Informed trading, institutional trading, and spread

Malay K. Dey & B. Radhakrishna

# Springer Science+Business Media New York 2013

Abstract We use transactions data from TORQ and present empirical evidenceon the cross sectional relation between institutional trading and effective spreadafter controlling for trading volume denoting inventory and order processingcosts and probability of informed trading (PIN) denoting risk of informedtrading. We find that volume, information risk premium denoted by PIN timesprice, and institutional trading are significant determinants of bid ask spreadsfor a sample of 65 NYSE listed securities. We also find that institutionaltrading increases the adverse selection component but does not have a signif-icant effect on order processing costs. The net effect of institutional trading onspread depends on the dominant effect, information increasing adverse selectioncosts or liquidity decreasing order processing costs. In our sample the increasein adverse selection costs trumps the decrease in order processing costs and asa consequence spread increases as institutional trading rises.

Keywords PIN . Institutional trading . Spread decomposition . Liquidity

JEL Classification G19

J Econ FinanDOI 10.1007/s12197-012-9249-4

A previous and different version of this paper was circulated as “Institutional Trading and Spread”. We aregrateful for the many helpful comments we received from an anonymous referee.

M. K. Dey (*)Cornell University, Ithaca, NY, USAemail: mdey77@yahoo.com

M. K. DeyFINQ, Fairfield, CT, USA

B. RadhakrishnaIndependent, Darien, CT, USAemail: r.radhakrishna@gmail.com

1 Introduction

McInish and Wood (1992) classify the significant cross sectional determinants of bidask spread in the equity market into activity, risk, information, and competition in thedealers market. Empirical research using spread decomposition techniques pioneeredby Glosten and Harris (1988) and Stoll (1989) and enabled by intraday order andquotes data finds order processing cost to be the dominant component of bid askspread followed by the adverse selection cost component, a measure of informationrisk premium included in the spread.1 Bollen et al. (2004) skillfully connect the twostrands of literature, determinants and components of spread.

More recently, the finance literature has focused on how ownership structure,a presumed conduit for private information affects bid ask spread and itsadverse selection component. Empirical findings indicate that insider, block,and institutional holding considered proxies of private information have mixedeffects on adverse selection costs and spreads.2 Further, based on the intuitionthat trades reveal information, (Easley et al. 1996, 1998) compute probability ofinformed trading (PIN), a measure of information content in trades from excessbuy or sell orders and find it to be a significant determinant of spread aftercontrolling for trading volume and financial analyst coverage as denoting orderprocessing cost and information respectively. Chung and Li (2003) and Lei andWu (2005) confirm PIN as a significant determinant of adverse selection costand spread.

In this paper, using TORQ data from the NYSE, a unique transactiondatabase that identifies institutional trades, we extend and refine Easley et al.(1998) to empirically investigate how institutional trading may affect spreadsand further the components of spreads after controlling for among others PINand volume as a proxies for multiple motives including liquidity and informa-tion. This investigation is important because institutional trading in equity inthe US has grown significantly during the last two decades; yet, its impact onaggregate market indicators is uncertain due to the lack of clarity as to whetherand to what extent institutional trades are motivated by information and/orliquidity needs. Bozcuk and Lasfer (2005) and Kaniel et al. (2008) contendinformation motive for institutions, while Seppi (1990) and Dey and Kazemi(2008) argue that institutional trades reflect information and liquidity motives. Empir-ically, Sias et al. (2006) find that institutional trades reflect information, price pressure,and/or positive feedback effects in the market; Griffin et al. (2003) confirm positivefeedback trading by institutions; and Dey and Radhakrishna (2007) document multipletrading motives for institutions. Therefore, institutional trades may increase the risk ofinformation trading but may also provide liquidity, with the two counteracting effectshaving an uncertain impact on spreads.

1 George et al. (1991), Affleck-Graves et al. (1994), Lin et al. (1995) empirically estimate order processingand adverse selection costs components of spread while Huang and Stoll (1997) expand Stoll (1989) andestimate order processing, inventory, and adverse selection costs components in spread.2 Kini and Mian (1995), Heflin and Shaw (2000), Chung and Charoenwong (1998), Fehle (2004), Jiang andKim (2005), Rubin (2007), Schnatterly et al. (2008), Brockman et al. (2009), Jiang et al. (2011) provideempirical evidence on the effect of block, insider, and institutional ownership on spread.

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The introduction of PIN as a proxy for information content in trades furthercomplicates the discovery of information content in institutional trading. Easley etal. (1998) find PIN denoting the information content in trades subsumes the infor-mation content in analyst coverage, while Dey et al. (2011) find institutional tradingis a determinant of PIN only for large but not small trades. Hence, besides inconclu-sive/lack of evidence on the information content in institutional trades, it is alsounclear how institutional trading may affect spread and its adverse selection compo-nent after controlling for PIN denoting information content in trades. We empiricallyinvestigate these issues as below.

First, we compute PIN from the parameters of a maximum likelihood modelof securities trading with competitive market makers as in Easley et al. (1996).Second, we estimate the parameters of a linear model for spread in which theindependent variables are institutional trading, PIN, trading volume, and buysell ratio (net order flow), which are accepted proxies for order processing cost,liquidity, information, and momentum. Hence we not only determine the infor-mation content in institutional trades, if any, but also disentangle the informa-tion contents in institutional trading and PIN. Finally, we decompose spreadinto adverse selection and order processing components and test a linearrelation between these spread components and changes in trading volume, net(buy over sell) order flow, PIN, and institutional trading.

We find that after controlling for PIN and volume among others, institutionaltrading explains a significant portion of the variation in effective spreads acrossstocks. The significantly positive coefficients for both PIN and institutional tradingconfirm information contents in both PIN and institutional trading. However simplecorrelation test finds zero (not significant) correlation between institutional tradingand PIN, which raises question as to the nature of information that PIN and institu-tional trading reveal.

We find spread increases as institutional trading increases across both highand low levels of institutional trading and that adverse selection costs increasewhile order processing costs decrease, albeit not statistically significant withincreased institutional trading. Taken together, these two findings indicate thatthe net effect of institutional trading as a cross sectional determinant on spreadis positive but may net to zero where the positive and negative effects ofadverse selection and order processing costs respectively cancel each other.Based on the findings in Dey and Radhakrishna (2007), we conjecture thatthe increase in spread is due to the increase in volatility associated withincreased institutional trading.

The rest of this paper is organized as follows. Section II provides a brief andprecise literature review followed by two testable hypotheses. Section IIIintroduces the data and further details the computations of bid ask spread,institutional trades, and PIN. It also contains sample statistics and a briefdiscussion of those statistics. In section IV, we present a least squares regres-sions model for quoted spreads and discuss the results relating institutionaltrading and PIN to spreads. In section V, we describe spread decompositionsand report results from our analysis of the PIN and institutional trading asdeterminants of adverse selection and order processing cost components ofspread. Section VI concludes the paper.

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2 Literature review and hypotheses development

This research touches on multiple well studied areas of research on market micro-structure, specifically, determinants and components of spread, measuring informa-tion content in trades, and the impact that trades and institutional trading may have onspread and its components, particularly the adverse selection component of spread.Partial reviews of many of these areas already exist in the published literature andhence we provide only a very brief summary of only those parts of the empiricalliterature which have a direct bearing on our empirical model and results.3

Bollen et al. (2004) provide an excellent survey of the empirical research on thedeterminants of bid ask spread and classify the observed significant factors underthree components of spread, order processing, inventory, and adverse selection costs.Spread decomposition studies for example, Glosten and Harris (1988), Stoll (1989),George et al. (1991), Affleck-Graves et al. (1994), Lin et al. (1995), and Huang andStoll (1997) based on transactions data from principal trading venues in the US findthat order processing cost is the single largest component of spread followed byadverse selection cost. Further, those studies indicate that surrogates of information,for example, trade size and market structure (order vs. quote) affect spread andits components. Kini and Mian (1995), Heflin and Shaw (2000), Chung andCharoenwong (1998), Fehle (2004), Jiang and Kim (2005), Rubin (2007),Schnatterly et al. (2008), Brockman et al. (2009), Jiang et al. (2011) findmixed evidence on the effect of block and insider holding, and changes ininstitutional holding on spread and its adverse selection component.

Sias et al. (2006), Griffin et al. (2003), and Kaniel et al. (2008) find evidence ofinformed trading by institutions while Easley et al. (1996) find PIN, the informationcontent in trades determined from excess buys and sales to be a significant determi-nant of spread. Contrary to the findings by Jackson (2007), Chung and Li (2003) andLei and Wu (2005) find results confirming PIN as a significant determinant ofadverse selection cost and spread. Easley et al. (1998) find PIN denoting theinformation content in trades subsumes the information content in analyst and henceanalysts’ coverage is not a significant determinant of spread but Dey et al. (2011)report correlation only between institutional trading and PINs for large trades but notsmall trades. Based on this literature review, we propose the following testablehypotheses:

i) Institutional trading impacts bid ask spread;ii) PIN and institutional trading are directly related to the adverse selection compo-nent of spread.

The first hypothesis embodies the notion that institutional trading contains infor-mation related to future values of securities. Institutions have many resources at theirdisposal to gather, analyze, and process information to secure competitive advantagein securities trading. Market makers are aware of the likely information content in

3 For an empirical literature review of one or more areas of market microstructure research, please seeMadhavan (2000), O’Hara (2003), Francioni et al. (2008), Biais et al. (2005), and Cougheneur and Shastri(1999). On cross sectional determinants of bid-ask spreads in equity markets, please refer to McInish andWood (1992), Laux (1993, 1995), Harris (1994), Kini and Mian (1995), Kim and Ogden (1996), and Klockand McCormick (1999) among others.

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institutional trades, observe those trades, make inferences about such information,and set bid and ask prices accordingly. Hence spread reveals information about futuresecurities values implicit in institutional trading.

The second hypothesis breaks down the impact of institutional trading on spreadinto size impact and future value related information content. Since institutions tendto trade in large sizes, the impact of institutional trading on spread may be due to boththe size and the information content. While order processing cost is largely a functionof size, adverse selection cost is due to information. Hence by decomposing spreadinto adverse selection and order processing costs, we are able to hypothesize a directrelation between institutional trading and the adverse selection costs, the informationrelated component of spread.

3 Data description, computation, and sample statistics

3.1 Sample selection and measurement of spread and institutional trading

We use the NYSE TORQ data for our empirical analysis. TORQ is the result ofmerging TAQ (trades and quotes) and audit trail data for a size stratifiedrandom sample of 144 NYSE stocks for three months (63 trading days) fromNovember 1990 through January 1991. TORQ allows identifying the partici-pants on both sides of any trade. Koski and Scruggs (1998) note that TORQ isan improvement over other publicly available quotes and trades databases likeTAQ because it provides identification for traders’ classes, as agencies, propri-etary, individuals, and dealers.4

For our empirical analysis, we compute spread from the data available in TORQ asfollows. First, in order to ensure adequate data for determining effective spreads andtheir components, we limit our analysis to 65 securities which have on average 20trades per day or more during the sample period. Whenever there are executions ofmultiple orders on the active side of a trade, we assign the trader class of the largestorder on the active side as the trade initiator. The active side of the trade is determinedusing the Lee and Ready (1991) algorithm.5 Second, for each security, we calculateeffective bid-ask spread for each trade as twice the absolute value of the differencebetween trade price and the prevailing mid-quote i.e., [Abs (price-mid quote)*2].

4 Until recently due to lack of easily accessible institutional trading data, in empirical research, large tradesand institutional holding or periodic, most often quarterly changes in institutional holding have beenfrequently used to denote institutional trades. Large trades as proxies for institutional trades ignore thefact that institutions often break up trades to minimize price impact adding noise and potential bias tomeasured relations between trading cost and information. Institutional holding or changes thereof do notaccount for inter temporal variation in institutional trading. TORQ does not identify institutional tradesdirectly; however, we follow Dey and Radhakrishna (2007) and use “Agency-A” coded trades as institu-tional trades.5 Finucane (2000), Odders-White (2000), Ellis et al. (2000) revise and extend the trade classificationalgorithm proposed in Lee and Ready (1991). Lee and Radhakrishna (2000) report that the Lee-Readyalgorithm correctly identifies a buy or sale in the TORQ database 91 % of the times. Odders-White (2000)and Boehmer et al. (2007) demonstrate how trade misclassification could bias PIN estimates. Lin and Ke(2011) rectify a computing bias in PIN. Jackson (2007) computes PIN from total number of trades ratherthan numbers of buys and sales.

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Finally, we compute mean effective spread as the time series average of all effectivespreads across all intraday trades.

Next, we compute institutional trading as the fraction of institutional trades over alltrades in a day from intraday transactions data for each of the 65 sample securities.TORQ identifies general participants in each trade, including a trader’s classificationbased on member firm disclosure. For example, trades are identified as proprietary‘P’ (if a trade stems from a member firm’s personal account), individual ‘I’ (for anindividual investor), and agency ‘A’ (where the member broker acts as an agent forother traders, mostly institutions).6 We use the classification used by Dey andRadhakrishna (2007) and identify “A-agency” trades as institutional trades. For eachfirm, we measure institutional trading by the proportion of trades by institutions in astock, for each day. We also compute institutional buy and sell proportions for eachfirm day as the number of institutional buy (sell) over total number of buy (sell).

Table 1 reports time series means and standard deviations of spread and institu-tional trading for our sample of 65 securities. As expected, the computed statisticsindicate a wide diversity in the trading characteristics of the sample securities. Forexample, the mean (standard deviation) spread of the sample securities ranges from0.001 (0.0001) to 0.034 (0.007), while similar statistics for institutional trading rangesfrom 0.09 (0.03) to 0.48 (0.21). Compared to spread, institutional trading seems tohave a lower variance, symmetric empirical distribution. Institutions generally tradeon a regular basis. Of the 4,075 (51 securities for 63 trading days plus 14 securitieswith 62 or 61 trading days) stock trading days covered in our sample there were only10 security-days when there was no institutional trading, and one security-day whenall the trading was initiated by institutions.

3.2 Computing PIN

As in Easley et al. (1996), we compute PIN denoting the information content intrades using count data on buys and sales from the estimated parameters givenby the vector ¼ "; d; a; θð Þ of the following likelihood function.

L ΘjB; Sð Þ ¼ 1� "ð Þ � e�aT aTð ÞBB! e�aT aTð ÞS

S!

þed � e�aT aTð ÞBB! e� aþθð ÞT aþθð ÞT½ �S

S! þ e 1� dð Þ � e�aT aTð ÞSS! e� aþθð ÞT aþθð ÞT½ �B

B!

ð1Þ

The four parameters denote the following: the probability that there is an informationevent (ε), the probability that the information is bad news (δ), and the mean arrival ratesof uninformed liquidity (α) and informed (θ) traders in the market respectively. We usethe data matrix [BS|t] denoting numbers of buys (B) and sales (S) during any interval t toempirically estimate the parameter vector by maximizing the likelihood function in Eq.(1). We estimate the parameters using a gradient search algorithm for nonlinear optimi-zation using S-Plus and find the estimates to be robust with respect to different initialvalues. We compute the standard errors of the estimated parameters from the asymptoticdistribution of the transformed parameters using the delta method.

6 Many recently published papers, for example, Lee and Radhakrishna (2000), Koski and Scruggs (1998),Chakrovarty (2001), Wald and Horrigan (2005), Dey and Radhakrishna (2007), and Dey et al. (2011) haveused TORQ data primarily due to its unique feature of identifying trade originators.

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Table 1 Time series averages and the corresponding standard deviations of spread, proportion of institu-tional trades, and PIN of 65 NYSE listed securities for the sample period, November 1990 to January 1991

Name Ticker Days Spread Inst. prop. PIN

ACUSON CORPORATION CAN 63 Mean 0.0063 0.47 0.21024

Std. 0.0013 0.10 0.03089

ALCAN ALUMINIUM LTD. AL 63 Mean 0.0061 0.32 0.17708

Std. 0.0018 0.10 0.02802

ADVANCED MICRO DEVICES AMD 63 Mean 0.0225 0.21 0.21849

Std. 0.0050 0.07 0.03698

ALLSTATE MUNI INC. TRUST AMO 63 Mean 0.0138 0.36 0.21718

Std. 0.0008 0.12 0.03161

ASARCO INCORPORATED AR 62 Mean 0.0054 0.27 0.15781

Std. 0.0011 0.12 0.03809

ATLANTIC ENERGY INC. ATE 63 Mean 0.0037 0.32 0.19620

Std. 0.0011 0.14 0.03336

BOEING COMPANY BA 61 Mean 0.0024 0.43 0.19978

Std. 0.0012 0.06 0.02546

BROWN GROUP INC. BG 63 Mean 0.0071 0.32 0.27601

Std. 0.0018 0.14 0.03430

CALFED INC. CAL 63 Mean 0.0340 0.22 0.26587

Std. 0.0058 0.08 0.03374

COLGATE-PALMOLIVE CO. CL 63 Mean 0.0022 0.43 0.14827

Std. 0.0004 0.09 0.01913

CLAIRE S STORES INC. CLE 62 Mean 0.0117 0.45 0.20215

Std. 0.0030 0.12 0.03616

CLAYTON HOMES INC. CMH 62 Mean 0.0095 0.42 0.24370

Std. 0.0019 0.15 0.03151

COMMUNITY PSYC. CENTERS CMY 63 Mean 0.0035 0.38 0.18290

Std. 0.0008 0.09 0.03345

CANADIAN PACIFIC LIMITED CP 63 Mean 0.0065 0.40 0.28440

Std. 0.0017 0.12 0.02509

CPC INTERNATIONAL INC. CPC 63 Mean 0.0019 0.46 0.13322

Std. 0.0002 0.09 0.03912

QUANTUM CHEMICAL CORP. CUE 62 Mean 0.0086 0.25 0.21511

Std. 0.0017 0.08 0.02526

CYPRUS MINERALS CO. CYM 63 Mean 0.0059 0.30 0.20932

Std. 0.0013 0.11 0.02916

CRAY RESEARCH INC. CYR 63 Mean 0.0045 0.29 0.20124

Std. 0.0011 0.10 0.02512

DANA CORPORATION DCN 63 Mean 0.0049 0.30 0.30833

Std. 0.0013 0.13 0.02606

DRESSER INDUSTRIES INC. DI 63 Mean 0.0058 0.32 0.19230

Std. 0.0009 0.07 0.02692

EMC CORPORATION EMC 63 Mean 0.0148 0.27 0.24507

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Table 1 (continued)

Name Ticker Days Spread Inst. prop. PIN

Std. 0.0021 0.08 0.03490

FEDERAL PAPER BOARD INC. FBO 63 Mean 0.0057 0.37 0.19118

Std. 0.0013 0.13 0.02798

FEDERAL EXPRESS CORP. FDX 62 Mean 0.0036 0.38 0.19609

Std. 0.0010 0.10 0.03136

FIRST FIDELITY BANCORP. FFB 61 Mean 0.0055 0.30 0.19609

Std. 0.0015 0.11 0.03136

FED. NATL. MORT. ASSON. FNM 60 Mean 0.0030 0.48 0.14825

Std. 0.0003 0.05 0.02147

FERRO CORPORATION FOE 62 Mean 0.0066 0.37 0.21607

Std. 0.0018 0.12 0.03435

FLORIDA PROGRESS CORP. FPC 63 Mean 0.0033 0.28 0.11186

Std. 0.0005 0.09 0.03219

FPL GROUP INC. FPL 63 Mean 0.0039 0.20 0.32938

Std. 0.0005 0.07 0.01692

GENERAL ELECTRIC CO. GE 62 Mean 0.0022 0.37 0.16654

Std. 0.0002 0.04 0.01666

GLAXO HOLDINGS PLC. ADR GLX 63 Mean 0.0043 0.30 0.21292

Std. 0.0005 0.05 (0.02509)

HANSON PLC ADR HAN 63 Mean 0.0065 0.40 0.12013

Std. 0.0005 0.07 0.02017

HAWAIIAN ELECTRIC IND. INC HE 63 Mean 0.0057 0.30 0.25789

Std. 0.0011 0.11 0.04099

HYPERION TOTAL RET. FUND HTR 63 Mean 0.0123 0.09 0.16218

Std. 0.0006 0.06 0.02688

INTL. BUS. MACHINES CORP. IBM 61 Mean 0.0010 0.44 0.14251

Std. 0.0001 0.05 0.02248

IP TIMBERLANDS LTD. CL A IPT 63 Mean 0.0078 0.18 0.18550

Std. 0.0022 0.11 0.04424

KROGER COMPANY KR 63 Mean 0.0084 0.18 0.17450

Std. 0.0013 0.08 0.03356

LOUISIANA-PACIFIC CORP. LPX 62 Mean 0.0050 0.33 0.21070

Std. 0.0012 0.10 0.02893

MCN CORP. HLDG CO. MCN 63 Mean 0.0081 0.29 0.22123

Std. 0.0020 0.12 0.03198

MEREDITH CORPORATION MDP 63 Mean 0.0066 0.19 0.22258

Std. 0.0020 0.13 0.03894

PHILIP MORRIS COMP. INC. MO 63 Mean 0.0022 0.40 0.11703

Std. 0.0001 0.04 0.01682

NATIONAL SERVICE IND. INC. NSI 63 Mean 0.0074 0.28 0.28091

Std. 0.0013 0.12 0.03064

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Table 1 (continued)

Name Ticker Days Spread Inst. prop. PIN

NORTHERN STATES POWER NSP 63 Mean 0.0082 0.21 0.29065

Std. 0.0041 0.07 0.02482

NORTHERN TELECOM LTD. NT 63 Mean 0.0040 0.25 0.22477

Std. 0.0007 0.11 0.01724

PITTSTON COMPANY PCO 63 Mean 0.0063 0.33 0.21532

Std. 0.0013 0.11 0.03851

PARKER-HANNIFIN CORP. PH 63 Mean 0.0060 0.34 0.16174

Std. 0.0013 0.12 0.03381

PUTNAM MASTER INT. INC. TR PIM 63 Mean 0.0183 0.14 0.30253

Std. 0.0009 0.09 0.03068

PREMARK INTL INC. PMI 63 Mean 0.0083 0.34 0.24592

Std. 0.0020 0.13 0.03068

POTOMAC ELEC. POWER CO. POM 63 Mean 0.0056 0.23 0.32691

Std. 0.0005 0.05 0.01458

PENN. POWER LIGHT CO. PPL 63 Mean 0.0030 0.38 0.20035

Std. 0.0007 0.10 0.02098

PROMUS COMPANIES INC. PRI 63 Mean 0.0088 0.29 0.23008

Std. 0.0021 0.14 0.03340

READERS DIGEST ASSO. CL A RDA 63 Mean 0.0044 0.42 0.22422

Std. 0.0009 0.12 0.02989

SCHLUMBERGER LTD SLB 62 Mean 0.0023 0.46 0.18712

Std. 0.0004 0.07 0.02004

SONAT INCORPORATED SNT 63 Mean 0.0034 0.33 0.20824

Std. 0.0009 0.09 0.02273

STANDARD PACIFIC LP SPF 63 Mean 0.0212 0.20 0.18979

Std. 0.0035 0.08 0.03269

SAFEWAY INC. SWY 63 Mean 0.0093 0.21 0.21053

Std. 0.0030 0.13 0.03412

AM. TEL. AND TELEGRAPH CO. T 61 Mean 0.0032 0.25 0.14385

Std. 0.0005 0.03 0.02541

TEKTRONIX INC. TEK 63 Mean 0.0078 0.23 0.23321

Std. 0.0017 0.09 0.02796

UNION ELECTRIC COMPANY UEP 63 Mean 0.0039 0.39 0.22775

Std. 0.0008 0.09 0.02304

USLIFE CORPORATION USH 63 Mean 0.0066 0.31 0.19597

Std. 0.0021 0.14 0.04202

VARCO INTL. INCORPORATED VRC 63 Mean 0.0135 0.33 0.19386

Std. 0.0068 0.21 0.05734

WESTVACO CORPORATION W 63 Mean 0.0050 0.30 0.19672

Std. 0.0009 0.10 0.02975

WABAN INC. WBN 63 Mean 0.0117 0.30 0.19566

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Next, as in Easley et al. (1996), we compute the conditional probability that theopening trade for any given interval contains information, good or bad, or equiva-lently the opening trade is informed as

PINð0Þ ¼bθbe

2ba þ bθbewhere b"; ba;bθ are the estimated parameters of the likelihood model.

Table 1 reports the PIN estimates and the corresponding standard errors for eachsecurity. For two securities (Ticker symbols AL and AMD), which are commonbetween our sample and the sample in Easley et al. (1996), the estimated PINs arequite comparable. Easley et al. (1996) report PINs for AL and AMD to be 0.18, and0.23 where as we find PINs for those securities to be 0.177 and 0.218 respectively.

Table 2 reports sample statistics for the estimated parameters, b";bd; ba; and bθ of thelikelihood function and PIN computed from those estimates respectively. The meansof the probabilities of an information event and bad news are 0.40 and 0.29 respec-tively; Easley et al. (1998) report those estimates as 0.39 and 0.38 respectively. Themeans and standard deviations of the computed probability of information trading are.209 and .049 respectively. These PIN estimates are comparable to similar estimatesreported in (Easley et al. 1996, 1998) but different from those reported by Jackson(2007).7

4 Regression model and results on PIN, institutional trading, and spread

Easley et al. (1998) test whether probability of information trading and analysts as jointproxies for information trading are significant determinants of spread after controllingfor trading volume as a measure of inventory and order processing cost. We replace

Table 1 (continued)

Name Ticker Days Spread Inst. prop. PIN

Std. 0.0067 0.10 (0.03406)

WALLACE COMP. SERV. INC. WCS 63 Mean 0.0081 0.35 0.15366

Std. 0.0014 0.10 (0.02770)

WINN-DIXIE STORES INC. WIN 63 Mean 0.0032 0.18 0.29624

Std. 0.0012 0.11 (0.03113)

EXXON CORPORATION XON 63 Mean 0.0019 0.35 0.18077

Std. 0.0002 0.05 (0.00959)

7 The reported biases in Odders-White (2000) and Boehmer et al. (2007) are sample specific and not theoretical.Since we (other authors as well) use anML estimator (function) of the parameters of the corresponding mixturemodel, theoretically, given a random large sample, a maximum likelihood function yields unbiased parameterestimates. With reference to specific sources of bias outlined by Odders-White (2000), TORQ is a size stratifiedsample and hence either large capitalization or frequently traded stocks are not disproportionately heavilyweighted in the sample; further, the 20+ average trades per day filter used in our sample selection reduces theincidence too many small trades and frequent quote revisions. Dey et al. (2011) note that average and maximumnumbers of trades for the 65 sample securities in TORQ are 98 and 614.

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number of analysts in Easley et al. (1998) with proportion of institutional trades andestimate the OLS parameters for the following single equation regression model:

Spread ¼ a0 þ b1volumeþ b2 price � PINð Þ þ b3instrading þ es ð2Þwhere,

Spread Mean of daily effective spreadPrice Mean of daily closing priceVolume Log of mean daily trading volumePIN probability of information trading computed from the estimates of the

information based pricing model as in Easley et al. (1996) andInstrading Mean of proportion of daily institutional trading.

Institutions are generally considered to be conduits for private information andthey are presumed to reveal such information through trading (Bozcuk and Lasfer2005; Kaniel et al. 2008). Hence there may be a direct positive relation betweeninstitutional trading and spread. However, Fehle (2004) documents a significantlyinverse relation between proportion of institutional trades and alternative measures ofspread. This evidence seems contrary to institutional trades revealing private valuerelated information content and supports a liquidity-enhancing role of institutionaltrades. Contrary to prior studies, which assume private information content in

Table 2 Descriptive statistics on MLE estimates of the parameters of a mixed model and Probability ofinformation trading (PIN) computed from those estimates

Eventuncertainty(ε)

Informationuncertainty (δ)

Arrival rate of liquiditytraders (α)

Arrival rate of informedtraders (θ)

PIN

Mean 0.402 0.284 66.399 75.416 0.209

Median 0.363 0.259 27.783 40.866 0.208

Standarderror

0.019 0.023 13.577 12.501 0.048

Maximum 0.867 0.935 481.868 508.904 0.329

Minimum 0.181 0.000 8.153 7.574 0.112

Skewness 1.152 1.233 2.890 2.833 0.397

PIN, probability of information trading is computed from the ML estimates of the parameters the dealershipmarket model for valuing risky securities, namely probability of a news event, probability of bad/goodnews, and arrival rates of informed and liquidity traders to trade securities as follows

PINð0Þ ¼ θ "2aþθ"

Where,

ε is the probability that an information event occurs

α is the rate at which small liquidity traders arrive to either buy or sell in small size

θ is the rate at which small information traders arrive to buy when there is good news and sell when there isbad news.

The above parameters belong to the following likelihood function:

L DjB; Sð Þ ¼ 1� "ð Þ � e�aT aTð ÞBB! e�aT aTð ÞS

S!

þ"d � e�aT aTð ÞBB! e� aþθð ÞT aþθð ÞT½ �S

S! þ " 1� dð Þ � e�aT aTð ÞSS! e� aþθð ÞT aþθð ÞT½ �B

B!

J Econ Finan

institutional trades, we argue that institutional trading reveal divergence of opinionaround public information releases, which increases volatility and is accordingly pricedin spread. This argument is based on the recent evidence in Dey and Radhakrishna(2007) that institutional trading denotes divergence of opinion that Harris and Raviv(1993) consider as the essence of volatility, which in turn affects spread. Under thispremise, there exists a direct relation between institutional trading and spread.

The control variable trading volume represents inventory control and order pro-cessing costs (Acker et al. 2002; McInish and Wood 1992). However, Brown et al.(2009) indicate that trading volume may denote liquidity, information, and momen-tum as well and hence the impact of volume on spread is uncertain. Nevertheless,following McInish and Wood (1992) and (Easley et al. 1996, 1998), we hypothesizean inverse relation between spread and volume.

PIN, another control variable denotes information content in order imbalance i.e.,buys more than sales indicate good news and sales more than buys indicate bad news.We multiply PIN with price to indicate dollar value of PIN risk, information riskpremium. A high PIN increases the risk of trading and thus the market maker seeks ahigher compensation through increased spread. Hence we expect a significantlypositive coefficient associated with PIN in a regression model for spread.

Table 3 reports OLS estimates for the parameters of the model in Eq. 2 with spreadas the dependent variable. The F-statistics (not reported in the table) indicate that allmodels are significant at 1 % level. Model 1 is a modified version of Easley et al.(1998). All three independent variables- log (volume), PIN*price, and institutional

Table 3 OLS estimates (t-statistics) of a regression model for spread

Model 1 Model 2 Model 3 Model 4

Intercept 0.26 (12.58)* 0.344 (10.10)* 0.265 (12.94)* 0.254 (12.11)*

Volume −0.014 (−7.20)* −0.029 (−5.46)* −0.013 (−6.59)* −0.013 (−6.63)*

Numtrd 0.021 (2.99)*

Buysell (ratio) −0.012 (−1.81)*** −0.014 (−2.00)***

Price*PIN 0.002 (2.80)** 0.001 (0.89) 0.002 (2.95) ** 0.002 (2.93) **

Instrading 0.055 (2.16)** 0.091 (3.42)* 0.050 (2.01) ** 0.102 (2.71) **

Inst. Hilo dummy −0.012 (−1.81) ***

Adj. R2 0.44 0.50 0.46 0.48

The regression model and variables definitions are as below:

Spread ¼ a0 þ b1volumeþ b2 price � PINð Þ þ b3instrading þ es

Spread = Mean of daily effective spread

Volume = Mean of natural log of daily trading volume

Numtrd = Average daily number of trades

Price = Average transaction price over all trades during a day

PIN = Probability of information trading computed from the estimates of a mixture model

Instrading = Mean of daily institutional trades as a fraction of all trades

Inst. Hilo dummy = Indicator variable that assumes a value 1 if mean institutional trading proportion for theith firm is greater than/equal to the median of institutional trading; else it assumes a value 0

All averages are computed over all observations available in the TORQ data set for a specific firm

J Econ Finan

trading are significant determinants of spread. Information risk premium denoted byprobability of information trading times price (coefficient 0.002) and institutionaltrading proportion (coefficient 0.055) are both significant at less than 5 % level.Trading volume denotes inventory and order processing costs and is a significant(−0.014, t-stat −7.20) determinant of spread. As a determinant of spread, the signif-icantly positive coefficient of institutional trading suggests its information content.Higher level of institutional trading increases volatility and thus spread increases.McInish and Wood (1992) and Klock and McCormick (1999) document a directrelation between spread and volatility. The adjusted R2 for the model is 44 %.

In Model 2, we add number of trades to determine whether PIN, an estimatedmeasure of information content based on number of trades has more explanatory powerthan the observed measure number of trades. Not surprisingly, since PIN is estimatedwith error, it drops out of the model when the observed measure, log (number of trades)is included. However there may be another interpretation of this result. Like institutionaltrades, number of trades may denote two counteracting effects, an information effect anda liquidity effect. PIN captures only the information component of ‘number of trade’effect and so drops out since there is a net liquidity effect. Later, we test this hypothesiswith order processing and adverse selection components of spread.

In model 3, we replace number of trades with buy sell ratio to capture net order flow orthe buy sell asymmetry. Again while all the remaining parameter estimates remainunchanged, buy sell ratio enters into the model (−0.012, t-stat −1.81) as a significantdeterminant of spread. These results confirm the explanatory power of net order flow inthe determination of spread; however, institutional net order flow (buy sell ratio) does notappear to be a significant determinant, which contradicts the findings of Shu (2006). Thisis a surprising finding given the evidence in Bozcuk and Lasfer (2005) that the asym-metry in trader type e.g., institutions vs. individuals drives the asymmetric price effect inLondon stock Exchange. There are two possible cause and effect scenarios, which mightexplain this puzzle. One, it is possible that the institutional buy sell asymmetry causes anasymmetric price effect in buys vs. sales, which then feeds into a market wide buy or sellsignal (Harris and Raviv 1993); two, institutions learn from each other and henceinstitutional buy sell asymmetry mutates into a market level buy sell asymmetry due tocorrelated trading by institutions as reported by Sias (2004) that eventually leads to anasymmetry in the bid and ask prices (Saar 2001). The adjusted R2 increases to 46 %.

Finally in Model 4, we include a dummy variable to indicate high vs. lowinstitutional trading among the securities. The dummy variable (−0.012) is significantat less than 10 % level suggesting a higher intercept for high institutional trading(dummy variable=1), high volatility stocks. Note that a positive correlation betweeninstitutional trading and spread as indicated by the slope of the regression line impliesa higher spread for securities with higher institutional trading. Further, in support ofDey and Radhakrishna (2007), we posit that a relatively higher level of institutionaltrading implies higher volatility and consequently higher spread. All other parameterestimates are literally unchanged. As one moves from low to high institutional tradingstocks, the direct relation between spread and institutional trading shifts without anychange in the slope thus forming two upward sloping parallel straight lines.

In order to provide an intuitive understanding of the hypotheses and the ensuingeconometrics results, in Figs. 1, 2, and 3, we present three scatter plots, spread *institutional trading, spread * PIN, and PIN * institutional trading. Note the

J Econ Finan

Fig. 1 Scatter plot of Mean spread and PIN for our sample of 65 NYSE securities

Fig. 2 Scatter plot of Mean spread and institutional trading for our sample of 65 NYSE securities

Fig. 3 Scatter diagram of PIN and proportion of institutional trading for our sample of 65 NYSE securities.A line fit indicates ‘not significantly different from zero’ correlation

J Econ Finan

similarities between Figs. 2, and 3. Both indicate positive correlation coefficients,clustering near the origin, and sparseness at the higher end of spread resulting inheteroscedasticity. The positive slope coefficients of institutional trading and PINwith respect to spread indicate a net increasing relation that may be attributed toinformation. However, Fig. 3 indicates an inverse correlation bρ ¼ �0:38ð Þ althoughthe correlation coefficient turns out to be insignificant. Thus while there might beinformation content in both institutional trading and PIN, but the information contentrevealed through those are uncorrelated, if not contrary to each other.

Table 4 presents a correlation coefficient matrix for the independent variables usedin the regression models. As expected, the correlation between volume and number oftrades is quite high, 0.95; all other correlation coefficients are below 0.5. PIN isinversely correlated with all other independent variables which indicate positivecorrelation vis-à-vis one another.

5 Regression model and results on PIN, institutional trading, and the componentsof spread

We extend our analysis by decomposing bid ask spread into its order processing andadverse selection components and further investigating the effect of PIN and institu-tional trading on those individual components. We use a technique from Lin et al.(1995) based on Stoll (1989) to decompose the spread into order processing andadverse selection components.8 We choose this technique to compare our results withthose from Heflin and Shaw (2000) which provides empirical evidence on how theextent of block holding impacts spread and its adverse selection component estimatedusing Stoll (1989). The parameters λ and γ of the following regression modelsrepresent the adverse selection and the order processing components of the spread.

$Qtþ1 ¼ lzt þ etþ1 ð3Þ

$Ptþ1 ¼ �gzt þ utþ1 ð4Þwhere,

Table 4 Matrix of correlation coefficients between pairs of independent variables used in regression

PIN Price Nos. of trade Volume Instrade

PIN 1

Price −0.367762491 1

Nos. of trade −0.379508935 0.560020702 1

Volume −0.429187921 0.531228954 0.954947501 1

Instrade −0.362823606 0.481918017 0.243736974 0.335014 1

8 Huang and Stoll (1997), Glosten and Harris (1988), and George et al. (1991) provide alternativedecomposition techniques. Although different methods find differing impacts of the components of spread,Heflin and Shaw (2000) report that Stoll (1989) and Huang and Stoll (1997) decompositions result insimilar estimates. Van Ness et al. (2001) report that the existing spread decomposition methodologieshowever, do not yield consistent estimates of adverse selections costs.

J Econ Finan

Pt log (trade price at time t)Qt log (quote midpoint at time t)

Δ Change in the relative variable from t to t+1zt Pt – Qt andet+1,ut+1 Random error terms with zero mean and constant varianceE(et+1ut+1) 0

We estimate λ and γ via OLS regressions. Table 5 reports descriptive statistics onthe components of the spread for 65 securities in the sample. We find that the adverseselection costs for our sample securities is about 25 %, with order processing costs onthe order of 40 %. Our order processing cost estimates are higher, and adverseselection cost estimates are lower than those reported by Lin et al. (1995) in general,but correspond closely with the estimates within their largest volume sub-sample.

We test the prediction of Dey and Kazemi (2008) that there are two simultaneousand opposing pressures on the relation between institutional trades and spreads – anupward pressure due to the information component of institutional trading and adownward pressure due to the trade size and hence liquidity component of institu-tional trades. We surmise that, if institutional trading combines information andliquidity effects, then the information effect should be reflected in the adverseselection component and an opposite liquidity effect should be reflected in the orderprocessing component. The resultant net effect on spreads is indeterminate and canonly be resolved empirically.

We use a system of equations approach and a SUR (Seemingly Unrelated Regres-sion) method to estimate the parameters of the following equations denoting the orderprocessing adverse selection components of daily spread for our sample firms:

OPC ¼ a1 þ b11Volumeþ b12Numtrd þ b13 PIN � priceð Þ þ b14Instrading þ eo ð5Þ

ADV ¼ a2 þ b21Buysell þ b22Numtrd þ b23 PIN � priceð Þ þ b24Instrading þ ea ð6Þ

where,

OPC Order Processing costADV Adverse Selection costVolume Log (Average daily trade volume for firm)Numtrd log (Mean daily number of trades for firm)

Table 5 Descriptive Statistics on adverse selection (ADV) and order processing (OPC) components aspercentages of spread

Mean Median Minimum Maximum Standard deviation

ADV- Adverse Selection Componenta 25.3 % 25.2 % 2.8 % 46.1 % 11.5 %

OPC - Order Processing Component 40.1 % 39.2 % 13.4 % 79.8 % 15.9 %

a The effective spread is decomposed into its components using methodology originally proposed by Stoll(1989) and used recently by Lin et al. (1995). The descriptive statistics presented above is for the cross-section of 65 securities in the sample and is calculated from the Mean proportion for each stock in oursample, of the adverse selection and order processing components of the spread

J Econ Finan

Price* PIN PIN * Average closing priceInstrading Mean institutional trading proportion for firmBuysell (ratio) Mean of daily (buy volume over sell volume)

The SUR model assumes that

E eoð Þ ¼ E eað Þ ¼ 0 and E eoieaj� � ¼ σoa if i ¼ j and 0 otherwise for i; j 2 1:65ð Þ:

In the model above, we recognize that while volume denoting total order flow isperhaps the primary determinant of order processing costs, buy sell ratio denoting netorder flow (buy volume over sell volume) will be a primary driver of adverse selectioncosts. Accordingly, we include log (volume) and buy sell ratio in the equations for orderprocessing and adverse selection costs respectively. This differentiation between theorder processing and adverse selection costs equations also enable us to estimateunbiased and efficient parameters via an SUR model, which requires that the twoequations in this model do not have identical independent variables.

Table 6 reports the results of the SUR model with columns 2 and 3 containingresults for Model 1 and columns 4 and 5 containing results for Model 2. Model

Table 6 SUR results of system of equations with Order Processing Cost (OPC) and Adverse SelectionCost (ADV) as dependent variables

OPC ADV OPC ADV

Intercept 0.191 (5.52)* 0.066 (6.55)* 0.195 (5.47)* 0.061 (5.45)*

Volume −0.20 (−3.64)* −0.20 (−3.62)*

Numtrd 0.026 (3.60)* −0.014 (−6.25)* 0.026 (3.58)* −0.014 (−6.23)*

Buysell (ratio) −0.009 (−1.84)*** −0.010 (−1.92)***

Price*PIN −0.003 (−3.58)* 0.003 (4.73)* −0.003 (−3.54)* 0.003 (4.69)*

Instrading −0.010 (−0.34) 0.053 (2.70)** −0.028 (−0.64) 0.082 (2.58)**

Inst. Hilo dummy 0.004 (0.56) −0.007 (−1.15)Cross correlation −0.50 −0.50 −0.50 −0.50System R2 0.44 0.44 0.45 0.45

The System of equations regression model and variables definitions are as follows:

OPC ¼ a1 þ b11Volumeþ b12Numtrd þ b13 PIN � priceð Þ þ b14Instrading þ eo

ADV ¼ a2 þ b21Buysell þ b22Numtrd þ b23 PIN � priceð Þ þ b24Instrading þ ea

OPC = Order Processing component of the average effective spread for firm

ADV = Adverse Selection component of the average effective spread for firm

Volume = Natural Log of average daily trade volume

Numtrd = Average number of trades per day

Buysell (ratio) = Average buy volume/Average sell volume

Price = Mean of daily closing price

PIN = Probability of information trading computed from the estimates of a mixture model

Instrading = Average institutional proportion of trades

Inst. Hilo dummy = Indicator variable that assumes a value 1 if mean institutional trading proportion for theith firm is greater than/equal to the median of institutional trading; else it assumes a value 0

All averages are computed over all observations available in the TORQ data set for a specific firm

J Econ Finan

1 results indicate that volume (−), number of trades (+), and information riskpremium denoted by PIN*price (−) are significant determinants of order process-ing costs while number of trades (−), buy sell ratio (−), PIN*price (+), andinstitutional trading (+) are all significant determinants of adverse selection costs.We find support for Dey and Kazemi (2008) that institutional trading has asignificantly positive relation with the adverse selection component of the spreadand a negative, albeit not significant, relation with the order processing costcomponent. These results suggest that the specialist acknowledges institutionaltrading is motivated by both information and liquidity motives which are fac-tored into determining effective spreads; however they also weigh in which ofthe two motives, if any dominates the other. In this sample, the informationmotive dominates the liquidity motive and as such the net effect on spread ispositive as we confirm the results with respect to spread (Table 3). The numberof trades is significantly negative in the adverse selection equation and positivein the order processing equation – also reflecting a dual effect on the spreadcomponents (Table 6).

The gross order flow variable (volume) is significantly negative, indicating thatorder processing costs are significantly reduced with higher levels of institutionaltrading. As volume increases, order processing costs decrease perhaps througheconomies of scale. In addition, the net order flow variable (buy sell ratio) issignificantly negative, indicating that adverse selection costs also decrease with anincrease in buying over selling activity.

In Model 2, we estimate the parameters of a similar system of equations as inModel 1 while adding a dummy variable indicating high and low institutional tradingas before. We find the dummy variable to be insignificant with regard to both orderprocessing and adverse selection costs indicating that the intercept does not changebetween high and low institutional trading. In this regard, unlike spread unconditionalorder processing and adverse selection costs remain unchanged between high and lowinstitutional trading or equivalently high and low liquidity stocks. All other parameterestimates remain unchanged from those in Model 1 suggesting a stable relationbetween order processing and adverse selection costs and their determinants acrosshigh and low volatility via institutional trading stocks.9

6 Conclusion

Institutional trades may be information and/or liquidity motivated and hence theeffect of institutional trading on bid ask spread is unclear. However, primarily dueto data constraints, there is little existing empirical evidence on the relation betweeninstitutional trading and spreads. Using transactions level data from TORQ, wedetermine how institutional affects spread after controlling for trading volume andPIN, both of which may contain information about future prices. The inclusion ofPIN, a measure of information content in trades as a control variable is particularlyimportant since it empirically observed to be a significant determinant of spread.

9 We do not find any evidence of institutional buys and sales affecting spread or its components.

J Econ Finan

We find spread increases as institutional trading increases across both high and lowlevels of institutional trading. We conjecture that the increase in spread is due to theincrease in volatility associated with increased institutional trading. We also findlimited support for the predictions of Dey and Kazemi (2008) - an increase ininstitutional trading is associated with a statistically significant increase in the adverseselection costs and a corresponding decrease, albeit statistically insignificant, in theorder processing costs. Taken together, these two findings indicate that the net effectof institutional trading as a cross sectional determinant on spread is positive but maynet to zero where the positive and negative effects of adverse selection and orderprocessing costs respectively cancel each other.

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