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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Control and Optimization in Applied Mathematics
- نوع مقاله: Journal Article
- کلمات کلیدی: Network data envelopment analysis,Ranking function,Revenue efficiency,Full fuzzy linear programming
- چکیده: هدف این مقاله، ارزیابی کارایی درآمد در تحلیل پوششی دادههای شبکهای تمام فازی میباشد. اندازهگیری دقیق دادهها در دنیای واقعی عملا امکانپذیر نمیباشد، بنابراین فرض دقیق بودن دادهها در حل مسائل، فرض درستی نمیباشد. یکی از راههای مواجهه با دادههای نادقیق، دادههای فازی میباشد. در این مقاله از توابع رتبهبندی خطی، برای تبدیل مدل تمام فازی کارایی درآمد به یک مسئله برنامهریزی خطی دقیق استفاده میشود و با فرض اعداد فازی مثلثی، کارایی درآمد فازی تصمیمگیرندهها اندازهگیری میشود. در پایان، یک مثال عددی روش پیشنهادی را نشان میدهد.
- چکیده انگلیسی: The purpose of this paper is to evaluate the revenue efficiency in the fuzzy network data envelopment analysis. Precision measurements in real-world data are not practically possible, so assuming that data is crisp in solving problems is not a valid assumption. One way to deal with imprecise data is fuzzy data. In this paper, linear ranking functions are used to transform the full fuzzy efficiency model into a precise linear programming problem and, assuming triangular fuzzy numbers, the fuzzy revenue efficiency of decision makers is measured. In the end, a numerical example shows the proposed method.
- انتشار مقاله: 25-02-1398
- نویسندگان: Mohsen Rostamy-Malkhalifeh,Elham Poudineh,Ali Payan
- مشاهده
- جایگاه : پژوهشی
- مجله: Advances in Mathematical Finance and Application
- نوع مقاله: Journal Article
- کلمات کلیدی: Financial statement,Fraud,fuzzy neural network
- چکیده:
- چکیده انگلیسی: Fraud is a common phenomenon in
business, and according to Section 24 of
the Iranian Auditing Standards, it is the
fraudulent act of one or more managers,
employees, or third parties to derive
unfair advantage and any intentional or
unlawful conduct. Financial statements
are a means of transmitting confidential
management information about the
financial position of a company to
shareholders and other stakeholders. In
this paper, by reviewing the literature, 6
indicators of current ratio, debt ratio,
inventory turnover ratio, sales growth
index, total asset turnover ratio, and
capital return ratio as input and detection
of financial fraud as output are
considered for the fuzzy neural network.
The database was compiled for 10
companies in the period from 2010 to
2018 after clearing and normalizing
qualitatively between 1 to 5 discrete
numbers with very low or very high
meanings, respectively. The fuzzy neural
network model with 161 nodes, 448
linear parameters, 36 nonlinear
parameters, and 64 fuzzy laws with two
methods of accuracy approximation of
mean squared error and root mean squared error has been set to zero and
0.0000001 respectively. This neural
network can be used for prediction.- انتشار مقاله: 12-11-1398
- نویسندگان: Mohsen Rostamy-Malkhalifeh,Maryam Amiri,Mehrdad Mehrkam
- مشاهده
- جایگاه : پژوهشی
- مجله: Advances in Mathematical Finance and Application
- نوع مقاله: Journal Article
- کلمات کلیدی: Data envelopment analysis,Portfolio optimization,Multi-Objective Decision Making,Negative data,Conditional Value at Risk
- چکیده:
- چکیده انگلیسی: The purpose of this study is to develop portfolio optimization and assets allocation using our proposed models. The study is based on a non-parametric efficiency analysis tool, namely Data Envelopment Analysis (DEA). Conventional DEA models assume non-negative data for inputs and outputs. However, many of these data take the negative value, therefore we propose the MeanSharp-βRisk (MShβR) model and the Multi-Objective MeanSharp-βRisk (MOMShβR) model base on Range Directional Measure (RDM) that can take positive and negative values. We utilize different risk measures in these models consist of variance, semivariance, Value at Risk (VaR) and Conditional Value at Risk (CVaR) to find the best one as input. After using our proposed models, the efficient stock companies will be selected for making the portfolio. Then, by using Multi-Objective Decision Making (MODM) model we specified the capital allocation to the stock companies that selected for the portfolio. Finally, a numerical example of the Iranian stock companies is presented to demonstrate the usefulness and effectiveness of our models, and compare different risk measures together in our models and allocate assets.
- انتشار مقاله: 22-12-1397
- نویسندگان: Sarah Navidi,Mohsen Rostamy-Malkhalifeh,Shokoofeh Banihashemi
- مشاهده
- جایگاه : پژوهشی
- مجله: Advances in Mathematical Finance and Application
- نوع مقاله: Journal Article
- کلمات کلیدی: Insurance Companies,Stocks Ranking,Fuzzy DEA
- چکیده:
- چکیده انگلیسی: The main goal of this paper is to propose a new approach for efficiency measurement and ranking of stocks. Data envelopment analysis (DEA) is one of the popular and applicable techniques that can be used to reach this goal. However, there are always concerns about negative data and uncertainty in financial markets. Since the classical DEA models cannot deal with negative and imprecise values, in this paper, possibilistic range directional measure (PRDM) model is proposed to measure the efficiencies of stocks in the presence of negative data and uncertainty with input/output parameters. Using the data from insurance industry, this model is also implemented for a real case study of Tehran stock exchange (TSE) in order to analyse the performance of the proposed method.
- انتشار مقاله: 27-09-1397
- نویسندگان: Pejman Peykani,Emran Mohammadi,Mohsen Rostamy-Malkhalifeh,Farhad Hosseinzadeh Lotfi
- مشاهده