Lada A. Adamic, Celso Brunetti, J. Harris, A. Kirilenko
{"title":"论交易网络的信息属性","authors":"Lada A. Adamic, Celso Brunetti, J. Harris, A. Kirilenko","doi":"10.2139/ssrn.1361184","DOIUrl":null,"url":null,"abstract":"We apply network analysis to trace patterns of information transmission in an electronic limit order market. If market orders or large executable limit orders are submitted by informed traders, then resulting star-shaped or diamond-shaped patterns – or trading networks – should be associated with large changes in returns, smaller volume, and short duration between trades. In contrast, the execution of small limit orders from uninformed traders should result in networks with many triangular and reciprocal patterns and be associated with smaller changes in returns, larger volume and longer duration between trades. We compute a time series of trading networks using audit trail, transaction-level data for all regular transactions in the September 2008 E-mini S&P 500 futures contract – the cornerstone of price discovery for the S&P 500 Index. We find that network metrics that quantify the shape of a network are statistically significantly related to returns, volatility, volume, and duration.","PeriodicalId":440574,"journal":{"name":"ERN: Asymmetric & Private Information (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"On the Informational Properties of Trading Networks\",\"authors\":\"Lada A. Adamic, Celso Brunetti, J. Harris, A. Kirilenko\",\"doi\":\"10.2139/ssrn.1361184\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We apply network analysis to trace patterns of information transmission in an electronic limit order market. If market orders or large executable limit orders are submitted by informed traders, then resulting star-shaped or diamond-shaped patterns – or trading networks – should be associated with large changes in returns, smaller volume, and short duration between trades. In contrast, the execution of small limit orders from uninformed traders should result in networks with many triangular and reciprocal patterns and be associated with smaller changes in returns, larger volume and longer duration between trades. We compute a time series of trading networks using audit trail, transaction-level data for all regular transactions in the September 2008 E-mini S&P 500 futures contract – the cornerstone of price discovery for the S&P 500 Index. We find that network metrics that quantify the shape of a network are statistically significantly related to returns, volatility, volume, and duration.\",\"PeriodicalId\":440574,\"journal\":{\"name\":\"ERN: Asymmetric & Private Information (Topic)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Asymmetric & Private Information (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.1361184\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Asymmetric & Private Information (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.1361184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On the Informational Properties of Trading Networks
We apply network analysis to trace patterns of information transmission in an electronic limit order market. If market orders or large executable limit orders are submitted by informed traders, then resulting star-shaped or diamond-shaped patterns – or trading networks – should be associated with large changes in returns, smaller volume, and short duration between trades. In contrast, the execution of small limit orders from uninformed traders should result in networks with many triangular and reciprocal patterns and be associated with smaller changes in returns, larger volume and longer duration between trades. We compute a time series of trading networks using audit trail, transaction-level data for all regular transactions in the September 2008 E-mini S&P 500 futures contract – the cornerstone of price discovery for the S&P 500 Index. We find that network metrics that quantify the shape of a network are statistically significantly related to returns, volatility, volume, and duration.