fuzzy mathematical programming

The purpose of the present study is to provide a fuzzy window data envelopment analysis (FWDEA) model in order to financial performance evaluation of stocks over different time periods under the uncertainty of the data. In other words, in this research, we will try to present a new approach to assess the stock's performance with the ability to be implemented in the presence of uncertain panel data. Because the use of information about several time periods rather than a time period, as well as taking into account the uncertainty in the data, can lead to more reliable results in the process of stock evaluation. It is necessary to explain that in modeling and presenting the mentioned approach, data envelopment analysis, window analysis and possibilistic programming have been used. Finally, the fuzzy window data envelopment analysis model was implemented on 5 stocks of the chemical industry in Tehran stock exchange for four periods from 2013 to 2016, and the results indicate that the FWDEA method is effective.

Portfolio selection is one of the problems which is very important for the investors in the exchange market. In order to invest on several kinds of shares instead of a specific one so that they can earn maximum profit through a certain level of risk or have minimum risk with a certain level of profit. What have been introduced concerning financial calculations and the choice of portfolio or selection of an investment up to now, in some ways, determines priority in accordance with degree of risk and rate of return respectively; so that the investor can select his favourite portfolio through financial facilities and other confronting policies. When the investor is faced with different properties, he should make decisions about the number of properties as well as the amount of investment. So in some ways, he faces uncertain decisions in his choices. We are considering fuzzy concepts in the discussion of portfolio selection optimization in order to pursue this uncertainty. Then by using Bonison method, we determine priority and preference among the portfolios so that the investor can decide without confusion and finally through introducing combined metaheuristic algorithm for variable neighbourhood search and genetics, can optimize the resulting model of previous process and comparing it with other solving algorithms.