Decision based on Neural Networks/ Deep Learning
Aminollah Zarghami; Meysam Doaei; Abtin Boostani
Abstract
Purpose: Delisted companies, despite their importance in the economic and social issues of society, is less considered in the financial literature. This issue is important because for each country, one of the criteria for economic measurement is the size of the capital market. Therefore, the delisted ...
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Purpose: Delisted companies, despite their importance in the economic and social issues of society, is less considered in the financial literature. This issue is important because for each country, one of the criteria for economic measurement is the size of the capital market. Therefore, the delisted companies not only destroys the company's reputation, its stock price and the market for the sale of its shares, but also affects the growth of the market and the economy of each country. The present study seeks to review the financial statements and audit reports of active companies and compare it with delisted companies to design a model for forecasting delisted companies in the Tehran Stock Exchange with the help of artificial intelligence modeling techniques.Methodology: In this study, which was conducted on companies of the Tehran Stock Exchange, data related to three years before the delisting of 73 companies removed from the stock exchange from 2003 to 2019 in the first group and data of 148 active companies that are continuously. They were present in the stock market in the second group and were selected by systematic elimination method. Then, with data mining techniques, which are among the most efficient and up-to-date models of artificial intelligence, and with the help of multi-layered perceptron neural network classifiers, decision tree, and Bayesian theory classifiers, stock delisted companies have been predicted.Findings: The findings show that the Bayesian classifier had the best performance and the multilayer perceptron neural network was in the second place and the decision tree classifier was in the third place.Originality/Value: Little research has been done in the field of predicting delisted companies from the Iran capital market. This study by filling this gap, suggests to researchers to use other classifiers, combine several classifiers together to better cover the errors of each, combine classifiers with each other and weigh in a way that is more accurate, add other variables influential in the dismissal of companies, including the ownership structure and shareholder composition can have other results.
Decision based on Neural Networks/ Deep Learning
Mohamad Ali Khatami Firoz Abadi; Mona Jahangir Zade; Amir Mazyaki; Seyed Soheil Fazeli
Abstract
Purpose: Nowadays insurance companies, same as other companies, are facing massive competition. This issue indicates the value of customer loyalty also a predictive model. Customers play a crucial role in the sustainability of organizations by constant repurchasing. Companies with loyal customers have ...
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Purpose: Nowadays insurance companies, same as other companies, are facing massive competition. This issue indicates the value of customer loyalty also a predictive model. Customers play a crucial role in the sustainability of organizations by constant repurchasing. Companies with loyal customers have more market share, and more money may return on investment. This article's main aim is to identify the factors affecting customer loyalty in insurance companies.Methodology: This research was quantitative, analytical-descriptive. In gathering information, Data was collected through the survey, and the findings are practical. In this way, two methods, Confirmatory Factor Analysis (CFA) and Artificial Neural Networks (ANN) were used. For localizing the factors extracted from other similar prior literature, first, the elements were examined by CFA with SMART PLS application due to some conflicts in the literature to evaluate whether each factor affects customer loyalty or not. Then, the elements were introduced to the ANN for training by this program.Findings: In this article, by using the MORGAN table, the sample size detected 384 people in 0.05 error. Questionnaires were distributed randomly between four Iranian insurance companies, ASIA insurance company, ALBORZ insurance company, and PARSIAN insurance company. Based on Confirmatory Factor Analysis, elements of commitment, perceived quality, trust, perceived value, empathy, brand image, the attraction of other alternatives, and customer satisfaction impact the customer loyalty of insurers in these companies. The cost of change, nevertheless, did not have a significant effect on customer loyalty. Then, the factors used as inputs for the multi-layer perceptron training also customer loyalty are indicated as output. The model was designed with eight inputs, 110 nodes in the hidden layer, and one output the error was E= 0.00992 and the regression = 0.98684.Originality/Value: the finding of this research is, expanding a model for predicting customer loyalty in Iranian insurance companies.
Decision based on Neural Networks/ Deep Learning
Yousef Ebrahimi; Yagoub Alavi Matin; Sahar Khoshfetrat; Hasan Refaghat
Abstract
Purpose: Banks as a service and financial economic enterprise, while accompanying the economic programs of countries, seek to benefit their stakeholders. In order to achieve this goal, they must be able to equip and allocate their resources optimally. One of the important issues is to identify the factors ...
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Purpose: Banks as a service and financial economic enterprise, while accompanying the economic programs of countries, seek to benefit their stakeholders. In order to achieve this goal, they must be able to equip and allocate their resources optimally. One of the important issues is to identify the factors affecting the absorption of resources that the purpose of this study is to provide a suitable model to identify the factors affecting the supply of resources.Methodology: To achieve the purpose of the research, by reviewing the research background, mission of the bank and the opinions of banking experts, 62 factors were presented in the form of a questionnaire. After approval by banking experts, the questionnaire was distributed to a sample of 30 employees of Tejarat Bank in Zanjan province for pre-testing. Then its reliability was tested and confirmed by Cronbach's alpha. After field collection of research data, the effective components were divided into two main groups of external and internal organizational factors. Then the factors within the organization into four subgroups; Financial, physical, service and communication and human factors were separated. Finally, the main research model was extracted using the model of unattended neural networks (self-organized maps) and the research data were analyzed.Findings: Research findings show that, From the set of factors affecting the provision of banking resources, communication and human factors had the most impact and external factors had the least impact. Also, due to the lack of similarity between the models of research input vectors, the correlation between each of the factors affecting resource equipping was not confirmed.Originality/Value: In this study, using a new approach of neural network model (self-organized mapping) to identify and weigh the factors affecting the equipping of bank resources, the findings of which help to develop the literature in the field of resource equipping.