The value of non-anonymity on the NASDAQ. Arze Karam* Queen s University Belfast. (September 2012)

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1 The value of non-anonymity on the NASDAQ Arze Karam* Queen s University Belfast (September 2012) Abstract: This paper examines why dealers select to display liquidity non-anonymously when it is optional for them to do so on the NASDAQ limit order book. On a sample of 2004 and across the 500 most liquid stocks on the NASDAQ, I find that non-anonymous quotes are more likely to be at the inside of the market when information is greater and when the quoted spread is high. In contrast to the general view, results suggest that non-anonymous activity is associated by greater price impacts, despite the fact that anonymity is provided to dealers through a direct access from the book. Dealers appear to select the non-anonymity based on many factors including liquidity and the level of information asymmetry in the stocks. Results also suggest that non-anonymity is valuable to dealers on the NASDAQ by influencing market quality variables. I discuss the implications for market design and relate the findings to the current changes in today s market settings. * a.karam@qub.ac.uk. 185 Stranmillis Road, Belfast, BT9 5EE. Tel: + (44) This paper is part of author PhD dissertation defended at University of Paris X in November I am indebted to Thierry Foucault, Vikas Agarwal and Didier Folus for their support. I am thankful to Chen Yao, Bruce Mizrach, Stefano Lovo, Michael Moore, Richard Payne, Vincent Van Kervel and Dale Rosenthal for their comments and suggestions to improve the paper. I also thank participants at Midwest Finance Association Meetings 2012 in New Orleans, at Warwick Business School Frontiers of Finance Meetings 2012 and at Georgia State University finance seminar in March Any remaining errors are the responsibility of the author. 1

2 Introduction Traders have traditionally little ability to influence the transparency of their quotes. However, this is no longer the case. An ever-expanding landscape of trading venues now offers considerable heterogeneity in the amount of pre-trade transparency traders are afforded, from platforms offering complete non-anonymity such as the Hong Kong or Australian Stock Exchanges to ones offering none such as BATS or Euronext; and finally to few which offer anonymity as an option with identity disclosure as default setting, namely NASDAQ or Toronto Stock Exchange. Characteristics of traders play a role in how much the market can infer about their identities. Now more than ever, traders are likely to hide their identities by escaping disclosure requirements, but also are able to disguise their identity with strategies, e.g. breaking up large orders or routing through multiple venues. Yet, despite the increasingly demand of trading anonymously, much less is known about the role of non-anonymity when it is optional and required. This paper is an attempt to improve our understanding of this issue, specifically by considering the determinants of non-anonymity on the NASDAQ and how it influences strategies and the resulting price dynamics. This topic lies at the heart of controversial debates about the informational advantages of market makers, and inter-market competition between trading systems with different regime of identity disclosure. For instance, the argument of identity disclosure rests on the idea that some degree of transparency may be needed to enhance price discovery process since market makers positions are publicly exposed. The benefits of identity disclosure appear however to be conditioned by the fact that market makers are reluctant to provide free options through their quotes in the presence of better informed traders, which may result in increased transaction costs or reduced liquidity. Further, following the evolution of dark pools and the dark liquidity, the paper is presumed to shed light on the role that the displayed quotes play in the price discovery process to which dark pools are not particularly contributing. Currently, dealers on the NASDAQ have the ability to select quoting either anonymously or non-anonymously through the INET book. Legally, they have the obligation to display twosided quotations under their market participant identifiers at least once per day for the stocks in which they have decided to make the market. They can also monitor liquidity through ECNs or off exchanges, in OTC markets for instances. One would expect them to display liquidity through the NASDAQ book when they need to attract order flow away from other 2

3 venues and other dealers. To this end, this paper seeks to answer two basic questions: first, why do dealers select to display liquidity non-anonymously when it is optional for them to do so? Second, does their selection have an impact on market quality in the sense that it affects execution costs and price volatility? The analysis yields some surprising results across a large sample of NASDAQ stocks. First, the findings suggest that dealers select to attract order flow non-anonymously when they are less likely to face information asymmetries for stocks with wide spread and low information risk. Further, the findings suggest that dealers identities seem to signal information leading to a higher impact of trading. Across the five hundred most liquid stocks ranked by activity quintiles, results show that non-anonymous activity leads to higher price impacts as opposed to anonymous activity, except for the stocks in the least active quintile. In fact, dealers quotes are more frequently revealed as such, leading other market participants to adjust prices accordingly. In such instances, there is an indirect opposing effect stemming from a dealers response to such an increase in the chance that their quotes are revealed. Research predicts that dealers will use the increased visibility to their advantage. Clearly, they attempt to mislead other market participants by bluffing them, i.e. Foucault, Moinas and Theissen (2007). The increased liquidity may result in higher expected profits for dealers. But, bluffing itself may not increase dealers expected profits since they are more likely to face the winner curse. Rather, it is possible that bluffing may result in the increased liquidity associated with nonanonymous trading facilities. In such instances, if bluffing is known to occur, the market is deeper. A second class of closely related implications speaks broadly to the impact of nonanonymous activity on market liquidity and price efficiency. Research on this topic predicts that bluffing has a destabilizing effect in that it moves prices away from fundamentals and increases adverse selection costs. Cross sectional results here suggest that the ability to quote non-anonymously is likely to increase slightly the spreads but to lower price volatility in the short run, except for the stocks in the least active quintile. I argue that this occurs when there is a high chance that the bluff strategies are noticed by the market. Because the linkage between dealers liquidity supply and the information is weakened, dealers are more willing to adjust prices quickly. Additional results show that non-anonymous activity appears to 3

4 result in large trades and tends to reach its highest level in the morning where dealers are known to be aware about the events that cause morning volatility. Also of interest is the relationship between non-anonymity and the level of information asymmetry in the stocks. I conjecture that dealers are more likely to bluff when they face less information asymmetry. This suggests that when the liquidity risk is high in less active stocks, dealers are less likely to make the market in these stocks non-anonymously since bluffing is more likely when losses can be recovered. Cross sectional results here indeed suggest that dealers are less likely to be at the inside non-anonymously for the stocks in the less active quintiles which exhibit highest liquidity risk. Univariate results show that dealers appear to maintain a market presence in these stocks throughout the day across all quintiles; the percentage of quotes at the inside increases in both categories, anonymous and nonanonymous, as the stocks became less active. Research suggests that the displayed liquidity on the dealers system is stable in the least active stocks of NASDAQ 100 index at times where ECNs have withdrawn from the market, i.e. Goldstein, Shkilko, Van Ness and Van Ness (2008). In the same line, I argue further that working less liquid stocks would seem to increase the supply of liquidity on the system anonymously for the less active stocks, beyond the NASDAQ 100 stocks. This paper contributes to the growing literature on pre-trade anonymity in financial securities markets. The non-anonymity is one of many aspects of pre-trade transparency 1. Foucault, Moinas and Theissen (2007) show that non-anonymity encourages expert brokers in Euronext-like market to engage in bluffing strategies 2. Their study is empirical and theoretical and consists of a comparison between two separate regimes of pre-trade transparency. I rely on their analysis in the non-anonymous market to develop hypotheses related to the effects of non-anonymous activity where dealers have simultaneous access to different identity disclosure regime. Simaan, Weaver and Whitcomb (2003) study the collusion among dealers in the NASDAQ at times where the quote montage was only displaying dealers non-anonymous quotes prior the implementation of SuperMontage and its execution anonymous facility. The authors argue that non-anonymous bid and ask quotes 1 The literature on the effects of pre-trade transparency is large and has led to mixed conclusions depending on the market settings and the type of information traders observe. For instance, Admati and Pfleiderer (1991) argue that large liquidity suppliers would sunshine their trading to reduce adverse selection costs. However, on the non-anonymity topic, the literature is sparse and often focused on the effects of removing brokers identities or switching to the full anonymity regime across different securities markets. 2 The main theme in this model is information asymmetry: there are some traders that are more informed than others and transparent market affects these two categories differently. 4

5 posted by dealers facilitate collusion since it is easy to identify and punish offending dealers. They show results that anonymous bid and quotes, posted by dealers through ECNs more frequently at the time of the study, make it more difficult to maintain a collusive equilibrium. On a more recent sample, studies on the NASDAQ show that the anonymity provided by the book was not used significantly by dealers as the one provided by ECNs, e.g. Mizrach (2006) and Benhami (2007) 3. The anonymity provided by the book is perceived by the market differently than the one provided by ECNs, as in Mizrach (2008). This has led to some disagreement over the role of non-anonymity in the presence of anonymous substitutes features or venues. Therefore, because of fundamental differences in market structure with their opponent venues, the exact extent to which non-anonymity played a role in attracting order flow in the NASDAQ new system remains unclear. The cross sectional results here suggest that dealers are likely to post informative quotes nonanonymously, after being able to assess their exposure to informed traders and choose their quotes accordingly. It appears that dealers are not only using such strategies but complementing it by using the anonymity of the book to efficiently manage their exposure to information risk. Broadly speaking, patient uninformed dealers may use the anonymity to make it more difficult for others to pick off their limit orders and thus reduce the price impacts of their trades. Due the fact that they are constrained to provide non-anonymous quotes, their non-anonymous activity is perceived as informative. In such instances, nonanonymous activity on the NASDAQ can be modelled as information disclosure to less informed market participants consistent with the asymmetric information and rational price frameworks. Clearly, dealers identities may play the role of signalling in a competitive setting à la Calgano and Lovo (2006). The empirical evidence here and in Karam (2012) support this logic 4. Recent contribution focuses on the selection of anonymity on the Toronto Stock Exchange. Comerton-Forde et al. (2011) argue that designated market makers trade anonymously to conceal information by using aggressive market orders more frequently while their anonymous limit orders are passive. Although the analysis arises under different microstructure environments, which suggests that the market setting is crucial for 3 Benhami (2007) shows that the occurrence of non-anonymous quotes posted by dealers is higher relative to anonymous quotes posted by them anonymously. 4 One might argue however that the price impact of non-anonymous trades occur as a result of a dealer trying to punish another dealer who have temporarily change his spreads, as has occasionally been alleged on the NASDAQ. I explore this hypothesis in other work. 5

6 interpretation of the results. The argument is broadly consistent with the analysis by Charitou and Panayides (2008) who note in their review on market making systems that liquidity supply and hence strategic interactions among market participants are different in a setting where designated market makers and traders coexist compared to a setting where traditional market makers and traders coexist. Specifically, the NASDAQ is more concentrated on a large number of dealers who compete constantly to provide liquidity among each others 5, and compete with ECNs. They typically invest in information gathering and thus play the role of informed traders to attract order flow. In such context, the dissemination of pre-trade information may be quite effective and therefore may have a major effect on liquidity provision. Thus, by revealing less information about dealers, anonymous limit orders make other dealers and market participants less reluctant to take up positions and to trade less frequently that recognize a patient liquidity suppliers pattern, as shown here. This paper contributes also to the growing literature on fragmented dealers markets. Several papers on the Forex markets show that certain banks with large customer order flow lead the market in terms of price discovery; clearly their prices tend to be more informative relative to the ones posted by their competitors [e.g. Sapp (2002) among others]. The Forex markets are interdealer markets where dealers can use a mix of direct voice-brokered trades, direct nonanonymous electronic trades (Reuters D2000-1) and anonymous electronic brokered systems (Reuters D and EBS). In such markets, there is a high level of pre-trade transparency with quotes transmitted through different platforms in real time. Due to data limitations, there is no evidence which provides a comprehensive study of venue selection by Forex dealers. On a different dealers market, Reiss and Werner (2005) study the choices of London dealers in brokered interdealer markets between separate anonymous and anonymous platforms. They show that informed dealers migrate their trades to non-anonymous venues rather than anonymous in attempt to reduce adverse selection costs. The main difference between a brokered interdealer market and the NASDAQ market I study is that I examine the determinants of dealers choice when they have access to different identity disclosure regime on the same platform. I provide insights on the motives for dealers to select non-anonymity across a large sample of stocks ranked based on their trading activity. I further examine the impact of non-anonymous activity on market quality. I employ the two stages of the 5 TSX market is composed of a large number of traders including designated market maker responsible to supply liquidity at a particular stock and trading at a constrained spread. Limit order traders are the main source of liquidity and may engage in informed liquidity supply. 6

7 Heckman technique where the first stage is a probit model to estimate the determinants of non-anonymous quotes; and the second stage is an endogenous switching regression that uses the first stage to overcome the selection bias in estimating the execution costs of dealers choice of quoting non-anonymously. I use the NASTRAQ data which allows differentiating between the quotes posted by dealers non-anonymously from the ones posted anonymously through the NSDQ feature on the NASDAQ quote montage. The analysis draws in the second semester of a period where the NASDAQ was about to join the US National Market System. It is also a period of significant competition and fragmentation. The next section describes the institutional background. The paper is organized as follows. Section one describes the non-anonymity on the NASDAQ while the section two details testable hypothesis. Section three describes the dataset and provides descriptive statistics. Section four presents results on the determinants of nonanonymity and its impact on execution costs and price volatility. The last section concludes. 1. The non-anonymity on the NASDAQ The NASDAQ order book provides an interesting context to examine why dealers choose to quote non-anonymously when they have simultaneous access to anonymity - directly from the NASDAQ book 6. In this section, I describe the NASDAQ book to assess the effect of dealer s behavior and the role of non-anonymity. During the sample period, major big ECNs like Instinet 7 and Island, which have not decided to participate to SuperMontage, were displaying liquidity outside the book. Even though trading was fragmented on NASDAQ, SuperMontage provided a centralized view of the available liquidity at that time. In figures, SuperMontage was executing 60% of the overall volume share in NASDAQ issues, as discussed by Goldstein, Shkilko, Van Ness and Van Ness (2008). In such setting, SuperMontage and all ECNs private books have incentives to be at the inside of the market to attract order flow in NASDAQ issues. Within the order book, dealers and participating ECNs can display liquidity at multiple levels. The book shows the first five price levels (tiers) of the bid and the first five price 6 Recall that dealers have access to the anonymity through ECNs. Given that the aim of this study is restricted to examine the choice of non-anonymity when anonymity is provided on the same platform, the analysis includes only attributed and non-attributed quotes posted by dealers on the NASDAQ book. 7 Instinet which was identified as the main source of market makers quotes via ECNs, was among the ECNs that decided not to participate to SuperMontage. The liquidity offered by Instinet was displayed on separate book anonymously. 7

8 levels of the ask for every stock. In the complete display, there are 29 non-zero bid and ask prices for every stock. Recall that dealers non-anonymous quotes can only be displayed on the book. The market participants observe the identities associated with the quote or the order on the quote montage. For instance, if Goldman Sachs or Knight quote at the bid and ask under their identities, their quotes will be displayed via the TotalView System and the Level II and associated to their MPIDs, i.e. MSCO and NITE respectively. But if they decide to quote anonymously, their quotes are associated to NSDQ feature, named SIZE at the time of the study. 8 From their knowledge of the identity of the dealer for this quote, market participants can infer the probable source of the quote. Although dealers display their clients orders, a proportion of their orders are proprietary. The inside market consists of the best bid and ask prices with the largest depth. This would be the quote displayed on more real time quote services and it would not offer the identity of the liquidity supplier. Note that in 2004, the NASDAQ had already implemented the postanonymous regime, i.e. transactions reports do not reveal counterparties identities. In the rest of the paper, a quote is defined to be non-anonymous when dealers identities are observed by market participants prior the trade via the TotalView system. Thus, when we say the quote is non-anonymous, we mean the displayed limit order does carry dealers identity. This view of non-anonymity discussed here in this paper is similar to the ones developed by Foucault, Moinas and Theissen (2007). There are multiple dealers for NASDAQ issues who are required to quote on both sides of the market under their identities, at least once per day to fulfill their obligations. Prior SuperMontage, evidence suggests that dealers tend to post competitive prices on only one side of the quote, e.g. Chung and Zhao (2004) for stocks with smaller market making costs related to adverse selection costs. In such instances, dealers select to be at the inside of the market to increase their profits by attracting order flow. It is interesting to examine the motives of dealers presence at the inside non-anonymously. These motives will provide the background for the interpretation of the results. Prior SuperMontage, Simaan, Weaver and Whitcomb (2003) show that dealers post more competitive quotes anonymously (through ECNs) than non-anonymously (through the quote montage at the time of their study). The authors argue that revelation of dealers identities on 8 Order entry firms were also allowed to post orders on the NASDAQ book but only through NSDQ feature. A study issued by NASDAQ authorities show that participation of entry firms to the book is about 1.5%. See results on the introduction of NASDAQs SuperMontage, by NASDAQ economic research, February

9 the quote montage enforces the implicit collusion among dealers to keep spreads wide. That is any dealer setting narrower spreads can be identified and reprimanded by other dealers. After the introduction of SuperMontage and its anonymous execution facility, recent studies show however that dealers were not attracting significant order flow through NSDQ. In proportion, the occurrence of NSDQ quotes at the inside is lower as opposed to quotes posted by ECNs, as in Mizrach (2006), and lower as opposed to quotes posted non-anonymously, as in Benhami (2007). So, dealers appear to have different economic reasons to seek the anonymity of the book. For instance, it is possible that dealers negotiate trades without revealing their identities to manage their exposure to informational risk in respect to their best execution obligations. This explanation can be rationalized by using the model of Copeland and Galai (1983). According to the model, dealers non-anonymous quotes could be seen as an option which informed traders may exercise, thus creating an adverse selection problem. Foucault, Roëll and Sandas (2003) develop a model where dealers are better off exploring the market to reduce this risk of exposure. In the same line of the model, using anonymity would allow dealers to efficiently manage their positions while monitoring the market. Recall that NASDAQ system allows also dealers to negotiate additional trades anonymously through reserve orders. There is prior evidence that the hidden liquidity is used by traders passively in attempt to reduce their order exposure to better informed traders, see for instance Bessembinder, Panayadis and Venkatarman (2009). To summarize, having access to these anonymous options, dealers would be more willing to reduce their exposure to information risk and avoid the risk their quotes being picked off by better informed traders. This leads us to conjecture that dealers would select to reveal their identities when they are likely to face less information asymmetries. For instance, some dealers are known by having a superior expertise in choosing their quotes and better assessing their exposure to informed trading. Hence, their activity could be informative about the cost of liquidity to other nonexpert dealers or traders. In particular, if expert dealers hit the bid, this may signal that the cost of liquidity is large. Other traders take it as a signal not to improve the offer probably. Also, if traders observe that there is strong selling or buying pressure by the expert dealers, they take it as a signal and act accordingly. Dealers would respond to the fact their identity signals such information by bluffing the market. These strategic interactions among asymmetrically informed traders were developed by Foucault, Moinas and Theissen (2007) in an order driven market by comparing two different regimes of pre-trade transparency. I rely 9

10 on their analysis which provides insights into dealers non-anonymous activity and its effect on market quality. 2. Hypothesis development I next translate this discussion into testable hypothesis. The fact that dealers on the NASDAQ are constrained to provide quotes makes it more likely that the information they possess or they gather will be reflected into their non-anonymous quotes. Dealers may use market orders to take positions 9. Unlike market orders that execute as just quickly as submitted whether submitted non-anonymously, limit orders provide the market with an option to trade. By revealing more information about dealers, non-anonymous limit orders may encourage other traders to trade more frequently. Expert dealers have greater incentives to make the market in stocks that have large observed spreads because they have too much to gain. Given the same market spread in both anonymous and non-anonymous facilities, expert dealers may prefer to adjust their quotes non-anonymously simply because they exploit the fact that their activity is viewed as informative. This leads us to test the following hypothesis: Hypothesis 1: Expert dealers are more likely to position their quotes at the inside of the market non-anonymously when the market spread is large. The hypothesis 1 does not necessarily mean that expert dealers post non-anonymous quotes unconditionally when the market spread is wide. They might be using both options to position their quotes at the inside in an effort to attract order flow. Further, prior studies document that anonymous activity through NSDQ tends to be passive as opposed to the quoting activity that happened elsewhere on SuperMontage be it through ECNs or dealers MPIDs [see Mizrach (2006), Benhami (2007) for more details]. Thus, non-anonymous option is more likely to be preferred by expert dealers to attract order flow and to increase their profits. In such instances, non-anonymous quotes may encourage predatory trading and thus, increase trading costs. I examine the interaction, if any, between non-anonymity, trade size and execution costs. Comerton-Forde et al. (2011) show that anonymous activity by TSX traders 9 It should be emphasized however that dealers may use also market orders to take positions. For instance, Boulatov and Georges (2008) argue that informed dealers use a mix of market and limit orders even in a transparent regime. This is an interesting issue but beyond the scope of this paper. 10

11 results in large anonymous trades. The authors argue that informed traders choose to submit large anonymous orders which result in huge market impact. In the current paper, expert dealers identity is modelled as being a signal to other traders in the market. In such instances, non-expert dealers or traders would avoid buying (selling) when a major expert dealer is frequently at the inside of the market selling (buying). This interaction among dealers and traders would create incentive for expert dealers to trade quickly because the risk is that other traders would eliminate the profitable trading opportunities by moving the price in the direction of the signal. To this end, I examine whether non-anonymous activity results in large trades and is associated with high price impact. Non-anonymity may also encourage predatory trading in the spirit of Kyle (1985). The implication of the model is that the informed traders act strategically in fragmenting their orders depending on the liquidity of the stock, which affects their ability to trade. The alternative hypothesis states that anonymous quotes result in small and medium sized trades. The discussion above leads to test the following: Hypothesis 2A: Non-anonymous quotes at the inside bid (ask) are associated with higher price impact and are more likely to result in large buying (selling) trades. Hypothesis 2B: Non-anonymous quotes at the bid (ask) inside are more likely to result in small and medium sized buying (selling) trades. I next discuss the impact of non-anonymous quotes on market quality, i.e. execution costs and price volatility. Foucault, Moinas and Theissen (2007) predict that when some dealers are perceived as informed in transparent market, liquidity deteriorates; because dealers quotes may encourage predatory trading and thus increase trading costs. The resulting equilibrium is the one where expert dealers end up posting wider spreads in attempts to bluff their competitors. It is possible however that the signal contingent to the identity lowers the uncertainty about the value of the asset. Thus, the effect on price volatility is unclear. On the one hand, identity disclosure may result in decrease of liquidity in the market, i.e. increase volatility. On the other hand, it may lower adverse selection costs if it provides information to other dealers in the market, in line with the predictions of Forster and Georges (1992) and Pagano and Roëll (1996). Thus, the alternative hypothesis is the one where I suppose that non-anonymous activity leads to lower uncertainty and thus reduce price volatility. Hypothesis 3A: Non-anonymous quotes lead to a decrease in the liquidity measured by the spreads and price volatility. 11

12 Hypothesis 3B: Non-anonymous quotes lead to lower uncertainty and thus reduce price volatility. From the literature we know that the level of asymmetry in a stock also influence dealers preference for non-anonymity. For instance, Theissen (2002) shows that traders choose to trade on German non-anonymous trading systems for less liquid stocks. Since, inventory costs are larger for these stocks, dealers are less likely however to maintain the inside as they face greater adverse selection costs. In line with the hypothesis 1, I expect dealers to post non-anonymous quotes at the inside in these stocks when dealers are likely to be aware of the nature of informational events that cause the uncertainty. Hypothesis 4: On average, the frequency of non-anonymous quotes tends to be higher in less active stocks. The willingness of dealers to be at the inside non-anonymously depends however on the level of uncertainty they face in these stocks. 3. Data and descriptive statistics This study uses trades and quotes for the 500 most liquid stocks traded on the NASDAQ during 24 days in the second semester of The data are extracted from the (1) Nasdaq Trade and Quote dataset (NASTRAQ), and (2) CRSP on market capitalization and daily trading volume. Nastraq quote file provides information about dealers identities. Although, NASDAQ TotalView provides a real time display of the entire limit order book to the market, NASTRAQ only records each market participants best quotes in their historical database (i.e. the Best Bid and Offers). In line with the Simaan, Weaver and Whitcomb (2002) study, I examine the quoting behavior of dealers, in each category, with respect to the inside market. Indeed, dealers best quotes that, in part, determine the inside, may reflect the prices that dealers are willing to offer or to take at any time. Thus, I construct the National best bid and offer quotes for the stocks in the sample to reflect all available quotes in the spreads. The sample includes trades and quotes that occur during regular market hours, i.e. 9:30 a.m. to 4:00 p.m. Quotes and trades are matched on a second basis using the Lee-Ready Algorithm (1991) as modified by Bessembinder (2003a) 10. The following quotes and trades are omitted, if ask and/or bid are equal to zero or non positive, or if the ask is equal to the bid, if prices and/or volume are equal to zero and if the bid-ask spread is greater than $5 or non positive. 10 Bessembinder (2003a) shows that no allowance for trade-reporting lag is needed to determine whether a trade is a buyer or a seller-initiated. 12

13 The sample includes about million orders 11. Through the analysis, I divide the sample stocks by activity quintiles to highlight aspects related to liquidity. In this manner, I investigate the economic value of non-anonymous activity for actively as well as less actively traded stocks on the NASDAQ. The categories are defined by ranking the 500 stocks by the total dollar volume reported by CRSP data during June-December For instance, Q1 contains the most 100 active stocks while Q5 contains the least 100 active stocks. [Insert table 1] Table 1 provides descriptive statistics for the stocks in the sample using information from CRSP and the NASTRAQ dataset. Panel A summarizes the measures obtained from CRSP. On average, the most active stocks are followed by 73 market makers and exhibit an average daily volume of 9 million shares; average closing prices is $22 in these stocks. As shown further in the table 1, these figures decrease for the least active stocks. Further, the NASTRAQ data reveals that the number of transactions of highly active stocks is higher than the number of transactions of the least active stocks. Similar pattern is observed for the trade size averages. On average for all quintiles, non-anonymous quotes present higher quoted spread than anonymous quotes, suggesting that dealers identities are associated with wider spreads. This is consistent with the fact that dealers post wide spreads non-anonymously either for inventory or information considerations. Further, I investigate the role played by nonanonymous quotes by computing the percentage of time these quotes are alone at the inside. This measure which assesses the contribution of quotes to the narrowing of the spreads was introduced by Barclay et al. (1995) and used by many empirical papers [see for instance Simaan, Weaver and Whitcomb (2002)]. Given the signalling argument, I would expect dealers to be more likely to position themselves at the inside non-anonymously. [Insert table 2] Obtained results presented in table 2 show the percentage of time that dealers are at the inside anonymously versus non-anonymously per quintiles. Non-anonymous best quotes constitute slightly a higher proportion of SuperMontage relatively to those quoted by dealers 11 Few dealers in the sample quote only once per day under their identities, in other words they sub-quote. These dealers are not usually interested in attracting order flow through SuperMontage and thus appear not to be active throughout the day. They trade OTC or through ECNs mainly. They quote once per day using their IDs to fulfil the obligation any registered market maker has on the NASDAQ. 13

14 anonymously. On average for the most liquid stocks, anonymous quotes hit the inside 9% of the time while non-anonymous quotes presence at the inside is slightly higher (11%). Some dealers may identify themselves in NASDAQ issues so that investors could send them orders. This is observed in particular case of least active stocks where non-anonymous quotes reach a higher percentage (20% of the time alone at the inside). It is possible that dealers not only use such strategy, but complement it by using the anonymity of the book to manage their exposure to information risk. In the same line, I examine whether the inside alone positions result in reducing the quoted spread. For instance, if one category reaches the best quotes from active quoting behavior voluntarily, this may result in a change in the inside bid or the ask accordingly. In such circumstances, the quoted spread on the platform would be reduced as a result of active quoting. Indeed, the higher the improvement, the lower the spread would be. To investigate the improvement provided by non-anonymous quotes, I measure the price improvement as the difference between the dealers price and the best market price for every stock and then take the average across stocks and quintiles. Panel B of Table 2 shows the average best quotes improvement of non-anonymous compared to anonymous category. The figures are expressed as percentage of the prevailing quote midpoint. On average, non-anonymous quotes improvement ranges from 0.01% to 0.07%. Both categories present similar average of best quotes improvement on both sides of the market across quintiles. And, the results from t- statistics suggest that the difference seems not to be significant among the two categories. Thus, there is no difference in patterns in terms of price improvement. 4. Results 4.1. Quoting intraday patterns Next, I investigate whether the percentage of quoting at the inside non-anonymously and anonymously vary throughout the day. One of the most common evidence in intraday analysis is that liquidity and volume cluster at the beginning and the end of the day [see for instance Admati and Pfleiderer (1988)] as a U-shape pattern. Accordingly, NASDAQ dealers will have to revise their quotes more often throughout the day to ensure that their quotes are current and consistent with their inventory management. For this, I examine whether quote revisions, as participation at the inside, change with the information risk throughout the day for each category. I compute the percentage at the inside of anonymous and non-anonymous 14

15 quotes during the sample period by activity quintiles. I separate the time of the day into 13 consecutive periods of half an hour. [Insert figure 1] The average levels of non-anonymous and anonymous usage at each half hour of the trading day are depicted in figure 1 for every quintile. The vertical bars indicate the percentage of non-anonymous quotes using the left chart axis and the line plots show the percentage of anonymous quotes using the right chart axis. To facilitate comparison, all statistics for each stock in every quintile are expressed as percentage deviations from their respective full-day means. Examining the intraday quoting patterns shows that anonymous activity is at its maximum in the first half-hour of trading days but stays relatively low throughout the day. Then, it raises again prior the close. The shape of NSDQ graph could be seen as the familiar U-shape documented in the empirical literature. Non-anonymous activity tends to have similar pattern than anonymous activity in terms of appearing at the inside. Particularly, the graphs indicate that both anonymous and non-anonymous activities are at their maximum in the first half hour of the trading day. At that moment, the uncertainty about the value of the asset tends to be at its highest; thus, the increase in quoting activity around the opening might be consistent with the view that some dealers revise more frequently their quotes. Since anonymous quotes are documented to be less informative, see for instance Benhami (2007) and Mizrach (2006); the variation in the anonymous quoting might reflect the fact that dealers learn about the information as the day progress in attempt to reduce price impacts. Moreover, the fact that the percentage of quoting anonymously increases again around the close suggests that dealers might be using NSDQ to layoff positions. Further, there is prior evidence that NASDAQ dealers are informed at the opening of the trading day [see for instance Cao, Ghysels and Hathaway (2000) 12 ]. In such instances, a question remains on whether some dealers select to be at the inside non-anonymously when they are likely to be aware of the nature of informational events that cause morning volatility. I examine this issue later in this paper in a set of logistic regressions Price Impacts, information shares of non-anonymous quotes NASDAQ dealers are free to revise their quotes at any time during the day. If their quote changes in any category - non-anonymous relative to anonymous - are permanent, then, 12 This study reports that the pre-opening process conveys significant information to dealers. 15

16 microstructure theory tells that the new information has been reflected into their particular quotes. In this case, we can attribute that information to a particular category. Previous evidence on the NASDAQ shows that dealers do not choose NSDQ feature to execute informed trades. That meant if some dealers use strategically their identities to execute their informative quotes, their decision to do so may have an impact on execution costs. Basically, their order placement strategy can be motivated either by minimization of execution costs or information considerations. In order to assess the effect of order choice across stocks, I use two measures defined by the literature: (1) the price impact since it measures the price reaction of the limit order book quotes in a short time period. This measure represents the cost that liquidity traders bear as a result of insufficient liquidity in the book; and, it measures the extent to which information held by informed traders is being impounded into prices; (2) the Hasbrouck information shares methodology (1995) which consists on using the Vector Error Correction Model (VECM) of prices to compute each categories information shares. [Insert table 3] The price impact measure is computed as a signed difference between the midpoint ten minutes after a trade and the midpoint at the time of the trade. I first identify trades as buyerinitiated or seller-initiated using Lee-Ready algorithm and then summing the sequence of buyer and seller-initiated trades that occur at the same price when dealers are at the inside anonymously and non-anonymously respectively. Table 3 presents the results of Price Impact and Information shares across the two categories of trades as well as paired t-test statistics. The price impacts in both of categories increase from the most to the least active stocks. This is expected given that the visibility of the most active stocks may attract uninformed traders, while less active stocks attract more informed traders. The table shows also that nonanonymous quotes tend to be associated with greater price impacts than anonymous quotes across all quintiles. The magnitude is in the order from 19 to 46 cents per share for nonanonymous orders which point out that non-anonymous venues tend to execute more informed trading. Consistent with this view, the non-anonymous activity leads to a wider increase in trading costs, in particular for the less active stocks. To examine further this matter, the information shares are determined stock by stock for the two categories using the Vector Error Correction Model (VECM) of transaction prices. Then, the stocks are divided by quintiles, and maximum, minimum and the mean information shares 16

17 are determined. Results are displayed in panel B of table 2 in the forms of information share mean and ranges, with lower bound to the highest. Results confirm the findings in the panel A. For the most active stocks, non-anonymous shares of informed trading are higher relative to the ones of NSDQ. Additionally, the results here suggest this difference increases as we move to the less active stocks. Taken together, results on price impacts and information shares seem to agree that non-anonymous venue executes the most informed trading and emerges with the highest information shares Determinants of quoting non-anonymously I question the validity of the univariate results documented in the last section in logistic regressions in line with Bessembinder (2003b) and Comerton-Forde et al. (2011). I use the two-stage Heckman technique (1979) where the first stage is a probit model; and, the second stage is an endogenous regression that uses the first stage to overcome the selection bias in estimating execution costs and price impacts of dealers choice of using the non-anonymous quotes. The design allows us to examine the determinants of non-anonymous quotes and the possible market reaction based on the signal that traders can reasonably infer from dealers identity. In the first specification, I relate non-anonymous quotes presence at the inside to variables that determine preceding market conditions to the quote and the current liquidity. In the first stage, the dependent variable is equal to one if a non-anonymous quote is at the inside and zero otherwise. The independent variables include trading volume and price volatility ten minutes preceding the quote that characterize market conditions, the width of the NBBO spread and the size of the trade and the number of trades at the time of the quote. Dbid is a dummy equals to one if the buyer trade and 0 otherwise. Dfirst and Dlast are dummy variables for quotes submitted in the morning and last half-hour of the trading day, respectively. I include fixed effects for stocks to control for unobservable cross-sectional characteristics, such as stock size. All variables, except the dummies, are standardized to have a mean of zero and standard deviation of one. For identification purpose, I should include a variable as an instrument in the probit model which can affect the selection of non-anonymity but not execution costs. It is not an easy task though. Two possible instruments are to be used. The first one is related to the impact of subpenny pricing rule on dealers behavior. One would expect dealers to be worried about someone stepping ahead of their quotes and they want less transparency of their identity. 17

18 Since the analysis is carried after the abolishment of the rule, i.e. March 2004, I would expect to see the opposite effect. The second instrument which I consider in this study is end of months dummy when mutual funds deploy retirement account contributions deducted from pay-checks. In these days, working large orders would seem to increase the demand for pretrade anonymity. I include a dummy inst_day equals to one for end of months days and 0 for other days in the sample. I would expect a negative coefficient for this dummy. [Insert table 4] Table 4 reports the results for the first stage probit model estimated for non-anonymous quotes for every quintile. As expected, the negative coefficient on the dummy variable Inst_day shows an increase (decrease) in anonymity (non-anonymity) at the end of month s days compared to others days in the sample. Holding all variables constant, for the most liquid stocks in Q1 and Q2, the NBBO_width and volat10 coefficients suggest that dealers select to be non-anonymous when spreads are wide and where information risk is low. The effects are particularly strong for the spreads. A one standard deviation increase in spreads from the mean increases the probability of non-anonymity by 8%. A plausible explanation is that environment characterized by wide spreads creates incentives for dealers to attract order flow while quoting non-anonymously. For the less active stocks belonging to Q4 and Q5, the width of the market spread lowers the incentive of market makers to use their identities. This is expected since these stocks exhibit high adverse selection costs. Thus, market makers prefer not revealing their identities to the market to avoid being targeted by better informed traders. To illustrate the magnitude of these results, marginal effects estimates suggest that for market makers, a one standard deviation increase in the spread from the mean decreases the probability that an order is quoted non-anonymously by 9%. For less active quintiles, holding all variables constant, the size of the trade is associated with a higher probability that the quote at the inside is non-anonymously. Hasbrouck (1991) finds that large trades lead to wider spreads and attributes this to the fact that dealers infer from the large trade that information has occurred. The implication is that traders with high sensitive information do not have the time for order splitting strategy. Dealers are more likely to execute more informed trades non-anonymously in less active quintiles. To illustrate the magnitude of the coefficients on Nsize, the marginal effects suggest that a one standard deviation increase in the size of the trade from the mean increases the probability of quoting non-anonymously by 2 to 6 % as we moved from quintiles 2 to 5. 18

19 The coefficient for the Dbid suggests that dealers are more likely to quote non-anonymously while buying across all quintiles. The coefficients for Dlast and Dfirst suggest that dealers prefer to use their identities more in the morning than in the late afternoon across all quintiles (for instance, coefficient 0.17 and respectively for the most liquid stocks). There is prior evidence that NASDAQ dealers tend to be more informed at the opening of the trading day [see for instance Cao, Ghysels and Hathaway (2000) 13 ]. Dealers thus are more likely to use their identities to post their firm quotes after the opening when they are likely to be informed; and they are less likely to use their identities at the close of the trading days when they are laying off positions. This is consistent with the intraday quoting pattern documented in the figure 1 of this paper. These results suggest on differences in non-anonymous quoting behavior across quintiles, as suggested by the hypothesis 1 and 4. To summarize, dealers are more likely to reveal their identities in high active stocks where information sharing is significant; whereas they prefer for the uncertainty to be resolved before revealing them in the less active stocks. Their decision to submit a quote non-anonymously appears to be sensitive to the size of the spread, the size and the sign of the trade and finally the time of the day. This is expected in a highly fragmented market like NASDAQ where dealers post their non-anonymous quotes voluntarily. They have access to other options, such as anonymity and hidden liquidity, to better manage their exposure to information risk Effects of quoting non-anonymously on price impact In the second stage, I estimate the price impact as dependent variable. Table 5 reports the results with all variables standardized by their means and standard deviation. For the independent variables, I include the vector of order characteristics as well as non-anonymous dummy and the fixed effects. The order characteristics contain the following variables: Nvol, the number of trades; NBBO_width is the width of the NBBO spread at the time of the trade; Dvol10, the dollar volume 10 minutes prior the trade; Volat10, is the realized volatility 10 minutes prior the trade; Mom10 is the average midpoint-to-midpoint return over the 10 minutes prior the trade (signed by the trade direction, i.e. multiplied by negative one for sell orders). Nsize is the size of the trade. Dbid is a dummy that equals to one if the trade is buyerinitiated. Dfirst and Dlast are dummy variables for quotes submitted in the morning and last 13 This study reports that the pre-opening process conveys significant information to dealers. 19

20 half-hour of the trading day, respectively. I include the mills ratio which correct for the selectivity bias. [Insert table 5] Results suggest that price impacts increase with trade size and number of trades. Large orders are perceived as relatively informed, thus causing prices to follow in the same direction. Buyer-initiated trades are associated with greater price impacts than seller-initiated ones. This is consistent with prior evidence that suggests buy orders have larger effect on prices than sell orders [see for instance Allen and Gorton (1992)]. Quotes in the first hour of the day are associated with greater price impacts. An explanation is that proportionally more informed trading tends to occur early in the morning in response to the market opening and overnight news. Consistent with the univariate results documented in table 3, submitting a quote nonanonymously signals that it is more informed causing more price impacts, than submitting a quote anonymously on SuperMontage as indicated by the positive coefficient on the Nonanonymity dummy. Apparently, the presence of dealers at the inside is perceived to be a signal. This is consistent with the regression coefficient which suggests that price impact, and therefore informativness, is greater for non-anonymous quotes rather anonymous quotes Effects of quoting non-anonymously on market quality I also examine the effect of quoting non-anonymously on other aspects of market quality: liquidity and price volatility. The results so far suggest that dealers use non-anonymity strategically. I test the last hypothesis related to the impact of non-anonymity on overall market quality, in terms, of liquidity and price volatility across quintiles. This question is important for market authorities who are concerned on the impact of market making on market quality, i.e. by reducing transaction costs and offering stable prices. For this, I examine how non-anonymous quotes affect market quality by measuring the effects of their submission on transaction costs and price volatility. Similar to price impact, I use the selectivity correction model, utilizing the same first stage, given that the degree of information and market conditions change can affect the use of non-anonymity. Since the decision to go non-anonymously cannot affect the current spread at the time of the trade; I therefore examine the change in spread after 10 minutes of the submission of quotes. If the spread is widening, this implies that order submission is followed by a decrease of liquidity which comes as result of information asymmetry. As well, I measure the change in volatility 20