Cluster Analysis of Liquidity Measures in Stock Market
Liquidity is one of the crucial factors in economy which reflects smooth operation of the market. In a liquid market, traders are able to trade large quantities of security quickly with minimal trading cost and little price impact. Many researchers have investigated the relationship between market liquidity and trading activity which are two important features of financial market. Huge amount of research has been done in this area including trying to find patterns in liquidity and trading activity. According to the literature, it is shown that liquidity can measure different aspects of market such as trading time, tightness, depth, and resiliency. This study tries to examine the relationship between liquidity measures in order to get more insight as to compare and contrast them. To this end, we utilize a hierarchical clustering method to analyze the correlations between different liquidity measures. 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). We show that the clustering method can be used to reduce the number of liquidity measures without loss of information.