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• W4309903464 abstract "Today the accurate prediction of stock price movement is one of the most effective tools for investors. This prediction becomes more challenging when the stock market is naturally chaotic and uncertain. These stock attributes prevent most forecasting models from valuable stock data. In this sense, In this research, our purpose is to use a suitable structure to predict the trading signals of the stock market with high accuracy. For this purpose, two models for the analysis of technical adaptation were used. It can be seen that suport vector machine (SVM) is used with particle swarm optimization (PSO) where PSO is used as a fast and accurate classification to search the problem-solving space and finally the results are compared with the performance of two other meta heuristic algorithm including the neural network and Cuckoo search algoritm (CS). Based on the result, we can say that all the new models are trustworthy in 6 days, however, SVM-PSO is better than basic research. The hit rate of SVM-PSO is 77.5%, but the hit rate of neural network (basic research) and SVM-CS are 71.2, 71.4 respectively. In this study, we examined the years 2013–2021, and if longer periods are used, it may now be possible to achieve more optimal results. The results show that the performance of SVM-PSO is superior to the performance of the SVM-CS and most importantly the feed-forward static neural network algorithm of the literature as the standard one. In this research, two approaches (raw-based and signal-based) have been developed to generate input data for the model while, we use historical data, unexpected events have not been determined. However, for comparison, the hit rate is considered the percentage of correct predictions for 16 days." @default.
• W4309903464 created "2022-11-30" @default.
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• W4309903464 date "2022-11-23" @default.
• W4309903464 modified "2023-10-01" @default.
• W4309903464 title "A developed stock price forecasting model using support vector machine combined with metaheuristic algorithms" @default.
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• W4309903464 doi "https://doi.org/10.1007/s12597-022-00608-x" @default.
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