Factors Affecting Commercial Banks Profitability in Pakistan

Due to significant contribution of commercial banks in the economic progress of Pakistan, this research has been carried out to analyse the possible effect of different relevant factors on the profitability of commercial banks in the country. Profitability is measured by return on assets (ROA). Using pooled regression analysis on yearly data collected from the annual reports for a panel of 14 commercial banks for eight years from 2007 to 2014, it was found that equity to assets, debts to assets, deposits to assets, bank size and assets management have a significant influence on the commercial banks profitability in Pakistan.

Self-Organising (Kohonen) Maps for the Vietnam Banking Industry

This is the first study to use the self-organisation (Kohonen) map technique, an artificial neural network based on a non-supervised learning algorithm, to categorise Vietnamese banks into super-class groups. Drawing on unbalanced yearly data from 2008 to 2017, this study identifies two super-class groups (one and two). While group one consists of joint stock banks, group two consists of commercial state and joint stock banks. Using the non-structural indicator, the Lerner index, to capture market power, and the data enveloped analysis technique to measure bank performance, our result shows significant differences in Lerner scores (which represent bank market power) of the two groups of banks. Differences in the Lerner scores provide evidence of a group of strong banks that is isolated from other banks. This implies that this strong bank group has the potential to be monopolist and impairs Vietnam’s competitive banking environment. The reason is that group two banks may be more profitable due to greater market power, whereas group one banks may struggle to cut costs to remain viable. These findings provide a better understanding for bank executives, policymakers and regulators of the Vietnam banking industry, and ensure an efficient and competitive Vietnam banking environment.

EBITDA Index Prediction Using Exponential Smoothing and ARIMA Model

Forecasting has become essential in different economic sectors for decision making in local and regional policies. Therefore, the aim of this paper is to use and compare performance of two linear models to predict future values of a measure of real profit for a group of companies in the fashion sector, as a financial strategy to determine the economic behavior of this industry. With forecasting purposes, Exponential Smoothing (ES) and autoregressive integrated moving averages (ARIMA) models were used for yearly data. ES and ARIMA models are widely used in statistical methods for time series forecasting. Accuracy metrics were used to select the model with best performance and ES parameters. For the real profit measure of the financial performance of the fashion sector in Colombia EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) was used and was calculated using multiple SQL queries.

Impact of Outflows Migration on Inflows Remittance of Different Countries in SAARC Region

The main objective of the study is to determine the impact of migration on remittance for some different countries in SAARC region. Country wise yearly data of outflows migrations and inflows remittances from 1990 to 2017 are collected from 6 countries in SAARC region which are considered in the analysis. To complete this study, some statistical analysis like as multivariate analysis and panel analysis are computed. The highest number of average migrant is found in India with yearly average number of out migrant is 209992 and average inflow remittances is 33214.45 million US dollars which is also high among these six countries, whereas the lowest number of out migrated people are found in Maldives with a yearly average number of migrants is 437 and the amount of average inflows remittances is 3.08 million US dollars which is also lowest among in SARRC countries. India, Bangladesh and Pakistan are comparatively high out migrated countries as well as high remittance receiving countries. In MANOVA analysis, significant Hotelling test statistic indicates the population mean vector with migration and remittances for different countries in SAARC region are not equal. LM test statistic supported to run a panel regression model for analyzing the data. Furthermore significant Hausman test statistic suggests for analyzing fixed effect panel regression model. Outputs from panel regression model show that there have significant positive contributions of migration for all of these countries to the remittance. i.e. if the outflows migration of these region increases, the yearly average inflows remittance will be increased. Considering Bangladesh as a base country in dummy variable regression model in panel analysis, all others countries have significant contribution of migrations to remittances compared with that of Bangladesh. One can use simulation study for getting fruitful results. Also the researcher can apply discrimination analysis to get better results.

The Effect of Foreign Direct Investment and External Debt on Economic Growth in Indonesia

Indonesia as a development country, has a good economic growth in the 1990's. It shows by increasing of GDP year by year, stabilization of inflation, etc. But since 1997's economic crisis in Asia's countries, Indonesia's economic growth has been declining. It effected the monetary sector and real sector, and add again with progressively the amount of foreign debt of Indonesia, so that effect of Rupiah rate wich progressively weakening. This paper will analyze the foreign direct investment also foreign debt, on the economic growth of Indonesia. By using the OLS model on Indonesia yearly data from 1975-2009 and the confirm the significant of these independent variables as the factors that effected the economic growth of Indonesia. Foreign direct investment and foreing debt represent the way able to be gone through by government in overcoming deficit of national saving utilize to push the national development to get the good economic growth. Pursuant to things told above, writer try to study the problem of economic growth in Indonesia in its relation with the foreign direct investment and foreign debt by lifting title “Influence on The Foreign Direct Investment and The Foreign Debt to Economic Growth of Indonesia”.

How does Financial Development and Petroleum Price cause Trade Openness in Pakistan?

In the past financial development and petroleum prices have been identified as acrucial factor influencing economic growth. This provoked us to explore the way financial development and petroleum prices influence the trade openness in Pakistan. The sample of yearly data is collected from 1980 to 2016 in order to apply ARDL cointegration method. Our results reflect the presence of long term cointegration between trade openness and its factors. This suggest that with the rise in credit in private sector there is eventual impact on imports and exports whereas the international petroleum prices also impact the same by pushing the prices of goods. Hence it is recommended that hedging the oil prices and the expansion of credit in Pakistan is worthwhile in terms of trade openness.

The Nexus between Digital Finance and Economic Development: Evidence from China

In the context of the digital economy and based on the characteristics of digital financial development in China, this paper investigates the effect of digital finance on economic growth and explores its influencing mechanism. A panel econometric model, mediating effect model, and instrumental variable method were employed to evaluate yearly data from 30 provinces of China from 2011 to 2018. The results show that the development of digital finance has significantly driven economic growth, which is quantitatively robust after the selection of historical data as instrumental variables and other robustness tests. A heterogeneity analysis proved that provinces in the central and western regions, which have a lower urbanization rate and lower physical capital, more clearly embody the facilitating impacts of digital finance on economic growth compared to their counterparts in other regions. Further analysis found that the development of digital finance has spurred the liberation of regional entrepreneurship, which in turn promoted economic growth—that is, there is an entrepreneurial channel by which digital finance could boost economic growth.

Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm

Although the literature on the relationship between economic growth and CO2 emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960–2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP). Despite the phase of low Italian economic growth, results reveal that CO2 emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO2 increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.

TERRORIST ATTACKS AND CORPORATE INVESTMENT IN INDONESIA

Using yearly data from 1997 to 2017, this paper studies the effect of terrorism (number of attacks) on corporate investment in Indonesia. Applying an investment-type model, we show that firms reduce their capital expenditure due to an increase in the number of terrorist attacks. On average, a one standard deviation increase in the number of terrorist attacks reduces corporate investment by 9.23%. We also find heterogenous reactions of firms to terrorism across different sectors and different panels based on firm characteristics. Finally, our main results remain consistent after performing several robustness tests.

Understanding the effects of banking profitability in Pakistan

The purpose of this study is to investigate the bank specific determinants related to the performance of public and private sector banks in Pakistan. Using strongly balanced panel yearly data from 2010 to 2017, Pooled OLS, fixed effect, Random effect and Random Effect Mundlak Transformation (REMT) have been utilized to provide the empirical evidences in credit risk management in Pakistan. The identification of suitable explanatory variable that explains the banking profitability wisely is made possible by using the panel data techniques. In this study, impact of bank specific variables are: Return On Assets, Capital Ratio, Credit Risk, Credit deposit ratio, Liquidity Ratio, Interest expended to interest earned, bank size and ownership on the profitability of banks in Pakistan has been assessed using four different panel data techniques. Out of the four estimation strategies Random Effect with Mundlak (1978) transformation raises the overall variation of the baseline model to 63% that is explained by banking profitability. Ignoring the time-invariant characteristics in the model, credit deposit ratio and interest expanded to interest earned possess negative relationship with return on assets of banks. Size of the bank is positive and significant when with-in and between banks information is augmented in Radom effect method of estimation. However the size of banks may not affect the banking profitability by allowing correlation between unobservable heterogeneity using Random Effect with Mundlak (1978) transformation.