Journal of Quantitative Methods

Impact of Profitability on Trade Credit Supply of Pakistani Firms: A Moderating Role of Financial Distress

Sadaf Noreen, Ahsan Riaz, Nimra Riaz, Nadeem Nazir, Nadia Sadiq — Wed, 27 Dec 2023 00:00:00 +0000

This research aims to elucidate the relationship between trade credit (supply) and the profitability/growth of non-financial companies in Pakistan. The 386 unbalanced panels listed in Pakistan Stock Exchange between 2004 and 2017 are analyzed through GMM with 3976 annual observations. The research indicated the insignificant relationship between profitability and growth with the net trade credit of non-financial companies. On the other hand, net trade credit positively correlates with return on equity, company size, and age. Financial distress has a significant moderating effect on net trade credit, and firms need to increase return on equity and growth to overcome financial distress.

Is this a reality? Occupational stress impacts on Employee Turnover Intention mediated by Democratic Leadership

Ahmad Tisman Pasha, Shahroze Habib, Syed Faraz Ahmed Gilani — Wed, 12 Jul 2023 09:11:02 +0000

Abstract:

Purpose – Human Resource is an essential asset of any organization. Competent pool of employees imparts their potential for the success and sustainability of the organization but the democracy which prevail in some organizations render the potential of employees and employees suffered stress and anxiety which ultimately lead them towards quitting their jobs.

Design/methodology/approach – Self-administered, and online method for surveying was used the gathering of data of the Pakistani banking sector. The data were collected from the 315 respondents. 400 questionnaires were distributed but get back only 315 responses for analysis.

Findings – The structural model of Smart PLS and construct reliability and validity model were used which inclined towards the significant relationship between the variables. Results show that occupational stress and democratic leadership have contingent effect on the employee turnover intention.

Practical Implications – organizations should eradicate the occupational stress on employees. They should develop the self confidence among the employees. Democracy should be minimized, and employees should be rewarded according to their performance not favoritism. These practices can eliminate the turnover intentions from the mind of employees.

Originality/Value – This study suggests that Occupational stress is the important factors that influence directly on the Employee Turnover Intention and Democratic Leadership also mediated the effect of Occupational stress and Employee turnover intention.

Inflation Forecasting Under Different Macroeconomic Conditions: A Case Study of Pakistan

Iqra Iqbal, Ahsan Satti — Wed, 12 Jul 2023 09:10:09 +0000

Inflation forecasting has been important task for monetary authorities, policy makers and government. Prediction about inflation confer us a precise image of how the economy is expected to accomplish in the future. It is essential job for researchers to examine which methods are suitable for inflation forecasting. We have used Naive model, ARIMA model, Philips curve model and Philips Curve (TAR) under different macroeconomic conditions with reference to real-time, revised and final data from 1974 to 2014 and predicted out-of-sample inflation forecast for 2015, afterward we roll-forward our regression from 1975 to 2015 to forecast inflation for 2016.We have analyzed naive model is superior to other modelsbecause RMSE and MAE of naive model are less than other models by using real-time, revised and final data for one year-ahead out-of-sample inflation forecasting. On the other hand for two years ahead out of sample inflation forecast, according to real-time data RMSE shows that naive model is most superior to other models whereas MAE shows that Philips curve Threshold auto regressive model is most superior to other models. According to revised and final data for two years ahead out of sample inflation forecasting both forecasting accuracy measures shows Naive model is most superior to other models.

Economic Welfare and the COVID-19 Pandemic in Nigeria: An Overview of the First Half of 2020

Olakunle Jimoh — Wed, 12 Jul 2023 09:09:07 +0000

Corona virus pandemic is a current misery not only to health status but also to economic wellbeing and to the world at large. This study examines the relationship between the pandemic outbreak and economic welfare using daily data on proximate economic welfare measures-real Gdp and changes in consumer price index-and the disease indicator variables-discharge rate, fatality rate, spread rate and number of tested cases. The Prosperity theory is adopted and the estimation issue is rooted from the robust least squares technique due to the failed normality assumption of the conventional least squares. Results indicate that corona virus due to its spread lowers labour supply and causes production shortages and subsequently results in hike in price and loss of real income value. High discharge rate can increase real income value and hence economic welfare. Immediate Financing, regulatory, equity and diversification strategies are needed to revive the Nigerian economy.

Unraveling the Mystery of Default Prediction: A Study on the Textile Industry in Pakistan

Jahanzaib Alvi, Imtiaz Arif — Mon, 26 Jun 2023 11:07:24 +0000

Default events are inevitable in any economy and can have a considerable impact on economic stability. However, predicting defaults before occurrence has always been a challenging task for researchers around the world. In Pakistan, the textile industry experiences a high rate of default, which motivated us to conduct a study on predicting default in this sector. We analyzed data from 134 listed companies in the textile industry between 2000 and 2020, and segregated the industry into three sub-sectors (Composite, Spinning and Weaving with Textile Associated Products) for better analysis. After reviewing the literature, we identified five widely-used default prediction models which led us to perform a comparative study to validate their performance. Findings revealed that Grover’s G-Score Model was the best predictor of default, followed by Springate’s S-Score Model based on both model accuracy and model validation. However, it is important to note that our study is limited to the textile sector and future studies could include other sectors and more advanced methods to improve accuracy. This study can be useful for investors and financial analysts in assessing the risk of default in the textile industry and making informed investment decisions.

Hybrid Modeling of Arima, ANN and SVM for Macro Variables Forecasting in Pakistan

Hafsa Hina, Rizwan Ali, Amena Urooj — Fri, 24 Feb 2023 13:08:56 +0000

Time series forecasting remains a challenging task owing to its nonlinear, complex and chaotic behavior. The purpose of this paper is to analyze the forecast performance of different models for Pakistan’s macroeconomic variables such as inflation, exchange rate and stock return. In which linear Autoregressive integrated moving average (ARIMA) model and nonlinear models like artificial neural networks (ANN) and Support vector machines (SVM) are employed. Then a hybrid methodology is used which combines the linear ARIMA with nonlinear models of ANN and SVM. The forecasting performance of all models i.e., ARIMA, ANN, SVM, ARIMA-ANN and ARIMA-SVM are compared on the basis of RMSE and MAE. The results indicate that the best forecasting model to achieve high forecast accuracy is the hybrid ARIMA-SVM.

Empirical Assessment of Business Competitiveness and Income Distribution: A Case of Selected Lower-Middle-Income Economies

Muhammad Ibrahim Saeed, Saima Sarwar — Wed, 04 Jan 2023 11:06:28 +0000

The present study attempts to quantify the quadratic nexus between income distribution and business competitiveness. The study also adds interaction term of development expenditure with business competitiveness to examine impact of both on income inequality. The present study uses yearly panel data from 2008 to 2018 for 27 lower-middle-income economies. The results of the study are based on Quantile Regression for Panel Data (QRPD). The findings of the study reveal a U-shaped pattern between business competitiveness and income inequality. Further, an insignificant negative impact of development expenditure on income inequality is observed. However, if lower-middle-income economies take into account development expenditure with business competitiveness then the impact on income inequality gets significant with the same sign. The variable urban population is significant and decreases income inequality while broad money (proxy of financial development), trade, and rule of law have a significant role in increasing income inequality. The study suggests that development expenditure and business competitiveness may increase simultaneously for more equal distribution of income. Moreover, standardization of rule of law in lower-middle-income economies is also very important for equal distribution of income.

Performance of Modality Tests on Existence of Bimodality

Abdul Ghafar Shah, Shahid Akbar, Habib Nawaz — Wed, 04 Jan 2023 00:00:00 +0000

In this paper, the Robertson’s and Fryer’s (1969) conditions are applied for the existence of bimodality in the mixture of normals. Best modality test is the pre-requisite to evaluate whether the existing data is a unimodal or bimodal. On the same data the researchers also assessed the bimodality through modality tests. For this purpose the modality tests are compared on size and power properties designed by Monte Carlo simulation. After using the simulated critical values, the results showed that the entire modality tests have stable sizes. In power assesment, mostly the Silverman Bandwidth test was found the best test.

Triple Bottom Line Corporate Sustainability and Organizational Performance: The Mediation of Employees Work Engagement

Fizza Kanwal, Umer Ayub, Kashif Rathore — Wed, 04 Jan 2023 00:00:00 +0000

Management scholars are currently focusing on designing research that could cater the big issues of 21^st century organizations including sustainability. Current research uses and extends Triple Bottom Line perspective that conceptualizes sustainability as comprising of economic, social and environmental dimensions. Though most of the previous research has focused on sustainability as a consequence of innovation and quality, present research contributes to literature by studying corporate sustainability as an antecedent of quality performance and innovation performance. Employees’ work engagement has also been incorporated in the framework as a potential mediator. To test the conceptual framework, a positivist research philosophy was utilized with cross-sectional design and quantitative approach via structured questionnaires. Data was collected from Iron and Steel Industry of Pakistan that has second highest growth rate for year 2017-18, and the responses aggregated to 216 in number. Structural equation modeling was used to validate the constructs through measurement model, and to test the hypotheses through structural model. The results revealed that corporate sustainability (economic, social and environmental) positively influences organizational performance (quality and innovation performance), and employees’ work engagement partially mediates this relationship. In Pakistan, the GDP and the cost of environmental degradation are increasing simultaneously. Current research offers a solution to this problem through suggesting that adopting sustainable practices can help resolve country’s environmental issues without taking a toll on organizational performance. It also explains that corporate sustainability leads to organizational performance through employees’ work engagement, thus providing an effective way to enhance employees’ work engagement as well.

Evaluating Nonresponse Bias for a Hypernetwork Sample Generated from a Probability-Based Household Panel

Thu, 20 Oct 2022 00:00:00 +0000

Hypernetwork sampling aims to generate representative samples of populations for which a sample frame does not exist or is too costly to construct. This multi-level sampling method relies on nominations from one sample source (Stage 1 sample) to construct another sample (Stage 2 sample). However, nonresponse from the Stage 1 sample has the potential to produce bias in Stage 2 of the hypernetwork sample if Stage 1 respondents differ from nonrespondents. This paper examines nonresponse in a hypernetwork sample of religious congregations in the U.S. generated from a probability-based household panel that includes background information for all panelists including Stage 1 nonrespondents. This study also illustrates the benefits of constructing a hypernetwork sample by using a sample of already recruited panelists for whom information has already been collected. We find Stage 1 nonrespondents tend to be from rural areas and not from the Midwest, compared to Stage 1 respondents. Results also suggest that the impact of subsequent survey reminders on key Stage 1 estimates decreased after the third reminder during Stage 1 fielding. Additionally, we find that Stage 1 nonresponse impacts the Stage 2 estimates for congregational characteristics. Specifically, the congregations nominated by Stage 1 late respondents tend to have the following characteristics: located in the South, predominantly African American, more likely to be conservative/evangelical Protestant or black Protestant, younger, urban or suburban, helped people register to vote, less likely to have a school, and have fewer child participants. Post-survey weighting adjustment of the Stage 1 sample decreased the risk for nonresponse bias in the Stage 1 hypernetwork sample and in the Stage 2 sample of congregations.