Today, there is a huge opportunity for after sales services companies to get more value in selling spare parts. To reach this ultimate goal as well as to have more market share, the key issue is to distinguish between valuable and invaluable authorized dealers as direct customers and main distribution channels of spare parts. Therefore, appropriate evaluation of these dealers is an important task for these companies. Here, we take an outranking approach for this evaluation. In order to rank a finite number of dealers, several criteria have to be defined. Hence, we are dealing with a multi criteria decision-making problem. The purpose of this paper is to develop a decision-making model for authorized dealer's evaluation. In this regard we determine suitable criteria based on the main concepts of customer value namely customer current value, customer potential value and customer loyalty. We run a survey to finalize the evaluation criteria as well as to determine the weights of these criteria. Our respondent were relevant expert who worked in an after sales services company. We applied the PROMETHEE method as a multi criteria decision making model for ranking of the authorized dealers at Saipa Yadak as a real case in the area of automotive after sales services. The ranking results showed that the proposed ranking model of this study can be considered as an appropriate guideline to deal with the authorized dealers of the company in terms of discounts, prices and other terms of the contracts between them.

In this paper, a fuzzy multiple attribute decision making model which is based on group decision methods is proposed for selecting the best alternative for investment in transportation projects. Here, fuzzy Topsis method has been applied along with qualitative and quantitative criteria for decision making. “Confirmatory Factor Analysis” has been utilized to determine the decision criteria. In order to incorporate vagueness of the information, Trapezoidal-fuzzy numbers are used in this study. To determine the weights of decision criteria, we used “Fuzzy Delphi method”. To implement the proposed selection procedure, this study uses data of "Oroomieh" as a case study. Our results were confirmed by the experts who contributed in this research. The PROMETHEE as another multi-criteria decision making method is also used to show the appropriateness of application of Fuzzy Topsis in this study.

Determination of cut-off grade strategy is one of the most important stages of open pit mine planning and design. It is the parameter directly influencing the financial, technical, economic, legal, environmental, social and political issues in relation to mining operation. Choosing the optimum cut-off grade strategy that maximises the economic outcome has been a major topic of research workers for nearly one century. Many researchers have contributed in devising methods and algorithms, such as dynamic programming, linear programming, optimal control and so on for various aspects of its determination. In this paper, a non-linear mathematical programming for cut-off grade strategy optimisation is presented, considering the three main stages of mining operation introduced by K F Lane. In this model maximisation of net present value of mining operation, under the three constraints of mining stages’ capacities, considered as the optimisation criteria. Due to the discrete representation of the mining resource, the proposed non-linear formulation is approximated by a non-linear signomial geometric programming. According to non-convexity and the complexity of the proposed model, an augmented Lagrangian genetic algorithm was used to fi nd the optimum cut-off grade strategy under varying and fi xed price circumstances. To validate the proposed non-linear model effi ciency, the results were compared with the results obtained by the K F Lane methodology. It was found that the proposed non-linear model works effi ciently in the determination of cut-off grade strategy. According to the simplicity of the structure of non-linear programming modelling in comparison with dynamic programming it is hoped that, further development of this model would certainly provide the ability of considering managerial and technical fl exibilities as well as incorporating more real mining conditions in the determination of cut-off grade strategy optimisation.

The main purpose of forecasting in financial markets is to estimate future trends and to reduce risks of decision making. This research suggests an ANFIS model to improve prediction accuracy in stock price forecasting. For doing so, we applied fuzzy subtractive clustering for structure identification of our ANFIS model. We implemented the proposed model for predicting Tehran Stock Exchange Price Index (TEPIX) using a dataset including TEPIX data from 25 March 2001 until 25 September 2010. To demonstrate the advantages of this model, first we compared our results with an Artificial Neural Network (ANN) model of type Multi Layer Perceptron (MLP). Then, we compared our results with ANFIS models using grid partitioning and Fuzzy C-Mean (FCM) clustering. The comparative results show the superiority of our proposed ANFIS model against ANN model and ANFIS models with no clustering and FCM clustering.

Technical trading rules can be generated from historical data for decision making in stock markets. Genetic programming (GP) as an artificial intelligence technique is a valuable method to automatically generate such technical trading rules. In this paper, GP has been applied for generating risk-adjusted trading rules on individual stocks. Among many risk measures in the literature, conditional Sharpe ratio has been selected for this study because it uses conditional value at risk (CVaR) as an optimal coherent risk measure. In our proposed GP model, binary trading rules have been also extended to more realistic rules which are called trinary rules using three signals of buy, sell and no trade. Additionally we have included transaction costs, dividend and splits in our GP model for calculating more accurate returns in the generated rules. Our proposed model has been applied for 10 Iranian companies listed in Tehran Stock Exchange (TSE). The numerical results showed that our extended GP model could generate profitable trading rules in comparison with buy and hold strategy especially in the case of risk adjusted basis.

Technical trading rules can be generated from historical data for decision making in stock trading. In this study, genetic programming (GP) as an evolutionary algorithm has been applied to automatically generate such technical trading rules on individual stocks. In order to obtain more realistic trading rules, we have included transaction costs, dividends and splits in our GP model. Our model has been applied for nine Iranian companies listed on different activity sectors of Tehran Stock Exchange (TSE). Our results show that this model could generate profitable trading rules in comparison with buy and hold strategy for companies having frequent trading in the market. Also, the effect of the above mentioned parameters on trading rule’s profitability are evaluated using three separate models.

Technical trading rules can be generated from historical data for decision making in stock trading. In this study, genetic programming (GP) as an evolutionary algorithm has been applied to automatically generate such technical trading rules on individual stocks. In order to obtain more realistic trading rules, we have included transaction costs in our GP model. Our model has been applied for 9 Iranian companies listed in different activity sectors of Tehran Stock Exchange (TSE). Our results showed that this model could generate profitable trading rules in comparison with buy and hold strategy especially for companies having frequent trading in the market.

Nowadays because of the complicated nature of making decision in stock market and making real-time strategy for buying and selling stock via portfolio selection and maintenance, many research papers has involved stock price prediction issue. Low accuracy resulted by models may increase trade cost such as commission cost in more sequenced buy and sell signals because of insignificant alarms and otherwise bad diagnosis in price trend do not satisfy trader’s expectation and may involved him/her in irrecoverable cost. Therefore, in this paper, Neuro-Fuzzy Inference System adopted on a Takagi–Sugeno–Kang (TSK) type Fuzzy Rule Based System is developed for stock price prediction. The TSK fuzzy model applies the technical index as the input variables and the consequent part is a linear combination of the input variables. Fuzzy C-Mean clustering implemented for identifying number of rules. Initial membership function of the premise part approximately defined as Gaussian function. TSK parameters tuned by Adaptive Nero-Fuzzy Inference System (ANFIS). Proposed model is tested on the Tehran Stock Exchange Indexes (TEPIX). This index with high accuracy near by 97.8% has successfully forecasted with several experimental tests from different sectors.

There is a lot of pertinent information concerning fixed-income markets, and it is important for interested practitioners and researchers to have an understanding of it. The main objective of this paper is to present a framework of the wide range of information relating to fixed-income trades. Through a comprehensive review of the literature, it has been found that more than 170 variables have a significant influence on bond prices. These variables have been categorized into two major groups: i) fundamental variables and ii) market microstructure dynamics. Fundamental variables have been categorized as macroeconomic, regional and corporate variables. Market microstructure variables have been categorized as price formation, market structure and trading information. Other reported variables concern bond features. As most of the reported variables do not directly relate to bond prices, the mediated variables have been reported for further clarification. The causal relationships between macroeconomic variables have been displayed in terms of announcement effects, surprise effects and domestic variables.

Information systems can serve as intermediaries between the buyers and the sellers in a market, creating an “electronic marketplace” that lowers the buyers’ cost to acquire information about sellers’ prices and product offerings. Although electronic trading systems provide potential to create an effi cient market structure, we witness that a $45 trillion fi xed-income market still makes little use of these systems. Low penetration of electronic trading systems in the marketplace is at odds with the existing information technology research doctrine. The reason is that the creation of effi cient market structure through an electronic marketplace is based on macro-level interfi rm relationships that do not take into account the recurrent micro-level, interpersonal interaction among the market actors. Our empirical investigation, based on face-toface interviews with 90 fi xed-income senior managers and traders from 25 fi nancial institutions, provides a unique insight into the social capital based on social networks of interpersonal relationships in the fi xed-income market. Our research fi ndings show that the market structure of embedded interpersonal ties enables participants to take advantage of information asymmetry for profi t taking. As a result, imposition of solely electronic trading systems on the present fi xed-income market structure is at odds with the present interfi rm market norms and business processes enacted for large transactions among market makers and institutional investors.

In this paper, a model has been provided for selection of the right portfolio in stock exchange. Industries ranking and companies ranking have been applied for selection of the right stocks in this model. These rankings have been done through the PROMETHEE decision making method. Two linear programming problems have been used for determining the amounts of investment per superior industries and superior companies in proportion to capital. Given the investor's strategies, these problems can be solved. A survey has been done for determining the effective criteria over industry and company evaluation. The developed model has been applied in Tehran Stock Exchange (TSE) as a real case and sample problems have been solved. It is concluded that industries ranking, companies ranking and then portfolio selection results could be different due to using different investment strategies. Therefore model results are largely dependent upon the investor's strategies and the investor should determine these strategies accurately.

The key issue for decision making in stock trading is selection of the right stock at the right time. In order to select the superior stocks (alternatives) for investment, a finite number of alternatives have to be ranked considering several and sometimes conflicting criteria. Therefore, we are faced with a special multicriteria decision-making problem. The purpose of this paper is to develop a decision-making model for selecting superior stocks in stock exchange and a model is provided in order to structure this problem. The proposed model is structured around two pillars: Industry evaluation and Company evaluation. The preference ranking organization method for enrichment evaluation (PROMETHEE) has been used for solving the problem. The model has been applied at Tehran Stock Exchange (TSE) as a real case and a survey from the experts in order to determine the effective criteria for industry evaluation and company evaluation has been conducted.