We identify and characterize order splitting strategies in an automated limit order market. We model the market conditions and order characteristics, which lead to the use of order splitting strategies. We find a positive correlation between price aggressiveness and the propensity to split an order, which implies, in a utility maximization framework, that traders trade-off one type of aggressiveness for another. We are able to identify two types of traders that use order splitting strategies for different purposes: the reduction of execution costs through split market orders, and voluntary supply of liquidity through split limit orders. The interpretation that split limit orders are submitted by voluntary market makers is consistent with the findings in the experimental study Bloomfield, O’Hara, and Saar (2005).
我们在自动限价订单市场中识别和描述订单分割策略。我们建立了市场条件和订单特征的模型，这导致了订单分割策略的使用。我们发现价格侵略性与拆分订单的倾向之间存在正相关关系，这意味着，在效用最大化框架中，交易者将一种侵略性与另一种侵略性进行权衡。我们能够确定两种类型的交易者使用订单分割策略来实现不同的目的:通过分割市场订单来降低执行成本，以及通过分割限价订单来自愿提供流动性。分割限价单由自愿做市商提交的解释与Bloomfield, O 'Hara, and Saar(2005)的实验研究结果一致。
{"title":"Strategic Order Splitting in Automated Markets","authors":"I. Tkatch, Zinat S. Alam","doi":"10.2139/ssrn.1400307","DOIUrl":"https://doi.org/10.2139/ssrn.1400307","url":null,"abstract":"We identify and characterize order splitting strategies in an automated limit order market. We model the market conditions and order characteristics, which lead to the use of order splitting strategies. We find a positive correlation between price aggressiveness and the propensity to split an order, which implies, in a utility maximization framework, that traders trade-off one type of aggressiveness for another. We are able to identify two types of traders that use order splitting strategies for different purposes: the reduction of execution costs through split market orders, and voluntary supply of liquidity through split limit orders. The interpretation that split limit orders are submitted by voluntary market makers is consistent with the findings in the experimental study Bloomfield, O’Hara, and Saar (2005).","PeriodicalId":447775,"journal":{"name":"Capital Markets: Market Microstructure","volume":"103 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131850463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce a novel estimator of the quadratic variation that is based on the theory of Markov chains. The estimator is motivated by some general results concerning filtering contaminated semimartingales. Specifically, we show that filtering can in principle remove the effects of market microstructure noise in a general framework where little is assumed about the noise. For the practical implementation, we adopt the discrete Markov chain model that is well suited for the analysis of financial high-frequency prices. The Markov chain framework facilitates simple expressions and elegant analytical results. The proposed estimator is consistent with a Gaussian limit distribution and we study its properties in simulations and an empirical application.
{"title":"Quadratic Variation by Markov Chains","authors":"P. Hansen, Guillaume Horel","doi":"10.2139/ssrn.1367519","DOIUrl":"https://doi.org/10.2139/ssrn.1367519","url":null,"abstract":"We introduce a novel estimator of the quadratic variation that is based on the theory of Markov chains. The estimator is motivated by some general results concerning filtering contaminated semimartingales. Specifically, we show that filtering can in principle remove the effects of market microstructure noise in a general framework where little is assumed about the noise. For the practical implementation, we adopt the discrete Markov chain model that is well suited for the analysis of financial high-frequency prices. The Markov chain framework facilitates simple expressions and elegant analytical results. The proposed estimator is consistent with a Gaussian limit distribution and we study its properties in simulations and an empirical application.","PeriodicalId":447775,"journal":{"name":"Capital Markets: Market Microstructure","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114079645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-08DOI: 10.1111/j.1468-2443.2009.01086.x
Daphne Yan Du, Qianqiu Liu, S. Rhee
Using the Korea Stock Exchange's transaction data and limit order book, we document the accelerating patterns of market activity before limit hits. We confirm the existence of the magnet effect from several key market microstructure variables, using a parsimonious quadratic function of the time until the price limit hit. In addition, this paper is the first to isolate the intraday momentum effect from the magnet effect during the period before stock prices hit daily price limits.
{"title":"An Analysis of the Magnet Effect under Price Limits","authors":"Daphne Yan Du, Qianqiu Liu, S. Rhee","doi":"10.1111/j.1468-2443.2009.01086.x","DOIUrl":"https://doi.org/10.1111/j.1468-2443.2009.01086.x","url":null,"abstract":"Using the Korea Stock Exchange's transaction data and limit order book, we document the accelerating patterns of market activity before limit hits. We confirm the existence of the magnet effect from several key market microstructure variables, using a parsimonious quadratic function of the time until the price limit hit. In addition, this paper is the first to isolate the intraday momentum effect from the magnet effect during the period before stock prices hit daily price limits.","PeriodicalId":447775,"journal":{"name":"Capital Markets: Market Microstructure","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125332137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When a bank defaults or stops trading in the interbank market, both a liquidity shortage in the market itself and mounting trading losses should be anticipated. To gain more insight into the way a liquidity crisis spreads, we apply network topology techniques to monthly data on deposits exchanged by Italian banks, from 1990 to 2008. Our research yields three main results: first, only a few banks are today pivotal in the redistribution of liquidity across the system, while banks close to, but outside this core circle, weigh less than they used to; secondly, the halt in operations in a second set of banks may cut off some of their counterparts from the rest of the network, with increasingly less negligible effects; finally, only 2-3 banks out of the 10 we identify as most interconnected within the network are currently also among the top 10 banks by volume of traded deposits.
{"title":"The Topology of the Interbank Market: Developments in Italy Since 1990","authors":"Carmela Iazzetta, Michele Manna","doi":"10.2139/ssrn.1478472","DOIUrl":"https://doi.org/10.2139/ssrn.1478472","url":null,"abstract":"When a bank defaults or stops trading in the interbank market, both a liquidity shortage in the market itself and mounting trading losses should be anticipated. To gain more insight into the way a liquidity crisis spreads, we apply network topology techniques to monthly data on deposits exchanged by Italian banks, from 1990 to 2008. Our research yields three main results: first, only a few banks are today pivotal in the redistribution of liquidity across the system, while banks close to, but outside this core circle, weigh less than they used to; secondly, the halt in operations in a second set of banks may cut off some of their counterparts from the rest of the network, with increasingly less negligible effects; finally, only 2-3 banks out of the 10 we identify as most interconnected within the network are currently also among the top 10 banks by volume of traded deposits.","PeriodicalId":447775,"journal":{"name":"Capital Markets: Market Microstructure","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114904868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We examine the performance of three spread decomposition models which provide estimates of the inventory holding component of the bid-ask spread: the Stoll (1989), Huang and Stoll (1997), and Bollen, Smith, and Whaley (2004) models. As a benchmark for the analysis, we use the order imbalance metrics of Bessembinder’s (2002) and Chordia and Subrahmanyam’s (2002) as well as the percentage spread. We find that the Bollen, Smith, and Whaley (2004) model has the largest number of predicted relations (although the Stoll, 1989 model also performs well) with proxies for inventory holding costs. We recommend that researchers needing to use the inventory holding component of the spread use either the Bollen, Smith, and Whaley (2004) or Stoll (1989) model.
我们检验了三个价差分解模型的性能，这些模型提供了买卖价差的库存持有成分的估计:Stoll (1989)， Huang和Stoll(1997)，以及Bollen, Smith和Whaley(2004)模型。作为分析的基准，我们使用了Bessembinder(2002)和Chordia和Subrahmanyam(2002)的订单不平衡指标以及百分比价差。我们发现Bollen, Smith, and Whaley(2004)模型与库存持有成本的代理具有最多的预测关系(尽管Stoll, 1989模型也表现良好)。我们建议需要使用价差的库存持有成分的研究人员使用Bollen, Smith, and Whaley(2004)或Stoll(1989)模型。
{"title":"An Analysis of the Inventory Holding Components of the Bid-Ask Spread","authors":"Birsel T. Pirim, Bonnie F. Van Ness, R. Van Ness","doi":"10.2139/ssrn.1404804","DOIUrl":"https://doi.org/10.2139/ssrn.1404804","url":null,"abstract":"We examine the performance of three spread decomposition models which provide estimates of the inventory holding component of the bid-ask spread: the Stoll (1989), Huang and Stoll (1997), and Bollen, Smith, and Whaley (2004) models. As a benchmark for the analysis, we use the order imbalance metrics of Bessembinder’s (2002) and Chordia and Subrahmanyam’s (2002) as well as the percentage spread. We find that the Bollen, Smith, and Whaley (2004) model has the largest number of predicted relations (although the Stoll, 1989 model also performs well) with proxies for inventory holding costs. We recommend that researchers needing to use the inventory holding component of the spread use either the Bollen, Smith, and Whaley (2004) or Stoll (1989) model.","PeriodicalId":447775,"journal":{"name":"Capital Markets: Market Microstructure","volume":"131 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134161824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose an origination-and-contingent-distribution model of banking, in which liquidity demand by short-term investors (banks) can be met with cash reserves (inside liquidity) or sales of assets (outside liquidity) to long-term investors (hedge funds and pension funds). Outside liquidity is a more efficient source, but asymmetric information about asset quality can introduce a friction in the form of excessively early asset trading in anticipation of a liquidity shock, excessively high cash reserves, and too little origination of assets by banks. The model captures key elements of the financial crisis and yields novel policy prescriptions.
{"title":"Outside and Inside Liquidity","authors":"P. Bolton, T. Santos, J. Scheinkman","doi":"10.1093/qje/qjq007","DOIUrl":"https://doi.org/10.1093/qje/qjq007","url":null,"abstract":"We propose an origination-and-contingent-distribution model of banking, in which liquidity demand by short-term investors (banks) can be met with cash reserves (inside liquidity) or sales of assets (outside liquidity) to long-term investors (hedge funds and pension funds). Outside liquidity is a more efficient source, but asymmetric information about asset quality can introduce a friction in the form of excessively early asset trading in anticipation of a liquidity shock, excessively high cash reserves, and too little origination of assets by banks. The model captures key elements of the financial crisis and yields novel policy prescriptions.","PeriodicalId":447775,"journal":{"name":"Capital Markets: Market Microstructure","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127331215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study directly compares the level and return predictability of short selling for NYSE stocks to a matched sample of Nasdaq stocks. When considering trading that executes on all exchanges, we document that the Nasdaq has greater levels of short selling, relative to total trading activity, than the NYSE. However, Nasdaq has less relative short activity than the NYSE when considering short selling that executes on the primary exchange. When comparing the contrarian trading behavior and the return predictability of short sellers, we show that Nasdaq short sellers are more contrarian in contemporaneous and past returns and better at predicting negative returns than NYSE short sellers. These results are robust in each trade-size category.
{"title":"Information in Short Selling: Comparing NASDAQ and the NYSE","authors":"Benjamin M. Blau, Bonnie F. Van Ness, R. Van Ness","doi":"10.2139/ssrn.1309801","DOIUrl":"https://doi.org/10.2139/ssrn.1309801","url":null,"abstract":"This study directly compares the level and return predictability of short selling for NYSE stocks to a matched sample of Nasdaq stocks. When considering trading that executes on all exchanges, we document that the Nasdaq has greater levels of short selling, relative to total trading activity, than the NYSE. However, Nasdaq has less relative short activity than the NYSE when considering short selling that executes on the primary exchange. When comparing the contrarian trading behavior and the return predictability of short sellers, we show that Nasdaq short sellers are more contrarian in contemporaneous and past returns and better at predicting negative returns than NYSE short sellers. These results are robust in each trade-size category.","PeriodicalId":447775,"journal":{"name":"Capital Markets: Market Microstructure","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126333530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Otavio Ribeiro de Medeiros, Gustavo R. De Oliveira, B. V. Doornik
This article examines the existence of lead-lag effects between the U.S. stock market, represented by NYSE and the Brazilian stock market, represented by Bovespa, i.e., whether upward and downward price movements in the NYSE are followed, on average, by similar movements in Bovespa, which would enable predicting stock prices in the Brazilian market, thus providing arbitrage opportunities. The existence of this effect would indicate a relative segmentation between these two markets, which would violate the efficient market hypothesis, whereby stock prices are unpredictable. Cointegration between the two markets was identified as well as the existence of bi-directional causality (Granger test). The results obtained from VECM, TSLS and GARCH regressions showed that the two markets are segmented and that returns of the Bovespa Index (Ibovespa) are to a large extent explained by the stock price movements in the Dow Jones Index some minutes beforehand. However, the results also show that the practice of arbitrage based on the lead-lag effects is not economically feasible due to transaction costs.
{"title":"Testing for Lead-Lag Effects Between the American and the Brazilian Stock Markets","authors":"Otavio Ribeiro de Medeiros, Gustavo R. De Oliveira, B. V. Doornik","doi":"10.2139/ssrn.1365322","DOIUrl":"https://doi.org/10.2139/ssrn.1365322","url":null,"abstract":"This article examines the existence of lead-lag effects between the U.S. stock market, represented by NYSE and the Brazilian stock market, represented by Bovespa, i.e., whether upward and downward price movements in the NYSE are followed, on average, by similar movements in Bovespa, which would enable predicting stock prices in the Brazilian market, thus providing arbitrage opportunities. The existence of this effect would indicate a relative segmentation between these two markets, which would violate the efficient market hypothesis, whereby stock prices are unpredictable. Cointegration between the two markets was identified as well as the existence of bi-directional causality (Granger test). The results obtained from VECM, TSLS and GARCH regressions showed that the two markets are segmented and that returns of the Bovespa Index (Ibovespa) are to a large extent explained by the stock price movements in the Dow Jones Index some minutes beforehand. However, the results also show that the practice of arbitrage based on the lead-lag effects is not economically feasible due to transaction costs.","PeriodicalId":447775,"journal":{"name":"Capital Markets: Market Microstructure","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125567732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Equity markets world-wide have seen a proliferation of trading venues and the consequent fragmentation of order flow. In this paper, we examine how fragmentation of trading is affecting the quality of trading in U.S. markets. We propose using newly-available TRF (trade reporting facilities) volumes to proxy for fragmentation levels in individual stocks, and we use a matched sample to compare execution quality and efficiency of stocks with more and less fragmented trading. We find that market fragmentation generally reduces transactions costs and increases execution speeds. Fragmentation does increase short-term volatility, but prices are more efficient in that they are closer to being a random walk. Our results that fragmentation does not appear to harm market quality have important implications for regulatory policy.
{"title":"Is Market Fragmentation Harming Market Quality?","authors":"Maureen O'Hara, Mao Ye","doi":"10.2139/ssrn.1356839","DOIUrl":"https://doi.org/10.2139/ssrn.1356839","url":null,"abstract":"Equity markets world-wide have seen a proliferation of trading venues and the consequent fragmentation of order flow. In this paper, we examine how fragmentation of trading is affecting the quality of trading in U.S. markets. We propose using newly-available TRF (trade reporting facilities) volumes to proxy for fragmentation levels in individual stocks, and we use a matched sample to compare execution quality and efficiency of stocks with more and less fragmented trading. We find that market fragmentation generally reduces transactions costs and increases execution speeds. Fragmentation does increase short-term volatility, but prices are more efficient in that they are closer to being a random walk. Our results that fragmentation does not appear to harm market quality have important implications for regulatory policy.","PeriodicalId":447775,"journal":{"name":"Capital Markets: Market Microstructure","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125470646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines the dual e ffects of informed trading on expected returns: information transmission and information risk. We show that the relationship between informed trading and expected returns can be non-linear in theory and is indeed non-linear empirically. Specifically, the relationship turns out to be U-shaped. Further, we demonstrate that these information eff ects are more apparent under high information uncertainty i.e. young, volatile, or no analyst coverage stocks.
{"title":"Does More Informed Trading Necessarily Lead to Higher Expected Returns?","authors":"Eric N. Hughson, Moonsoo Kang","doi":"10.2139/ssrn.1364038","DOIUrl":"https://doi.org/10.2139/ssrn.1364038","url":null,"abstract":"This paper examines the dual e ffects of informed trading on expected returns: information transmission and information risk. We show that the relationship between informed trading and expected returns can be non-linear in theory and is indeed non-linear empirically. Specifically, the relationship turns out to be U-shaped. Further, we demonstrate that these information eff ects are more apparent under high information uncertainty i.e. young, volatile, or no analyst coverage stocks.","PeriodicalId":447775,"journal":{"name":"Capital Markets: Market Microstructure","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124600872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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