LIQUIDITY RISK VS. UNDERINVESTMENT PROBLEM : AN EMPIRICAL STUDY OF THE TEXTILE SECTOR OF PAKISTAN

This research study tests the underinvestment hypothesis andthe liquidity risk hypothesis in the textile sector of Pakistan. A paneldata set of 105 textile companies has been employed over eight yearsextending from 2004-2011. Using 2-Stage Least Square Estimationprocedure (2SLS) and the Generalized Method of Moments (GMM),the empirical findings reveal that textile firms in Pakistan use highlevel of long term debt to shrink the liquidity risk which allows thefirms to use more debt. Moreover growing firms use less leverage thanthe non growing firms when exposed to high growth opportunities.The growth opportunities exhibited a negative relationship with debtmaturity but no significant economic relationship with leverage. Inthe textile sector of Pakistan leverage and short term debt maturitystructure tends to complement each other to hedge the firms againstthe liquidity risk

Which Pairs of Stocks should we Trade? Selection of Pairs for Statistical Arbitrage and Pairs Trading in Karachi Stock Exchange

Pairs Trading refers to a statistical arbitrage approach devised to take advantage from short term fluctuations simultaneously depicted by two stocks from long run equilibrium position. In this study a technique has been designed for the selection of pairs for pairs trading strategy. Engle-Granger 2-step Cointegration approach has been applied for identifying the trading pairs. The data employed in this study comprised of daily stock prices of Commercial Banks and Financial Services Sector. Restricted pairs have been formed out of highly liquid log share price series of 22 Commercial Banks and 19 Financial Services companies listed on Karachi Stock Exchange. Sample time period extended from November 2, 2009 to June 28, 2013 having total 911 observations for each share prices series incorporated in the study. Out of 231 pairs of commercial banks 25 were found cointegrated whereas 40 cointegrated pairs were identified among 156 pairs formed in Financial Services Sector. Furthermore a Cointegration relationship was estimated by regressing one stock price series on another, whereas the order of regression is accessed through Granger Causality Test. The mean reverting residual of Cointegration regression is modeled through the Vector Error Correction Model in order to assess the speed of adjustment coefficient for the statistical arbitrage opportunity. The findings of the study depict that the cointegrated stocks can be combined linearly in a long/short portfolio having stationary dynamics. Although for the given strategy profitability has not been assessed in this study yet the VECM results for residual series show significant deviations around the mean which identify the statistical arbitrage opportunity and ensure profitability of the pairs trading strategy. JEL classifications: C32, C53, G17 Keywords: Pairs Trading, Statistical Arbitrage, Engle-Granger 2-step Cointegration Approach, VECM