 12.5% longer in duration. Up paths also average greater volume and trades, so the near 

equal returns imply that prices trend up slower than they trend down. There are somewhat 
18

more than 80 participants on average on these paths, suggesting for a competitive 

environment for price discovery.17 

b. Identifying Anticipatory Participants

The FDR method uses the binomial statistics in equations (1) and (2) to identify Type E 

and Type R participants. This method produces a two-tailed test in which our focus is on the 

upper tail results. We examine results for 2.5% and 5% FDR control rates. Note that the 5% 

control rate includes all of the 2.5% control rate accounts as it is less restrictive. The 2.5% 

(5%) tests found 112 (301) Type E and 196 (542) Type R participant accounts. We also 

identified 4 (25) overlaps between Type E and Type R groups for the 2.5% (5%) control rate. 

These accounts appear to have exceptional timing skills. Within these two groups, we find a 

total of 150 are algorithmic traders; 52 in the Type E group and 98 in the Type R group in the 

5% control rate tests. The remaining traders use manual order entry methods.

Table 2 also provides path characteristics when Type E and Type R accounts are active 

anywhere on a price path. For these entries, the average number of unique participants refers 

to anticipatory traders, not to all traders. Thus, there are on average one to two Type E and 

seven Type R traders active on these paths. Notably, when Type E accounts are active,

average and median path returns are greater in absolute value, volume and trades increase, and 

path duration is greater. When Type R accounts are active, average and median path returns 

are about same as the all path statistics. Thus, Type E accounts may be somewhat more 

successful than Type R accounts in identifying paths with larger price changes. We explore 

whether this suggests informed trading by either anticipatory type below.

 

17 Additional summary data on the sample and price paths are available from the internet Appendix for this 

paper. See https://sites.google.com/view/anticipatorytrading/.
19

c. Aggregate Sharpe Ratios 

Sharpe ratios examine if the traders identified as anticipatory use a strategy that offers 

benefits relative to the risk. Otherwise, such strategies will become ephemeral as other, more 

dominant strategies are recognized, or such traders lose their skills. Both Menkveld (2013) 

and Conrad and Wahal (2016) compute Sharpe ratios using high frequency trading data. Our 

approach closely follows Menkveld as his method incorporates trading fees and margins. The 

primary difference here is that we do not know a participant’s inventory at the start of the 

sample, because our sample begins months after trading starts in the December 2011 

expiration. Thus, we focus on the gross cash flows from trades to compute Sharpe ratios.

Following Menkveld (2013), a participant’s average gross cash flow from trading on a 

given day is given by:

π̅i =

1

T

∑ −∆(nt

iPt) − |∆nti

| τ

T

t=1

(3)

where T is the total number of trades for participant i during the day, nt

i measures the 

cumulative net position after the t

th trade, Pt

is the price of the futures contract, and τ captures 

the exchange and clearing fees paid by the participant.18 The capital employed (mi) is 

estimated from the Nymex daily margin requirements for the highest absolute position held 

during the day.19 Daily returns are estimated by: ri = π̅i − rfmi, where the risk-free rate (rf) 

is proxied using daily one-year constant maturity treasury bill rates. These returns are 

averaged across trading days to compute the mean return (μi) and standard deviation (σi) for 

 

18 The cumulative net position is set to zero at the start of the sample. As the position accumulates, the first trade 

on a new day is netted against the last cumulative position of that participant, generally the previous day’s last 

net position. The end-of-day mark-to-market approach is not appropriate here because the initial inventory 

position of each participant is unknown.

19 Exchange and clearing fees may vary across participants because of exchange incentive programs. We could 

classify many participants into specific programs, but not all. Thus, we applied the worst-case fees (and margins) 

to all participants, thereby reducing the estimated Sharpe ratios.
20

the Sharpe ratio, which is given by, Si =

a(ri)μi

σi

, where a(ri

) is an annualization factor that 

may be used to adjust for autocorrelation in returns (Lo 2002; Table 2).

Table 3 shows the estimated Sharpe ratios for Type E and Type R anticipatory traders 

identified using the 2.5% and 5% FDR control rates. Sharpe ratios for the “All Other” group 

and the sub-group of participants who are both Type_E and Type R are also included. Sharpe 

ratio results for an unadjusted annualization factor (√254) and one adjusted for first-order 

autocorrelation—coefficient is ‘rho’—are shown in the table. 

Sharpe ratios reveal that the Type_E group performs better than Type_R group with both 

groups exceeding the average performance of the All Other group. Notably, the overlapping 

participants within the Type_E and Type_R groups show exceptional performance. This latter 

group is both trading early and late on a price path, which would be an optimal reversal 

strategy. For comparison, Nagel (2012) finds annualized Sharpe ratios between 4.91 and 9.58 

using a daily reversal strategy on individual equities between 1998 and 2010. This range is 

similar to the 4.78 to 5.48 range reported for the overlapping Type_E and Type_R 

participants. The daily return ratios reported by Menkveld (2013, Table 3) for a single HFT 

are also close to these values; as are the very short horizon annualized Sharpe ratios found by 

Conrad and Wahal (2016) using realized spreads. 

Conrad and Wahal (2016) find that as the calculation horizon for spreads increases, the 

Sharpe ratio decreases to values similar to those for the All Other participants group. While 

some in the All Other group trade frequently, the majority do not which makes them more 

comparable to a longer horizon result. Nagel also reports a realized Sharpe ratio of 0.26 for 

the CRSP value-weighted index, also similar to that found for the All Other group. As these 

participants are close to representative of the overall futures market, they may perform similar 
21

to an overall market index. In addition, because the contract here is a derivative, there is a 

zero-sum effect that suggests the average gross cash flows aggregated over all trades during 

the life of a contract approach zero (without fees). Although the sample is only over the 

ending months of the contract, this effect may also help explain the relatively low Sharpe 

ratios found for the All Other group.

d. Speed Characteristics 

A central question is whether anticipatory traders are high frequency traders in the usual 

sense of the term. The Securities and Exchange Commission (2014) offers five criteria to 

identify high frequency traders: (1) high speed in routing and executing orders, (2) use of colocation services, (3) short time between establishing and liquidating positions, (4) using a 

submit and cancel approach to orders, and (5) ending trading sessions with near zero 

inventories. We have information on items (1), (3), and (4) from the order book data, which is 

the source for participant speeds.

A participant’s speed is found by comparing the initial order submission time to the exit 

time of each order.20 Specifically, we calculate the time between order entry and either 

cancellation or execution.21 For cancels, order duration is the difference between the time the 

CME/Nymex received the cancel message and the initial order confirmation time. If an order 

executes, duration is the difference between the CME/Nymex confirmation time and the initial 

order confirm time. This execution duration depends on a host of factors that affect the order 

book, such as liquidity flows and new information about price, as well as the initial and 

 

20 We do not use modification messages to measure speed because they do not remove an order from the book, so 

they are not part of the exit accounting.

21 Orders may also exit by administrative action, but we exclude these from the sample.
22

subsequent decisions of the participant placing the order. A comparison of these speeds across 

all orders for Type E and Type R participants is shown in Table 4. 

In Table 4, Panels A and B report average (minutes) and median (seconds) speeds, 

respectively, and Panel C reports the distribution of messages by Type E, Type R, and all 

other participants. There are clear differences between average and median speeds across 

participant types. Average speeds are slow, measured in minutes suggesting that account level 

behavior is very different than market level speeds.22 These median and average speed 

differences show that skewness (and possibly outliers) affects the statistics for all groups 

including the all other category. Thus, for inferences we focus on the median data.

Considering the SEC criteria, we see that Type E and Type R accounts are slower to 

cancel than the median cancel speed of all other participants, with or without modification 

messages. Type R algorithmic accounts are more than ten times slower than the All Other 

algorithmic accounts. All groups use cancellations, but Type E—particularly algorithmic 

entry—is below the frequencies shown in Panel C for All Other accounts, while Type R is 

above for manual and below for algorithmic entry. For algorithmic comparisons, both Type E 

and Type R fall short of using a “submit and cancel” approach compared to other participants. 

Execution speeds for Type R accounts are also slower than the All Other group. However, 

execution speeds for Type E accounts are faster, which may be due to the greater use of 

market orders and marketable limit orders by Type E participants. This is consistent with the 

high execution percentage reported in Panel C, suggesting that Type E accounts place orders 

with the intention of execution, with few cancellations. In contrast, Type R participants have 

 

22 Market level speed computes the difference between two successive cancelations or executions without regard 

to who is the participant. For example, Hasbrouck and Saar (2013) find 2-3 millisecond peaks in hazard rates that 

measure market responses after a quote improvement, although there are long tails in these rates. 
23

much higher modification rates compared to All Others and lower execution rates. This 

suggests that they modify their orders often and relatively few of those orders are executed. It

appears that Type R accounts may be modifying to search for the end of a price path. In 

general, these statistics do not suggest that HFT-like speeds are necessary to implement an 

anticipatory strategy.

To validate our interpretation of the summary statistics in Table 4, we specify a regression 

model with characteristics partitioned by a set of dummy variables, some of which identify 

these anticipatory groups. In the regression model, the effects of the anticipatory participants 

are measured relative to other omitted groups. Table 5 shows regression results using order 

cancellation speed as the dependent variable. The transformation, natural logarithm of one 

plus the cancellation speed (in seconds with fractional milliseconds), is used in these

regressions.

23 The variables, "First 10% of Path Volume", "Last 10% of Path Volume", and 

"Between 10% to 50% of Path Volume", indicate whether the cancellation message occurs 

during the first 10% of path volume, last 10%, or during the first half of volume that excludes 

the first 10%, respectively. The "Type E Trader" and "Type R Trader" variables indicate 

whether the message is by a Type E or Type R participant, respectively. Type E and Type R 

participants are those identified using the 5% control rate for the FDR. Interaction terms are 

included in selected models: (1) Type E trader in the first 10% volume bin; (2) Type R trader 

in the last 10% volume bin; (3) Type E trader that is also algorithmic (ATS); and (4) Type R 

trader that is also algorithmic. Additional binary variables are "Modified Order" if there are 

any modification messages to the original order, "Proprietary trader" if the trade is made from 

a proprietary account, "Buy-Sell Indicator" which is one if this is a buy order, and the 

 

23 The dependent variables in Tables 5 and 6 are log(1+duration) because the order confirmation time of a market 

order equals its execution time, so duration will be zero for these orders.
24

"Algorithmic Trader" variable which is one if this side of the trade was submitted by a 

computer algorithm. As all variables are binary, the intercept captures the omitted categories. 

The parentheses below each coefficient in Table 5 shows p-values, computed using 

heteroscedasticity-consistent standard errors. The adjusted R-squared for each model is shown 

at the bottom of the table. The sample size is 30,870,518 observations.

The intercept term captures the average decision speed of the omitted group, which in the 

most general case (Model VI) is a non-anticipatory, non-proprietary, manual participant, 

canceling during the 50-90% of path volume with no order modifications. The average 

cancellation speed of the omitted group is 27.361 seconds, which decreases to an average of 

3.094 seconds if these are algorithmic participants. The estimates in Models II-IV show that 

Type E and Type R participants are slower than the omitted group until we control for other 

order characteristics. Specifically, Model VI shows that the average cancellation speed of 

manual Type E (Type R) traders is 38.375 (68.622) seconds. Type E (Type R) traders who are 

algorithmic are slower that the omitted algorithmic group with an average cancellation speed 

of 9.169 (10.252) seconds. From these estimates, the algorithmic and modified order 

coefficients have the largest impact on average cancellation speeds. 

Table 5 also shows that cancels during the first and last 10% of path volume are 

marginally faster for all traders. However, the interaction terms for Type E and Type R 

participants show no significant effect for Type E participants and a slowdown in speed for 

Type R participants during the last 10% of path volume. Combined with the results above, 

these findings suggest that fast cancellation speeds are not a significant characteristic of 

anticipatory trading. 
25

Table 5 includes regression estimates under the "Bootstrap" column heading. These 

estimates are averages of coefficient results from 1,500 random samples (with replacement) in 

which each trader account is chosen once per sample. The purpose is to equal weight each 

account in the regression so that the numerous orders from HFT participants do not skew the 

resulting coefficients (Fishe and Smith 2017). The 95% confidence intervals (“C.I.”) from 

these simulations are shown below the average coefficient value.

The confidence interval results show that there are fewer significant variables. Of those 

that are significant, the Type E and Type R dummies continue to indicate slower cancel 

speeds relative to the omitted group. The effects of HFT trading volumes are clear in the new 

algorithmic trader coefficient, which decreases by 71% in absolute value from Model VI. 

Interestingly, the proprietary trader coefficient nearly quadruples, suggesting that those 

participants are much faster than the omitted group. While inferences about the duration of 

orders ending in a cancel message are correctly derived from Models I-VI, inferences about 

participants from such message durations are better reflected in the bootstrap results. These

results continue to show that being fast as measured by cancellation speeds is not a 

meaningful determinant of who is an anticipatory trader.

Table 6 presents speed results using the data on trade executions. In addition to the binary 

variables in Table 5, we include a dummy variable for whether a participant was on the 

aggressive side of the trade. Model VI coefficient estimates evaluated at the mean show a 

sample execution speed of 0.818 seconds. This is somewhat faster than equity execution 

speeds for Nasdaq- and NYSE-listed securities in 2011 and 2012 (Angel, Harris, and Spratt 

2015). Part of the reason for this low average is the effect of aggressive orders, which lower 

the base speed of the omitted group from 6.034 seconds to 2.233 seconds. In isolation, a 
26

manual Type E (Type R) participant is 0.655 (2.337) seconds slower with executions than the 

base speed of the omitted group. However, being an algorithmic trader increases these speeds 

with the base group executing in an average of 3.38 seconds and the Type E (Type R) group 

slower with an average speed of 5.832 (6.878) seconds. Even so, these anticipatory traders are 

not faster than non-anticipatory traders as measured by execution speeds.

In addition, some of these observations no longer hold in the bootstrap results for the 

execution sample. These coefficients were created in the same manner as described for the 

cancelation sample. The interaction between Type E and Type R groups with the first and last 

10% of volume is not significant, so there is no acceleration of speed in those periods based 

on participant inferences. Also, the interaction terms between Type E or Type R groups and 

the algorithmic variable are not significant. Thus, in an equal-weighted sample, Type E and 

Type R participants are no faster than other algorithmic participants.

e. Measuring Information and Volume Effects

Two important issues in this analysis are whether anticipatory traders have an information 

advantage and/or a skill at short-term price prediction, and whether the volume of other 

traders is affected by these participants. We first examine whether new information is 

provided by anticipatory traders. Because anticipatory traders are selected using a volume 

decile rule, investigating price changes using volume deciles presents an endogeneity issue. 

To avoid that problem, we divide each price path into time deciles. We examine the first and 

second time decile bins for Type E traders and the last bin in the previous path as well as the 

first bin in the current path for Type R traders. We compute the price change in each bin 

relative to the absolute price change in the path. Table 7 reports these average relative price 

changes for bins with and without anticipatory traders and for “up” and “down” price paths 
27

separately. These results are reported for groups selected using both the 2.5% and 5% FDR 

control rates. The averages using all bins are also shown for comparison.

For Type E comparisons, we examine the bin averages conditional on whether Type E 

traders are transacting in Bin#1. Similarly, for Type R comparisons, we condition on whether 

Type R traders are transacting in Bin#10 on the previous path. If these traders bring new 

information to the market, then in the current or next bin we expect a difference in relative 

prices when they are in the current (Type E) or previous (Type R) path. We find no significant 

differences in relative price changes for bins with and without Type E traders using 2.5% or 

5% control rates. When the average is across all bins, the up paths show a difference when the 

highly skilled Type E group (2.5% control rate) is in time bin#1 of a path, consistent with our 

findings in Table 2. However, this result is not due to a nearby bin so it is not closely 

connected to the early presence of a Type E trader. 

In contrast, the Type R results suggest an information effect if a Type R participant trades 

in the last time bin on the previous path. The effect is not in the last bin, but appears in Bin#1 

on the next path where relative prices change by 3.1% to 3.7% in absolute value. This result 

holds for both the 2.5% and 5% control rate results for the Type R group. It is also focused on 

the nearby bin as the “all bins” results show no significant relative price change. These results 

suggest that Type R participants appear to have valuable short-term price information while 

the Type E participants appear to have superior forecasting skills.

Our second set of results address whether anticipatory traders affect the volume of trading 

by other market participants. If such traders increase volume by other participants, then price 

moves may be “caused” by their anticipatory trading, a “momentum-ignition” effect. In this 

event, they are not “anticipatory” because, instead of indirectly anticipating other’s actions, 
28

they directly caused them. We use a VAR model of price and volume to examine this 

question. This model is estimated using bin data across the 15,765 price paths. Table 8 shows 

reduced form results for the VAR model.

In Table 8, first differences of price and volume are used to improve series stationarity.24

Results are shown in Panel A for the anticipatory participants identified using the 2.5% FDR 

control rate. Panel B shows estimates for the 5% FDR control rate. As the data are bin 

specific, the differences for price and volume are relative to the previous bin.25 End-of-bin 

prices are used to calculate price deltas. Both price and the volume of all other participants 

(i.e., not Type E or Type R) are endogenous, while the trading volume of Type E or Type R 

participants is exogenous, which implies that these variables enter these models

contemporaneously. We also include a dummy exogenous variable for whether Type E (Type 

R) participants trade in Bin#1 (Bin#10 of the last path). This variable is the focus here because 

we want to know whether the volume of trading by other participants is affected by 

anticipatory traders, especially in those bins where they are likely implementing their 

anticipatory strategy. We consider effects for participants in the full sample and for the Type 

E group in Bin#2, and for the Type R group in Bin#1.26

The results in Table 8 suggest that contemporaneous, same bin trading by Type E 

participants is associated with reduced volume deltas by other traders, but the reverse holds 

for Type R participants (Model 1). Important for momentum claims, we observe that trading 

by Type E participants in Bin#1 is correlated with higher volume deltas for all other 

 

24 Adjusted Dickey-Fuller tests indicate that bin prices are stationary in first differences. Volume by trader type 

is generally stationary in levels and always stationary in first differences.

25 These price changes differ from those in Table 7, which are relative to the absolute value of the path’s price 

change. Thus, the dependent variable ∆Ptis the price change observed for a specific bin. 

26 We also estimated the VAR model without overlaps between Type E and Type R traders on a given path. This 

filter did not change our conclusions.
29

participants (Model 2). This effect appears to be driven by actions later in the price path 

because the Bin#2 results show no significant effects when Type E participants are in Bin#1. 

In contrast, Type R participants in Bin#10 on the previous path have a significant negative 

effect on volume deltas in the next path, both on average across bins and in Bin#1 on the next 

path. In short, Type R participants are not associated with a “momentum ignition” effect, but 

Type E participants may incite some volume effect, but this effect is not contiguous to activity

in Bin#1.27

f. Skill Retention

Lastly, we examine whether these traders retain their skills in a subsequent sample. 

Unfortunately, the WTI crude oil futures data from 2014 shows substantial attrition among the 

2011 accounts with only 35% of the 2011 accounts included. This attrition is similar to that 

found by Boyd and Kurov (2012) in energy futures after side-by-side electronic trading began 

on the Globex platform in 2006. Several factors contribute to this result, including oil price

volatility, rule changes under Dodd-Frank raising capital requirements, and flows into and out 

of energy markets after the 2008-09 financial crises.

28 Notably, the bankruptcy of MF Global 

on October 31, 2011 forced thousands of customers to find alternative clearing arrangements, 

which generally led to new account designations.29 Thus, a caveat to the out-of-sample results 

 

27 The specific bin results on Type E participants were also estimated for Bin#3. There is a significant negative 

effect from contemporaneous volume and an insignificant effect for the Bin#1 dummy variable.

28 These factors also contributed to a decline in Futures Commission Merchants (FCM) and rotation of customers 

to new executing brokers. See http://www.thetradenews.com/Asset-Classes/Derivatives/FCM-client-clearingnumbers-in-steep-decline/.

29 At the time, MF Global was the eighth largest bankruptcy in U.S. history. It managed over 36,000 customer 

accounts, many of which were involved with futures and options trading (Gibbons 2012). Approximately 800 of 

our sample participants used MF Global as the executing broker, and we expect that many of these may still be 

trading but active with another commodities broker.
30

is that they may under-state the success of our two groups as some participants may appear 

successful under a new executing broker. 

Table 9 summarizes our findings for the 2014 sample. Panel A shows the size of the Type 

E and Type R groups in both 2011 and 2014, Panel B shows the counts for those who are 

found in both samples, and Panel C identifies the number of overlaps who retain the 

anticipatory strategy. The percent of sample figures in Panel A indicate that the anticipatory 

strategy was relatively more difficult to execute in 2014: There are 24% (11%) fewer 

anticipatory participants at the 2.5% (5%) FDR control rate. These decreases are not as large 

for the 2011 participants that overlap the 2014 data as shown in Panel B. Those successful at 

the 2.5% control rate show a decrease of 12%, but there is an increase of 3% for the 5% 

control rate results. In effect those who do continue to trade three years later are more 

experienced and perhaps better able to maintain a strategy compared to newer participants.

Only a small number of the Type E overlaps—7 participants or 9%—continue to 

implement that strategy in 2014. Skill retention for Type R participants is higher—27 

participants or 20%—indicating that this group may have adopted a more durable strategy. 

These counts are likely lower bounds because of the need for many participants to find new 

executing brokers during the gap. Thus, some continuing but unidentified accounts may be 

among those identified as successful in the 2014 sample. Even so, the results indicate that 

anticipatory trading became more difficult between 2011 and 2014 and that the Type E or 

early trading strategy was the relatively more difficult strategy to maintain.

Finally, we examined the speed of the 2,669 participants who overlapped between the 

2011 and 2014 samples using data for both samples. Table 10 reports these results following 

the regression methods in Tables 5 and 6, and modeling both cancellation and execution 
31

speeds. The Type E and Type R participants are those who were significant at the 5% control 

rate in the 2011 sample. We include binary variables for algorithmic traders, a dummy for the 

2014 observations, and control variables for participants who are Type E or Type R in both 

samples. The latter variables show incremental effects for these participants compared to 

those same types in 2011 who did not maintain these strategies. We want to determine 

whether those who successfully continued as anticipatory traders in 2014 were aided by 

operating at faster speeds, thereby making speed important to strategy survival.

The base models for cancel and execution speeds in Table 10 show that manual entry 

cancel and execution speeds decreased between samples, indirectly supporting Hoffmann’s 

(2014) theory that slower traders will place orders with lower execution probabilities when 

competing with faster traders. Algorithmic entry speeds increased for executions in 2014, but 

cancel speeds decreased, suggesting that electronic traders adjusted the role of cancelations in 

their strategies, possibly affected by new anti-spoofing laws.

30 Cancel speeds for Type E 

participants did not change, but execution speeds increased. For Type R, execution speeds 

decreased, while cancel speeds increased. These are mixed findings on the role of speed 

between the 2011 and 2014 samples.

Importantly, these results are for anticipatory traders identified in 2011, who also are 

identified as trading in 2014. As Table 9 shows, only a small number of these participants 

continued to successfully implement the anticipatory strategy. Table 10 identifies these 

special participants as “Participant ‘X’ in both samples.” We want to determine if speed 

increased or decreased for these continuing participants.

 

30 Section 747 of the Dodd-Frank legislation of 2010 specifically prohibited any trading, practice, or conduct that 

“...is commonly known to the trade as, ‘spoofing’ (bidding or offering with the intent to cancel the bid or offer 

before execution).”
32

Table 10 shows that the continuing Type E participants are no faster at cancelations or 

executions than non-continuing Type E participants. If they are algorithmic, however, they are 

faster with both actions. The 2014 dummy is not significant alone for either cancelations or 

executions, but combined with the algorithmic indicator it shows a significant positive effect 

on execution speeds. Thus, the only clear effect for continuing Type E participants is that they 

are slower to execute in 2014 if they are algorithmic.

Estimates for the continuing Type R participants show that they are faster (slower) at 

cancellations (executions) than non-continuing Type R, while they are slower for both actions 

if they are algorithmic. Curiously, they are slower (faster) with cancelations (executions) in 

2014, but if they are algorithmic these findings reverse themselves. In 2014, manual entry, 

continuing Type R participants appear to be enabled by faster (slower) execution (cancelation)

speeds, but algorithmic entry participants operate with slower (faster) execution (cancelation) 

speeds. It appears that continuing the Type R strategy required adjusting the use of cancel 

messages as manual-entry participants slowed and algorithmic entry increased cancel speeds, 

making the importance of speed ambiguous to continuing the anticipatory strategy.

VI.CONCLUSION

We identify and examine a select group of participants who can anticipate short term, intraday

price movements. The purpose is to understand whether such strategies require high-speed 

actions or rather, are populated with HFTs. We identify two groups of anticipatory traders 

who are prescient early in a price path (Type E) or who trade immediately before a reversal in 

the price path (Type R). Sharpe ratios show that their risk-adjusted performance exceeds that 

of other participants. Importantly, there is a mixture of both algorithmic- and manual-entry 
33

traders within these groups, so that speed per sé is not necessary or sufficient to conduct an 

anticipatory strategy.

Regression results show that a number of factors affect message speed. Algorithmic and 

proprietary traders are faster than manual-entry, customer-directed participants. Those who 

use modifications are much slower than other participants are, and aggressive traders have 

demonstratively faster execution speeds, likely because they are entering market orders. Type 

E and Type R groups are found to be slower than other traders, confirming the result that 

speed is not an essential characteristic of anticipatory trading.

We also investigated whether anticipatory traders bought new information to the market 

and whether they caused momentum trading by others. There is some evidence that Type R 

traders are informed in nearby bins, but no evidence of information in Type E trades. Volume 

effects are present, but weak for Type E traders and are of the wrong sign for Type R traders. 

Lastly, we examined whether anticipatory traders retained their skills three years later in a 

similar sample. Attrition from the original sample qualifies these claims. Even so, the 

evidence suggests that the skills held by Type E participants are relatively ephemeral, but that 

Type R participants show more durability with their strategies, with the role of speed in 

strategy survival being ambiguous between manual- and algorithmic-entry participants.
34

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Figure 1. Price paths and anticipatory traders 

This figure illustrates a sequence of trades (the “x’s”) from participant order flow. When new 

information about the value of the asset arrives or liquidity demand changes, the market price 

reacts until the information is impounded in the price structure or new liquidity arrives to resolve 

the imbalance. Non-price moving liquidity based trading is shown as trades along a bid-ask 

bounce sequence (shaded areas). Price reversals occur at the specified local price extrema (the 

circled trades). A participant that processes the skills to process order flow and trade information 

to systematically forecast the short-term direction of trade prices is known as a Type E 

anticipatory traders. A participant who processes new information or market flow signals to 

systematic forecast the subsequent reversal in local trade price trend is known as a Type R 

anticipatory trader.

Time

Trade

Price

Trades

Local Price Extrema

Bid-Ask Tunnel

Type E 

Anticipatory 

traders

Type R 

Anticipatory 

traders
Characteristics Average Median

Standard

Deviation Minimum Maximum

Panel A: 2011 Sample - December 2011 Expiration

Trading Volume 178,447 53,456 181,564 8,243 565,140

Count of:

 - Algorithmic Traders 421 280 318 47 890

 - Manual Traders 1,864 1,088 1,474 242 4,388

 - Order Entry Messages 819,799 506,624 562,948 7,649 2,095,500

 - Modification Messages 552,411 390,795 400,678 5,475 1,579,307

 - Cancellation Messages 705,919 480,767 451,195 3,837 1,764,984

 - Market Orders 11,075 3,711 11,429 431 34,926

 - Limit Orders 816,439 505,980 559,644 7,408 2,087,561

Panel B: 2014 Sample - October 2014 Expiration

Trading Volume 225,723 270,396 156,284 8,401 486,328

Count of:

 - Algorithmic Traders 704 934 397 29 1,262

 - Manual Traders 3,469 4,692 2,287 243 6,533

 - Order Entry Messages 781,966 762,280 301,255 216,777 1,415,200

 - Modification Messages 484,818 500,726 137,082 220,791 829,322

 - Cancellation Messages 640,131 663,227 226,690 192,038 1,134,177

 - Market Orders 17,673 20,012 12,099 1,068 41,268

 - Limit Orders 776,262 760,971 297,646 215,233 1,402,853

Table 1

Daily Characteristics of Sample Data

Summary statistics are shown for messages and orders in the WTI crude oil contract expiring in

December 2011 in Panel A and expiring on October 2014 in Panel B. These data are for outright or

outright-to-spread orders as these orders are price informing for each expiration. The 2011 sample

covers all trading days from September 12 to November 18, 2011 and the 2014 sample covers August 1

to September 19, 2014. The table provides average, median, standard deviation, minimum and

maximum statistics across sample days for volume, number of unique traders (algorithmic or manual

order entry methods), message types (entry, modification, or cancellation), and order types (market or

limit). Note that message and order counts may not add up exactly as some order types arise from

different message origins, such as a market order being generated from a modified message that was

originally a limit order.
Description

Path 

Direction

Path 

Count

Average 

Path Ret. 

(%)

Median 

Path Ret. 

(%)

Path 

Duration 

(sec)

Path 

Volume

Number of 

Trades

Number of 

Unique 

Participants

down 7,903 -0.148 -0.119 115.6 569.5 447.6 88.8 

up 7,916 0.149 0.118 102.8 513.7 402.3 82.0 

down 2,455 -0.193 -0.156 190.7 892.8 692.8 121.6 

up 2,290 0.204 0.166 175.6 876.2 678.1 120.5 

down 7,140 -0.148 -0.120 114.4 612.8 482.3 95.2 

up 6,996 0.152 0.122 102.5 562.5 441.1 89.1 

Table 2

Local 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. The average number of unique participants is shown for all paths. Type E and Type R participants are

selected using the 5% FDR control rate.

Average

Price Paths with Type 

E Participants

Price Paths with Type 

R Participants

All Price Paths
Variable 2.5% C.R. 5% C.R. 2.5% C.R. 5% C.R. 2.5% C.R. 5% C.R. 2.5% C.R. 5% C.R.

Sharpe Ratio 1.046 0.830 0.523 0.531 na 4.784 0.371 0.376 

Adj. Sharpe Ratio 1.126 1.046 0.531 0.535 na 5.858 0.353 0.362 

rho -0.082 -0.139 -0.023 -0.015 na -0.109 0.043 0.034 

p-value(rho) 0.304 0.179 0.569 0.651 na 0.434 0.151 0.292 

Count of Participants 112 301 196 542 na 25 7,250 6,736 

Overlaps for 

Type_E and Type_R

Table 3

Sharpe Ratios for Anticipatory Traders

The table shows annualized Sharpe ratios for selected groups of participants. The Sharpe ratios are estimated for each

participant using the method described by Menkveld (2013). Trade-to-trade cash flows are averaged daily to compute mean

returns, which net out capital employed based on daily margin requirements and an estimate of the risk-free rate obtained from

daily one-year Treasury bill yields. Daily returns are averaged across trading days to compute the mean and standard deviation

of daily returns used in the Sharpe ratio. Both annualized and adjusted ratios are computed. The adjusted ratios follow the

methods in Lo (2002) for a first-order autocorrelation model of returns. The reported ratios are averaged over each participant

within a group. Groups are those participants identified as Type_E or Type_R using 2.5% and 5% control rates (C.R.) for the

FDR tests, and the collection of "All Other" participants who are those found not significant in these FDR tests. The overlap

group includes participants who are in both Type_E and Type_R groups. Heteroscedastic-consistent p-values are shown for the

estimated first-order autocorrelation coefficient ('rho'). The "na" indicates that the number of observations is too small for

meaningful inferences.

Type E Participants Type R Participants All Other Participants
Ending Message Man-Entry Algo-Entry Man-Entry Algo-Entry Man-Entry Algo-Entry

Cancellation 25.04** 7.59** 5.82** 0.68** 9.44 1.29

Cancellation (No Mods) 24.76** 3.98** 5.11** 0.51** 8.67 1.11

Execution 16.19** 7.03 8.07** 2.76 11.55 3.03

Execution (No Mods) 4.73** 2.03* 3.33** 0.58 2.24 0.59

Cancellation 54.56** 0.83* 60.07** 7.93** 23.18 0.73

Cancellation (No Mods) 35.49** 0.27 59.92** 7.42** 20.11 0.40

Execution 4.32** 0.01** 17.43** 1.83** 8.18 0.10

Execution (No Mods) 0.07** 0.00** 9.21** 0.18** 0.87 0.04

Cancellation 8.5% 2.8% 32.7% 13.4% 15.8% 57.8%

Modification 4.3% 59.5% 22.7% 84.0% 12.8% 33.1%

Execution 87.3% 37.7% 44.6% 2.6% 71.4% 9.1%

Total Messages 68,128 51,454 294,194 5,409,423 3,995,440 54,335,721 

Table 4

Speed Characteristics from Order Book Messages

The table shows the average (minutes) and median (seconds) durations between four message types: order entry to

either order cancellation or execution, and order entry to cancellation or execution with no intermediate

modification messages (No Mods). These calculations are grouped by Type_E, Type_R, and "All Other"

participants, and partitioned into manual-entry and algorithmic-entry methods. The Type_E and Type_R

participants are those found using the 5% control rate in FDR tests. Averages and medians shown are across

messages for each group and entry type. Panel A shows summary statistics for averages across messages, Panel B

shows median statistics, and Panel C shows the distribution and total number of messages by message type. A

"**" ("*") indicates that tests of whether Type E or Type R are different from the corresponding average for All

Other participants are significant at the 1% (5%) level of significance. Wilcoxon and Median tests are used to test

whether the medians are also different between Type E or Type R and the All Other group durations. The least

significant result is reported for the latter tests. 

Type E Participants Type R Participants All Other Participants

Panel A: Average Duration between Messages (minutes)

Panel B: Median Duration between Messages (seconds)

Panel C: Message Distribution
Bootstrap

Independent Variable I II III IV V VI Average/95% C.I.

-0.037 -0.035 -0.036 -0.027 0.038

(<0.001) (<0.001) (<0.001) (<0.001) (-0.09; 0.16)

-0.046 -0.046 -0.047 -0.036 -0.002

(<0.001) (<0.001) (<0.001) (<0.001) (-0.12; 0.11)

-0.015 -0.014 -0.014 -0.013 0.029

(<0.001) (<0.001) (<0.001) (<0.001) (-0.05; 0.11)

1.929 1.930 1.933 0.328 0.455

(<0.001) (<0.001) (<0.001) (<0.001) (0.18; 0.74)

1.430 1.430 1.422 0.898 0.423

(<0.001) (<0.001) (<0.001) (<0.001) (0.23; 0.60)

-0.271 -0.024 -0.176

(0.766) (0.772) (-1.03; 0.72)

0.063 0.074 -0.285

(<0.001) (<0.001) (-0.81; 0.26)

1.091 1.094 1.080

(<0.001) (<0.001) (0.96; 1.19)

-0.778 -0.516 -1.975

(<0.001) (<0.001) (-2.05; -1.91)

-0.001 -0.002 0.097

(0.002) (<0.001) (0.03; 0.17)

-1.834 -1.935 -0.553

(<0.001) (<0.001) (-0.63; -0.48)

0.582 -0.098

(0.029) (-0.72; 0.57)

0.113 -0.043

(<0.001) (-0.40; 0.30)

1.111 1.057 1.072 1.072 3.505 3.345 4.301

(<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (4.24; 4.36)

Adjusted R squared 0.0% 2.8% 2.8% 2.8% 13.3% 14.5% 12.1%

Regression (p-value) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (11.4% - 12.8%)

Interaction Term: Type R and 

Last 10% of Path Volume

Table 5

Participant Cancellation Speeds

This table shows regression results for the speed of cancellation controlling for characteristics of the participants, position within

a price path, and other features of the trade. The speed of cancellations equals the difference between the time the Nymex

receives a cancellation message and the confirmation time of the original order (in seconds with fractional milliseconds). The

dependent variable is the natural logarithm of one plus the cancellation speed. The variables, "First 10% of Path Volume", "Last

10% of Path Volume", and "Between 10% to 50% of Path Volume", are binary variables indicating whether the cancellation

message occurs during the first 10% of path volume, last 10%, or during the first half of volume that excludes the first 10%,

respectively. The "Type E Trader" and "Type R Trader" are binary variables indicating whether the cancel message is made by a

Type E or Type R trader, respectively. There are interaction terms in selected models: (1) Type E trader in the first 10% volume

bin, (2) Type R trader in the last 10% volume bin, (3) Type E trader that is also algorithmic, and (4) Type R trader that is also

algorithmic. Additional variables are "Modified Order" if there are any modification messages to the original order, "Proprietary

trader" if the order is made from a proprietary account, "Buy-Sell Indicator" which is one if this is a buyside order, and the

"Algorithmic Trader" variable which is one if this cancel message was submitted by a computer algorithm. The intercept captures

the omitted categories. The p-values are computed using heteroscedastic-consistent standard errors and are shown in parentheses

below each coefficient. The "Bootstrap" column shows coefficient results from 1,500 random samples in which each account is

chosen only once per sample. For these regressions, Type E and Type R traders are defined using the 5% FDR control rate. The

95% confidence intervals ("C.I.") are shown below the average coefficient value for these simulations. The full sample size is

30,870,518 observations.

Dependent Variable: Ln(1+Cancellation Speed)

First 10% of Path Volume

Last 10% of Path Volume

Between 10% to 50% of Path 

Volume

Type E Trader

Type R Trader

Interaction Term: Type E and 

First 10% of Path Volume

Intercept

Modified Order

Proprietary Trader

Buy-Sell Indicator

Algorithmic Trader

Interaction Term: Type E and 

Algorithmic Trader

Interaction Term: Type R and 

Algorithmic Trader
Bootstrap

Independent Variable I II III IV V VI Average/95% C.I.

-0.024 -0.022 -0.018 -0.041 -0.092

(<0.001) (<0.001) (<0.001) (<0.001) (-0.16; -0.03)

-0.038 -0.058 -0.066 -0.031 0.079

(<0.001) (<0.001) (<0.001) (<0.001) (0.01; 0.15)

-0.060 -0.052 -0.052 -0.048 -0.110

(<0.001) (<0.001) (<0.001) (<0.001) (-0.15; -0.07)

0.571 0.570 0.630 0.504 0.126

(<0.001) (<0.001) (<0.001) (<0.001) (-0.07; 0.32)

1.388 1.388 1.363 1.205 0.409

(<0.001) (<0.001) (<0.001) (<0.001) (0.25; 0.56)

-0.292 -0.171 -0.109

(<0.001) (<0.001) (-0.46; 0.26)

0.166 0.107 0.080

(<0.001) (0.004) (-0.29; 0.46)

-1.596 -1.562 -2.696

(<0.001) (<0.001) (-2.73; -2.66)

0.887 0.875 1.582

(<0.001) (<0.001) (1.48; 1.67)

-0.079 -0.049 -0.432

(<0.001) (<0.001) (-0.50; -0.35)

0.003 0.004 -0.007

(0.002) (0.832) (-0.04; 0.03)

-0.508 -0.474 -0.063

(<0.001) (<0.001) (-0.10; -0.02)

-0.060 -0.312

(0.01) (-0.65; 0.06)

-0.618 0.269

(<0.001) (-0.07; 0.59)

0.940 0.855 0.884 0.885 2.011 1.951 2.876

(<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (2.82; 2.93)

Adjusted R squared 0.1% 2.7% 2.7% 2.7% 28.6% 28.8% 39.7%

Regression (p-value) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (38.9% -40.5%)

Interaction Term: Type R and 

Last 10% of Path Volume

Table 6

Participant Execution Speeds

This table shows regression results for the speed of execution controlling for characteristics of the participants, position within a

price path, and other features of the trade. The speed of an individual execution equals 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 dependent variable is the natural logarithm of one plus the execution speed. The variables, "First 10% of Path Volume", "Last

10% of Path Volume", and "Between 10% to 50% of Path Volume", are binary variables indicating whether the cancellation

message occurs during the first 10% of path volume, last 10%, or during the first half of volume that excludes the first 10%,

respectively. The "Type E Trader" and "Type R Trader" are binary variables indicating whether the trade is made a Type E or Type

R trader, respectively. There are interaction terms in selected models: (1) Type E trader in the first 10% volume bin, (2) Type R

trader in the last 10% volume bin, (3) Type E trader that is also algorithimic, and (4) Type R trader that is also algorithimic.

Additional binary variables are "Modified Order" if there are any modification messages to the original order, "Proprietary trader"

if the trade is made from a proprietary account, "Buy-Sell Indicator" which is one if this is the buyside of a trade, and the

"Algorithmic Trader" variable which is one if this side of the trade was submitted by a computer. The intercept captures the

omitted categories. The p-values are computed using heteroscedastic-consistent standard errors and are shown in parentheses

below each coefficient. For these regressions, Type E and Type R traders are defined using the 5% FDR control rate. The

"Bootstrap" column shows coefficient results from 1,500 random samples in which each trader account is chosen only once per

sample. The 95% confidence interval ("C.I.") is shown below the average coefficient value for these simulations. The full sample

size is 6,738,138 observations.

Dependent Variable: Ln(1+Execution Speed)

First 10% of Path Volume

Last 10% of Path Volume

Between 10% to 50% of Path 

Volume

Type E Trader

Type R Trader

Interaction Term: Type E and 

First 10% of Path Volume

Interaction Term: Type R and 

Algorithmic Trader

Intercept

Aggressive Side of Trade

Modified Order

Proprietary Trader

Buy-Sell Indicator

Algorithmic Trader

Interaction Term: Type E and 

Algorithmic Trader
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 (2.5%) in Current Path Time Bin#1 0.088 -0.089 0.271 0.070 -0.267 -0.075 

Type E (2.5%) in Current Path Time Bin#1 0.092 -0.077 0.264 0.094 -0.373 -0.046 

p-value for t-Test of Diff. (0.000) (0.627) (0.813) (0.281) (0.419) (0.315) 

No Type E (5%) in Current Path Time Bin#1 0.088 -0.088 0.273 0.070 -0.267 -0.075 

Type E (5%) in Current Path Time Bin#1 0.089 -0.089 0.245 0.077 -0.299 -0.064 

p-value for t-Test of Diff. (0.755) (0.921) (0.055) (0.526) (0.448) (0.436) 

No Type R (2.5%) in Previous Path Time Bin#10 0.088 -0.088 0.202 0.256 -0.181 -0.256 

Type R (2.5%) in Previous Path Time Bin#10 0.089 -0.089 0.194 0.292 -0.171 -0.289 

p-value for t-Test of Diff. (0.752) (0.954) (0.174) (0.000) (0.330) (0.005) 

No Type R (5%) in Previous Path Time Bin#10 0.088 -0.088 0.202 0.252 -(0.182) -(0.254) 

Type R (5%) in Previous Path Time Bin#10 0.089 -0.089 0.195 0.289 -(0.172) -(0.285) 

p-value for t-Test of Diff. (0.759) (0.875) (0.256) (0.000) (0.216) (0.002) 

All Bins

Table 7

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 across all bins. For these calculations, price paths are divided by ten equal time periods across

each path. Using time deciles avoids the endogeneity of volume deciles because volume is used in the selection process for Type E and Type R

participants. 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. Results for both the 2.5% and 5% FDR control rates are shown. The first two columns report averages across all bins separating the averages

for relative price changes into "up" and "down" trending paths, respectively. The remaining columns also average the relative price changes 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 ttest of comparing two means (unequal variances).

Means of Sample Data - Relative Price Changes by Bin

Up Price Paths Down Price Paths
Exogenous Variables ∆Pt ∆Vt ∆Pt ∆Vt ∆Pt ∆Vt ∆Pt ∆Vt

Model 1

Delta(Type 'X' Volume in Bin) - - - - - - - ns + + ns + +

- Cumulative Impulse Response -0.00 -0.29 -0.00 -1.03 ns 0.33 ns 3.64

- Akaike Info. Criterion

Model 2

Delta(Type 'X' Volume in Bin) - - - - - - - ns + + ns + +

- Cumulative Impulse Response -0.00 -0.29 -0.00 -1.03 ns 0.30 ns 3.36

Dummy for Type 'X' in Bin 'Z' ns + + - - ns ns - - ns - -

- Cumulative Impulse Response ns 4.98 -0.01 ns ns -4.57 ns -10.96

- Akaike Info. Criterion

Model 1

Delta(Type 'X' Volume in Bin) ns - - - - - - ns + + ns + +

- Cumulative Impulse Response ns -0.29 -0.00 -0.96 ns 0.35 ns 3.70

- Akaike Info. Criterion

Model 2

Delta(Type 'X' Volume in Bin) ns - - - - - - ns + + ns + +

- Cumulative Impulse Response ns -0.29 -0.00 -0.97 ns 0.32 ns 3.52

Dummy for Type 'X' in Bin 'Z' ns + + - - ns ns - - ns - -

- Cumulative Impulse Response ns 4.38 -0.00 ns ns -4.22 ns -8.67

- Akaike Info. Criterion

Panel B: Type E and Type R Participants found using 5% FDR Control Rate

262.455 -17.453 262.192 63.574

262.078 -17.469 261.160 63.514

Panel A: Type E and Type R Participants found using 2.5% FDR Control Rate

265.357 -18.679 265.130 64.582

265.208 -18.712 264.004 64.497

Table 8

Volume Effects of Anticipatory Traders

This table estimates VAR models of volume and price using bin data derived from sample price paths. Prices and volume are differenced for stationarity,

although volume is generally stationary without differencing. Delta price uses end-of-bin trade prices and delta volume is the change in bin volume for all

traders excluding Type E and Type R traders. These VAR models are estimated with one lag, but ten lags produced similar results. The exogenous

variables are delta volume for either Type E or Type R traders and dummies variables for whether Type E participants traded in Bin#1 of the current path

or Type R participants traded in Bin#10 of the previous path. Results are shown for the 2.5% FDR control rate in Panel A and for the 5% FDR control rate 

in Panel B. VAR estimates are calculated using all sample bins, and for Bin#2 only for Type E tests and Bin#1 only for Type R tests. Two models are

estimated in each panel to show the effects with and without anticipatory trades in either Bin#1 (Type E) of the current path or Bin#10 (Type R) in the last

path. The cumulative impulse responses are the coefficients on the associated variable in each model. The significance of these estimated coefficients is

indicated by a sign for the 5% level and a double sign for the 1% level. An 'ns' indicates that the coefficient was not significant. The Akaike information

criterion (AIC) is shown for model comparison. 

'X' = Type E Participant and 'Z' = #1 'X' = Type R Participant and 'Z' = #10 Last Path

All Bins Bin#2 Only All Bins Bin#1 Only
Descriptor 2.5% C.R. 5% C.R. 2.5% C.R. 5% C.R.

Panel A: Type E and Type R participants in 2011 and 2014 samples

Type E Participants 112 301 101 322 

Percent of Sample 1.5% 4.0% 1.2% 4.0%

Type R Participants 196 542 133 425 

Percent of Sample 2.6% 7.2% 1.6% 5.3%

Sample Size

Panel B: Only accounts included in both 2011 and 2014 samples

Type E Participants 35 81 38 95 

Percent of Sample 1.3% 3.0% 1.4% 3.6%

Type R Participants 65 138 50 131 

Percent of Sample 2.4% 5.2% 1.9% 4.9%

Sample Size

Panel C: Skill retention in 2014 for accounts in both samples

Type E in 2011 1 (2.8%) 7 (8.6%) 0 (2.6%) 7 (7.4%)

Type R in 2011 5 (7.7%) 27 (19.6%) 5 (10.0%) 27 (20.6%)

2,669 2,669

2011 Sample 2014 Sample

Table 9

Type E and Type R Participants in 2011 and 2014

Counts of Type E and Type R participants are reported from FDR tests using

crude oil futures data in 2011 and 2014. The 2011 sample is for the December

expiration and includes all trading days between September 12th to November

18th. The 2014 sample is for the October expiration and includes trading days

from August 1st to September 18th. Significant accounts are those identified as

Type_E or Type_R using 2.5% and 5% control rates (C.R.) in the FDR tests.

Panel A identifies Type E and Type R participants found in the two samples and

the sample size. Panel B identifies the number of Type E and Type R participants

who traded in both samples. There were 2,669 accounts identified as trading in

both samples. Panel C identifies whether the Type E and Type R participants

found in 2011 retained those characteristics in the 2014 sample. The first two

columns in Panel C report the retention counts by control rate and the percentage

of that count relative to the "type" totals observed in 2011 for the overlapping

acounts. The last two columns report the retention counts and percentages

measured relative the 2014 total for overlapping accounts.

7,554 8,087
Independent Variable Base Model X = 'Type E' X = 'Type R' Base Model X = 'Type E' X = 'Type R'

3.704 3.700 3.242 1.928 1.920 1.833

(<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001)

-0.260 -0.257 0.128 0.588 0.596 0.609

(<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001)

-2.838 -2.833 -2.384 -0.682 -0.672 -0.586

(<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001)

0.961 0.956 0.563 -0.731 -0.743 -0.763

(<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001)

1.302 1.291 0.533 1.318

(<0.001) (<0.001) (<0.001) (<0.001)

-0.193 -1.019 -0.477 0.642

(0.323) (<0.001) (<0.001) (<0.001)

-0.270 -0.416 -0.825 -1.717

(0.124) (<0.001) (<0.001) (<0.001)

0.742 1.197 1.019 0.203

(0.002) (<0.001) (<0.001) (<0.001)

-0.061 -0.064 -0.108 0.711

(0.865) (0.009) (0.199) (<0.001)

-2.165 1.346 -0.686 0.228

(<0.001) (<0.001) (<0.001) (<0.001)

-0.430 1.192 -1.088 -1.468

(0.445) (<0.001) (0.445) (<0.001)

na -2.443 0.619 1.728

na (<0.001) (0.046) (<0.001)

Adjusted R squared 8.5% 8.6% 9.2% 4.4% 4.5% 5.3%

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

Table 10

Speed Characteristics of Participants in both 2011 and 2014 Samples

In the sub-sample of 2,669 accounts that traded in both 2011 and 2014, this table shows regression estimates of cancel and

execution speeds using the logarithmic transform for the dependent variable defined in Tables 5 and 6. The independent variables

are all binary. The base case model includes a dummy for observations in the 2014 sample, dummy for algorithmic traders (ATS),

and an interaction term between ATS and the dummy for the 2014 sample. Type E and Type R models include the base case

variables, a dummy if the participant is Type 'X' as identified in the 2011 sample, where 'X' is either Type E or Type R in the

regression, and interaction terms for observations in the 2014 sample and/or algorithmic participants. The final set of variables are

defined for those Type E or Type R particpants who are also identified as that same type in the 2014 sample; that is, the

overlapping anticipatory traders. These variables include interaction terms for the 2014 sample and whether the participant is

algorithmic. The intercept captures the omitted categories, which in a manual-entry participant. For these regressions, Type E and

Type R traders are defined using the 5% FDR control rate in the 2011 sample. The p-values are computed using heteroscedasticconsistent standard errors and are shown in parentheses below each coefficient. An 'na' indicates that there were not enough

independent observations to include that variable. The sample size of the cancel (execution) speed regression is 17,729,475

(4,789,309). 

Dependent Variable

Ln(1+Cancel Speed) Ln(1+Execution Speed)

Intercept

Sample 2014 Dummy

Algorithmic Trader (ATS)

Dummy 2014 and ATS

Participant 'X'

Participant 'X' and Dummy 

2014

Participant 'X' and ATS and 

Dummy 2014

Participant 'X' in both samples 

and Dummy 2014

Participant 'X' in both samples 

and Dummy 2014 and ATS

Participant 'X' and ATS

Participant 'X' in both samples

Participant 'X' in both samples 

and ATS