November 2017 

The Third Dimension of Financialization:
 

Electronification, Intraday Institutional Trading, and Commodity
 

Market Quality
 

by 

Vikas Raman Michel A. Robe ° Pradeep K. Yadav 

° Vikas Raman is at the Warwick Business School, Coventry CV4 7AL (United Kingdom). Tel: (+44) 24-761-

50438. Email: vikas.raman@wbs.ac.uk. Michel A. Robe (corresponding author) is at the U.S. Commodity 

Futures Trading Commission (CFTC) and at the College of ACES, University of Illinois at Urbana-Champaign 

(UIUC), 1301 W. Gregory Drive, Urbana, IL 61801. Tel: (+1) 217-300-6276. Email: mrobe@illinois.edu. 

Pradeep K. Yadav is at the Division of Finance, University of Oklahoma, 307 West Brooks Street, Norman, OK 

73019. Tel: (+1) 405-325-5591. Email: pyadav@ou.edu. We thank the late Jim Moser, Kumar Venkataraman, 

Wei Xiong, Hank Bessembinder, Scott Irwin, and Pat Fishe for many suggestions that substantially improved 

the paper. June Harding receives the expression of our most sincere gratitude for her cheerful help with raw 

data. We also thank Andrei Kirilenko, Albert Menkveld, P.K. Jain, Kelley Anderson, Ing-Haw Cheng, Jim 

Overdahl, Craig Pirrong, Talis Putnins, Phil Garcia, Ajeyo Banerjee, Eleni Gousgounis, Esen Onur, Scott 

Mixon, Casey Petroff, Jonathan Wallen, seminar participants at the CFTC, Virginia Tech, George Mason 

University, the University of Illinois, Université Paris Dauphine, the University of Technology Sydney, as well 

as participants at IPAM’s “Workshop III: Commodity Markets and their Financialization” (UCLA), Institut 

Louis Bachelier’s 1st Summer School on Commodity Derivatives Markets (Florence, Italy), the 12th Energy and 

Commodity Finance Conference (ESSEC Business School, Paris), the 2017 World Finance Conference 

(Cagliari, Italy), the J.P. Morgan Center for Commodities’ 1st International Symposium (Denver, CO), and the 

2017 University of Oklahoma Energy and Commodities Finance Research Conference for useful comments. 

Michel Robe gratefully acknowledges the financial support received in his capacity as The Clearing Corporation 

Foundation Professor in Derivatives Trading at UIUC. Any errors and omissions are the authors' sole 

responsibility. 

The research presented in this paper was co-authored by Michel Robe, who performed work on this paper in his 

official capacity as a part-time consulting senior economist at the CFTC; and, Vikas Raman and Pradeep Yadav, 

who performed work as CFTC contractors under CFTC OCE contracts CFCE-10-CO-0237 and CFOCE-13-

0064. The Office of the Chief Economist and CFTC 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-reviewed 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. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The Third Dimension of Financialization: 

Electronification, Intraday Institutional Trading, and Commodity 

Market Quality 

Abstract 

We provide the first detailed empirical evidence on the financialization of 

intraday trading activity in the world’s largest commodity market and show that 

this development had a first-order positive impact on market liquidity and pricing 

efficiency. We use a unique, confidential regulatory dataset to show that the 

electronification of U.S. crude oil futures trading in 2006 brought about a massive 

growth in intraday activity by “non-commercial” institutional financial traders. 

We exploit differences in the post-electronification growth rates of institutional 

financial trading in crude oil futures contracts of different maturities to tease out 

the effect of financialization on key metrics of commodity market quality. We 

show that increased institutional financial trading reduces the variance of pricing 

errors, narrows bid-ask spreads, and improves market depth. Our inferences are 

robust to differences in the nature and volume of non-financial trading. Finally, 

we provide novel evidence of notable differences between the respective 

contributions of high-frequency traders (HFTs) vs. other (non-HFT) institutional 

financial traders to different market quality attributes. 

JEL Classification: Q02, G10, Q49, G12, G13, G23 

Keywords:	 Financialization, Commodities, Institutional Trading, Intraday activity, 

Competition, Pricing efficiency, Liquidity, HFT. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1. Introduction 

In the early 2000s, institutional financial entities, driven purely by “non-commercial” 

financial motives, started assuming a sharply greater role in commodity markets. This 

development has been dubbed the “financialization” of commodities (UNCTAD, 2011; 

Cheng and Xiong, 2014). It comprises three main dimensions, corresponding to the arrival of 

three distinct kinds of new commodity market participants trading across multiple 

commodities: first, commodity index traders (“CIT”), known as “massive passives;” second, 

managed money traders (hedge funds) that are active in multiple asset classes; and third, 

institutional financial intraday traders (including algorithmic traders). 

While the financialization of commodities has spawned much academic research, its 

focus to date has been on the first two dimensions of financialization mentioned above (i.e., 

the positions held overnight and the longer-term trading strategies of CITs and hedge funds) 

and their effects on select aspects of the daily, weekly, monthly, or quarterly distributions of 

commodity returns: price levels (e.g., Irwin and Sanders, 2012; Sockin and Xiong, 2015), risk 

premia (e.g., Acharya, Lochstoer, and Ramadorai, 2013; Singleton, 2014; Hamilton and Wu, 

2014), realized volatility (e.g., Brunetti, Büyükşahin, and Harris, 2016; Kim, 2015), and comovements between markets (e.g., Tang and Xiong, 2012; Büyükşahin and Robe, 2014; 

Başak and Pavlova, 2016).1 This paper addresses the third dimension of financialization: the 

massive growth in intraday trading, often across multiple commodities, by institutional 

financial traders who have no commercial interest in the underlying physical commodity. 

Prior research shows that the financialization of commodity markets’ “buy-side” 

transformed commodities from a type of asset whose price is driven by physical consumption 

and supply considerations into a class of financial assets valued in terms of their interactions 

with other classes of financial assets. Crucially, it links “the changing face of participants in 

commodity futures markets over the past ten years to changes in commodity futures price 

dynamics” (Cheng, Kirilenko, and Xiong, 2015, p. 1735). This development, together with 

1 Other contributions include, among others, Bessembinder, Carrion, Tuttle, and Venkataraman (2016); Brunetti 

and Reiffen (2014); Bruno, Büyükşahin, and Robe (2017); Etula (2013); Hamilton (2009); Hong and Yogo 

(2012); Kilian and Murphy (2014); Knittel and Pindyck (2016); Korniotis (2009); Sanders, Irwin, and Merrin 

(2010); and Stoll and Whaley (2010). See Cheng and Xiong (2014) for a detailed literature review. 

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the electronification of commodity futures markets, paved the road for the third dimension of 

financialization that is the subject of our paper. This “intraday financialization” reflects the 

sharply greater role of institutional financial traders in providing liquidity and their active 

trading on short-term information (e.g., information in the order flow). It includes within its 

ambit the financialization of commodity markets’ “sell-side,” in the sense of Hasbrouck and 

Saar (2013).2 However, despite its size and importance, it has hitherto received surprisingly 

little attention in the academic literature due to a dearth of publicly available high-frequency 

information identifying the makeup of intraday commodity trading activity. 

Our paper utilizes a comprehensive, non-public, trader-level dataset from the market 

regulator (the U.S. Commodity Futures Trading Commission or CFTC) to provide the first 

quantitative evidence on this intraday dimension of financialization, and to investigate its 

impact on commodity market quality. Specifically, we test hypotheses regarding its effects on 

bid-ask spreads, depth, and the absolute magnitude of customers’ demand imbalances (i.e., 

the difference between customer buys and sells).3 We also test hypotheses about the impact 

of financialization on commodity pricing efficiency at intraday horizons by examining the 

variance of futures “pricing errors,” i.e., deviations of market prices from informationally 

efficient “fundamental” values. Finally, we investigate heterogeneity among the new market 

entrants, and ask whether high-frequency (institutional financial) traders (“HFTs”) and other 

institutional financial traders (hereafter “non-HFTs”) contribute differently to market quality. 

Overall, this paper adds to two strands of the finance literature—financialization of 

commodities and market microstructure—and makes three main contributions. First, our 

results show that financialization has a significantly beneficial impact on market quality. 

Second, given that intraday financialization took place around the electronification of 

commodity markets, we contribute by showing that the entry of new types of institutional 

financial traders is an important channel through which electronification boosts market 

2 Unless otherwise indicated, our use of the term “financialization” in the rest of this paper relates to this third 

intraday financialization dimension: the growth in intraday trading/positions of institutional financial traders. 3 These three market liquidity measures present the double advantage of widespread use and of computability 

with trade data. Access to order-book data would be necessary to compute other useful market quality measures, 

such as the availability of a two-sided market and the order-book depth or dispersion. 

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liquidity and pricing efficiency. Third, we provide novel evidence that, in an environment 

where HFT and non-HFT institutional financial traders coexist and where both account for 

substantial shares of the total trading activity, there exist notable differences between their 

respective contributions to market quality. 

Our findings are based on the world’s largest commodity market—the New York 

Mercantile Exchange’s (NYMEX) West Texas Intermediate (WTI) light sweet crude oil 

futures market. This market underwent a major structural change on September 5th, 2006, 

when the NYMEX first allowed electronic trading on the Globex platform alongside face-toface trading in the NYMEX pits. Before electronification, the NYMEX’s WTI futures trading 

was physically confined to the pits during business hours. Pit trading was intermediated 

largely by “Locals,” i.e., by individual traders functioning as scalpers (Silber, 1984) and 

acting as voluntary providers of immediacy and liquidity in the market (Manaster and Mann, 

1996). Importantly, Globex trading brings about a transformative easing of access for traders 

without physical access to the pits, enabling them to trade remotely, and thereby compete 

with Locals in supplying liquidity (through limit orders to buy or sell) or otherwise (e.g, 

through short-horizon trading on information in the order flow). Consistent with evidence on 

structural reforms at the NASDAQ (Barclay et al., 1999) and the London Stock Exchange 

(Naik and Yadav, 2003) allowing public traders to compete with traditional market makers in 

supplying liquidity, and in line with evidence on the benefits of electronification in equity 

markets (e.g., Jain, 2005), we document that electronification results in significantly higher 

commodity pricing efficiency and significantly lower trading costs. 

That said, our paper’s focus is specifically on the financialization that resulted from 

electronification, not on the electronification per se. We identify institutional financial traders 

using our regulatory data, and we document several facts that characterize the financialization 

of intraday crude oil futures trading following the NYMEX’s electronification. First, many of 

the institutional financial traders that start trading WTI futures on the Globex platform are 

new to the NYMEX. In total, over four hundred new institutional financial traders (including 

almost two dozen HFTs) enter the WTI market in the six months immediately following 

electronification—a rate of entry that is almost three times what it was in the first eight 

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months of 2006. Second, the fraction of all Globex WTI trades involving an institutional 

financial trader on either side grows to almost 60 percent of the total volume—a figure 

double what it used to be in the WTI pits. Third, both the HFT and the non-HFT institutional 

financial traders that enter the WTI market after September 5th, 2006 engage in much shorterhorizon strategies compared to their predecessors in the pits before electronification. Finally, 

these new entrants are three times more likely to also trade other commodity futures (beside 

crude oil) than are their counterparts in the WTI pits. These new financial intermediaries’ 

cross-market footprints thus echo the simultaneous presence in multiple commodity markets 

characteristic of the longer-horizon, buy-side institutions that started the financialization of 

commodities at the beginning of the last decade—i.e., commodity index traders (Tang and 

Xiong, 2012; Cheng et al., 2015) and managed money traders (Büyükşahin and Robe, 2014). 

Electronification enhances market quality through three main channels: it improves 

pre- and post-trade market transparency (Boehmer, Saar, and Yu, 2005), trims fixed operating 

and order-processing costs (Jain, 2005), and increases competitive efficiency by removing or 

reducing barriers to entry into liquidity provision services (Barclay et al., 1999; Naik and 

Yadav, 2003), thereby attracting new traders, e.g., institutional financial traders in the case of 

commodity markets. What is relevant from our perspective is that, while the first and second 

channels arguably impact all futures contract maturities similarly, not all contract maturities 

can expect the same interest from intermediation-focused institutional financial traders. In 

particular, because the two nearest-month futures and the three nearest December futures 

(commonly used for associated calendar spread positions) correspond to the maturities most 

commonly held overnight or for longer periods by commercial crude oil traders—producers, 

refiners, wholesalers, etc.—both before (Neuberger, 1999; Ederington and Lee, 2002) and 

after (Büyükşahin, Haigh, Harris, Overdahl, and Robe, 2015) electronification, 

financialization should manifest itself mainly in these contract maturities. Consistent with this 

intuition, we find that entry by new intraday-focused institutional financial traders after 

electronification does take place chiefly in those specific maturities, which we loosely label 

“short-term” even though they also include the associated December contracts. 

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Our empirical design exploits the fact that, post-electronification, financialization is 

largely concentrated in these “short-term” contracts. We exploit the resulting large postelectronification differences across the crude oil futures term structure to determine the effect 

of financialization on market quality. Specifically, we use electronification as an instrument 

for the relative financialization of short-term contracts (vs. long-term contracts) in two-stage 

regressions in order to tie the relative improvements in market liquidity and pricing efficiency 

for short-term contracts to their relatively greater rate of financialization.We document that a 

one-standard deviation increase in the percentage difference in the rate of financialization 

(between short-term and long-term contracts) widens the percentage differences (between 

short-term and long-term contracts) in the magnitude of pricing errors by 0.43 standard 

deviations; in bid-ask spreads, by 0.84 standard deviations; in market depth, by 1.07 standard 

deviations; and in customer trade imbalances, by 0.60 standard deviations. 

In our analysis as described, we use: (a) electronification as our instrument; (b) the 

difference in financialization (i.e., financial traders’ shares of total trading) between shortterm and long-term futures contracts as our explanatory variable; and, (c) the corresponding 

difference in market quality variables (spreads, depth, order-imbalances, and pricing errors) 

as our dependent variables. Using electronification (as an exogenous event to tease out the 

effect of financialization on market quality) is predicated on the following logic. One, 

electronification unquestionably influences entry by financial traders (i.e., the intensity of 

financialization) and, specifically, our explanatory variable (the difference in the extent of 

financialization between short-term and long-term contracts). Two, while electronification 

affects the overall levels of market quality, it should arguably have no independent effect on 

the difference in market quality between short-term and long-term contracts—except any 

impact arising through the channel of the difference in financialization. 

Indeed, our finding that financialization improves both pricing efficiency and market 

liquidity is robust also to using an alternative instrumental approach. Inspired by Muravyev 

(2016), we exploit the day-to-day persistence in cross-maturity financialization differences 

and we use lagged values of this difference—together with a set of exogenous variables likely 

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to generate fluctuations in institutional financial trading—in the first stage regression. Again, 

we find that greater financialization leads to higher liquidity and price efficiency. 

While institutional financial traders’ entry into the crude-oil market is exogenous, 

their choices of which contract maturities to trade could be based on the order-flow of 

external “customers,” i.e., of non-financial traders—and it is arguably possible that the 

observed financialization-related improvements in market quality arise because the nature of 

these customers changes after electronification or simply because of greater overall or 

customer trading volume. We accordingly control in multiple ways for potentially 

confounding influences of post-electronification differences in the nature of customers, and in 

customer and overall trading volume. First, after incorporating regression-based controls for 

overall and customer trading volumes, financialization’s impact on liquidity and pricing 

efficiency remains robustly significant. Second, when we stratify the regressions based on 

terciles of cross-maturity differences in customer volume, we find very similar 

financialization-related market quality improvements across terciles. Even in the lowest 

tercile (consisting only of days when customer trading volume is actually lower in contracts 

in which financialization is on average greater), we observe that financialization similarly 

improves each of our market quality metrics. Third, we utilize the granularity of our 

regulatory data to identify exchange customers who traded WTI futures in the pits prior to 

electronification and who continue to trade (now electronically on Globex) after September 

5th, 2006. We find that the spreads paid by such “continuing customers” are also reduced by 

financialization—consistent with the inference that financialization-related improvements in 

liquidity post-electronification are not driven by changes in the nature of customers. Overall, 

irrespective of differences in the nature and volume of non-financial trading, institutional 

financial trading continues to be the driver for market quality improvement. 

We confirm the robustness of this conclusion in yet another way by carrying out a 

vector auto-regression (VAR) analysis of the endogenous relation between the participation 

of institutional financial traders and market quality. To do so, we extract information from the 

same regulatory dataset but from a later, non-overlapping time period in which no structural 

market change takes place. Using data from April 2007 to May 2008, we again show that 

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increases in institutional financial trading contemporaneously narrow bid-ask spreads, curtail 

customer trade imbalances, and cut the variance of pricing errors. 

Having established that financialization improves liquidity and pricing efficiency, we 

then provide novel evidence of differences between the respective roles of HFT and non-HFT 

institutional financial traders. Importantly, we document that non-HFT institutional financial 

trading activity significantly improves market depth, lowers bid-ask spreads, and reduces the 

imbalances between customer buys and sells. At the same time, we show that it is HFT 

activity that significantly reduces the variance of pricing errors. HFTs also contribute to 

reducing spreads and trade imbalances, but we find no evidence of a contribution to market 

depth.4 These results support the view that, in a market where HFT and non-HFT insitutional 

financial traders coexist, and each group has a large share of the total trading activity, market 

quality improvements are driven by the overall flow of institutional risk capital into intraday 

voluntary liquidity provision rather than just the activities of HFTs per se. 

Section 2 motivates our empirical hypotheses. Section 3 discusses our rich regulatory 

dataset and our measures of intraday market quality. Section 4 documents electronification’s 

huge impact on the latter and the oil market’s intraday financialization post-electronification. 

Sections 5 analyzes financialization’s impact on market quality. Section 6 shows that, in the 

causal link from financialization to market quality improvements, HFT and other (non-HFT) 

institutional financial traders play separate and important roles for different attributes of 

market quality. Section 7 concludes and outlines avenues for further research. 

2. Research Questions 

2.1. Intraday Pricing Efficiency 

Our first research question is whether financialization improves intraday commodity 

pricing efficiency, and in particular, the variance of “pricing errors,” i.e., the deviations of 

4 Our evidence relating specifically to HFTs complements findings in Brogaard, Hendershott, and Riordan 

(2014, equities) and Chaboud, Chiquoine, Hjalmarsson, and Vega (2014, currencies) for pricing efficiency, and 

in Hendershott, Jones, and Menkveld (2011, equities) for spreads. Jones (2013) and Menkveld (2016) provide 

thorough reviews of the extant HFT literature on high frequency trading. 

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observed market prices from information-efficient random-walk or “fundamental” values—as 

in Hasbrouck (1993), Boehmer and Kelly (2009), or Fotak, Raman, and Yadav (2014). 

Prior research asks whether buy-side financialization can drive prices away from their 

fundamental values. On the theory side, Singleton (2014) and Sockin and Xiong (2015) argue 

that, amid a globalization of the world’s economic activity, commodity market participants 

face severely heightened informational frictions (regarding physical supply, demand, and 

inventories) and that these frictions, and the associated financial speculation, may cause the 

magnitude of pricing errors to increase for extended periods.5 Similarly, there exist several 

empirical studies on whether trading activities related to commodity index derivatives or 

other commodity-linked financial products have deleterious effects on prices at daily or 

longer horizons.6 The focus of all those papers, however, is on pricing errors that may persist 

for long periods of time—days, weeks, or months. Thus, while those papers make important 

contributions to the literature, their relevance to the present paper is relatively limited given 

that our focus is entirely intraday. Indeed, ours is the first study of institutional financial day 

traders’ impact on the intraday efficiency of commodity prices. 

In the same vein, extensive empirical evidence from equity markets suggests that 

institutional investors are more informed than other investors. However, that evidence largely 

focuses on institutional investors (who typically own shares and engage in fundamental 

research on earnings and other news or announcements) rather than on institutional traders— 

who are the focus of the present paper.7 As such, that prior work does not deal with intradayfocused institutional traders engaged primarily in short-horizon liquidity provision and 

trading on the order flow. For such traders, the literature on institutional investors with daily 

(or quarterly) horizons may be informative but is not necessarily conclusive. 

5 See also Goldstein and Yang (2016) and other recent theoretical papers cited therein.
 6 See, e.g., Hamilton and Wu (2015), Henderson, Pearson, and Wang (2015), and Irwin and Sanders (2012).
 7 For example, Boehmer and Kelly (2009) find a positive relation between institutional shareholdings and the
 

relative informational efficiency of stock prices. Badrinath, Kale, and Noe (1995) find that returns of stocks with
 

high institutional ownership leading those with low institutional ownership. Chordia, Roll, and Subrahmanyam
 

(2011) find that institutional trading leads to an overall increase in information-based trading. Campbell,
 

Ramadorai, and Schwartz (2009) find that institutions arbitrage stock mispricings around earnings
 

announcements.
 

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More directly relevant to the present paper is the extant research showing that, on 

average, HFTs—who likely are institutional—improve price discovery in equity markets (e.g. 

Brogaard, Hendershott, and Riordan, 2014) and pricing efficiency in currency markets (e.g., 

Chaboud, Chiquoine, Hjalmarsson, and Vega, 2014). For intraday-focused institutional 

traders, one can argue, after Hendershott, Lidvan, and Schürhoff (2015), that institutions 

should be better informed because of superior access to information (in terms of gathering 

and processing skills) and better financial and analytical resources. Thus, increased intraday 

trading by institutional financial traders in commodity markets should reduce informational 

imperfections and pricing errors at intraday horizons. At the same time, though, we know 

from De Long, Shleifer, Summers, and Waldman (1990) and the related later literature that 

short-horizon investors could have adverse effects on pricing efficiency because of “shorttermism” (reluctance to arbitrage pricing inefficiencies as the latter may last beyond the 

arbitrageurs’ trading horizon, causing pricing errors to persist). 

Irrespective of which of these two effects dominates, the financialization of intraday 

trading should clearly increase the amount of capital available for financial traders to fulfill 

more continually the risk-sharing role of futures markets (i.e., the capacity to absorb the other 

side of the hedging-related futures positions taken by commercially-motivated or 

“commercial” traders). From this perspective, the financialization of commodity markets 

should also reduce the magnitude of pricing errors by making risk-sharing more efficient. 

In short, it is an empirical question whether institutional financial trading increases 

pricing efficiency. In view of the above, our first hypothesis is: 

H1: Financialization lowers the variance of intraday pricing errors. 

Finally, given extant findings that high frequency trading improves price discovery in 

other financial markets, and given that HFTs may be organized as institutional traders, we 

test for the beneficial effects on the variance of pricing errors of increases in (i) overall 

institutional financial trading, as well as (ii) a narrow measure of financialization that only 

considers non-HFT traders (i.e., that subtracts from overall institutional financial trading all 

observations related to HFTs). 

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2.2. Intraday Liquidity 

Our second research question is whether financialization—with the resultant flow of 

institutional risk capital into intraday commodity futures trading—improves market liquidity 

as manifested in bid-ask spreads, depth, and imbalances between customer buys and customer 

sells. Ours is the first paper to shed light on this question. 

The reasons to expect a beneficial impact of financialization on intraday liquidity 

arise from factors similar to those cited by Hendershott, Lidvan, and Schürhoff (2015, pp. 

249-250) for institutional traders to be relatively more informed. First, insofar as institutional 

financial traders have more financial resources than individual traders do, their market entry 

should significantly increase the access to, and the overall availability of, capital available for 

liquidity provision—thus increasing depth and reducing customer trade imbalances. Second, 

insofar as institutional financial traders have greater direct access to information and greater 

resources for processing that information, they are better able at estimating short-term price 

changes based on information and liquidity flows and have a greater ability to effectively 

manage their inventories and control risks. Consequently, they can take greater position risks 

in individual liquidity-provision trades and can supply liquidity at lower costs. This should 

cut spreads and increase depth. Third, an increase in institutional intraday trading increases 

competition among liquidity providers, potentially leading to more aggressive pricing and 

participation, again reducing spreads and customer trade imbalances. 

In addition, to the extent that institutional financial traders are better informed than 

other market participants at intraday horizons, financialization should increase the extent of 

information-based trading. The theoretical models of Boulatov and George (2008, 2013) and 

Goettler, Parlour, and Rajan (2009) predict that informed agents gravitate towards supplying 

(rather than taking) liquidity, which is consistent with evidence from Kaniel and Liu (2006). 

When the (more informed) institutional financial traders gravitate toward supplying (rather 

than demanding) liquidity, they should be able to do so at lower cost since they need to make 

a relatively lower provision for adverse selection losses to more informed traders (Glosten 

and Harris, 1988). This competitive advantage in liquidity provision should also mean that 

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the presence of (the relatively more informed) institutional financial traders in the market 

should, at the margin, lead to greater depth and lower trade imbalances. 

Overall, in view of the above, our second set of hypotheses, for both our measures of 

financialization, are: 

H2: Financialization reduces bid-ask spreads. 

H3: Financialization increases market depth. 

H4: Financialization reduces the absolute magnitude of customer trade imbalances. 

As Section 2.1 noted for pricing efficiency, there is evidence (e.g., Hendershott, 

Jones, and Menkveld, 2011) that, on average, algorithmic trading improves intraday liquidity 

in equity markets. Thus, given that algorithmic traders may be organized as institutional 

traders, we also undertake tests that identify the beneficial impact on market liquidity of two 

alternative measures of financialization: (i) an overall measure that includes HFTs and (ii) a 

narrower measure that excludes high-frequency institutional financial traders. 

3. Data: Measuring Institutional Financial Trading and Market Quality 

For the purposes of this paper, we were granted access to non-public regulatory data 

from January 2006 to May 2008 for the world’s largest commodity market—the NYMEX’s 

WTI light sweet crude oil futures market. The NYMEX introduced electronic trading of WTI 

futures (alongside face-to-face pit trading) on September 5th, 2006. Our data originate with 

the market regulator, the U.S. Commodity Futures Trading Commission (CFTC). 

3.1. Data 

The CFTC collects data on every WTI futures transaction at the NYMEX, and for 

every trading account in that market. Each futures trade is recorded twice in the dataset, once 

for the buyer and once for the seller. The buyer and the seller are each identified only by an 

identity code. Those anonymizing codes are assigned by the CFTC to each trading account so 

as to conceal the actual identities of market participants. Hence, while the data to which we 

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had access provides a complete WTI trading history for every trader in our sample, each 

trader’s true identity remains confidential and unknown to us. 

The CFTC dataset includes details such as the commodity and delivery month; the 

quantity traded; the trade type (outrights, spreads, trades at settlement,...), price, and direction 

(i.e., whether the transaction was a buy or sell); and the transaction date and time. For 

electronic trades on Globex, the latter is the time stamp assigned to a trade when both sides 

were matched. For open outcry trades in the pits, it is the imputed trade time stamp. For our 

analysis, we use pit trades time-stamped during business hours (pre-electronification) and 

Globex trades time-stamped between 9AM-2:30PM (post-electronification).8 

The dataset classifies the traders on each side of a given transaction using one of four 

customer type indicators (“CTI”). The three main categories, comprising over 95 percent of 

all trades, are Locals (CTI-1 or individual exchange members trading for accounts they own 

or control), institutional exchange members trading for accounts they own or control (CTI-2), 

and non-member customers of the exchange or external traders (CTI-4). The rest, about four 

percent of all trades in our sample, are classified as CTI-3 (individual member trading on 

behalf of another member); such trades are largely not relevant for this paper. 

In our analysis, we aggregate the account-level data across multiple contracts and by 

CTI trader category. Each CTI category comprises dozens to thousands of trading accounts, 

so the information we provide respects the CFTC’s confidentiality statutes by not allowing 

the reader to identify any individual trader’s position(s), trade secrets, or trading strategies. 

3.2. Institutional Financial Trading 

The trader category of primary interest to us is CTI-2, which captures the participation 

of institutional traders whose trading activity is large enough to warrant corporate exchange 

membership for their proprietary trading desks. Such membership allows a firm to obtain 

8 Pits used to be open from 10AM—2:30 PM prior to January 31st, 2007. Starting on February 1st, 2007, pit 

business hours were increased to 9AM—10:30 PM. We exclude from the sample the Friday immediately after 

Thanksgiving as well as the entire week from Christmas to New Year (starting the last full trading day before 

Christmas and ending the first trading day after New Year). Before aggregating the data, we carry out a number 

of quality checks; for example, we exclude transactions whose reported prices are clearly erroneous. 

12
 
 

 

 

 

 

 

 

 

 

 

 

 

 

	 	 	

 

 

 

 

 

 

 

 

 

preferential fees and other benefits on its proprietary futures trading, and it is particularly 

useful for short-horizon trading. CTI-2 traders include banks, hedge funds, commodity pool 

operators, futures commission merchants, commodity trading advisors, foreign and domestic 

broker/dealers, introducing brokers, proprietary trading firms, and other eligible entities. We 

use CTI-2 traders’ share of the trading volume to compute our financialization proxy. 

3.3. Measures of Market Quality 

We investigate several measures of market quality: (a) the volatility of pricing errors, 

i.e., of deviations of prices from their “fundamental” values; (b) bid-ask spreads; (c) depth; 

and (d) the absolute magnitude of customer trade imbalances. On any given day, futures with 

up to 84 different maturities are traded. We start by computing market quality variables for 

each contract maturity in five-minute non-overlapping intervals. Then, we compute daily 

volume-weighted averages of the five-minute figures during business hours. 

We estimate the volatility of pricing errors using Hasbrouck’s (1993) widely-used 

approach. The logarithm of the observed transaction price, !" , is expressed as the sum of the 

logarithm of the efficient price, $" , and the pricing error, %", as follows: 

!" = $" + %"

The pricing error is a measure of how efficiently the transaction price tracks the 

(unobserved) fundamental price represented by an information-efficient “random walk price.” 

Since the pricing error is a zero-mean process, its absolute magnitude is a good measure of its 

volatility. We follow Hasbrouck (1993) and Boehmer and Kelly (2009) and estimate a lower 

bound of the volatility of the pricing error, (), using a VAR system comprising four 

variables: ∆!", trade sign indicator (estimated using Lee and Ready’s (1991) “tick test”), 

signed trading volume, and signed square root of the trading volume. We compute () on 

every trading day for each contract maturity for which at least 50 trades take place. In our 

tables, the variable called “PE_Proportion” is the daily ratio of the variance of pricing errors 

(PE), estimated as in Hasbrouck (1993), to the volatility of intraday (log) transaction prices. 

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We estimate daily bid-ask spreads to approximate the cost of transacting for exchange 

customers. In the absence of order-book information, we estimate bid and asked prices for 

each contract in each 5-minute interval after classifying trades as buyer- vs. seller-initiated 

using the Lee and Ready “tick-test.”9 In our tables, the variable called “Spread” is the daily 

volume-weighted average of these 5-minute bid-ask spreads. 

We calculate the inverse measure of depth as in Amihud (2002). In our tables, the 

variable called “Amihud” is the daily volume-weighted average of the ratio of absolute return 

to volume calculated in 5-minute non-overlapping intervals throughout the trading day. 

We compute returns tick by tick. We then calculate daily volatility of returns using 

five-minute non-overlapping intervals throughout the trading day. 

Finally, we measure daily customer trade imbalances, reported in our tables as the 

variable “AbsOIB”, as the daily volume-weighted average of the ratio of five-minute absolute 

trade imbalances (buyer-initiated minus seller-initiated trades) to trading volume. 

4. The Impact of Electronification 

Section 4.1 documents changes in market quality measures around electronification. 

Section 4.2 documents the financialization of intraday activity following electronification by 

showing the relative contributions to total market activity of individual vs. institutional 

financial traders. It provides novel empirical evidence of entry by new institutional financial 

traders (Section 4.2.1) and of a resulting change in the nature of financial intermediaries and 

intraday trading by identifying major differences between these new entrants and other types 

of traders (size and short-term focus, Section 4.2.2; cross-market presence, Section 4.2.3). 

Section 4.3 shows that this intraday financialization subsequent to electronification affects 

short-term more than longer-term contracts. 

9 Raman, Robe, and Yadav (2017) verify the tick test’s accuracy using data from a time period (post-2009) 

when “aggressive” traders start being identified by a flag in the CFTC’s non-public intraday dataset. For WTI 

futures, those authors find that the tick-test successfully identifies the (actual) aggressive trader in more than 

75% of the cases—similar to the 73% figure found by Theissen (2001) using Frankfurt Stock Exchange data. 

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4.1. Market Quality Metrics 

Effective September 5, 2006, the NYMEX introduced electronic trading on Globex in 

parallel with face-to-face trading in the pits intermediated largely by “Locals.” Given that this 

market reform significantly increased market access for traders without physical presence in 

the NYMEX pits, and thus increased competition in liquidity provision services, one should 

expect a marked improvement in each of our market quality measures. Table 1 and Figure 1, 

which present respectively statistical and visual analyses of changes in our market quality 

measures (defined in Section 3.3 above) surrounding the electronification of WTI futures, 

show that such is indeed the case. 

The sample period for the univariate tests in Table 1 runs from January 3rd, 2006 to 

May 31st, 2008. Pre-Electronification in Table 1 refers to the period from January 3rd, 2006 to 

September 1st, 2006. Post-Electronification refers to the period from September 6th, 2006 to 

May 31st, 2008. Figure 1 relates to WTI sweet crude oil futures trading in the pits during the 

Pre-Electronification period, and on the Globex platform Post-Electronification. 

The t-tests in Table 1 show that the estimated bid-ask spreads drop from an average of 

37 basis points to just 3 basis points, a drop of more than 90 percent. Absolute customer trade 

imbalances drop by about 40 percent, from about 24 percent to about 13 percent. The ratio of 

the variance of pricing errors to the variance of log transaction prices falls from about 59 

percent to about 4 percent, i.e., by more than 90 percent. Each change is statistically highly 

significant (p-value < 0.001). The Amihud measure of depth also improves substantially, 

although the change is not statistically significant. 

4.2. Electronification and the Financialization of Intraday Activity 

4.2.1. Institutional Financial Trading Activity 

Following electronification, one also expects an increase in institutional financial 

traders’ contribution to WTI futures market activity. This is exactly what we find. The results 

are in Table 1 and in Figure 2, describing the nature and respective extents of participation by 

Locals and by institutional financial traders before vs. after September 5th, 2006. In Table 1 

and in Figure 2, FIN is the proportion of trading volume involving the participation of 

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institutional financial traders (traders classified as CTI-2 traders in the CFTC database). It is 

our proxy for the extent of financialization. 

Figure 2 provides visual, trading-activity-based evidence of the WTI futures market’s 

financialization by aggregating the CFTC’s account-level intraday information for the CTI-1 

and CTI-2 trader categories. Prior to electronification, in line with Manaster and Mann 

(1996), Locals (CTI-1) dominated pit trading: in the first eight months of 2006, Locals were 

involved on at least one side of approximately 80 percent of all transactions. In contrast, CTI2 traders were involved in only about 30 percent of all WTI pit trades in the same period.10 

In the six months immediately following electronification, we find that over 400 new 

institutional financial traders enter the WTI market—a rate of entry that is almost three times 

what it was in the first eight months of 2006.11 As a result of this inflow, Figure 2 shows that, 

within a few months of electronification, the fraction of all Globex WTI futures trades 

involving an institutional financial trader is almost 60 percent of the total volume—double 

what the fraction used to be in the WTI pits.12 Since the overall trading volume also grew 

substantially in the same period, institutional financial traders captured a bigger slice of a 

growing WTI pie. The t-tests reported in Table 1 confirm this visual evidence. They show 

that the average proportion of trading activity involving institutional financial traders almost 

doubles, from 29.6 percent pre-electronification to 55.0 percent post-electronification.13 

4.2.2. Characteristics of the Institutional Financial Trader Group 

Because electronification allows for new kinds of institutional financial trading 

(automated order placement and execution, competition with locals with respect to liquidity 

provision, etc.), one expects institutional financial traders’ trading patterns to differ before 

10 These percentages generally do not sum up to 1, because these figures refer to the proportion of all trades 

involving a trader of a given category on at least one side of a transaction. 

11 These figures are consistent with Boyd and Kurov (2012), who note a sharp increase in the entry of new 

traders overall into NYMEX energy futures markets following their electronification in September 2006. That 

study, however, focuses on the profitability of Locals before and after electronification; it does not provide 

information on the activities of financial traders. 

12 Meanwhile, the proportion of trades with Locals falls to approximately half of its pre-electronification value. 13 The significant increase in the proportion of financial institutional trading after the onset of electronic trading 

echoes the massive growth of the overnight WTI futures positions held by hedge funds and other financial 

traders during the same period (Büyükşahin et al., 2015). 

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and after electronification. To confirm this intuition, Table 2 shows mean and median hourly 

trading volumes, trading frequencies, and closing ratios for Locals (CTI-1), institutional 

financial traders (CTI-2), and customers (CTI-4) from January 3rd, 2006, to March 31st, 2007. 

Consistent with the expected differences between institutional and individual traders, 

CTI-2s in the post-electronification period trade more than twice as much as Locals do. Most 

notably, comparing Panels A and B in Table 2 shows that the median value of CTI-2 traders’ 

absolute closing ratio—the average ratio of individual traders’ ending-of-hour inventory to 

their hourly trading volume—declined sharply from 83 percent pre-electronification to only 8 

percent post-electronification. This finding indicates that the institutional financial traders 

that enter the WTI futures market after September 5th, 2006 are focused on much shorter 

horizon strategies (similar to Locals’ strategies) compared to the institutional financial traders 

that had been active in the pits prior to electronification. 

4.2.3. Institutional Financial Traders’ Cross-Market Presence 

In contrast to the specialized non-commercial traders that used to populate commodity 

futures markets in prior decades, a characteristic of the buy-side institutions that started the 

financialization of commodities fifteen years ago is their simultaneous presence in multiple 

commodity markets (Tang and Xiong, 2012) and also equity markets (Büyükşahin and Robe, 

2014; Cheng, Kirilenko, and Xiong, 2015). If the phenomenon we describe truly amounts to 

intraday financialization, then one would expect the new institutional day traders in the WTI 

futures space—unlike their pit counterparts—to be simultaneously active in other markets. 

Verifying this conjecture requires intraday data from financial or from non-energy 

commodity markets, in which individual traders have the same IDs as in the WTI crude oil 

futures markets. For the period of our study, we are aware of only two such markets: the gold 

and silver futures marketplaces of the NYMEX’s COMEX division. 

The COMEX introduced electronic gold and silver futures trading on December 4th, 

2006—three months after the NYMEX’s energy futures started trading electronically. We use 

CFTC pit and electronic audit trail data for both of these metals’ futures markets, which are 

available for the last three quarters of 2007. Using the IDs of individual traders from all three 

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futures markets, we identify “common” traders, i.e., traders active in more than one of these 

markets. For each day in our 2007-Q2 to 2007-Q4 sample, we then compute the proportion of 

the total WTI crude oil futures trading volume (either in the pits or on Globex) that either 

involves a “common” Local (CTI-1) or involves a “common” institutional trader (CTI-2). 

Table 3 summarizes our analysis of cross-market activity following electronification. 

It highlights two novel empirical facts. First, “common” institutional financial traders who 

trade WTI futures on Globex (and who are almost all new to the WTI crude oil market—see 

Section 4.3.2 below) account for three times as much of the electronic platform’s median 

daily volume as their institutional pit-trading counterparts do of the median daily pit volume. 

Second, “common” institutional financial traders are involved in several orders of magnitude 

more trades than are Locals in the WTI pits (almost none of whom trade metals). A similar 

analysis of gold futures yields similar results. Put differently, Locals really are “local” to the 

market in which they operate. In all, both empirical regularities clearly suggest that, after 

electronification, the nature of financial intermediaries and institutional day traders active in 

commodity futures markets changed drastically, and that intraday trading became dominated 

by financial traders focused on the very short term and active across multiple markets, rather 

than traders focused on a single commodity. 

4.3. Institutional Financial Trading: Short-term vs. Long-term Futures Contracts 

Sections 4.1 and 4.2 show, respectively, that the introduction of electronic trading 

dramatically improved market quality in the crude oil futures market (as it earlier did in the 

equity markets—see, e.g., Barclay et al. (1999) and Jain (2005)) and that it also triggered 

financialization, i.e., a massive growth in institutional intraday financial trading activity. To 

isolate the impact of financialization, which is the primary focus of this paper, we turn to 

differences in the participation of institutional financial traders in different segments of the 

crude oil futures term structure. 

4.3.1. Intuition 

With a view to teasing out the impact of financialization from the overall impact of 

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electronification, we note, as discussed earlier in the introduction, that electronic trading can 

improve market quality via three main channels. First, it reduces information asymmetries by 

improving pre- and post-trade market transparency. Second, it curbs operating and order 

processing costs. Finally, it drastically cuts the costs of entry into providing liquidity services. 

It enables all exchange members, irrespective of their physical location, to exploit deviations 

from fundamental values and to provide liquidity, thereby significantly increasing 

competition: the open and transparent electronic market, where all traders have an equal 

opportunity to voluntarily provide and demand liquidity, attracts new traders—particularly 

institutional financial traders. 

Of these three channels, the first and second should impact different futures contracts 

similarly, regardless of maturity. Both short-term and long-term contracts should therefore 

similarly benefit from the improvements in transparency and the reduction of fixed ordering 

and trading costs. 

In contrast, not all contract maturities are expected to experience the same amount of 

interest from institutional financial traders. First, institutional financial traders have shorter 

trading horizons than other traders. Intuitively, they should thus trade more in short-term than 

in long-term contracts (Ederington and Lee, 2002). Second, the two front contract months and 

the nearest three December contracts (because of related calendar spreads) account for the 

preponderance of the intraday directional and calendar spread trading in the WTI futures 

market. Notably, both before (Neuberger, 1999) and after (Büyükşahin et al., 2015) 

electronification, positions in these five contracts are the most commonly held overnight or 

for longer periods by producers, refiners, and wholesalers, i.e., by key demanders of WTI 

futures market liquidity. Hence, with electronification’s attracting new financial traders intent 

on competing to provide liquidity to such users, one would expect financialization to be more 

pronounced for those five contract maturities. 

4.3.2. Evidence 

To verify this conjecture, we compute the participation rates of institutional financial 

traders separately for two groups of futures: short-term vs. long-term contracts. Given that 

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approximately half of the total WTI futures trading volume involves calendar spreads in our 

sample period, we select the “short-term contracts” bin to comprise the two front months 

(precisely, contracts with 62 days or less to expiration) and the three December contracts with 

which these two nearest-dated contracts are most often paired for spread trades.

14 Our “longterm contracts” bin consists of the trading activity in the remaining 79 contracts (on any 

given day) with more than 62 days to expiration. 

Figure 3, Panel A plots the evolution of institutional financial traders’ participation in 

short-term and long-term contracts. As anticipated, Panel A shows a much stronger rate of 

financialization for short-term contracts relative to long-term contracts. 

Figure 3, Panel B plots the number of new financial institutional traders entering the 

crude oil futures market in short-term and in long-term contracts. In line with the increased 

CTI-2 participation rates following electronification discussed in Section 4.2.1 above, Panel 

B shows a significantly greater number of new institutional financial traders entering the 

trading of short-term rather than long-term contracts after September 5th, 2006. Overall, the 

bottom line is that the introduction of electronic trading encouraged an influx of new 

financial institutional traders, albeit largely in the short-term contracts. 

Table 4 presents a more formal comparison of the pre- and post-electronification 

differences between the extents of financialization in short-term vs. long-term futures. The 

fraction of the total trading volume in short-term contracts involving institutional financial 

traders (denoted FIN_Short-Term) increases from 28.1 percent for the first eight months of 

2006 to 41.4 percent for the six months following electronification. This increase of almost 

45 percent is highly statistically significant (p-value < 0.001). During the same period, the 

equivalent measure for long-term contracts (denoted FIN_Long-Term) does not change 

significantly, remaining at around 36-37 percent of the total trading volume at such maturities 

both pre- and post-electronification. Consequently, the percentage difference in the 

contribution of institutional financial trading to the short-dated vs. long-dated trading volume, 

denoted ∆FIN = (FIN_Short-Term–FIN_Long-Term)/FIN_Short-Term, increases from minus 

14 In our sample period, crack spreads account for about 3.7% of all transactions and 1.8% of the WTI futures 

trading volume. Calendar spreads account for 22.2% of the futures trade count and 50.1% of the trading volume. 

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30.4 percent pre-electronification to plus 9.5 percent after the electronification of the futures 

market. This event-related increase is statistically significant at the 1 percent level. 

In sum, the participation rates of institutional financial traders (financialization) in 

short- and long-term contracts change differentially as a consequence of electronification, an 

exogenous exchange-mandated intervention. In the next Section, we exploit these differences 

in the extent of financialization along the futures term structure to formally test, in an 

econometrically rigorous framework, for the impact of financialization on market quality. 

Essentially, even though the market quality metrics for all contracts improve postelectronification, what we test empirically is whether the market quality improvements postelectronification are greater for contracts with a greater entry of new institutional financial 

traders, after controlling for any other factors that could be relevant. 

5. Financialization and Market Quality 

We saw in Section 4.1 that our market quality metrics improve significantly overall 

post-electronification. We also saw that financialization, in response to electronification, is 

significantly greater in short-term contracts than in long-term contracts on the same (WTI) 

commodity. In this Section, we seek to identify the effect of financialization on crude oil 

futures market quality by exploiting the observed variation in the intensity of financialization 

for short-term and long-term contracts following the (exogenous) exchange-mandated 

electronification of crude oil futures. Our contention is that the average improvement in 

market quality variables across all maturities is the average effect of electronification 

(whether due to improved transparency, lower operating and processing costs, or increased 

competition amid financialization), but the relatively larger improvements observed for shortterm (vs. long-term) contracts are due to their relatively greater financialization. 

Section 5.1 analyzes differences, pre- and post-electronification, in our market quality 

metrics for short-dated and long-dated futures contracts. It is a simple event study conducted 

around the introduction of electronic trading. Section 5.2 carries outs two-stage regressions to 

establish causality between financialization and improvements in market quality. Section 5.3 

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discusses the validity of this empirical strategy and shows that our conclusions are 

qualitatively robust to using alternative methodologies, including a VAR model examining 

the endogenous relationship between institutional financial trading and market quality using 

data between April 2007 and March 2008—when electronic trading had become entrenched. 

5.1. Descriptive Analysis—Event Study 

We start with an event study on a sample comprising all transaction records for WTI 

futures on NYMEX from January 3rd, 2006 to March 31st, 2007. We split the period between 

an eight-month period before and a seven-month period after the onset of electronic trading. 

We employ the market quality measures discussed in Sections 3.3 and 4.1. We 

compute daily averages separately for two groups of contracts: short-term and long-term. 

Table 5 presents the pre- and post-electronification values of our market quality variables for 

short-term and long-term contracts separately, as well as the average percentage differences 

(D) between the daily values of these short- and long-term estimates. 

Panels A to D of Figure 4 plot the evolution of these percentage differences (tradevolume weighted for short- vs. long-term contracts). For all four measures, the improvement 

after electronification benefited short-term futures more than long-term futures. 

First, in Table 5, the pre-electronification average ratio of the volatility of pricing 

errors to the volatility of log transaction prices is 28 percent higher for long-term contracts 

than for short-term contracts. The difference is 132 percent post-electronification. That is, 

while both long- and short-term contracts have lower pricing errors post-electronification, the 

pricing errors of short-term contracts improve five times more than those of long-term 

contracts (a relative improvement that is highly statistically significant with a p-value<0.001). 

Second, the pre-electronification average bid-ask spread is about 28 percent wider for 

long-term contracts than for short-term contracts. The difference, post-electronification, more 

than decuples—to 304 percent. Again, while both long- and short-term contracts are more 

liquid post-electronification, the liquidity of short-term contracts improves significantly more 

than that of long-term contracts (p-value<0.001). 

22
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Third, the pre-electronification absolute magnitude of customer trade imbalances for 

long-term contracts is on average about 144 percent larger than that for short-term contracts. 

The same variable in the post-electronification period increases significantly to 175 percent. 

Here also both long- and short-term contracts display a post-electronification improvement 

(lower customer order imbalances), but again the improvement is significantly greater for 

short-term contracts than for long-term contracts (p-value<<0.001). 

Finally, similar to the depth findings for the WTI market as a whole (see Section 4.1), 

our t-tests suggest that the average depth for short-term contracts does not change statistically 

significantly after electronification. We find, however, a statistically significant relative 

improvement of depth for short-term vs. long-term contracts (p-value<0.001). 

Overall, as captured by our key market quality metrics, electronification benefits 

short-term contracts significantly more than it does long-term contracts. Meanwhile, as seen 

in Section 4.3.2, institutional financial traders’ contribution to the total volume increases 

significantly in short-term contracts while staying (statistically) the same in long-term ones. 

Together, these results are consistent with our contention that the average market 

quality improvement across all contracts is the effect of electronification, while the relatively 

greater improvement for short-term contracts is due to their relatively greater financialization. 

Prima facie, notwithstanding the lack of relevant controls (such as changes in relative trading 

volume and volatility), it appears that financialization improves market quality. The next two 

sub-Sections examine this conjecture more rigorously. 

5.2. Two-Stage Regression Analysis 

The NYMEX’s introduction of electronic trading removed barriers to trading crude 

oil futures and facilitated market participation by financial institutional traders. We have 

documented a massive increase in financial institutional trader activity post-electronification. 

Under the maintained assumption that the NYMEX’s decision to “go electronic” was 

exogenous with respect to pre-existing crude oil derivatives market conditions, we use the 

advent of WTI futures market electronification as an instrument to tackle the endogeneity 

issues between market quality and trading activity of financial institutional traders. 

23
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Our goal is to identify the impact on market quality of the financialization that 

followed electronification—not of electronification per se. In order to tease out the former, 

we exploit the exogenous increase due to electronification in the relative participation of 

financial institutional traders in short-term (vs. long-term) contracts. To be precise, we use the 

electronification of the NYMEX WTI crude-oil futures markets as an instrument for the 

relative participation of financial institutional traders in short-term (vs. long-term) contracts 

in that market, as follows: 

First Stage: ∆,-." = /0 + 1023456789:;:5<6:89 + =0>" + ?"

Second Stage: ∆@" = /A + 1A∆,-." + =A>" + =BC" + D"

where ∆,-." is the percentage difference between the rates of participation of institutional 

financial traders in short- vs. long-term contracts, i.e., ∆FIN = (FIN_Short-Term – FIN_LongTerm) / FIN_Short-Term; ∆@" is the relative difference between the value taken for short- vs. 

long-term contracts by a given market quality measure (bid-ask spreads, depth, etc.); 

23456789:;:5<6:89 is a dummy set equal to 1 after September 5th, 2006 (the NYMEX’s 

electronification date) and to 0 prior to that day; >" are exogenous control dummies; and C" is 

a set of control variables including trading volume and volatility. The coefficient 1A is our 

estimate of the (causal) effect of financialization on market quality. 

5.2.1. First Stage 

The first stage regresses the relative difference between financial institutions’ rates of 

participation in trades of short-term vs. long-term futures, ∆,-.", on the 23456789:;:5<6:89

dummy. We control for differences in trading patterns on: (i) prompt-futures expiration days; 

(ii) days when the U.S. Department of Energy’s Energy Information Administration (EIA) 

releases its weekly reports on petroleum inventories; (iii) the day just prior to an EIA newsrelease day; (iv) the five business days each month when commodity index traders that follow 

the GSCI indexing methodology roll their nearby-futures positions; (v) possible transitory 

anomalies in the month when electronification started; and (vi) day-of-the-week effects. 

24
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 6 summarizes the first-stage results. Consistent with the univariate analyses and 

the graphical evidence presented in Section 4.3.2, we find a statistically and economically 

significant link between 23456789:;:5<6:89 and the difference in institutional financial 

traders’ participation in short- vs. long-term contracts. Before electronification, institutional 

financial traders participated more in long-term than in short-term crude oil futures contracts, 

as indicated by the intercept of -0.30. The 23456789:;:5<6:89 dummy’s coefficient 10 = 0.41 

implies that, post-electronification, the relative participation of financial traders in short-term 

contracts increased by 41 percentage points. Importantly, the 23456789:;:5<6:89 dummy 

alone explains more than 30 percent of the variation in ∆,-., indicating that it is a strong 

instrument for the relative change in the proportion of institutional financial traders. 

5.2.2. Second Stage 

In the second stage, we regress our market quality measures (precisely, the percentage 

differences in depth, spreads, customer imbalances, and pricing error volatility for short-term 

vs. long-term contracts) on ∆FIN (instrumented from the first stage) and the binary controls 

from the first stage. Panel B in Table 6 shows the results from the second stage. Following 

Breusch-Godfrey serial correlation tests, all models include two lags of the dependent 

variables. We estimate standard errors using the Newey-West method with two lags. 

For each and every market quality metric, Panel B shows that ∆FIN consistently has a 

negative and statistically highly significant coefficient (p-value < 0.001). To interpret this 

result, recall that we are analyzing the relation between the percentage difference (for shortvs. long-term contracts) in a given market-quality variable and the corresponding percentage 

difference in the extent of intraday financialization. In this context, our results mean that an 

increase in the percentage (or relative) difference in participation of institutional financial 

traders in short- vs. long-term contracts causally influences the percentage (or proportional) 

difference in that market quality variable in short- vs. long-term contracts. 

In robustness checks (summarized as “Model B” in Panel B, Table 6), we include in 

the second stage two variables measuring the percentage differences in (i) crude oil return 

volatilities and (ii) trading volumes between short- and long-term contracts. The results are 

25
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

robust: when short-term contracts experience more financialization than long-term contracts 

do, the bid-ask spreads, Amihud inverse measure of depth, customer imbalances, and pricing 

errors all drop substantially more for short-term contracts than for long-term contracts. 

Let us consider each of the market quality variables separately. For spreads, the -4.36 

coefficient (Model A, Table 6) means that a one-unit increase in the percentage difference in 

financialization (with institutional financial trading increasing more in short- than in longterm contracts) decreases the percentage differences in bid-ask spreads (with bid-ask spreads 

decreasing more for short- than for long-term contracts) by 4.36 units. The standard deviation 

for the percentage difference in bid-ask spreads is 186 percent; thus, a one-standard deviation 

increase in the percentage difference in financialization widens the percentage differences in 

bid-ask spreads by 0.84 standard deviations, or 104 percent of its mean value. Model B in 

Table 6 shows that, as should be expected, the percentage difference in spreads is positively 

related to the percentage difference in volatilities between short-term and long-term contracts 

and negatively related to the percentage difference in trading volumes. 

Similar to the bid-ask spread results, we find a consistently negative and statistically 

significant relation between the difference in financialization intensity and the difference in 

Amihud inverse depth ratios or customer trade imbalances. A one-standard deviation increase 

in the percentage difference in financialization widens the percentage difference in Amihud 

ratios by 1.07 standard deviations (or 38 percent of the mean value), and in the absolute 

magnitudes of customer trade imbalances by 0.60 standard deviations (or 19 percent of the 

mean value). 

Last, but not least, a one-unit increase in the percentage difference in financialization 

between short- and long-term contracts, after controlling for all the relevant variables, further 

widens the percentage difference in pricing error volatility by 0.92 units in Model A (and by 

0.64 units in Model B15). A one-standard deviation increase in the percentage difference in 

the rate of financialization widens the percentage differences in pricing error volatility by 

15 In our analysis of the volatility of pricing errors, we follow Boehmer and Kelly (2009) in including the 

standard deviation of transaction prices as control while modeling the volatility of the pricing error. 

Accordingly, we employ the percentage difference in the volatility of transaction prices between short-term and 

long-term controls while modeling the percentage difference in pricing error volatilities. 

26
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0.43 standard deviations or 74 percent of its mean value. In other words, Table 6 shows that 

financialization significantly improves intraday pricing efficiency.16 

5.3. Controlling for Changes in “Customers” 

The two-stage approach in Section 5.2 is designed to identify a causal relationship 

from financialization to market quality. While the entry of institutional financial traders into 

the crude-oil market is exogenous, however, their choice of which contract maturities to trade 

could be based on the order-flow of external “customers”, i.e., of non-financial traders. In this 

sub-Section, we accordingly carry out additional analyses to control carefully, in multiple 

ways, for potentially confounding influences on our inferences of changes in customer 

trading volume and for the possibility that improvements in market quality might be observed 

because the nature of customers changes after electronification. 

5.3.1. Controlling for Post-Electronification Changes in Customer Volume 

In Section 5.2 above, we already showed that the impact of financialization on all of 

our market quality measures remains economically and statistically significant even after 

controlling (Model B, Panel B of Table 6) for differences (in short- vs. long-term contracts) 

in the post-electronification growth rates of the overall trading volume. We now show that 

the effect of institutional financial traders remains significant even after taking into account 

cross-maturity differences in customer trading (∆Customer Volume). We do so in two ways. 

First, we introduce ∆Customer_Volume in the second-stage regressions as an 

additional control. Table 7 shows that the contributions of increased institutional financial 

trading to all market quality improvements are economically and statistically significant even 

after controlling for differences along the futures term structure in the post-electronification 

16 Our two-stage analyses focus on Intitutional Financial (“CTI-2”) traders to the exclusion of Locals (“CTI-1” 

traders). One might expect that a two-stage regression analysis using the volume share of CTI-1s (rather than 

that of CTI-2s) could produce “mirror” results. In that case, it could be argued (albeit facetiously) that it is the 

crowding out of CTI-1 traders and of their trading practices in the futures pits (rather than the competition from 

institutional financial traders) that led to the post-electronification increase in market quality. However, this 

possibility is unlikely since CTI-1 trading volume (as opposed to the CTI-1 share of the total volume) actually 

increased following electronification—which indicates that it is indeed greater competition from CTI-2s, rather 

than the decline of CTI-1s, that drove market quality improvements. 

27
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

growth rates of non-financial (customers’) activity. While the regression results suggest that 

relatively greater changes in customer activity in near-dated (vs. long-dated) contracts do help 

explain part of the spread improvements, we find no empirical evidence that they improve 

our other measures of market quality. 

Second, to account for possibly non-linear effects of ∆Customer_Volume on the 

relation between institutional financial trading and market quality, we run the same secondstage regressions but now stratified by ∆Customer Volume (Tercile 1, 2, and 3 corresponding 

to the bottom third, medium, and highest values of ∆Customer Volume). Table 8 summarizes 

the results of this additional analysis, focusing for brevity on the ∆FIN coefficients. It shows 

similarly significantly beneficial effects of financialization on market quality, irrespective of 

the level of ∆Customer_Volume. Even in the lowest tercile (which consists solely of days 

when customer trading is actually more intense in long-term than in short-term contracts), we 

find that financialization improves each of our market quality metrics.17 These results further 

strengthen our argument that, economically and statistically, the main driver of change—for 

all our quality metrics—is the extent of institutional financial trading. 

5.3.2. Two-Stage Regression Analysis – “Continuing” Customers 

We next look at the effect of the exogenous entry of institutional financial traders on 

the spreads paid by customers who already traded WTI futures in the pits prior to 

electronification and who continue to trade (now electronically, on the Globex platform) after 

electronification. In other words, we examine the effect of the entry of institutional financial 

traders in short-term vs. long-term contacts while keeping both the nature of the customers 

and their demand for liquidity the same, before and after electronification. 

The general methodology is identical to the one described Section 5.2, except that we 

limit our analysis to bid-ask spreads (given that spreads are the only market measure that can 

be estimated separately for “continuing” customers). As shown in Table 9, the results of this 

additional analysis are consistent with our previous results: specifically, an increase in ∆FIN 

17 We obtain similar results if, rather than tercile regressions, we run the analysis separately for negative and 

positive values of ∆Customer_Volume. 

28
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

significantly reduces (by 4.43 percent on average across all six specifications) the spread paid 

by “continuing” customers. 

5.4. Other Robustness Tests 

5.4.1. Two-Stage Regression Analysis—Lagged Identification 

Our next robustness check relies on a different instrument to extract the component of 

institutional trading that is exogenous to market conditions. In all of our two-stage analyses 

so far, we have used the NYMEX’s introduction of electronic trading as our main instrument. 

Here, inspired by Muravyev (2016), we adopt an alternative instrumental variables approach 

predicated on the day-to-day persistence of the degree of financialization and we use lagged 

values of that degree of financialization (in addition to a set of exogenous variables likely to 

generate fluctuations in institutional financial trading) in order to predict its future levels. 

To be precise, we use in the first stage a one-day lag of ∆FIN together with dummy 

variables for (i) prompt-contract expiration days, (ii) EIA information days, (iii) the initial 

month of electronic trading, and (iv) GSCI roll days. As Panel A of Table 10 shows, these 

variables combine to form a strong instrument: they explain just over 25% of the variation in 

∆FIN. Moreover, ∆FIN is statistically significantly persistent (with a coefficient of 0.50), 

higher on EIA announcement days, and lower on contract expiration days. 

In the second stage, we use ∆FIN (now instrumented from this alternative first stage) 

as an explanatory variable for our market quality variables. The results for the second-stage 

regressions are presented in Panel B of Table 10. As in Panel B of Table 6, we find strong 

evidence that financialization improves both liquidity and intraday pricing efficiency. 

5.4.2. VAR Analysis 

Finally, we further examine the endogenous relation between participation by 

financial institutions and intraday market quality through a vector autoregression (VAR) 

analysis. We rely on the same 2006—2008 CFTC dataset of intraday transactions but restrict 

the sample period from April 1st, 2007, to May 31st, 2008. This choice of sample period rules 

out any overlap with the two-stage regression sample (which ends on March 31st, 2007). It 

29
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

also allows us to test whether the results obtained for the six months immediately following 

the onset of electronic trading persist even in more mature market conditions. 

As before, we compute percentage differences (for short- vs. long-term contracts) in 

the financialization rates (∆,-.), bid-ask spreads (∆E!74<F), depth (∆H$:hJF @4<%J74), 

and absolute customer trade imbalances (∆HKELMN). We proceed analogously for the daily 

ratio of the variance of pricing errors (PE) to the volatility of intraday (log) transaction prices 

(∆O2_O78!876:89). As well, we compute the percentage differences in realized return 

volatilities for short-term vs. long-term futures contracts (∆Q46J79_R83<6:3:6S).

18 

We propose a 6-variable VAR model of the roles of volatility and financialization in 

explaining the behavior of our four liquidity and pricing efficiency variables in 2007–2008. 

Hameed, Kang, and Viswanathan (2010) note the need for, yet also the difficulty in 

justifying, a specific ordering of a similar set of variables. We follow their approach, which 

consists of ordering the variables in a logical manner and then verifying robustness to 

alternative orderings. 

Specifically, in order to test whether the intensity of institutional financial trading (our 

proxy for the extent of commodity market financialization) impacts market quality, we order 

∆,-. and ∆Q46J79_R83<6:3:6S before the four market quality variables. This ordering 

implies that shocks to volatility or to institutional financial traders’ positions result in 

instantaneous adjustments in liquidity and pricing efficiency, whereas changes in market 

quality impact volatility and institutional trading activity with a lag. 

We obtain qualitatively similar results independent of whether ∆Q46J79_R83<6:3:6S

or ∆,-. is ordered first vs. second of the VAR variables. Likewise, we obtain qualitatively 

similar results with different orderings of the market quality variables. For tractability, we 

therefore focus our discussion below on a single specification. Reflecting our focus on the 

effect of financialization on market quality, we discuss results when ∆,-. is placed first with 

the following ordering: ∆,-., ∆Q46J79_R83<6:3:6S, ∆E!74<F, ∆H$:hJF @4<%J74, 

∆HKELMN, and ∆O2_O78!876:89. 

18 Augmented Dickey-Fuller (ADF) tests show that the percentage difference of all the variables in our SVARs 

are stationary. We select the number of lags for the ADF tests according to the Akaike information criteria. 

30
 
 

 

 

 

 

 

 

 

 

 

 

 

 		

	 	 

 

 

 

 

 

 		 	 	 	 

 

 		 

 

Formally, for the data series S" consisting of the vector S" of our six variables of 

interest, we consider the following reduced-form representation of the VAR model: 

H(U)S" = V",

where H(U) is a matrix of polynomial in the lag operator L, {I – A1L1 – A2L2 – ... ApLp}, S" is 

a (6 x 1) data vector, and V" is a vector of orthogonalized reduced-form disturbances. 

Specifically, for our six-variable VAR, we impose the standard Cholesky decomposition of 

the variance-covariance matrix (i.e., a lower triangular matrix with ones on the diagonal, and 

a diagonal matrix) to fit a just-identified model. With our ordering restrictions, we assume 

that the extent of financialization is not contemporaneously affected by market volatility or 

market quality. Likewise, we posit that market volatility is contemporaneously affected by 

the extent of financialization but not by various aspects of market quality. We also assume 

that all of our measures of liquidity and pricing efficiency are contemporaneously affected by 

the rate of financialization and by market volatility, but affect the latter two with a lag. 

Panels A to D of Figure 5 present the impulse response functions (IRFs) showing the 

effect of a one-standard deviation shock to ∆,-. on market quality measures: ∆E!74<F,

∆H$:hJF @4<%J74, ∆HKELMN, and ∆O2_O78!876:89. Consistent with the results of our 

earlier event-study and regression analyses, Panel A shows that an increase in ∆,-. results in 

a negative and significant effect on (i.e., an improvement in) ∆E!74<F on day t 

(contemperaneously) and on day t+1. That is, an increase in the relative financialization of 

short-term contracts leads to a decrease in their relative bid-ask spreads. While the effect of 

∆,-. on ∆H$:hJF is statistically insignificant (Panel B), we find a negative and significant 

effect on ∆HKELMN Panel C and on pricing errors, ∆O2^_`a`_"b`c Panel D . More 

specifically, a one-standard deviation increase in ∆,-. leads to a 0.19 and 0.15 standarddeviation decreases in contemperaneous ∆HKELMN and ∆O2^_`a`_"b`c, respectively, and leads 

to a further 0.13 standard deviation-decrease in ∆O2_O78!876:89 on day t+1. In sum, the 

VAR results are in line with the results of our other univariate and mutivariate analyses. 

31
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6. Electronification, Financialization, and Automation of Trading 

For equities, Hendershott, Jones, and Menkveld (2011) show that algorithmic trading 

improves several intraday market liquidity metrics. In a similar vein, we also know that high 

frequency trading improves intraday price discovery for equities (Brogaard, Hendershott, and 

Riordan, 2014) and pricing efficiency for currencies (Chaboud et al. 2014). 

For commodities, Section 5 establishes empirically that financialization, as measured 

through intraday institutional financial trading, improves both liquidity and pricing efficiency 

metrics. A natural question, then, is whether some of our results obtain wholly or in part due 

to the rise of high-frequency algorithmic trading by institutional traders. More broadly, in the 

light of ongoing academic and policy debates relating to constraining HFTs and boosting the 

market participation of non-automated liquidity providers,19 it is important to examine if HFT 

and non-HFT traders differ in their impact on key aspects market quality, particularly in an 

environment in which both groups co-exist and contribute substantially to trading volumes. 

In Section 6.1, we start by documenting that HFT and non-HFT institutional financial 

traders both make up significant shares of the daily trading during the last quarter of 2006 and 

the first quarter of 2007. In the NYMEX’s WTI futures market, there is obviously no HFT 

before September 2006, and the relative importance of non-HFT insitutional financial traders 

wanes after 2007. Our 2006Q4 to 2007Q1 sample therefore provides a unique opportunity to 

shed light on the above questions. Indeed, Section 6.2 provides empirical evidence—to our 

knowledge for the first time—of major differences in the respective contributions to pricing 

efficiency and market liquidity of HFT and non-HFT institutional financial trading. 

6.1. Institutional Financial Trading 

Inspired by Raman, Robe, and Yadav (2017), whose analysis of algorithmic liquidity 

provision also relies on CFTC non-public intraday data, we identify HFTs as CTI-2 traders 

who trade more than 990 times a day (3 times per minute, every minute) and carry less than 

5% of their daily trading volume overnight (making them largely intraday traders). This 

19 See, e.g., Budish, Cramton, and Shim (2015) and references cited therein. On the policy side, see also e.g. 

“Foresight: The Future of Computer Trading in Financial Markets (2012) Final Project Report,” The UK 

Government Office for Science, London; and the studies underlying the recommendations. 

32
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

identification allows us to create two new measures of intraday financial institutional trading: 

(i) FIN_Non_HFT, which we calculate after removing all HFTs from our set of institutional 

financial traders; and (ii) FIN_HFT, the component of financialization that is due to the onset 

of high-frequency trading. Analogous to our other analyses, we use the difference between 

short-term and long-term contracts for both of these measures of financialization. 

Table 11 provides summary information on HFT and non-HFT institutional financial 

traders, based on Globex data from the post-electronification period (September 5th, 2006, to 

March 31st, 2007). Predictably, HFTs trade much more, and much more frequently, than nonHFTs. Notably, both groups’ median hourly closing ratios (computed using each trader’s endof-hour inventory to that trader’s hourly trading volume on the Globex electronic platform 

during business hours) are massively lower than the corresponding ratio for institutional 

financial traders pre-electronification (1% for HFTs and 15% for non-HFTs in Table 11, vs. 

83% in Table 2). In other words, Table 11 shows that it is not only the 21 HFT institutional 

financial traders that begin to trade WTI futures following the market’s electronification, but 

also the over four hundred other (non-HFT) new institutional financial traders that enter the 

market for the first time in the same period (Figure 3, Panel B), that behave very differently 

from their pre-electronification counterparts. Finally, Table 11 shows that both the aggregate 

HFT and non-HFT components of the FIN variable are large and, on average in our twoquarter sample, quite similar in magnitude. 

6.2. Differential contributions of HFT and Non-HFT Traders to Market Quality 

For the revised two-stage regression analysis, we only apply the first stage procedure 

(see Section 5.2.1) to the non-automated component of financialization (i.e., excluding HFT 

CTI-2 traders). We then run the second stage using the same specification as in Section 5.2.2, 

but using the instrumented series of non-automated financialization as well as the actual level 

of HFT-based financialization.20 

20 Using predicted values for both financialization variables might introduce unacceptable levels of multicollinearity between them. 

33
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 12 presents the results from the second-stage of this analysis. It is clear that, for 

non-HFT financial institutional traders, the liquidity results in the columns corresponding to 

spreads, depth, and absolute customer trade imbalances in Table 12 are qualitatively similar 

to those corresponding to all institutional financial traders as reported respectively in Panel B 

of Table 6. For each liquidity metric: (a) ∆FIN_Non_HFT has a negative and statistically 

highly significant coefficient (p-value<0.001); (b) the R-squared of the Table 12 regression is 

even higher than the R-squared of the corresponding Model B regressions in Panel B of Table 

6; and (c) the magnitude of the coefficient for ∆FIN_Non_HFT in Table 12 is significantly 

higher—about double in every case—compared to the matching Model B coefficient in Panel 

B of Table 6. The impact of non-automated institutional financial trading in Table 12 appears 

to be at least as strong as the impact of overall institutional financial trading in Table 6. It is 

also clearly robust to all the specifications and controls we have utilized. 

Table 12 also shows that HFT institutional financial traders share some, but not all, of 

the credit for liquidity improvements: their trading contributes to the narrowing of bid-ask 

spreads (highly significant) and curtailing of customer trade imbalances (though statistical 

significance is weak). Their activity’s impact on depth, however, is statistically insignificant. 

Interestingly, the respective roles of HFT and of other (non-HFT) institutional 

financial traders are reversed in the case of pricing efficiency. In Table 6, financialization as a 

whole brings about an economically and statistically significant reduction in the variance of 

pricing errors. Table 12 indicates that, statistically speaking, the improvement in pricing 

efficiency may be attributed solely to the growth of HFT institutional financial traders. 

For robustness, we undertake further analysis using a different way of controlling for 

endogeneity, based on the alternative set of instruments introduced in Section 5.4.1. Namely, 

we include in the first stage the lagged values of our two FIN variables (corresponding to 

HFT and non-HFT institutional traders). Table 13 shows that the results of this additional 

analysis are qualitatively similar to those summarized in Table 12. 

In sum, our results on financialization’s beneficial impact on intraday market quality 

do not come about just because of HFTs. To wit, a financialization measure based only on 

non-automated institutional financial trading leads to a significant reduction in bid-ask 

34
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

spreads, a significant increase in depth, and a significant reduction in the absolute magnitude 

of customer trade imbalances. In that sense, our results provide novel evidence that different 

components of intraday financialization (HFT vs. non-HFT traders) contribute differently to 

different aspects of market quality in a market environment in which both types of traders 

coexist with significant fractions of the total trading volume. 

7. Conclusions 

On September 5th, 2006, the NYMEX introduced electronic trading to its energy 

futures marketplace, including the world’s largest commodity market: the WTI light sweet 

crude oil futures. We document that this structural change sharply increased intraday trading 

by institutional financial traders, i.e., electronification led to the crude oil market’s intraday 

financialization. We use this event to document the effect of intraday financialization on key 

measures of market quality: the volatility of intraday pricing errors as well as bid-ask spreads, 

depth, and the absolute magnitude of customer trade imbalances. Specifically, exploiting the 

electronification-induced differences in the extent of financialization between short-term and 

long-term futures contracts, we are able to tie economically and statistically significant 

improvements in all these market quality proxies to intraday financialization. Our inferences 

are robust to differences in the nature and volume of non-financial trading. 

Post-event, a VAR analysis of the endogenous relation between institutional financial 

trading and market quality metrics provides strong additional evidence that greater 

participation by institutional financial traders brings about significant improvements in 

pricing efficiency and market liquidity. 

Importantly, we also show that these results are not driven solely by the emergence 

and growth of high-frequency trading made possible by electronification. While the activity 

of fast algorithms (HFTs) has the biggest impact on intraday pricing efficiency, non-HFT 

institutional financial traders have an economically and statistically beneficial impact on 

market liquidity (bid-ask spreads, depth, and absolute customer buy-sell imbalances) that is 

robust to different specifications and controls. 

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Overall, we add significantly to the financialization literature by providing the first 

detailed empirical evidence on the financialization of intraday trading activity in the world’s 

largest commodity market and by showing that this development had a first-order positive 

impact on market liquidity and pricing efficiency. We also add to the large literature on the 

impact of institutional trading. In particular, we are first to investigate the impact of the short 

(intraday) horizon of both HFT and non-HFT institutional financial traders and to show that 

they both contribute to intraday market quality (albeit in different ways). While these findings 

pertain to commodity markets, they are directly relevant to all electronic order-driven 

markets where liquidity provision is voluntary. Indeed, insofar as most equity and other 

financial markets are now organized as electronic order-driven markets with voluntary 

liquidity provision, our results on the beneficial impact of the flow of institutional risk capital 

into liquidity provision and short-horizon trading are potentially of wide applicability. 

Our results point to several avenues for future research. First, given that liquidity 

provision in U.S. commodity futures markets is entirely voluntary, and given the significant 

increase that we document in the extent and influence of institutional financial trading, two 

important questions are: one, whether, in periods of stress, institutional financial traders make 

markets more or less fragile; and two, whether the financialization of commodity markets 

affects their resilience to exogenous shocks. Answering these questions would have 

implications for financial stability and the importance of systemic risk in the presence of 

electronic trading. 

Second, we provide empirical evidence that financialization has changed the nature of 

financial intermediaries in commodity futures markets. In particular, we show that, unlike 

their predecessors in the pits, the new institutional financial traders on the electronic platform 

are generally active also in other commodity markets. They are likely to be active also in 

markets for other assets than commodities. These facts and conjecture point to the need for 

new research on commonality in liquidity across asset classes, particularly in the context of 

funding liquidity constraints and associated systemic market risks. 

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Figure 1: Market Quality Measures, 2006–2008 

Figure 1 depicts the evolution of 15-day moving averages for various market quality measures at the New York Mercantile Exchange’s (NYMEX) West Texas Intermediate 

sweet crude oil (WTI) futures market between 2006 and 2008. The underlying measures are computed using pit data for the pre-electronification period (January 3rd, 2006, to 

September 1st, 2006) and Globex data for the post-electronification period (September 5th, 2006, to May 31st, 2008). In Panel A, PE_Variance is the daily average pricing 

error variance, estimated as in Hasbrouck (1993). Price_Volatility is the daily average of 5-minute volatility of intraday (log) prices for each futures maturity. In Panel B, 

Spread is the daily average of 5-minute Bid-Ask spreads, where bid and asked prices are estimated for each contract maturity in each interval (5 minutes) after classifying 

trades as buyer- vs. seller-initiated using the Lee and Ready (1991) tick test. Volatility is the daily average 5-minute (300 seconds) average return volatility. In Panel C, 

Amihud is an inverse measure of depth (Amihud, 2002) equal to the daily average of the ratio of absolute return to volume, calculated in 5-minute non-overlapping intervals 

throughout the trading day. In Panel D, AbsOIB is the daily average of 5-minute customer trade imbalances (where customers are the traders classified as CTI-4 traders in the 

CFTC database) calculated as the ratio of 5-minute absolute trade imbalances (buyer- minus seller-initiated trades) to trading volume. All the measures are estimated for each 

contract maturity and then volume-weight-averaged across all 84 futures contract maturities using trades time-stamped during business hours. Source: U.S. Commodity 

Futures Trading Commission (CFTC) and authors’ computations. 

Panel A: Pricing Errors (PE_Variance and Price_Volatility) Panel B: Spread and Volatility 

0.40% 1.00% 0.50% 

Return_Volatility (15-day MA) 0.80% 0.40% 0.30% Spread (15-day MA) 0.60% 0.30% 

0.20% 

0.40% 0.20% PE_Variance (15-day MA) 0.10% Price_Volatility (15-day MA) 0.20% 0.10% 

0.00% 0.00% 0.00% 

Panel C: Inverse Depth (Amihud Ratio) Panel D: Customer Demand Imbalances (AbsOIB) 

0.0008% 30% ABS_OIB (15-day MA) Inverse_Depth (15-day MA) 25% 0.0006% 20% 

0.0004% 15% 

10%

0.0002% 5% 

0% 0.0000% 

Jan-06

Mar-06

May-06

Jul-06

Sep-06

Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07

Nov-07

Jan-08 Jan-08

Mar-08 Mar-08

May-08 May-08

41

Jan-06

Mar-06

May-06

Jul-06

Sep-06

Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07

Nov-07

Jan-08

Mar-08

May-08

Jan-06

Mar-06

May-06

Jul-06

Sep-06

Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07

Nov-07
42

Figure 2: Trading Volume – Share of Locals vs. Institutional Financial Traders, 2006 – 2008

Figure 2 compares the respective evolutions between 2006 and 2008 of the fractions of the total trading volume 

involving institutional financial traders (dashed line) and Locals (solid line) in the NYMEX’s WTI sweet crude 

oil futures market. Volume shares are based on pit data for the pre-electronification period (January 3rd, 2006, to 

September 1st, 2006) and on Globex data for the post-electronification period (September 5th, 2006, to May 31st, 

2008). FIN is the proportion of trades involving the participation of one or two institutional financial traders 

(traders classified as CTI-2 traders in the CFTC database) in either or both legs of a trade. Local is the 

proportion of the total trading volume involving the participation of one or two “Locals” (i.e., traders classified 

as CTI-1 traders in the CFTC database) in one or both legs of a trade. Figure 2 plots moving averages of these 

daily volume shares based on trades time-stamped during business hours. Source: U.S. Commodity Futures 

Trading Commission (CFTC) and authors’ computations. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Jan-06

Mar-06

May-06

Jul-06

Sep-06

Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07

Nov-07

Jan-08

Mar-08

May-08

Prop_Local_Volume		(15-day	MA)

Prop_FIN_Volume	(15-day	MA)
43

Figure 3: Institutional Financial Trading Activity: Short-Term vs. Long-Term contracts

Figure 3 compares the evolution of institutional financial trading activity in short-term vs. long-term WTI sweet 

crude oil futures contracts on the NYMEX. Panel A compares the proportions of the total trading volume 

involving institutional financial traders in short-term vs. long-term WTI futures; Panel B compares the monthly 

number of new institutional financial traders (i.e., arrivals) in short-term vs. long-term WTI futures. The analysis 

is conducted on pit data for the pre-electronification period (January 3rd, 2006, to September 1st, 2006) and 

Globex data for the post-electronification period (September 5th, 2006, to May 31st, 2008). In Panel A, FIN is 

the proportion of the trading volume involving one or more institutional financial traders (traders classified as 

CTI-2 traders in the CFTC database): FIN_Short-Term is the daily, trading volume-weighted average of FIN 

across short-term contracts (contracts with up to 62 days to expiration); FIN_Long-Term is the daily, volume 

weighted average of FIN for long-term contracts (contracts with more than 62 days left to expiration). ∆FIN or 

DFIN is the daily percentage difference between the short- and long-term proportions: (FIN_Short-Term –

FIN_Long-Term)/ FIN_Short-Term. The vertical line in Panel A identifies the date of the introduction of 

electronic trading – September 5th, 2006. Source: U.S. Commodity Futures Trading Commission (CFTC) and 

authors’ computations.

Panel A: Financial trading volume in short-term vs. long-term crude oil contracts

Panel B: Entry of new institutional financial traders in short-term vs. long-term WTI crude oil futures

-100%

-50%

0%

50%

100%

150%

200%

-38%

-25%

-13%

0%

13%

25%

38%

50%

63%

75%

Jan-06

Mar-06

May-06

Jul-06

Sep-06

Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07

Nov-07

Jan-08

Mar-08

May-08

prop_FIN_volume_short-term	(15-day	MA)

prop_FIN_volume_long-term	(15-day	MA)

DFIN	(15-day	MA)

0

10

20

30

40

50

60

70

80

90

100

New	FIN	(Short-Term	Contracts)

New	FIN	(Long-Term	Contracts)
44

Figure 4: Institutional Financial Trading Activity and Market Quality measures: 

Short-Term vs. Long-Term contracts

Figure 4 compares the evolution of various market quality measures in short-term (up to 62 days to expiration) 

vs. longer-term (more than 62 days until expiration) crude oil futures contracts. The analysis is conducted for 

NYMEX WTI sweet crude oil futures trading during business hours in the NYMEX pits for the preelectronification period (January 3rd, 2006, to September 1st, 2006) and on the Globex platform for the postelectronification period (September 5th, 2006 to May 31st, 2008). In each Panel, we plot the daily percentage 

difference (denoted ∆) between the Short-Term and Long-Term values of the relevant variable(s). In Panel A, 

Spread refers to the daily average of 5-minute Bid-Ask spreads obtained using bid and asked prices estimated 

for each contract maturity in each interval (5 minutes) after classifying trades as buyer- vs. seller-initiated using 

the Lee and Ready (1991) tick test; Volatility or Std_Return_300 refers to the 5-minute (300 seconds) average 

return volatility. In Panel B, Amihud refers to an inverse measure of depth (Amihud, 2002) equal to the daily 

average of the ratio of absolute return to volume calculated in 5-minute non-overlapping intervals throughout 

the trading day. In Panel C, AbsOIB refers to the daily average of 5-minute customer (traders classified as CTI 4 

traders in the CFTC database) trade imbalances calculated as the ratio of 5-minute absolute trade imbalances 

(buyer-initiated minus seller-initiated trades) to trading volume. In Panel D, PE_Proportion is the daily ratio of 

PE_Variance and Price_Volatility. In Panel E, PE_Variance refers to the daily pricing error variance, estimated 

as in Hasbrouck (1993), while Price_Volatility refers to the daily average of 5-minute volatility of intraday (log) 

transaction prices for each contract maturity in each time interval. The dark vertical line in each plot identifies 

the date of the introduction of electronic trading – September 5th, 2006. Sources: U.S. Commodity Futures 

Trading Commission (CFTC) and authors’ computations.

Panel A: ∆Spread and ∆Volatility

Panel B: ∆Amihud (inverse depth)

-7

-6

-5

-4

-3

-2

-1

0

1

DReturn_Volatility	(15-day	MA)

DSpread	(15-day	MA)

-40

-35

-30

-25

-20

-15

-10

-5

0

DAmihud	(15-day	MA)
45

Panel C: ∆AbsOIB

Panel D: ∆PE_Proportion

Panel E: ∆PE_Variance and ∆Price_Volatility

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

Jan-06

Mar-06

May-06

Jul-06

Sep-06

Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07

Nov-07

Jan-08

Mar-08

May-08

D_ABS_OIB	(15-day	MA)

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

Jan-06

Mar-06

May-06

Jul-06

Sep-06

Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07

Nov-07

Jan-08

Mar-08

May-08

DPE_Proportion	(15-day	MA)

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

Jan-06

Mar-06

May-06

Jul-06

Sep-06

Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07

Nov-07

Jan-08

Mar-08

May-08

DPE_Variance	(15-day	MA)
46

Figure 5: Effect of Institutional Financial Traders – Impulse Response Functions

Figure 5 provides graphs of orthogonalized impulse response functions (red lines) with 10% confidence bands (dotted lines). Each Panel depicts the impact on measures of 

market quality and pricing efficiency, up to 10 days out, of a one-standard deviation shock to ∆FIN, the difference between the daily average trading volume shares of

institutional financial traders in short-term vs. long-term WTI sweet crude oil futures contracts. The analysis is conducted using Globex data from April 1st, 2007, to May 31st, 

2008. ∆Spread (Panel A), ∆Amihud (depth, Panel B), ∆AbsOIB (customer trade imbalances, Panel C), and ∆PE_Proportion (Pricing errors, Panel D). Impulse response 

functions are obtained for a VAR system consisting of 5 lags of ∆FIN, ∆Spread, ∆Amihud, and ∆AbsOIB and ∆PE_Proportion. 

Panel A: Effect of ∆FIN on ∆Spread Panel B: Effect of ∆FIN on ∆Amihud

Panel C: Effect of ∆FIN on ∆ABSOIB Panel D: Effect of ∆FIN on ∆ PE_Proportion

-0.40

-0.20

0.00

0.20

0 1 2 3 4 5 6 7 8 9 10

-0.20

-0.10

0.00

0.10

0.20

0 1 2 3 4 5 6 7 8 9 10

-0.40

-0.20

0.00

0.20

0.40

0 1 2 3 4 5 6 7 8 9 10

-0.40

-0.20

0.00

0.20

0.40

0 1 2 3 4 5 6 7 8 9 10
47

Table 1: Impact of Electronification on Institutional Financial Trading and Market Quality

Table 1 presents an analysis of institutional financial traders’ participation and measures of market quality 

surrounding the introduction of electronic trading in WTI sweet crude-oil futures by the NYMEX on September

5th, 2006. The sample period is January 3rd, 2006, to May 31st, 2008. Pre-Electronification refers to the period 

from January 3rd, 2006, to September 1st, 2006; Post-Electronification in this Table refers to the period from 

September 5th, 2006, to May 31st, 2008. The analysis is conducted on pit trading data for the PreElectronification period and on Globex data for the Post-Electronification period. FIN is the proportion of the 

total trading volume during business hours involving the participation of institutional financial traders (traders 

classified as CTI-2 traders in the CFTC database). Spread is the daily average of 5-minute Bid-Ask spreads 

obtained using bid and asked prices estimated for each contract maturity in each interval (5 minutes), after 

classifying trades as buyer- vs. seller-initiated using the Lee and Ready (1991) tick-test. Amihud, an inverse 

measure of depth (Amihud, 2002), is the daily average of the ratio of absolute return to volume calculated in 5-

minute non-overlapping intervals throughout the trading day. AbsOIB is the daily average of 5-minute customer 

(traders classified as CTI-4 traders in the CFTC database) trade imbalances calculated as the ratio of five-minute 

absolute trade imbalances (buyer-initiated minus seller-initiated trades) to trading volume. PE_Proportion is the 

daily ratio of the pricing error variance, estimated as in Hasbrouck (1993), to the volatility of intraday (log) 

transaction prices. All the variables are estimated for each contract maturity and daily volume-weighted 

averages of these figures are then computed and employed in the regressions. Two-tailed p-values are also 

reported. Source: U.S. Commodity Futures Trading Commission (CFTC) and authors’ computations.

PreElectronificationPostElectronification DifferencePct. 

Difference p-value

FIN 29.64% 55.01% 25.37% 85.59% <.001

Spread 0.37% 0.03% -0.34% -91.94% <.001

Amihud 4.90 2.66 -2.24 -45.76% 0.604

AbsOIB 23.85% 13.48% -10.37% -43.48% <.001

PE_Proportion 58.87% 3.73% -55.14% -93.66% <.001
48

Table 2: Trader Descriptions

Table 2 describes key attributes of three kinds of traders in the NYMEX’s WTI sweet crude-oil futures market 

in 2006–2007. The summary statistics in Panel A are based on pit data during the pre-electronification period 

(January 3rd, 2006, to September 1st, 2006); in Panel B, the information is based on Globex data for a sevenmonth post-electronification period (September 5th, 2006, to March 31st, 2007). Locals, Financial Institutions 

(“Fin. Inst.”) and Customers refer to traders classified respectively as CTI-1, 2 and 4 in the CFTC database. Abs. 

Closing Ratio refers to the average ratio of a trader’s ending-of-hour inventory to that trader’s hourly trading 

volume during business hours. Similarly, Trading Volume and Number of Trades are also hourly averages of a 

trader’s activity. Cross-sectional mean and median are also presented. Source: U.S. Commodity Futures Trading 

Commission (CFTC) and authors’ computations. 

Panel A: Pits, Pre-electronification (January 3rd, 2006 to September 1st, 2006)

Traders Trading Volume Number of Trades Abs. Closing Ratio

Mean Median Mean Median Mean Median

Locals 258 85 14 8 22% 3%

Fin. Inst. 269 81 6 3 57% 83%

Customers 87 10 4 2 75% 100%

Panel B: Globex, Post-electronification (September 5th, 2006 to March 31st, 2007)

Traders Trading Volume Number of Trades Abs. Closing Ratio

Mean Median Mean Median Mean Median

Locals 115 36 30 13 32% 12%

Fin. Inst. 298 50 114 19 38% 8%

Customers 77 10 23 4 60% 100%
49

Table 3: Locals and Institutional Financial Traders – Electronic vs. Pit Cross-Market Traders

Table 3 tabulates the average proportion of the pits or electronic (i.e., Globex) daily futures trading volume that involves a “Local” or a “Financial Institution” active not 

only in the NYMEX WTI crude oil futures market but also in both gold and silver COMEX futures (left columns) or only in gold COMEX futures (right columns). Panel A 

computes those proportions for the WTI futures market; Panel B, for the gold futures market. The sample period is March 27th through December 31st, 2007. All proportions

are computed across all contract maturities. Source: U.S. Commodity Futures Trading Commission (CFTC) and authors’ computations. 

Common to Crude Oil, Gold, and Silver Common to Crude Oil and Gold

Institutions 

(Electronic) Institutions (Pits)

Locals 

(Pits)

Institutions 

(Electronic)

Institutions 

(Pits)

Locals 

(Pits)

A. Crude Oil Futures

Mean 21.6% 10.4% 0.0% 23.8% 10.5% 0.2%

Median 21.8% 7.4% 0.0% 24.1% 7.5% 0.1%

Std Dev. 3.4% 8.8% 0.0% 3.8% 8.8% 0.1%

B. Gold Futures

Mean 20.2% 1.4% 0.0% 22.4% 1.5% 0.2%

Median 19.6% 0.1% 0.0% 22.0% 0.2% 0.0%

Std Dev. 5.9% 3.6% 0.3% 5.9% 3.6% 0.6%
50

Table 4: Effect of Electronification on Institutional Financial Trading – Short-Term vs. Long-Term

Table 4 presents a univariate analysis of institutional financial traders’ participation surrounding the introduction of 

WTI futures electronic trading by the NYMEX on September 5th, 2006. The sample period is January 3rd, 2006, to 

March 31st, 2007. Pre-Electronification refers to the period from January 3rd, 2006, to September 1st, 2006. PostElectronification refers to the period from September 5th, 2006 to March 31st, 2007. The analysis is conducted using pit 

data in the Pre-Electronification period and Globex data in the Post-Electronification period. FIN is the proportion of 

the futures trading volume during business hours that involves the participation of institutional financial traders (traders 

classified as CTI-2 traders in the CFTC database). All the variables are estimated for each contract maturity, with daily 

volume-weighted averages computed separately for (i) Short-Term (contracts with up to 62 days to expiration) and (ii) 

Long-Term (contracts with more than 62 days to expiration) futures. FIN_Short-Term is the daily, volume-weighted 

average of FIN in Short-Term contracts. FIN_Long-Term is the daily, volume-weighted average of FIN in Long –Term 

contracts. ∆FIN is the daily percentage difference in the two: ∆FIN = (FIN_Short-Term – FIN_Long-Term)/FIN_ShortTerm. Two-tailed p-values are reported in the last column. Source: U.S. Commodity Futures Trading Commission 

(CFTC) and authors’ computations. 

Pre-Electronification Post-Electronification Difference p-value

FIN_Short-Term 28.10% 41.42% 13.32% <.001

FIN_Long-Term 36.27% 37.48% 1.21% 0.312

∆FIN -30.42% 9.54% 39.96% <.001
51

Table 5: Effect of Electronification on Market Quality – Short-Term vs. Long-Term

Table 5 presents univariate analyses of key measures of market quality surrounding the introduction of WTI futures 

electronic trading by the NYMEX on September 5th, 2006. The sample period is January 3rd, 2006, to March 31st, 2007. 

Pre-Electronification refers to the period from January 3rd, 2006, to September 1st, 2006; Post-Electronification refers to 

the period from September 5th, 2006, to March 31st, 2007. The analysis is conducted using pit data in the PreElectronification period and Globex data in the Post-Electronification period. Spread is the daily average of 5-minute 

Bid-Ask spreads obtained using bid and asked prices estimated for each contract maturity in each interval (5 minutes), 

after classifying trades as buyer- vs. seller-initiated using the Lee and Ready (1991) tick-test. Amihud, an inverse 

measure of depth (Amihud, 2002), is the daily average of the ratio of absolute return to volume calculated in 5-minute 

non-overlapping intervals throughout the trading day. AbsOIB is the daily average of 5-minute customer (traders 

classified as CTI-4 traders in the CFTC database) trade imbalances calculated as the ratio of 5-minute absolute trade 

imbalances (buyer-initiated minus seller-initiated trades) to trading volume. PE_Proportion is the daily ratio of the 

pricing error variance, estimated as in Hasbrouck (1993), to the volatility of intraday (log) transaction prices. All the 

variables are estimated for each contract maturity with volume weighted averages computed separately for (i) ShortTerm (contract maturities with less than 62 days to expiration) and (ii) Long-Term (contract maturities with greater than 

or equal to 62 days to expiration) futures. For example, Spread_Short-Term is the daily, trading-volume-weighted 

average of Spread across Short-Term contracts. Spread_Long-Term is the daily, volume weighted average of Spread

across Long –Term contracts. ∆Spread is the daily percentage difference between the two: (Spread_Short-Term –

Spread_Long-Term)/ Spread_Short-Term. The other market quality variables are defined analogously. Two tailed pvalues are reported in the last column. Source: U.S. Commodity Futures Trading Commission (CFTC) and authors’ 

computations.

Pre-Electronification Post-Electronification Difference p-value

Spread_Short-Term 0.35% 0.03% -0.32% <.001

Spread_Long-Term 0.41% 0.10% -0.31% <.001

∆Spread -28% -304% -276% <.001

Amihud_Short-Term 1.04 1.07 0.03 0.604

Amihud_Long-Term 15.00 20.00 5.00 <.001

∆Amihud -14.87 -18.43 -3.56 <.001

AbsOIB_Short-Term 17.09% 12.36% -4.73% <.001

AbsOIB_Long-Term 40.85% 33.19% -7.66% <.001

∆AbsOIB -143.74% -175.26% -31.52% <.001

PE_Proportion_Short-Term 55.35% 4.12% -51.23% <.001

PE_Proportion_Long-Term 69.61% 8.90% -60.71% <.001

∆PE_Proportion -27.73% -132.27% -104.54% <.001
52

Table 6: Institutional Financial Traders and Market Quality – Two-stage Regression Analysis

Table 6 presents the results of a two-stage regression analysis of the impact of institutional financial trading on market quality. Results from the first stage are 

presented in Panel A; the second-stage results are presented in Panel B. The analyses are conducted using NYMEX pits data for the pre-electronification period 

(January 3rd, 2006, to September 1st, 2006) and Globex data for the post-electronification period (September 5th, 2006, to March 31st, 2007). FIN is the proportion 

of the total futures trading volume involving the participation of financial traders (traders classified as CTI-2 traders in the CFTC database). Electronification is 

a dummy variable that equals 1 in the post-electronification period and 0 otherwise. EIA_Inventory is a dummy variable that equals 1 on the day (usually 

Wednesday, otherwise Thursday) when the U.S. Department of Energy’s Energy Information Administration (EIA) releases its weekly report on crude oil stock 

levels and 0 otherwise. Lead_Inventory is a dummy variable that equals 1 on the day preceding the EIA announcement day and 0 otherwise. GSCI Roll is a 

dummy variable that equals 1 on the five business days when the monthly GSCI roll takes place and 0 otherwise. Contract_Exp_Day is a dummy variable that 

equals 1 on the day of the prompt contract’s expiration and 0 otherwise. September_2006 is a dummy variable that equals 1 in the calendar month when 

electronification took place and 0 otherwise. Day of the Week are three dummy variables set equal to 1 for Monday, Tuesday, or Friday and 0 otherwise. 

Volatility is the daily volume-weighted average of the 5-minute volatility of (mid-quote) returns estimated for each contract in each maturity interval. Volume is 

the daily volume-weighted average of 5-minute trading volume estimated for each contract in each maturity interval. Price_Volatility is the daily volumeweighted average of 5-minute volatility of intraday (log) transaction for each contract in each maturity interval. Amihud, an inverse measure of depth (Amihud, 

2002), is the daily average of the ratio of absolute return to volume calculated in 5-minute non-overlapping intervals throughout the trading day. AbsOIB is the 

daily average of 5-minute customer (traders classified as CTI 4 traders in the CFTC database) trade imbalances calculated as the ratio of five-minute absolute 

trade imbalances (buyer-initiated minus seller-initiated trades) to trading volume. AbsOIB_Short-Term is the daily, volume weighted average of AbsOIB across 

short-term contracts (up to 62 days to expiration). PE_Variance is the average daily pricing error variance, estimated as in Hasbrouck (1993). Spread is the daily 

volume-weighted average of 5-minute Bid-Ask spreads obtained using bid and asked prices estimated for each contract maturity in each interval (5 minutes), 

after classifying trades as buyer- vs. seller-initiated using the Lee and Ready (1991) tick-test. ∆Spread is the daily percentage difference between Spread_ShortTerm and Spread Long-Term. Spread_Short-Term is the daily volume-weighted average of Spread for short-term contracts (up to 62 days to expiration). 

Spread_Long-Term is the daily, volume weighted average of Spread for long-term contracts (more than 62 days to expiration). ∆FIN, ∆PE_Variance, ∆Amihud, 

∆AbsOIB, ∆Volatility, ∆Price_Volatility, and ∆Volume are defined analogously. ∆FIN is the dependent variable in the first stage and is instrumented by 

variables from the relevant Model in Panel A (i.e., Model 2 or 3), in the second stage. Two-tailed p-values, obtained using Newey-West standard errors with 5 

lags, are also reported.
53

Panel A: First Stage

Parameter Model 1 Model 2 Model 3

Intercept -0.30 <.001 -0.33 <.001 -0.33 <.001

Electronification 0.40 <.001 0.40 <.001 0.41 <.001

EIA_Inventory 0.07 0.212 0.07 0.206

Lead_Inventory 0.01 0.886 0.01 0.871

GSCI_Roll 0.07 0.088 0.07 0.085

Contract_Exp_Day -0.08 0.318 -0.08 0.325

September_2006 -0.07 0.336

Day of the Week YES YES

N 299 299 299

Adj RSq 30.65% 31.06% 31.04%

Panel B: Second Stage

Parameter ∆Spread ∆Amihud ∆AbsOIB ∆PE_Variance 

Model A Model B Model A Model B Model A Model B Model A Model B

Intercept -1.06 <.001 0.91 0.089 -9.01 <.001 9.15 0.010 -1.30 <.001 -1.16 <.001 -0.65 <.001 0.42 0.338

∆FIN -4.36 <.001 -3.89 <.001 -17.42 <.001 -11.76 <.001 -0.85 <.001 -0.81 <.001 -0.92 <.001 -0.64 <.001

∆Volume -2.42 <.001 -22.03 <.001 -0.17 0.682 -1.30 0.019

∆Volatility 0.83 <.001 0.80 <.001 5.95 <.001 5.63 <.001 0.07 0.263 0.06 0.299

∆Price Volatility 1.29 <.001 1.27 <.001

EIA_Inventory 0.28 0.132 0.28 0.129 0.32 0.689 0.27 0.732 -0.17 0.056 -0.17 0.056 0.01 0.908 0.01 0.896

Lead_Inventory 0.02 0.901 0.03 0.824 -0.71 0.397 -0.58 0.461 -0.13 0.138 -0.13 0.146 0.21 0.086 0.21 0.078

GSCI_Roll 0.53 <.001 0.49 <.001 -0.26 0.711 -0.78 0.247 0.18 0.004 0.18 0.005 0.24 0.001 0.22 0.001

Contract_Exp_Day -0.08 0.689 -0.11 0.513 -2.78 0.011 -2.94 0.014 -0.03 0.758 -0.21 0.088 -0.18 0.144 -0.19 0.129

September_2006 -1.10 0.041 -0.94 0.079 2.63 0.096 4.61 0.008 0.27 0.047 0.28 0.045 -0.53 0.034 -0.43 0.115

Dependent Lags 2 2 2 2 2 2 2 2

Day of the Week YES YES YES YES YES YES YES YES

N 299 299 299 299 299 299 299 299

Adj RSq 75.25% 75.81% 39.04% 44.45% 18.72% 18.48% 37.60% 38.49%
54

Table 7: Institutional Financial Traders and Customer Volume

Table 7 presents the results of the second-stage of a two-stage analysis of the effect of institutional financial trading on 

market quality while controlling for customer trading.The analyses are conducted using NYMEX pits data for the preelectronification period (January 3rd, 2006 to September 1st, 2006) and Globex data for the post-electronification period 

(September 5th, 2006 to March 31st, 2007). Customer_Volume is the daily, volume-weighted average of the proportion 

of 5-minute trading volume involving customers (traders classified as CTI-4 in the CFTC database) on at least one side 

of a trade. ∆Customer_Volume is the percentage difference between Customer_Volume_Short-Term and Customer 

Volume Long-Term. Customer_Volume_Short-Term is the daily, volume-weighted average of Customer_Volume for 

short-term contracts (up to 62 days to expiration). Customer_Volume_Long-Term is the daily, volume weighted average 

of Customer_Volume for long-term contracts (more than 62 days to expiration). ∆FIN, ∆Price_Volatility, ∆Spread, 

∆Amihud, ∆AbsOIB, ∆Volume, EIA_Inventory, Lead_Inventory, GSCI_Roll, Contract_Exp_Day, September_2006, 

and Day of the Week are defined in Table 6. ∆FIN is instrumented by the exogenous variables from Model 3 in Table 

6, Panel A. Two-tailed p-values are also reported.

Parameter ∆Spread ∆Amihud ∆AbsOIB ∆PE_Variance 

Intercept 0.89 0.088 7.97 0.024 -0.85 0.009 0.37 0.397

∆FIN -3.89 <.001 -11.34 <.001 -0.94 <.001 -0.61 0.003

∆Volume 0.80 <.001 -5.38 0.022 -0.52 0.183 -1.23 0.025

∆Customer Volume -2.40 <.001 5.67 <.001 1.54 <.001 -0.33 0.244

∆Volatility -0.11 0.823 -20.81 <.001 0.05 0.368

∆Price Volatility 1.26 <.001

EIA_Inventory 0.28 0.129 0.07 0.928 -0.11 0.163 0.00 0.971

Lead_Inventory 0.03 0.835 -0.66 0.406 -0.10 0.208 0.21 0.086

GSCI_Roll 0.49 0.001 -0.62 0.366 0.13 0.031 0.23 0.001

Contract_Exp_Day -0.11 0.543 -2.53 0.033 -0.33 0.005 -0.16 0.195

September_2006 -0.94 0.080 4.76 0.007 0.25 0.084 -0.42 0.115

Dependent Lags 2 2 2 2

Day of the Week YES YES YES YES

N 299 299 299 299

Adj RSq 76.80% 45.35% 30.01% 38.52%
55

Table 8: Institutional Financial Traders and Customer Volume Terciles

Table 8 presents the results of the second-stage of a two-stage analysis of the effect of institutional financial trading on 

market quality for different terciles of customer trading intensity. We use NYMEX pits data for the pre-electronification 

period (January 3rd, 2006 to September 1st, 2006) and Globex data for the post-electronification period (September 5th, 

2006 to March 31st, 2007). Customer_Volume is the daily, volume-weighted average of the proportion of 5-minute 

trading volume involving customers (traders classified as CTI-4 in the CFTC database) on at least one side of a trade.

∆FIN, ∆Price_Volatility, ∆Spread, ∆Amihud, ∆AbsOIB, ∆Volume, ∆Customer_Volume, EIA_Inventory, 

Lead_Inventory, GSCI_Roll, Contract_Exp_Day, and Day of the Week are defined in Table 7. Tercile Average is the 

average of ∆Customer_Volume in the corresponding tercile. ∆FIN is instrumented by the exogenous variables from 

Model 2 in Table 6, Panel A. The second-stage analyses are conducted as in Tables 6 and 7 but data are first sorted by 

tercile. Only the regression coefficients for ∆FIN are reported for brevity.Two-tailed p-values are also reported.

∆Customer Volume Tercile Tercile 

Average ∆Spread ∆Amihud ∆AbsOIB ∆PE_Variance 

∆Customer Volume - Low -12.97% -6.65 <.001 -14.15 0.001 -1.49 0.001 -0.61 0.006

∆Customer Volume - Moderate 0.64% -5.69 <.001 -11.81 0.001 -0.56 0.048 -0.91 0.008

∆Customer Volume - High 13.18% -7.22 <.001 -17.80 <.001 -0.95 0.058 -0.89 0.063
56

Table 9: Effect of Institutional Financial Traders on Spreads for Continuing Customers

Table 9 presents regression analyses of the effect of the percentage difference between ∆FIN (the daily trading volume shares of institutional financial traders in 

short-term vs. long-term contracts) on the percentage difference between the Bid-Ask spreads for short-term vs. long-term contracts paid by continuing 

customers—traders classified as “CTI-4” in the CFTC database who trade WTI futures in the pits before electronification and who continue to do so on the 

electronic (Globex) platform after electronification of the crude oil market. The analysis is conducted on WTI crude-oil futures trading in the NYMEX pits in the 

pre-electronification period (January 3rd, 2006, to September 1st, 2006) and on the Globex platform in the post-electronification period (September 5th, 2006, to 

March 31st, 2007). FIN is the proportion of the trading volume involving the participation of financial traders (traders classified as CTI 2 traders in the CFTC 

database). Volatility is the daily volume-weighted average of the 5-minute volatility of (mid-quote) returns estimated for each contract in each maturity interval. 

Volume is the daily volume-weighted average of 5-minute trading volume estimated for each contract in each maturity interval. Spread is the daily volumeweighted average of 5-minute Bid-Ask spreads obtained using bid and asked prices estimated for each contract maturity in each interval (5 minutes), after 

classifying trades as buyer- vs. seller-initiated using the Lee and Ready (1991) tick-test – it is calculated only for continuing customers. Spread_Short-Term is the 

daily volume-weighted average of Spread for short-term contracts (up to 62 days to expiration). Spread_Long-Term is the daily, volume weighted average of 

Spread for long-term contracts (more than 62 days to expiration). ∆Spread is the daily percentage difference between the two: (Spread_Short-Term – Spread 

Long-Term)/ Spread Short-Term, and is the dependent variable in the analysis. ∆FIN, ∆Volatility and ∆Volume are defined analogously. ∆FIN is instrumented 

by the exogenous variables from Model 2 & 3 in Table 6, Panel A. EIA_Inventory is a dummy variable equal to 1 during EIA announcement days. 

Lead_Inventory is a dummy variable set equal to 1 during the days prior to the EIA announcements. GSCI_Roll is a dummy variable equal to 1 on the five 

business days when a GSCI roll takes place. Contract_Exp_Day is a dummy variable equal to 1 on the day of the prompt contract’s expiration. September_2006

is a dummy variable equal to 1 in the calendar month when electronification took place and 0 otherwise. Day of the Week are three dummy variables set equal to 

1 for Monday, Tuesday, or Friday. Two-tailed p-values, obtained using Newey-West standard errors with 5 lags, are also reported.

Parameter Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Intercept -1.22 <.001 2.09 0.006 -1.21 <.001 1.93 0.006 1.85 0.011 2.00 0.007

∆FIN -5.09 <.001 -4.31 <.001 -5.02 <.001 -3.59 <.001 -4.31 <.001 -4.30 <.001

∆Volume -4.11 <.001 -3.57 <.001 -3.82 <.001 -4.01 <.001

∆Volatility 0.94 <.001 0.87 0.001 0.93 <.001 0.88 0.001 0.88 0.001 0.88 0.001

EIA_Inventory 0.24 0.192 0.24 0.192 0.24 0.194 0.19 0.282 0.22 0.236 0.22 0.235

Lead_Inventory 0.03 0.897 0.05 0.808 0.02 0.905 0.04 0.823 0.04 0.849 0.04 0.843

GSCI_Roll 0.41 0.005 0.35 0.013 0.42 0.005 0.19 0.276 0.38 0.009 0.37 0.008

Contract_Exp_Day -0.58 0.169 -0.62 0.112 -0.56 0.172 -0.41 0.278 -0.57 0.143 -0.58 0.142

September,2006 -0.51 0.222 0.53 0.339 -0.22 0.599

Dependent-variable Lags 2 2 2 2 2 2

Day of the Week YES YES YES YES YES YES

N 299 299 299 299 299 299

Adj RSq 59.43% 61.67% 59.73% 62.83% 61.66% 75.69%
57

Table 10: Effect of Institutional Financial Traders on Market Quality – Lag Identification

Table 10 presents the results of a two-stage analysis in which 1-day lagged values of the intensity of financialization are 

used as an instrument for the first-stage regression in Panel A; the second-stage regression results are presented in 

Panel B. The analyses are conducted using NYMEX pits data for the pre-electronification period (January 3rd, 2006 to 

September 1st, 2006) and Globex data for the post-electronification period (September 5th, 2006 to March 31st, 2007).

FIN is the proportion of trading volume involving the participation of financial traders (traders classified as “CTI-2”

traders in the CFTC database). ∆FIN is the percentage difference between the daily trading volume shares of 

institutional financial traders in short-term (less than 62 days to expiration) vs. long-term (62 or more days to expiration) 

WTI sweet crude oil futures contracts. ∆FIN is the dependent variable in Panel A. ∆FIN_Lag is the 1-day lag of 

∆FIN. ∆Price_Volatility, ∆Spread, ∆Amihud, ∆AbsOIB, ∆Volume, ∆Customer_Volume, EIA_Inventory, 

Lead_Inventory, GSCI_Roll, Contract_Exp_Day and Day of the Week are defined as in Table 6. ∆FIN is instrumented 

by variables from the first-stage regression in Panel A. Two-tailed p-values are also reported. 

Panel A: First Stage

Parameter Model 3

Intercept -0.11 0.012

∆FIN_Lag1 0.50 <.001

EIA_Inventory 0.10 0.083

Lead_Inventory 0.06 0.299

GSCI_Roll 0.02 0.590

Contract_Exp_Day -0.20 0.022

September_2006 0.11 0.131

Day of the Week YES

N 299

Adj RSq 25.26%

Panel B: Second Stage

Parameter ∆Spread ∆Amihud ∆AbsOIB ∆PE_Variance 

Intercept 2.83 <.001 14.91 <.001 -0.37 0.202 0.65 0.087

∆FIN -1.32 0.002 -5.39 <.001 -0.58 <.001 -0.54 <.001

∆Volume -4.26 <.001 -28.32 <.001 -1.08 0.003 -1.55 0.003

∆Customer Volume -0.38 0.479 -7.09 0.002 1.36 <.001 -0.48 0.088

∆Volatility 0.37 0.024 4.24 <.001 -0.06 0.254

∆Price Volatility 1.27 <.001

EIA_Inventory 0.19 0.317 -0.06 0.939 -0.12 0.132 0.00 0.985

Lead_Inventory 0.01 0.945 -0.67 0.399 -0.10 0.206 0.21 0.090

GSCI_Roll 0.29 0.035 -0.91 0.151 0.11 0.063 0.23 0.001

Contract_Exp_Day 0.06 0.799 -2.35 0.102 -0.32 0.012 -0.15 0.259

September_2006 -0.85 0.118 4.11 0.022 0.21 0.166 -0.45 0.086

Dependent-variable Lags 2 2 2 2

Day of the Week YES YES YES YES

N 299 299 299 299

Adj RSq 72.20% 42.29% 28.61% 38.63%
58

Table 11: Trader Characteristics—HFT vs. non-HFT Institutional Financial Traders

Table 11 describes key attributes of HFT and non-HFT institutional financial traders in the NYMEX’s WTI sweet crude-oil futures market in 2006–2007. The 

information is based on Globex data from the post-electronification period (September 5th, 2006, to March 31st, 2007). FIN_Non_HFT are institutional 

financial traders who trade less than 990 times a day. FIN_HFT are institutional financial traders who more than 990 times a day and go home flat. Abs. 

Closing Ratio refers to the average ratio of a trader’s ending-of-hour inventory to that trader’s hourly trading volume during business hours. Similarly, Trading 

Volume and Number of Trades are also hourly averages of a trader’s activity. Volume share is the proportion of all trades involving institutional financial 

traders on at least one side of the trade. The cross-sectional means and medians are presented. Source: U.S. Commodity Futures Trading Commission (CFTC) 

and authors’ computations.

Traders Trading Volume Number of Trades Abs Closing Ratio Volume Share

Mean Median Mean Median Mean Median Mean Median

FIN_FLP 1304 772 623 322 8% 1% 29% 30%

FIN_Non_FLP 168 42 49 16 42% 15% 26% 26%
59

Table 12: Effect of Non-HFT and HFT Institutional Financial Traders on Market Quality

Table 12 presents regression analyses of the effect of the percentage difference in the trading volume shares of Non-HFT and HFT institutional financial 

traders in short- vs. long-term contracts, on the percentage differences between various market quality measures for short- vs. long-term contracts. The 

analyses are conducted on WTI sweet crude oil futures trading in the NYMEX pit for the pre-electronification period (January 3rd, 2006, to September 1st, 

2006) and on the electronic (Globex) platform for the post-electronification period (September 5th, 2006, to March 31st, 2007). FIN is the proportion of 

trading volume involving the participation of financial traders (traders classified as CTI-2 traders in the CFTC database). FIN_Non_HFT is the daily 

weighted average trading-volume share of Non-HFT financial traders, i.e., of institutional financial traders who trade less than 990 times a day. 

FIN_HFT is the daily weighted average trading-volume share of institutional financial traders who more than 990 times a day and go home flat. 

∆FIN_Non-HFT is instrumented by variables from the first-stage regressions (analogous to Model 3 in Panel B of Table 6). ∆FIN, ∆Price_Volatility, 

∆Spread, ∆Amihud, ∆AbsOIB, ∆Volume, ∆Customer_Volume, EIA_Inventory, Lead_Inventory, GSCI_Roll, Contract_Exp_Day, September_2006, 

and Day of the Week are defined as in Table 6. Two-tailed p-values, obtained using Newey-West standard errors with 5 lags, are also reported.

Parameter ∆Spread ∆Amihud ∆AbsOIB ∆PE_Variance 

Intercept 0.06 0.923 4.52 0.279 -1.17 <.001 0.40 0.425

∆FIN_Non_HFT -6.24 <.001 -28.21 <.001 -1.75 <.001 0.10 0.842

∆FIN_HFT -1.24 <.001 1.73 0.085 -0.16 0.107 -0.69 0.002

∆Volume -2.72 <.001 -24.68 <.001 -0.57 0.132 -1.01 0.048

∆Customer Volume -0.45 0.333 -5.84 0.014 1.50 <.001 -0.43 0.104

∆Volatility 0.65 <.001 5.35 <.001 0.03 0.595

∆Price Volatility 1.25 <.001

EIA_Inventory 0.53 0.010 1.68 0.056 -0.03 0.746 -0.04 0.750

Lead_Inventory 0.07 0.655 -0.56 0.482 -0.09 0.261 0.22 0.070

GSCI_Roll 0.98 <.0001 2.00 0.042 0.27 <.001 0.18 0.041

Contract_Exp_Day -0.43 0.054 -4.51 0.001 -0.42 <.001 -0.08 0.555

September_2006 -0.55 0.321 3.92 0.030 0.34 0.034 -0.14 0.666

Dependent Lags 2 2 2 2

Day of the Week YES YES YES YES

N 299 299 299 300

Adj RSq 76.66% 45.10% 30.21% 42.27%
60

Table 13: Effect of Non-HFT and HFT Institutional Financial Traders on Market Quality– Lag Identification

Table 13 presents regression analyses of the effect of the percentage difference in the trading volume shares of Non-HFT and HFT institutional financial 

traders in short- vs. long-term contracts on the percentage differences between various market quality measures for short- vs. long-term contracts. The 

analyses are conducted on WTI sweet crude oil futures trading in the NYMEX pit for the pre-electronification period (January 3rd, 2006, to September 1st, 

2006) and on the electronic (Globex) platform for the post-electronification period (September 5th, 2006, to March 31st, 2007). FIN is the proportion of 

trading volume involving the participation of financial traders (traders classified as CTI-2 traders in the CFTC database). FIN_Non_HFT is the daily 

weighted average trading-volume share of Non-HFT financial traders, i.e., institutional financial traders who trade less than 990 times a day. FIN_HFT

is the daily weighted average trading-volume share of institutional financial traders who more than 990 times a day and go home flat. ∆FIN_Non-HFT

and ∆FIN_HFT are instrumented by variables from the first-stage regressions (analogous to Model 1 in Panel A of Table 10). ∆FIN, ∆Price_Volatility, 

∆Spread, ∆Amihud, ∆AbsOIB, ∆Volume, ∆Customer_Volume, EIA_Inventory, Lead_Inventory, GSCI_Roll, Contract_Exp_Day, September_2006, 

and Day of the Week are defined as in Table 6. Two-tailed p-values, obtained using Newey-West standard errors with 5 lags, are also reported.

Parameter ∆Spread ∆Amihud ∆AbsOIB ∆PE_Variance 

Intercept 2.62 <.0001 15.50 <.0001 -0.53 0.084 0.53 0.167

∆FIN_Non_HFT -0.95 0.081 -6.33 0.012 -0.42 0.094 0.26 0.416

∆FIN_HFT -1.35 0.000 -0.52 0.601 -0.33 0.001 -0.70 0.002

∆Volume -4.02 <.0001 -29.76 <.0001 -0.87 0.019 -1.11 0.017

∆Customer Volume -0.49 0.342 -6.97 0.003 1.43 <.0001 -0.38 0.135

∆Volatility 0.34 0.037 4.02 <.0001 -0.05 0.315

∆Price Volatility 1.23 <.0001

EIA_Inventory 0.17 0.348 0.14 0.871 -0.12 0.113 -0.05 0.657

Lead_Inventory 0.04 0.810 -0.67 0.404 -0.10 0.222 0.22 0.068

GSCI_Roll 0.33 0.027 -0.54 0.456 0.11 0.095 0.16 0.026

Contract_Exp_Day 0.05 0.828 -2.59 0.079 -0.30 0.017 -0.07 0.621

September,2006 -0.47 0.355 4.59 0.011 0.38 0.013 -0.20 0.497

Dependent Lags 2 2 2 2

Day of the Week YES YES YES YES

N 299 299 299 299

Adj RSq 73.56% 41.04% 29.10% 41.79%