Probability of Stock Price Crashes: A Closer Look towards Pakistan Stock Market

Abstract

The concept of stock price crash probability has become popular in recent years. In Pakistan, being an emerging economy, this area needs to be explored yet. This research investigates the impact of internal and external predictors effecting stock price crash probability for the period of 2006-2021. Pooled OLS is utilized as a baseline regression with clustering at firm level to test the hypothesis. For robustness analysis, in order to capture issues of serial correlation and cross sectional dependency, FGLS and GMM regression techniques are employed. Furthermore, in this study theoretical arguments are built on the basis of bad news hoarding theory, agency theory and information asymmetry. The findings suggest that corporate tax avoidance has an insignificant relation with stock price crash probability. While, other predictors have an inverse relation with stock crash probability. The results are consistent while using FGLS technique however, GMM estimates exhibit corporate tax avoidance as a significant predictor. Overall, the outcomes support H2 and H3, while H1 is rejected particularly for market of Pakistan. This study attempts to assist investors, regulators and policymakers to timely predict the chances of a stock price and thus make corrective decisions to mitigate its chances.