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Open Access Article
1 - Development of data envelopment analysis model for financial and social evaluation of companies based on stock returns and accounting value
Mohammad Mir Darikvandi Gholamreza Farsad Amanollahi Ali Esmaelzadeh Mogheri Amirreza KeyghobadiCorporate social responsibility has long attracted the attention of academics, researchers, NGOs, and the government, and has become an important aspect of corporate operations. Increasing the globalization of business, increasing the strategic importance of stakeholder MoreCorporate social responsibility has long attracted the attention of academics, researchers, NGOs, and the government, and has become an important aspect of corporate operations. Increasing the globalization of business, increasing the strategic importance of stakeholder relations and the growth of corporate image management are the three key factors and the main driver in increasing the importance of corporate social responsibility. Data envelopment analysis is a well-known methodology that is applied to evaluate the selected firms based on the most important features. Therefore, in this research, it is important to examine the social responsibility of companies with emphasis on stock returns and accounting value. For this purpose, information related to the member companies of the stock exchange during a ten-year period from 2010 to 2020 after performing the necessary statistical tests using linear regression and EViews 10 and SPSS 26 software to test We addressed the hypotheses. In this research, multivariate regression method has been used as a statistical method. The results show that there is no relationship between social responsibility and accounting value, but social responsibility has an inverse and significant relationship with corporate stock returns. Manuscript profile -
Open Access Article
2 - Presenting an Explanatory Model of Stock Price Using Deep Learning Algorithm
Mojtaba Bavaghar Zaeimi Gholamreza Zomorodian Mehrzad Minooee Amirreza KeyghobadiThis study aimed to present an explanatory model of stock price using deep learning algorithm for companies listed in the Tehran Stock Exchange. In this study, a deep learning network was used to predict stock prices. The study was applied-developmental research in term MoreThis study aimed to present an explanatory model of stock price using deep learning algorithm for companies listed in the Tehran Stock Exchange. In this study, a deep learning network was used to predict stock prices. The study was applied-developmental research in terms of purpose. To test the research questions, accounting data were prepared from 2011 to 2020 and input variables were calculated based on it for the model. The method of systematic elimination sampling has been used in this study. The results indicated that the precisions of prediction has a high precisions in the deep learning model. The proposed algorithm was reviewed according to its prediction accuracy and modeling cost. According to the data volume, it was found that the prediction accuracy in the deep learning model has a relative superiority and the diagram of performance characteristic and AUC criteria also showed this superiority in detection power. Manuscript profile
List of Articles Amir Reza Keyghobadi
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