Social Enterpreneurs: Emerging Trend in Health Sector of Pakistan

The term Social Entrepreneurs is relatively new in the country like Pakistan. These groups try to overcome the existing social problems and derive sustainable social change in the economy through their innovative ideas and plans. The purpose of this study is to compare and explore the emerging trends of social entrepreneurs with the government in the health sector of Pakistan. This study tries to cover the research gap in the area of social entrepreneurship and highlight the growing role of social entrepreneurs in providing healthcare services to the people of Pakistan. For empirical evidence, three different healthcare institutions are selected. They include: Indus Hospital, Sindh Institute of Urology Transplantation and Shaukat Khanum Memorial Hospital. The services provided by these Healthcare Institutions are compared over the period of time. Trend analyses are carried out to study the role of these institutions by using various healthcare indicators and quantify the contribution of these organizations towards health improvement facilities in Pakistan. Some financial variables are also used to investigate resource mobilization in the sector. The result of the analysis revealed that these institutions are providing quality healthcare services to the people of Pakistan that are increasing with the passage of time.

Does the use of a big data variable improve monetary policy estimates? Evidence from Mexico

We analyse the performance improvement on a monetary policy model of introducing non-conventional market attention (NCMA) indices generated using big data. To address this aim, we extracted top keywords by text mining Banco de Mexico’s minutes. Then, we used Google search information according to the top keywords and related queries to generate NCMA indices. Finally, we introduce as covariates the NCMA indices into a bivariate probit model of monetary policy and contrast several specifications to examine the improvement in the model estimates. Our results show evidence of the statistical significance of the NCMA indices where the expanded model performed better than models only including conventional economic and financial variables.

Socially Responsible Portfolio Selection: An Interactive Intuitionistic Fuzzy Approach

In this study, we address the topic of sustainable and responsible portfolio investments (SRI). The selection of such portfolios is based, in addition to traditional financial variables, on environmental, social, and governance (ESG) criteria. The interest of our approach resides in allowing socially responsible (SR) portfolio investors to select their optimal portfolios by considering their individual preferences for each objective and simultaneous definition of the degrees of acceptance and rejection. In particular, we consider socially responsible portfolio selection as an optimization problem with multiple objectives before applying interactive intuitionistic fuzzy method to solve the portfolio optimization. The robustness of our approach is tested through an empirical study on the top 10 Stocks for ESG values worldwide.

Ciclos económicos y variables monetarias y financieras en México, 1980-2019: un enfoque de cambio estructural

El objetivo de este documento es analizar la relación entre el ciclo económico y diversas variables monetarias y financieras con el fin de determinar si han sido estables en el tiempo, teniendo en cuenta los efectos de las modificaciones en las condiciones económicas generales, la estrategia específica adoptada de política monetaria en diferentes subperiodos y las condiciones institucionales en que esta se maneja. Para probar la estabilidad de estas relaciones, una vez que se obtienen los indicadores del ciclo mediante los filtros convencionales, se emplea la metodología de cambio estructural de Bai y Perron (1998). Los resultados destacan dos cambios estructurales en la relación entre el indicador del ciclo y las variables de inflación, tasa de interés, tipo de cambio nominal y agregados monetarios nominales, uno a mediados de los años ochenta y el otro a mediados de los noventa. Abstract This paper aims to analyze the relationship between the business cycle and various monetary and financial variables. In particular, the paper seeks to determine whether this relationship has been stable over time, considering the effects of the changes in the general economic conditions, the specific monetary policy strategy adopted in different subperiods, and the institutional framework in which this is managed. To test the stability of these relationships, once the cycle indicators are obtained using conventional filters, the structural change methodology of Bai and Perron (1998) is used. The results highlight two structural changes in the relationship between the cycle indicator and inflation, interest rate, nominal exchange rate, and nominal monetary aggregates, one in the mid-eighties and the other in the middle of the nineties.

Economic Policy Uncertainty and the Co-Movement between REITs and Exchange Rate

The REITs market has attracted a lot of interest among the academic, policymakers, and market participants. The linkages between REITs and macroeconomic and financial variables have been adequately explored in the literature, with more emphasis on linear models. This study expands the frontier of knowledge by examining the role of uncertainty in the comovement/spillover between REITs and the currency markets. Some interesting results were observed. First, using the Diebold and Yilmaz (2012) spillover test, we find that there is strong connectedness between the REITs and currency markets. Second, the BDS test shows that nonlinearity is a very crucial factor to be put into consideration when examining the role of EPU in affecting the interactions between REITs and exchange rate markets. Third, the non-parametric causality-in-quantile test confirms that the connectedness between the markets and EPU is stronger around the lower and middle quantiles. These results have important policy implications for policymakers and market participants. The study also offers suggestions for future research.

Non-financial variables related to governance and financial distress prediction in SMEs–evidence from Egypt

PurposeThis study examines the effect of non-financial variables related to governance on the accuracy of financial distress prediction among Egyptian listed small and medium-sized enterprises (SMEs), by using the logistic regression technique.Design/methodology/approachThis study used a sample of 24 Egyptian-listed SMEs in each year, totaling 120 firm observations, of which 25 were classified distressed and 95 of them non-distressed between 2014 and 2018. The variables for the study included five financial variables and thirteen non-financial variables related to governance. The models were developed using financial variables alone as well as combining financial and non-financial variables related to governance.FindingsThe results showed that the model with financial variables had a prediction accuracy of 91.7% , whereas models with a combination of financial and non-financial variables related to governance predict with comparatively better accuracy of 92.7 and 93.6% .Research limitations/implicationsAlthough the results seem to be conclusive, it could be noted that the non-distressed sample was not paired with the distressed sample. Other studies showed that paired samples increase the financial distress prediction rate. Furthermore, due to the small sample size, this study was unable to create a hold-out sub-sample for the accuracy test.Practical implicationsThe proposed distress prediction model for SMEs is effective for stakeholders, including banks and other financial institutions, in the assessment of the credit risk of SMEs. Using such a model, they could better identify SMEs with a higher risk of failure in their lending decisions. Moreover, SME managers' could be interested in using such models as a tool for planning corrective action, in addition to planning and controlling current operations to avoid financial failure in the future.Originality/valueThis study contributes to financial distress prediction literature in different ways. First, few studies were conducted in the area of financial distress among SMEs. Second, neither of these studies was conducted within the Egyptian context, nor any of them had used non-financial variables related to governance in the prediction of financial distress among SMEs.

Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction

“Going concern” is a professional term in the domain of accounting and auditing. The issuance of appropriate audit opinions by certified public accountants (CPAs) and auditors is critical to companies as a going concern, as misjudgment and/or failure to identify the probability of bankruptcy can cause heavy losses to stakeholders and affect corporate sustainability. In the era of artificial intelligence (AI), deep learning algorithms are widely used by practitioners, and academic research is also gradually embarking on projects in various domains. However, the use of deep learning algorithms in the prediction of going concern remains limited. In contrast to those in the literature, this study uses long short-term memory (LSTM) and gated recurrent unit (GRU) for learning and training, in order to construct effective and highly accurate going-concern prediction models. The sample pool consists of the Taiwan Stock Exchange Corporation (TWSE) and the Taipei Exchange (TPEx) listed companies in 2004–2019, including 86 companies with going concern doubt and 172 companies without going concern doubt. In other words, 258 companies in total are sampled. There are 20 research variables, comprising 16 financial variables and 4 non-financial variables. The results are based on performance indicators such as accuracy, precision, recall/sensitivity, specificity, F1-scores, and Type I and Type II error rates, and both the LSTM and GRU models perform well. As far as accuracy is concerned, the LSTM model reports 96.15% accuracy while GRU shows 94.23% accuracy.

The influence that Spanish Labour Reform represents on Madrid Stock Market: An empirical analysis

Empirical evidence for Spanish Stock Market shows that labour events, like a firm level collective agreement, have informative content for the market due to the loss of wealth that it implies for the investor. Labour Reforms which Spain experienced between the years 2010 and 2012 have allowed the jeopardising of employment and the destruction of jobs, substituting one well paid by another of lower cost for the firm, the cost of dismissal, or the proposals of substituting payoffs by the so-called Austrian backpack, and the elimination of the distinction between temporary and permanent contracts. These Labour Reforms affect many of the accounting and financial variables, which are the subject of analysis and follow-up by investors and analysts, next to the idiosyncrasy of the Open Shop System that is followed in Spain, the present article means to explore the effect on Madrid Stock Market. Our results, applying analysis techniques with decision trees where we control the effect of the economic crisis on the market reaction, show that the Labour Reforms of 2010 to 2012 are incorporated as negative, or positive, information when the investor perceives a possible decrease, or increase, in its future cash flows.

An Analysis of Sovereign Credit Ratings Impact on Financial Development in South Africa

Purpose: The study analyses the effects of sovereign credit ratings on financial development in South Africa. This became important considering that the country has been receiving negative ratings of late. Design/Methodology/Approach: Quarterly data for the period 1994-2017 was analysed using the Auto-Regressive Distributed Lag (ARDL) cointegration model and its associated statistics. The Error Correction Model (ECM) was implemented to augment the results of ARDL analysing the short run dynamics. The model was chosen given the order of integration of the variables. Financial development was selected since it influences financial conditions and financial sector stability. Findings: The statistical results revealed that sovereign ratings positively influence financial variables that is in other words higher ratings are found to contribute positively to the growth of the financial development sector. Negative ratings are likely to affect the financial system as due to low access to external funding and exodus of investors, financial development is halted or decreased. Implications/Originality/Value: The results suggest that authorities need to consider the factors which are targeted by rating agencies and ensure that they perform as expected. Governments should focus on raising sovereign ratings and avoiding downgrades to boost financial development.

Measuring the effectiveness and impact of COVID-19 health policies on firms and UNSDGs: evidence from China

PurposeThis paper documents and links firm- and country-level outcomes to the United Nations Sustainable Development Goals (UNSDGs) by portraying how the Chinese economy has fared during the COVID-19 crisis. It does so by shedding light on the factors that determine the effectiveness of health policies implemented in China.Design/methodology/approachUnlike the prior literature, in which lagging performance measures are used, the authors use leading indicators with event study methodology to develop effectiveness scores and identify the determinants of effectiveness, including financial variables, firm infection, geographical location of the spread, travel bans, lockdown periods, policies of home quarantine, health innovations and other innovative measures undertaken by the Chinese authorities.FindingsThe detailed disaggregated results show many dimensions where abnormal returns are indeed associated with various health policies and that the effectiveness, influenced by firm size, profitability, firm infection and location. The results remain robust when the authors control for various event windows and models and provide evidence of a strong UNSDG link, which the authors draw up a list.Research limitations/implicationsApart from the quantitative analysis approach, future studies can complement and add further insights by utilizing qualitative research approaches.Practical implicationsThe results offers robust evidence for policy-makers and firm managers on how a crisis of such proportions and subsequent health policies is affecting different firms and why.Social implicationsThe study shows how COVID-19 health policies open a new dimension in terms of energy demand reduction and lower emissions, factors linking to the UNSDGs.Originality/valueThe study is the first to show detailed disaggregated results across many dimensions where abnormal returns are indeed associated with various health policies and that the effectiveness, influenced by firm size, profitability, firm infection and location.