Applying GMDH-Type Neural Network and Genetic Algorithm for Stock Price Prediction of Iranian Cement Sector

The cement industry is one of the most important and profitable industries in Iran and great content of financial resources are investing in this sector yearly. In this paper a GMDH-type neural network and genetic algorithm is developed for stock price prediction of cement sector. For stocks price prediction by GMDH type-neural network, we are using earnings per share (EPS), Prediction Earnings Per Share (PEPS), Dividend per share (DPS), Price-earnings ratio (P/E), Earnings-price ratio (E/P) as input data and stock price as output data. For this work, data of ten cement companies is gathering from Tehran stock exchange (TSE) in decennial range (1999-2008). GMDH type neural network is designed by 80% of the experimental data. For testing the appropriateness of the modeling, reminder of primary data were entered into the GMDH network. The results are very encouraging and congruent with the experimental result

Combining Fuzzy MCDM with BSC Approach in Performance Evaluation of Iranian Private Banking Sector

The objective of this study is to construct an approach based on multiple criteria decision making (MCDM) and balanced scorecard (BSC) for evaluating performance for three nongovernmental Iranian's banks. Following the literature relating to banking performance and BSC concepts, experts and managers select 21 indexes for evaluation. Furthermore, fuzzy analytic hierarchy process (FAHP) calculated the relative weights of each chosen index in order to tolerate vagueness and ambiguity of information, and three MCDM analytical tools (TOPSIS, VIKOR, and ELECTRE) were adopted to rank the banking performance. The results indicate that a customer “” has the most significant BSC perspectives and the customer satisfaction “1” is the most major index in banking sector. This proposed fuzzy MCDM method combined with BSC approach is a comprehensive and up-to-date model that can be a useful and effective assessment tool

Combining fuzzy MCDM with BSC approach in performance evaluation of Iranian private banking sector

The objective of this study is to construct an approach based on multiple criteria decision making (MCDM) and balanced scorecard (BSC) for evaluating performance for three nongovernmental Iranian's banks. Following the literature relating to banking performance and BSC concepts, experts and managers select 21 indexes for evaluation. Furthermore, fuzzy analytic hierarchy process (FAHP) calculated the relative weights of each chosen index in order to tolerate vagueness and ambiguity of information, and three MCDM analytical tools (TOPSIS, VIKOR, and ELECTRE) were adopted to rank the banking performance. The results indicate that a customer "C" has the most significant BSC perspectives and the customer satisfaction "C 1 " is the most major index in banking sector. This proposed fuzzy MCDM method combined with BSC approach is a comprehensive and up-todate model that can be a useful and effective assessment tool

Developing Sustainable SCM Evaluation Model Using Fuzzy AHP in Publishing Industry

AbstractIn the new econo mic context the long term success of any organization is built not only on profits and profitability but also on its contribution to the future of people and the future of the planet. Being supply chains a key cornerstone in any organization the consideration of sustainability at the supply chain level is recognized as an emerging area that needs to be studied in a systematic way. In this paper we proposed a fuzzy AHP approach for evaluating of SCM sustainability in publishing industry. For this work, we define a hierarchical framework regarding with Ageron et al model and criteria and sub criteria based on viewpoints of practical and academic experts. Results show that criteria relating with company and supplier selection factors are most important criteria