Cluster Analysis of Liquidity Measures in a Stock Market Using High Frequency Data

Posted: 26 Oct 2017 Last revised: 23 Mar 2018

See all articles by Amin Salighehdar

Amin Salighehdar

Stevens Institute of Technology - School of Business

Yang Liu

Stevens Institute of Technology - School of Business

Dragos Bozdog

Stevens Institute of Technology

Ionut Florescu

Stevens Institute of Technology

Date Written: August 1, 2017

Abstract

Liquidity is one of the crucial factors in economy which reflects smooth operation of the markets. In a liquid market, traders are able to transact large quantities of security quickly with minimal trading cost and price impact. Many researchers have investigated the relationship between market liquidity and trading activity of a financial market. According to the existing literature, liquidity can measure different market characteristics such as trading time, tightness, depth, and resiliency. There is significant number of liquidity measures published in the literature. The main goal of this study is to use a hierarchical clustering algorithm to classify different liquidity measures. We examine the relationship between liquidity measures in order to detect commonality and idiosyncrasy among them. Then, we estimate the correlation among liquidity measures to quantify similarity between them and this quantity is used to develop a hierarchical clustering algorithm. At the end, we analyze the consistency in the structure of the clusters and we conclude that, clusters hold the same structure for almost 80% of the stocks in our sample. The data set that we are using for this study is NASDAQ High Frequency Trader (HFT) data. This data set contains trading and quoting activities of 26 HFT firms in 120 stocks on the Nasdaq exchange for various dates (in millisecond timestamp).

Keywords: Liquidity, High Frequency Trading, Correlation, Hierarchical Clustering

JEL Classification: C50

Suggested Citation

Salighehdar, Amin and Liu, Yang and Bozdog, Dragos and Florescu, Ionut, Cluster Analysis of Liquidity Measures in a Stock Market Using High Frequency Data (August 1, 2017). Stevens Institute of Technology School of Business Research Paper. Available at SSRN: https://ssrn.com/abstract=3059772

Amin Salighehdar

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Yang Liu

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Dragos Bozdog (Contact Author)

Stevens Institute of Technology ( email )

Hoboken, NJ 07030
United States
2012163527 (Phone)

HOME PAGE: http://personal.stevens.edu/~dbozdog/

Ionut Florescu

Stevens Institute of Technology ( email )

Castle Point on the Hudson
Hoboken, NJ 07030
United States

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