Stock Return Predictability and Market Efficiency in Pakistan; A Role of Asian Growing Economies of India and Malaysia

The purpose of this study is to determine the predictability of the Pakistani stock market's one-day forward returns by utilizing lagged daily returns for Pakistan, India, and Malaysia from 2006 to 2016. The findings indicate that lagged Pakistani market returns significantly predict Pakistani one-day ahead market returns. However, the other two growing stock markets, India and Malaysia, show no association with one-day ahead market returns. Mostly, stock market behavior in the pre-2008 and post-2008 eras was the same, although industry return behaviour was different due to the economic crisis of 2008. However, the Pakistani stock market one-day ahead returns predict the own Pakistani lag returns due to an inefficient market and prices do not follow a random walk. As a result, investors and financial analysts can foresee and generate anomalous returns by using previous data and information. Key words: Stock Market Returns Predictability, Stock Market crash, Market efficiency

An empirical testing of comparative efficiency of static and dynamic factor models towards stock returns’ predictability in capital market of Pakistan

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.