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Review of Quantitative Finance and Accounting

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01.02.2010 | Original Research

Dancing in the dark: post-trade anonymity, liquidity and informed trading

verfasst von: Alexandra Hachmeister, Dirk Schiereck

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 2/2010

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Abstract

We analyze the impact of post-trade anonymity on liquidity and informed trading in an order driven stock market. The German stock market introduced the Central Counterparty (CCP) in March 2003 for German equities traded on its anonymous electronic trading platform Xetra leading to a major change in its existing transparency regime. Before the introduction trader IDs were revealed to the counterparties of a trade, with the introduction of the CCP even after the transaction the traders remain anonymous. Previous theoretical and empirical research documents that pre-trade anonymity results in increased liquidity, while results on post-trade anonymity are mixed. We find a significant increase in liquidity measured through a reduction of 25% in implicit transaction costs. We also document that the arrival rate of informed traders is reduced in the anonymous setting. Following recent findings of Bloomfield et al. (J Finan Econ 75:165–199, 2005) that informed traders take on the role of liquidity providers we interpret our findings as indication that informed traders change their behavior in providing liquidity more aggressively in an anonymous environment.

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The controversial discussion concerning post-trade transparency was mainly conducted with respect to delayed trade reporting on the London Stock Exchange. While most continental regulators favored immediate publication of trade data the British regulator did not, which was a major issue in the drafting of the European Union’s Investment Services Directive as implemented in 1996. The effects were analyzed by Naik et al. (1999) and Saporta et al. (1999).

Market participants have openly stated that in the case of so called iceberg orders in the limit order book, traders tried to hit them with the intention of identifying the trading institution behind the large order.

Wells (2000) in his survey of the FIBV members analyses among other market design elements the three dimensions of transparency stating that pre-trade transparency i.e., the distribution of order book information is now common for exchanges with the upstairs market remaining opaque. Post-trade transparency has been discussed more controversial as dealers preferred delaying the information on trades; however limitations are allowed in Europe. For the third dimension anonymity FIBV members supported pre-trade anonymity while post trade-anonymity showed mixed results. Although there was concern by institutional traders for front-running and information leakage only a number of exchanges had moved to post-trade anonymity.

The complete pre-trade and post-trade information is provided to data vendors allowing for further distribution.

A detailed description of the German equity market is provided in Theissen (2003b).

Simaan et al. (2003) argue based on a collusion rationale that pre-trade anonymity reduces the probability for collusion; consequently spreads are lower leading to an increase in liquidity. We believe that the collusion argument will not hold in a pure limit order book market with a large number of intermediaries as for the DAX instruments on Xetra. In addition, a central feature of pure order book markets is the absence of dedicated market makers.

Effects to changes in pre-trade transparency may differ from the effects of changes in the degree of anonymity in a market. Boehmer et al. (2005) studying the introduction of pre-trade transparency at the NYSE while holding anonymity constant document improved liquidity along with increased pre-trade transparency (distribution of order book information). In contrast, Madhavan et al. (2005) report that the introduction of pre-trade transparency on the Toronto Stock Exchange led to a decrease in liquidity. From an empirical point of view the results concerning the effects of pre-trade transparency on market quality are less clear. See also Boehmer (2005) and Easley et al. (1997).

These results are also supported by Grammig et al. (2001), who compare the probability of informed trading for the Floor and fully automated trading system IBIS of FSE. They conclude that informed traders have a clear preference for anonymous markets.

Foucault et al. (2004) test their theoretical model for the CAC40, the largest and most actively traded French instruments. They assume that the fraction of informed traders is small which is in line with Easley et al. (1996) who find that large and actively traded firms have a lower probability of informed trading. The outcome of their model depends on the size of the fraction of informed traders. For their setting a positive relation between anonymity and liquidity is tested as significant. Our analysis is based on the DAX30 instruments which we expect to shows similar characteristics as the CAC40. In addition, if trader ID’s are informative for orders there is no obvious reason why they should not be for trades.

In order to test this hypothesis we will compare the results for a liquidity measure covering the different dimensions of liquidity for a defined period before and after the event. The null hypothesis tests that anonymity has no effect on liquidity. In order to reject the null hypothesis we would expect to find significant differences in means and medians for the pre and post event period for the defined liquidity measure. These results are further validated by a multivariate analysis.

We test against the null hypothesis that the difference in the mean and median in share of informed traders for the pre and post event period is not significantly different from zero. These results are also further validated by a multivariate analysis.

The index DAX is based on prices generated in the electronic trading system Xetra and measures the performance of the Prime Standard’s thirty largest German companies in terms of order book volume and market capitalization.

Irvine et al. (2000) are the first to suggest liquidity measures based on round trip costs.

The design and trading rules of the fully electronic trading system Xetra resemble other open limit order book markets such as Euronext as described in Foucault et al. (2004), the Hong Kong stock exchange as described in Ahn et al. (2001) and the Australian stock exchange as described in Cao et al. (2004).

The trading hours were reduced on 1 November 2003 to 5.30 p.m. after consultation with market participants and exchange council for the Xetra trading system only. The floor of the FSE is still open until 8 p.m.

In general, provision of additional liquidity through designated market makers so called designated sponsors is supported. Market Participants that act as designated sponsors can enter quotes into the system. In order to be traded in the trading model ‘continuous trading with auctions’ an instrument requires at least one designated sponsor. However DAX instruments which have sufficient liquidity as measured by the XLM and their trading volume are exempted from this rule. Designated sponsors are in contrast to specialist or market makers not obliged to provide quotes. Their quoting performance is measured daily and a ranking is published quarterly.

Marketable orders are either market or limit orders with limit prices that make them immediately executable.

In contrast to other exchanges e.g., Euronext, the tick size for equities traded in Xetra is not a function of the stock price level.

The ultimate and legally binding terms for trading at the FSE are laid down in the rules and regulations of the exchange, especially the exchange rules and the terms and conditions for transactions. The market model serves as a basis for the rules and regulations which, nevertheless, may contain additional terms. In addition to the market model for equity trading, Xetra provides a block crossing model and an additional model for warrant and bond trading (‘continuous auctions’). For details concerning the market model see Deutsche Boerse (2004): market model equities—Xetra release 7.1.

Central Counterparties have become popular not only for derivatives markets, where due to the high risks involved they are common for decades but also for equities. For US equities the NSCC (National Securities Clearing Corporation) is the defined central counterparty and was already introduced in 1977.

The parameters for trading activity count both sides of the transaction. For parameters on order book turnover a simple division by to two is required. However this still includes partial executions of orders.

StatistiX provided three separate files for all trading days during February–May 2003 for the 30 instruments constituting the DAX: trades file, BBA file, XLM file. The trades files contains all time stamped trades with related order information i.e., order number and order entry time stamp. The BBA file provides the best bid and asks during the trading day and the XLM file included intraday values for all available volume classes.

The XLM covers three dimensions of liquidity: market breadth, market depth and immediacy in execution. The fourth dimension ‘market resilience’ can be assessed through the change in results over the course of time. The XLM thus follows the concept of four dimensions of liquidity as described in Harris (1990). Gomber et al. (2005) analyze the time dimension of liquidity ‘resilience’ and find that large transactions are timed, i.e., discretionary traders with large orders wait until the liquidity in the market is high enough.

The liquidity premium added for both sides of the order book is the well known and often applied quoted percentage spread.

The XLM provides the weighted average price at which an order of a given size can be executed immediately at time t with P _B,t(V) and P _S,t(V) where the index (B, S) indicates if the transaction was buyer—or seller initiated and V denotes the order size and MQ_t the quote midpoint at time t:

\( {\text{XLM}}_{B,t} (V) = 10,000\;{\frac{{P_{B,t} (V) - {\text{MQ}}_{t} }}{{{\text{MQ}}_{t} }}} \) and \( {\text{XLM}}_{S,t} (V) = 10,000\;{\frac{{{\text{MQ}}_{t} - P_{S,t} (V)}}{{{\text{MQ}}_{t} }}} \) which leads to \( {\text{XLM}}_{t} (V) = {\text{XLM}}_{B,t} (V) + {\text{XLM}}_{S,t} (V) \).

The transaction sizes >500k were excluded from the sample as they are far from any typical order size. In addition 25k was excluded as in this case the LP would be much larger than the APM and as the quoted percentage spread is included as a liquidity measure no additional information is provided.

Adverse selection costs are part of costs imposed through the bid-ask spread (usually inventory, transaction and adverse selection components of the spread are distinguished). As such the XLM includes the adverse selection costs as part of the market impact calculation, but does not provide any information on their share of the market impact costs.

We follow Bloomfield et al. (2005) in assuming that liquidity is also provided by informed traders.

The EKOP model has been applied to order book markets for example by Brockman and Chung (2000), and Grammig et al. (2001).

Kyle (1985) concluded that a model with one market maker compared to several competitive market makers lead to the same results under a zero profit assumption. Thus, in the limit order book the sum of all liquidity providers resembles the market makers. Supporting this assumption Chung et al. (2004) find that almost 75% of the bid and ask quotes on the NYSE reflect interest of limit order traders on at least one side of the book. It should be noted that market maker inventory does not enter the EKOP model, thus there is no difference between market makers and liquidity providers in the costs they are facing, i.e., adverse selection and transaction costs. In addition, the EKOP model assumes only for liquidity takers (informed and uninformed) exogenous arrival rates while an exogenous arrival rate for liquidity providers is not necessary to ensure model consistency.

We implement the log likelihood function as presented in Easley et al. (2002): \( L\left( {\{ y_{t} \}_{t = 1}^{T} \theta } \right) = \sum\limits_{t = 1}^{T} {\left( { - 2\varepsilon + M\ln x + (B + S)\ln (\mu + \varepsilon )} \right)} + \sum\limits_{t = 1}^{T} {\left( {\ln (\alpha (1 - \delta )e^{ - \mu } x^{S - M} + \alpha \delta e^{ - \mu } x^{B - M} + (1 - \alpha )x^{B + S - M} )} \right)} \) where M ≡ min(B, S) + max(B, S)/2 and x ≡ (ε/μ + ε) ∈ [0,1].

Xetra attaches an order number to each order entered into the system. No matter how often an order is partially executed the information that the partials belong to one order can be identified with the unique order number.

During any type of auction the order book displays an indicative price (if available) instead of the best bid-ask. In addition, it is not possible to determine the trade initiator during batch auctions.

We estimated two regression equations differing with respect to the inclusion of TradedVol as independent variable. As expected we find collinearity between the arrival rate of uninformed investors (UnInformed) and average traded volume. Including TradedVol does not improve the results of the regression. The results are not reported but are available upon request.

For 18 out of 30 instruments significant results at the 0.01 level are reported.

For brevity reasons Table 4 only reports the APM for the 250k volume class. Results for the 500k volume class are comparable.

Bayer AG ranked as number 10 in terms of traded volume, five in terms of number of trades is only number 13 ranked in terms of liquidity as measured by XLM.

See, for example, Stoll (2000).

See Table 3.

The general notion of an inverse relation between PINF and average traded volume is also not supported by our individual data. However, when we arrange for two sub-samples with high average traded volume and low average traded volume the relationship holds.

In addition to the t-test a non parametric test is required for examination of the distribution of parameters as the restriction to non-negative numbers of the arrival rate parameters ε and µ and the restriction to [0,1] for the probability parameters α and δ violates the normality assumption.

The inclusion of TradedVol as independent parameter does not increase the explanatory results of the regression analysis and leads to collinearity with the parameter UnInformed.

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Boehmer E, Saar G, Yu L (2005) Lifting the veil: an analysis of pre-trade transparency at the NYSE. J Finan 60:783–815CrossRef

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Brockman P, Chung DY (2000) Informed and uninformed trading in an electronic, order-driven environment. The Finan Rev 35(2):125–146CrossRef

Deutsche Boerse AG (2004): Market model equities—xetra release 7.1, http://deutsche-boerse.com/dbag/dispatch/en/binary/gdb_content_pool/imported_files/public_files/10_downloads/31_trading_member/10_Products_and_Functionalities/20_Stocks/50_Xetra_Market_Model/Marktmodell_Aktien_R7.pdf

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Titel: Dancing in the dark: post-trade anonymity, liquidity and informed trading
verfasst von: Alexandra Hachmeister
Dirk Schiereck
Publikationsdatum: 01.02.2010
Verlag: Springer US
Erschienen in: Review of Quantitative Finance and Accounting / Ausgabe 2/2010
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-010-0165-4

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