QUANTITATIVE RESEARCHES IN MANAGEMENT
Year:2011 | Volume:1 | Issue:3|Papers Count:9

Data-mining can be considered a recently developed methodology and technology, coming into prominence only in 1994. It aims to identify valid, novel, potentially useful and understandable correlations and patterns in data. Data-mining can also be considered as a process and technology to detect the previously unknown in order to gain competitive advantage. In data- mining, there is a strong emphasis on combing through copious data sets to sniff out patterns that are too subtle or complex for humans to detect. Data-mining techniques also include non-traditional methods developed in the areas of artificial intelligence and machine learning. The two most important models of these are neural networks and decision trees. Neural networks are useful for recognizing patterns in the data, especially when the form of relationships between the target and input variables is unknown and/or complex. They are modeled after the human brain, which can be perceived as a highly connected network of neurons or nodes. Each node in a layer of nodes receives inputs from at least one node in a previous layer and combines the inputs and generates an output to at least one node in the next layer. Generally, the input variables comprise the input layer and the target variable comprises the output layer. Between the input and output layers, there may be one or more hidden layers of nodes. The objective of decision trees is prediction and/or classification by dividing observations into mutually exclusive and collectively exhaustive subgroups. This process of division or splitting usually continues until either no further splitting can produce statistically significant differences in the target variable in the new subgroups or the subgroups are too small for any further meaningful division. The subgroups and sub-subgroups are usually referred to as nodes. The end product can be graphically represented by a tree-like structure. This study goes through the definition of Data Mining and its Techniques and gives a case study about its applications in Credit Scoring.

Most theorists and researchers agree on the importance of two key concepts of customer satisfaction and quality of the services and they believe the said concepts as two fundamental ones. Looking over the literature of quality, we understand that most experts believe that the quality is the result of considering expectations and customer satisfaction. This study aimed to identify the best approach (strategy) to increase satisfaction of VAT electronic system users. In order to achieve this, Analytic Hierarchy Process (AHP) and zero-One programming is used. Based on the results of the Hybrid Model, strategy to increase intelligence of VAT electronic services selected as the best strategy to increase user satisfaction from VAT electronic.

This research has been done for designing and formulating portfolio strategies of Farhangian enterprise in insurance business. The research is an applied research from outcome point of view. Required data has been collected as survey research by using interview and questionnaire tools. Also, for formulating portfolio strategies, market attractive and competitive position methodology has been used. By paying attention to market attractive and competitive position of insurance businesses, results illustrate that suitable strategy for Farhangian enterprise is growth strategy that proposed as sub strategies such as penetration, product development and integration.

The aim of this research is to determine suitable locations and selecting the best location for fire station in Alborz industrial city of Qazvin. To achieve this, Geographic Information System (GIS), TOPSIS and SAW multi criteria decision-making methods have been used and compared with using Breda method. In this research, required factors for fire station site selection have been analyzed and evaluated. Firstly, factor maps have been prepared and then is reclassified and the suitable regions has been selected. Finally, the best site for fire station building has been selected according to the SA Wand TOPSIS methods as two multi criteria decision-making method.

One of the aims of accounting information is assist to users in predicting the efficiency of investment. Some of variables influenced on the stock efficiency in the stock market is provided from the financial information by the management. The measure of effecting this information is partly very complex and unknown. When investors invest in an economic unit, they have been possessing enough information about it (including financial information). Therefore, using of disclosure information policies and plans can transfer knowledge and information to top managers in the capital markets and they may influence on communication between managers and investors. This article also tried to evaluate review of voluntary disclosure information, its relationship with the rate of stock efficiency. In other words, purpose of this article is review of the market reaction to voluntary disclosure information.

Given the importance of investing in the stock market in each country and also according to the fluctuations in the different parts of the economic, this study has tried using econometric techniques and by using annual data of Iranian economy during the period between 1998 to 2008 to assess the impact of fluctuation in production in different economic sectors on investment in Tehran Stock Exchange (TSE). In this context, the Auto-regression Distributed Lag (ARDL) model has been used. Co- integration relationship between the variables has been studied. Results show negative relationship between fluctuations of Oil, services and Industries and Mines sectors with investment in Tehran Stock Exchange (TSE). Also results show no relation between fluctuation of Agriculture sector and dependant variable.

At the same time with increasing trend in day to day trading development in this world, needs to access to financing resources are becoming more necessary and this causes banks to emphasis more on credit granting and makes governments to establish new banks. One of the main problems in the banking system is credit risk management, because high amount of money are presented to applicants in this organizations as credit and repayment of this sources is necessary for their life and their development. Therefore considering applicants' creditworthiness is an important process for paying credit. In this paper data mining techniques has been used for defining credit risk.

Nowadays, loans have a critical role in the banking industry, so that much of the assets of a bank is composed of paid loans to the individuals and companies and thus increasing the number of loan requests from individuals and companies and its related risks, it is necessary to provide a method to manage loans. Banks are facing different risk including market risk, liquidity risk, credit risk and commercial risk among which credit risk is more important. Therefore, the risk is inherent component of banking activities, and due to limited financial resources and available banking financial resources, evaluation of customers' reimbursement ability before granting loan is one of the important challenges facing the country's banking system. One way of planning and measuring credit risks and therefore proper risk management is the use of credit scoring models. The model formulates characteristics and performance of the previous loans based on quantitative and qualitative factors so that can forecast the future performance of loans with similar situations. In this study, credit assessment of individual borrowers of the banking system has been done through credit scoring model. Qualitative and quantitative characteristics of customers such as age, sex, marital status, education, occupation, amount of loan, collateral and etc were considered as the independent variables. In this study, the relationship between the results of the model with actual customer credit status has been evaluated. According to the results of the research, no relationship and impact has been seen between age and education on credit status and were excluded from the model. Other variables had significant relationships with customers credit status. The results showed that the Logit prediction model has good predictability power.