Measuring changes in energy consumption and carbon dioxide emissions of various large economies is fundamental for analyzing the impact and effectiveness of various policies in this direction. This study analyzes intertemporal changes in energy and CO₂ emissions efficiency of economies by applying a network data envelopment analysis approach that takes into consideration the internal structure of the analysis units. We have applied two divisional network data envelopment analysis models for analysis of the economic and distributive efficiency of economies from 2001 to 2011. The results are very useful in analyzing the situation; we found that none of the economies was efficient in both aspects in the sample period, implying that none of the countries in the analysis was efficient in the production and distribution of economic outputs simultaneously. Brazil, Canada, China and Germany showed improvement in economic efficiency but the distribution efficiency of the most of the economies is low because of the increase in population and high-income class. Most of the countries had an increase in the high-income class but China performed better in the second division because it has managed to improve its middle-income class in the recent past by moving more people from low-income class to middle income class. It is suggested that countries should emphasize on economic restructuring and expansion of the middle-income class to improve their performance in the production and distribution of economic outputs.

This paper used artificial neural networks (ANNs) time series predictor for approximating returns of Pakistan Stock Exchange (PSX) listed 100 companies. These projected returns are then substituted into expected returns in the Markowitz’s Mean Variance (MV) portfolio Model. For comparison empirical data used is closing prices of PSX listed stocks, Karachi Inter Bank Offer Rates (KIBOR) as risk free rate and KSE-all share index as benchmark. The Portfolio returns are compared for two datasets by employing various constraints like budget, transaction costs, and turnover constraints. The value of portfolios is measured through Sharpe ratio and Information ratio. Both Sharpe and Information ratios support use of ANNs as return predictor and optimisation tool over simple MV model implemented for empirical data as well as predicted data. ANNs framework performed better in both Long and Short positions and its portfolio returns are significantly higher as compared with MV.

In the past few decades, the analysts have noticed an important change in consumers’ behavior while purchasing and picking products, especially in cosmetics. Now, when consumers purchase the product, they pay more attention to health and environment features of the product. Therefore, the purpose of this research is to analyze Pakistani Consumers’ buying behavior towards Eco-Friendly Cosmetic products. The theory of planned behavior has been applied for better understanding of consumer behavior. Moreover, analyzed the relationship of consumers’ values on their intention to buy eco-friendly cosmetics products. It is studied that consumers’ social class has a moderating effect on consumer’s intention toward purchasing eco-friendly cosmetic products. For quantitative data collection questionnaire is constructed. For questionnaire, variable scales are adapted from literature. Convenience sampling is used for distribution of questionnaire. Data is composed from 260 respondents in Punjab, Pakistan. For analyzing the data, statistical analysis will be done by using SPSS and AMOS. All variables are significantly influence consumer intention to purchase eco-friendly cosmetics.This research suggests marketers to develop their marketing strategies and promote eco-friendly cosmetics through effective advertising.

The research is aiming at assessing the effect of cash conversion cycle on profitability of the firm. Three components are used to measure cash conversion cycle (CCC); average receivable period (ARP), average inventory period (AIP) and average payable period (APP). Henceforth, cash conversion cycle and its determinants are taken as Independent variables. The dependent variable is profitability being measured by return on asset (ROA). The data was collected with the help of pooled data containing a sample of 10 firms of two manufacturing sector such as Oil & Gas and Engineering, listed on PSX for the period 2010-2018. Regression and correlation techniques were used for analysis and come up with the outcomes that average receivable period and average inventory period have an adverse significant association with profitability of the firm except average payable period. In the end, there exists a highly negative significant association among CCC and firm’s profitability as ROA. The results showed that lesser the no. of days of CCC, the firm has greater profitability. This paper contributes to the literature, which shows the association amongst CCC and ROA.

Purpose: The challenge of managing a portfolio effectively is allocating capital among numerous stock holdings to achieve maximum profit. Therefore, the purpose of this study is to guide investors in developing optimal portfolios in the stock market of Pakistan. Design/Methodology/Approach: To pick and optimize a portfolio in the most effective way possible, we used the daily closing stock prices of a sample of listed firms at the Pakistan stock exchange. The study applied the mean semi-variance approach and compared the performance of portfolios with equally weighted portfolios under artificial neural networks and historical-based return estimation in Pakistan. Findings: The result shows that artificial neural network-based estimation of the expected return vector has outperformed the historical return estimation under mean semi-variance portfolio optimization and constrained mean semi-variance portfolios based on the Sharp ratio in Pakistan. Implications/Originality/Value: The study suggests that investors, fund managers, and portfolio analysts should focus on the more sophisticated neural network-based choice for the development of portfolios in the equity market of Pakistan.

This study uses many portfolio approaches to optimize mean semi-variance portfolios using artificial neural networks for South Asian investors. These methods include the mean-semi variance strategy, minimal variance approach, limited portfolios with transaction costs and turnover constraints, constrained portfolios with risk and return diversification limits, and the equally weighted approach. These portfolio strategies are analysed using the excess Sharpe ratio and the Information Ratio for financial efficiency and diversity, respectively, and validated using the 130/30 portfolio strategy. The Pakistan Stock Exchange (PSX), Bombay Stock Exchange (BSE), Dhaka Stock Exchange (DSE), Columbo Stock Exchange (CSE), and Nepal Stock Exchange (NEPSE) supply daily data for empirical research. We use a large data collection from 2017 to 2021. The study found that ANN-generated estimators using a mean semi-variance optimization strategy outperformed alternative portfolio optimization methods in South Asian markets. The research reveals that ANN-based returns outperform correlation statistics, descriptive analysis, and mean square prediction error (MPSE). The study suggests utilizing naive diversification as a benchmark for other portfolio optimization methodologies.