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.

No abstract

Efficient Market Hypothesis has its supporters and critics as it has invited significant attention of research scholarship in recent years. The taxonomy and existence of this hypothesis is widely debated in terms of making economic decisions in the capital markets. Stock returns predictability has galvanized researchers to use forecasting models. Literature shows that forecasting is possible yet it debates problems associated with the techniques used for forecasting from the time series data. The study relies on stock returns for 67 randomly selected companies listed on the Pakistan Stock Exchange. The static and the dynamic factor models are compared in terms of forecast efficiency. The study also uses eight macroeconomic variables to forecast stock returns by including gold prices, crude oil prices, market capitalization, PSX- 100 index, PSX-100 index turnover, KIBOR 1-month rates, KIBOR 3 years rates and Rupee to Dollar rates. The results of the hit rates and out-of-sample forecasting technique suggest that dynamic factor model is the best multivariate time series forecasting model in the Pakistani context.