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In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Many researchers and analysts are interested in evaluating the performance of a system with a network structure as a decision-making unit. In this regard, fuzzy network data envelopment analysis (FNDEA) is a noticeable and worthy method for evaluating the efficiency of a system with fuzzy data. Based on the structure of a fuzzy network system, which consists of at least two serial stages, an intermediate factor has an output nature for the first stage and an input nature for the second stage. Hence, it is inappropriate to allocate the same weight for each stage using this factor. Unfortunately, contrary to real-world conditions, all previous conventional FNDEA studies have considered the same role for intermediate factors to linearize or simplify models. For the first time, this study attempts to determine the upper and lower bounds of the overall efficiencies of a fuzzy two-stage series system and its subprocesses with unequal intermediate product weights.

The proposed model remains in its original nature as a complex combinatorial problem in the nonlinear programming category of NP-hard problems. A genetic algorithm (GA) is utilized as a metaheuristic algorithm, and a novel hybrid GA-FNDEA algorithm is presented to solve the problem.

The findings of the study outlined several theoretical contributions and practical implications, including as compensatory property of DEA, determining upper and lower bounds, improving efficiency in nonlinear systems, reducing computational burden, enhancing evolutionary algorithms and retaining real-world conditions.

Contrary to real-world conditions, all previous conventional FNDEA studies have considered the same role for intermediate factors to linearize or simplify models. For the first time, this study attempts to determine the upper and lower bounds of the overall efficiencies of a fuzzy two-stage series system and its subprocesses with unequal intermediate product weights.