{"title":"Liquidity and Price Discovery of Algorithmic Trading: An Intraday Analysis of the SPI 200 Futures","authors":"Tina Prodromou, Hui Zheng, P. Westerholm","doi":"10.2139/ssrn.1913693","DOIUrl":"https://doi.org/10.2139/ssrn.1913693","url":null,"abstract":"We study the intra-day impact of algorithmic trading on the futures market to increase our understanding of algorithmic trading and its role in the price formation process. First, we find that algorithmic trading provides liquidity when the spread is wide and that algorithms enter the market at a series of intervals that decrease the spread. Second, we show that algorithmic trading is related to lower adverse selection and is unrelated to realised spreads. Third, we confirm that information asymmetry is highest at the beginning of the trading day, and as the price stabilises during the trading day, we find that the trade becomes the information carrier and algorithmic trading increases. Fourth, we find that algorithmic trades strategically enter the market during periods with less informed trading, while the period following exhibits higher public and private information. Our results suggest that algorithmic traders contribute to the price discovery process of financial markets.","PeriodicalId":192246,"journal":{"name":"Lead Session: Liquidity","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126022973","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}
{"title":"Systematic Liquidity Risk in the Australian Bond Market","authors":"Timothy Whittaker","doi":"10.2139/ssrn.1913926","DOIUrl":"https://doi.org/10.2139/ssrn.1913926","url":null,"abstract":"This research examines recent developments in asset pricing theories and their ability to explain Australian bond market returns. This study develops a multifactor bond pricing model in an Australian setting. We examine the Lin et al. (2011) systematic liquidity factor to evaluate its power in explaining Australian bond returns. This study shows that the term, default and liquidity factors are important systematic risk factors in explaining the variation of returns of individual bonds and bond portfolios in Australia. The Australian bond pricing model developed in this study allows market participants to evaluate the risk factors that drive Australian bond portfolio returns regardless of their credit rating, liquidity, duration or industry sector concentration. In a simple case study, the Australian bond pricing model explains more than 82 percent of the variation of returns of Public Private Partnership (PPP) bond portfolios comprising of firms that are financially solvent.","PeriodicalId":192246,"journal":{"name":"Lead Session: Liquidity","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124008397","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}