Forecasting stock returns and their risk represents one of the most important concerns of market decision makers. Although many studies have examined single classifiers of stock returns and risk methods, fusion methods, which have only recently emerged, require further study in this area. The main aim of this paper is to propose a fusion model based on the use of multiple diverse base classifiers that operate on a common input and a Meta classifier that learns from base classifiers' outputs to obtain more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes is determined using a methodology based on dataset clustering and candidate classifiers' accuracy. The results demonstrate that Bagging exhibited superior performance within the fusion scheme and could achieve a maximum of 83.6% accuracy with Decision Tree, LAD Tree and Rep Tree for return prediction and 88.2% accuracy with BF Tree, DTNB and LAD Tree in risk prediction. For feature selection part, a wrapper-GA algorithm is developed and compared with the fusion model. This paper seeks to help researcher select the best individual classifiers and fuse the proper scheme in stock market prediction. To illustrate the approach, we apply it to Tehran Stock Exchange (TSE) data for the period from 2002 to 2012.
Purpose
To achieve the optimum performance of electric transmission power system performance, the possibility of generators’ failure and the consequences are amongst the most important and real assumptions which should be taken into consideration. This paper aims to recognize the most influential factors on generators’ failures that can have a deep effect on the total cost and environmental issues. The integrated proposed approach is useful for investigating the generators’ failure effects on the performance of electric power transmission grids from the economic and environmental perspectives. In other words, the cost and pollution minimization policies are considered to decrease the unfavorable generators’ failure effects on electric power flow.
Design/methodology/approach
The data used in this study are gathered from a real case in USA in first step, the influential generator points that their failure has a significant effect on the objective function, have been recognized. Then, different failure scenarios are defined, and the optimum values in each of these scenarios through the GAMS modeling software are found. Consequently, by using a two-level factorial design approach, the critical generators across the power grid are determined.
Findings
The results show that by using such information, it is possible to detect the significant nodes in the power system grid and have a better maintenance plan. In addition, by means of this analysis and changing the capacity of main generators, it is possible to significantly reduce the operation costs. By comparing the indexes in case of the generator’s location, it seems that some of them are critical because of their capacity and position in the network (as their failure causes infeasibility in the model). Also, some of these deficiencies caused considerable index changes and critical consequences.
Practical implications
The integrated proposed approach is useful for investigating the generators’ failure effects on the performance of electric power transmission grids from the economic and environmental perspectives. In other words, the cost and pollution minimization policies are considered to decrease the unfavorable generators’ failure effects on electric power flow.
Social implications
This paper endeavors to recognize the most influential factors on generators’ failures that can have a deep effect on the total cost and environmental issues.
Originality/value
The integrated proposed approach is useful for investigating the generators’ failure effects on the performance of electric power transmission grids from the economic and environmental perspectives. In other words, the cost and pollution minimization policies are considered to decrease the unfavorable generators’ failure effects on electric power flow.
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