"Cluster Analysis of Liquidity Measures in a Stock Market Using High Frequency Data"
In this paper, we analyze commonality and idiosyncrasy pattern among existing liquidity measures for a stock market. To achieve this goal, we use correlation to quantify similarity between liquidity measures. This analysis is used to develop a hierarchical clustering algorithm to classify liquidity measures into different categories. Also, the consistency of the cluster structure has been studied during the sample period. This study has been done using high frequency data. The results of this study might be used to develop a new liquidity index which can capture most of the market features such as tightness, depth, and transaction cost.
Mr. Salighehdar is a Ph.D. candidate in Financial Engineering Division at Stevens Institute of Technology. His primary research interests include liquidity analysis and developing asset pricing models using liquidity factors. He earned his master degree in Financial Engineering Division from Stevens Institute of Technology in 2016. He also hold master and bachelor degrees in Electrical Engineering from Isfahan University of Technology, IRAN.
In addition to his Ph.D. program, Mr. Salighehdar is working as a lab assistant in Halon Financial System Center since 2014. Due to his diverse set of research interests he is working on interdisciplinary research projects such as liquidity, robotic arm, and storm surge prediction. He is also administrator for the Hadoop Cluster. Coming from his academic backgrounds, Mr. Salighehdar is teaching two lab courses, SAS in finance (FE517) and VBA in Finance (FE514). He was one of the Ph.D. candidates who was accepted as intern at JPMorgan & Chase, working as a Risk Analyst for the auto-finance team. His responsibility was to develop risk forecast models using statistical methods.