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- W4226072956 abstract "Bacanin, Nebojsa Zivkovic, Miodrag Jovanovic, Luka Ivanovic, Milica Rashid, Tarik A.The prediction of the stock market trends represents a challenge that many researchers, investment bankers and stockbrokers try to solve, as correct predictions of the stock market’s direction can be very rewarding. However, the stock market forecasting is also one of the hardest tasks, as the stock market is very unpredictable, and historical data is pretty much nonlinear. This research suggests a new machine learning approach to forecast the movement of the stock market index, on the case study of the Borsa Istanbul 100 index. To perform this task, a multilayer perceptron hybridized with a modified whale optimization algorithm has been used, in two different cases of the output functions, namely Tanh(x) and Gaussian function. The dataset used in this research included Borsa Istanbul historical data from period 1996–2020, where nine technical indicators have been monitored. The obtained experimental results were validated by using RMSE, MAPE and correlation coefficient, and the proposed method was compared to similar methods that have been executed on the same dataset recently. In conclusion, the suggested approach was able to improve the accuracy of the model and was superior when compared to other methods observed in the comparative analysis." @default.
- W4226072956 created "2022-05-05" @default.
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- W4226072956 date "2022-01-01" @default.
- W4226072956 modified "2023-10-16" @default.
- W4226072956 title "Training a Multilayer Perception for Modeling Stock Price Index Predictions Using Modified Whale Optimization Algorithm" @default.
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- W4226072956 doi "https://doi.org/10.1007/978-981-16-9573-5_31" @default.
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