<p>Bankruptcy prediction is one of the core area in finance that is quite rich in empirical and theoretical work. This study compares two models for measuring the financial position of financial firms listed in Karachi Stock Exchange. The study gives a comprehensive review of two models, namely Altman’s [1] Z-score and an O-Score derived from Ohlson [14]. The purpose of this paper is two folded. First to identify unique characteristics of business failure and to compare effective variables responsible for this response. Secondly to compare two popular accounting-based measures. summarize publiclyavailable information about bankruptcy. The sample period for this study is from 2009 to 2015. From the KSE listed financial firms, a total of 40 firms were selected and accounting ratios were extracted from balance sheet analysis reports published by State bank of Pakistan. The empirical results concluded that the logit model has a high rate of classification as compared to multiple discriminant analysis. The model has obtained overall 85.5% accuracy and identified three significant accounting ratios that are: retained earnings to total asset, earnings before income and taxes to the total asset, and current liabilities to total asset. The finding of this study would benefit stakeholders that are affected by bankruptcies. So in order to take an advantage, it is important to understand the phenomenon that causes bankruptcies.</p>

Bankruptcy prediction has long been an important concern for various stakeholders in an increasingly intricated business environment. Using a sample of 3,806 company-year observations of listed non-financial companies of Pakistan during 2005-2015, the paper compares models and identifies an optimal approach in terms of forecasting accuracy for predicting financial distress and bankruptcy. The purpose is to develop a model with relatively high predictability and figure out determinants of bankruptcy. By employing financial ratios, equity market variables and macroeconomic indicators; the hybrid artificial neural network (ANN) validates superior performance as opposed to dynamic panel probit and Merton-KMV models individually. Among financial ratios; quick ratio, cash ratio, current to total asset, quick to total asset, cash flow to short-term debt, gross profit margin, asset turnover, interest to debt, net working capital to net sales, and cash to net sales are crucial in examining firm’s financial status. Additionally, money supply, forex reserves, exchange rate, balance of trade, and real GDP growth rate are found statistically meaningful in predicting bankruptcy.

Currency devaluation plays an important role in reshaping trade balance counties like China and Malaysia have experienced export-led growth based on maintenance of their devalued currencies. This study provides the effect of recent currency devaluation of Pakistan on its trade with neighboring countries namely China, India, and Iran. The countries are of remarkable importance because the trade climate of the region is in transition in the essence of China-Pakistan Economic Corridor (CPEC). The paper adopts the elasticity approach to test the effect of devaluation by examining Marshall-Lerner condition. The study uses Panel data estimation models to determine import and export elasticities which serve as inputs for Marshall-Lerner condition. The data of monthly frequency is analyzed over the period of January 2005 to May 2018. The empirical results show that Marshall-Lerner condition does not hold for Pakistan bilateral trade to China and India, however, fulfilled for Iran. It suggests that devaluation of December 2017 of PKR has improved the trade balance of Pakistan with Iran but deteriorated with India and China. The results of the study would allow us to understand the cost and benefits of regional trade integration.

The purpose of this paper is to investigate the determinants of Asian rice exports to the European market that comprise of 27 member countries. The EU-27 emerged as the net importer of rice as well as the biggest importer of agricultural products. Privileged rice exporting countries including India, Pakistan, Thailand, and Vietnam mainly stem from preferential treatment given by the European Union. Using a 19-year panel data over 2001-2019 for four top rice exporting countries augmented gravity model is tested with additional variables including rice yield, exchange rate, the population of importing region, distance, and rice price indices in importing and exporting regions. Results of the study suggest that rice yield, exchange rate, the population of importing region, distance, and rice price in importing region are statistically meaningful in determining the level of exports of rice to the EU-27 region. Analysis in this paper contends that policymakers in top rice-producing countries should focus on competitive exchange rate regimes as well as set price below what is prevailing in the EU-27 region.