Corporate Social Responsibility and Corporate Financial Performance: Evidence from Korea

This paper studies the empirical relation between corporate social responsibility (CSR) and corporate financial performance in Korea using a sample of 1122 firm-years during 2002-2008. We measure corporate social responsibility by both an equal-weighted CSR index and a stakeholder-weighted CSR index suggested by Akpinar et al. (2008). Corporate financial performance is measured by ROE, ROA and Tobin’s Q. We find a positive and significant relation between corporate financial performance and the stakeholder-weighted CSR index, but not the equal-weighted CSR index. This finding is robust to alternative model specifications and several additional tests, providing evidence in support of instrumental stakeholder theory.corporate social responsibility; corporate financial performance; KEJI index; instrumental stakeholder theory

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Abstract The gravitational waves (GW170817) produced during a binary neutron star inspiral, followed by a gamma-ray burst (GRB 170817A) and afterglows from X-ray to radio wavelength, were observed. By combining the distance obtained from gravitational waves with the red shift obtained from electromagnetic waves, even the Hubble constant was estimated. This indicates the start of new era of multimessenger astronomy. In addition to the masses of inspiralling neutron stars, the tidal deformability, which depends on the inner structures of neutron stars, has been estimated from gravitational waves. This confirms that even strong interactions can be tested by using gravitational waves. In this article, we review the effect of the tidal deformability of neutron stars on the gravitational waves produced during the inspiral process and discuss the implications of the detected tidal deformability for the neutron star's equations of state

A Study on GBW-KNN Using Statistical Testing

In the 4th industrial revolution, big data and artificial intelligence are becoming more and more important. This is because the value can be four by applying artificial intelligence techniques to data generated and accumulated in real-time. Various industries utilize them to provide a variety of services and products to customers and enhance their competitiveness. The KNN algorithm is one of such analysis methods, which predicts the class of an unlabeled instance by using the classes of nearby neighbors. It is used a lot because it is simpler and easier to understand than other methods. In this study, we proposed a GBW-KNN algorithm that finds KNN after assigning weights to each individual based on the KNN graph. In addition, a statistical test was conducted to see if there was a significant difference in the performance difference between the KNN and GBW-KNN methods. As a result of the experiment, it was confirmed that the performance of GBW-KNN was excellent overall, and the difference in performance between the two methods was significant

A Study on Self-Diagnosis Method to Prevent the Spread of COVID-19 Based on SVM

In this paper, a study was conducted to find a self-diagnosis method to prevent the spread of COVID-19 based on machine learning. COVID-19 is an infectious disease caused by a newly discovered coronavirus. According to WHO(World Health Organization)’s situation report published on May 18th, 2020, COVID-19 has already affected 4,600,000 cases and 310,000 deaths globally and still increasing. The most severe problem of COVID-19 virus is that it spreads primarily through droplets of saliva or discharge from the nose when an infected person coughs or sneezes, which occurs in everyday life. And also, at this time, there are no specific vaccines or treatments for COVID-19.Because of the secure diffusion method and the absence of a vaccine, it is essential to self-diagnose or do a self-diagnosis questionnaire whenever possible. But self-diagnosing has too many questions, and ambiguous standards also take time. Therefore, in this study, using SVM(Support Vector Machine), Decision Tree and correlation analysis found two vital factors to predict the infection of the COVID-19 virus with an accuracy of 80%. Applying the result proposed in this paper, people can self-diagnose quickly to prevent COVID-19 and further prevent the spread of COVID-19