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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Information Technology Management
- نوع مقاله: Journal Article
- کلمات کلیدی: Genetic Algorithm,decision trees,Credit scoring,Features Selection,Clustering.
- چکیده:
- چکیده انگلیسی: Decision trees as one of the data mining techniques, is used in credit scoring of bank customers. The main problem is the construction of decision trees in that they can classify customers optimally. This paper proposes an appropriate model based on genetic algorithm for credit scoring of banks customers in order to offer credit facilities to each class. Genetic algorithm can help in credit scoring of customers by choosing appropriate features and building optimum decision trees. Development process in pattern recognition and CRISP process are used in credit scoring of customers in construction of this model. The proposed classification model is based on clustering, feature selection, decision trees and genetic algorithm techniques. This model select and combine the best decision tree based on the optimality criteria and constructs the final decision tree for credit scoring of customers. Results show that the accuracy of proposed classification model is more than almost the entire decision tree models compared in this paper. Also the number of leaves and the size of decision tree i.e. its complexity is less than the other models.
- انتشار مقاله: 11-10-1348
- نویسندگان: Mahmood I Alborz,Mohammad Ebrahim Mohammad Pourzarandi,Mohammad Khanbabaei
- مشاهده
- جایگاه : پژوهشی
- مجله: Iranian Journal of Finance
- نوع مقاله: Journal Article
- کلمات کلیدی: Artificial Intelligence,Markov switching model,Market Financial Cycles,Bear Market,Bull Market
- چکیده:
- چکیده انگلیسی: The stock exchange is considered to be an important establishment to finance long term projects, on one hand, and to collect savings and finance of private section. The stock exchange can be a safe and secure place to invest surplus funds to purchase corporate stocks. As recession and prosperity in this market can have a great role in stockholders` decision-making, it becomes vital to predict these cycles. In this paper, using model MSMH(4)AR(2), we extract the financial cycles of the market. Then, using the ant colony algorithm, we determine the most significant predictors and predict the market financial cycles using neural networks. The results show that the PNN model performs better in predicting the future market with respect to the criteria of mean squared error, the root mean squared error, the model accuracy and kappa coefficient.
- انتشار مقاله: 21-06-1398
- نویسندگان: Farzaneh Abdollahian,Mohammad Ebrahim Mohammad Pourzarandi,Mehrzad Minouei,Seyed Mohammad Hasheminejad
- مشاهده
- جایگاه : پژوهشی
- مجله: Iranian Journal of Finance
- نوع مقاله: Journal Article
- کلمات کلیدی: Data mining,Tehran Stock Exchange,Financial ratios,Bank Performance
- چکیده:
- چکیده انگلیسی: In order to survive in the modern world, organizations must be equipped with the mechanisms that not only maintain their competitive advantage, but also result in their progress and improvement. Prediction of banks’ performances is an important issue, and a poor performance in banks may primarily lead to their bankruptcy, thereby affecting national economics.
The bank performance prediction model uses scientific and systematic approaches to diagnose the financial operations of institutes. According to a precise and strict evaluation, the model can detect the weakness of institutions in advance and provide early warning signals to related financial governments. In the present study, we have used three data mining models to predict the future performance of the banks accepted in Tehran Stock Exchange (TSE) and Iran Fara Bourse. Initially, 53 financial ratios were selected and, consequently, reduced to 28 using the fuzzy Delphi technique. The statistical population included 18 banks listed on TSE and Iran Fara Bourse, which provided their financial statements during the period of 2011 to 2017. Data were collected from the Codal site based on 28 financial ratios using C4.5 decision tree, AdaBoost, and Naïve Bayes algorithm. According to the findings, the Naïve Bayes algorithm was the optimal predictive model with the accuracy of 88.89%.- انتشار مقاله: 31-04-1398
- نویسندگان: Elham Adakh,Arefeh Fadavi Asghari,Mohammad Ebrahim Mohammad Pourzarandi
- مشاهده
- جایگاه : پژوهشی
- مجله: Advances in Mathematical Finance and Application
- نوع مقاله: Journal Article
- کلمات کلیدی: Forecasting stock price,Industry average,Optimization algorithm,Fuzzy time series,Golden Ratio algorithm
- چکیده:
- چکیده انگلیسی: The effective role of capital in every country flows through giving guidelines for capital and resources, generalizing companies and sharing development projects with public, and also adding accredited companies stock market requires appropriate decision making for shareholders and investors who are willing to buy shares based on price mechanism. Forecasting stock price has always been a challenging task, since it is affected by many economic and non-economic factors and variables; therefore, selecting the best and the most efficient forecasting model is tough and essential. Up to now applying weighted mean called weighted mean price has been used to forecast industry average price for companies in the stock market and investors were forecasting based on this method. First we have identified 10 accredited banks in TSE and 10 banks in Iran Fara Bourse. In this article, by applying one of the mathematical optimizing techniques, industry means got calculated based on optimized parameters and compared with the industry average; in this statement we strived to find another variable that could forecast with less deviation. In the following study, by calculating frequency level of deviations, average for price forecasting in banking industry during five years is examined. Finally, the research suggests that, instead of using mean of industry average, it is better to use mean average of golden number, which will lead us to more accurate results.
- انتشار مقاله: 18-09-1397
- نویسندگان: Negar Aghaeefar,Mohammad Ebrahim Mohammad Pourzarandi,Mohammad Ali Afshar Kazemi,Mehrzad Minoie
- مشاهده