Integration of Intelligent Computing Architecture and Artificial Neural Networks in Supporting Decision-Making Processes: A Case Study of the Capital Market
DOI:
https://doi.org/10.29304/jqcsm.2026.18.33217Keywords:
Intelligent Computing Architecture; artificial neural networks; capital markets; decision support; feature extraction; time-series forecasting.Abstract
This paper presents research on the application of Intelligent Computing Architecture (ICA) with artificial neural networks (ANN) in the capital market environment for assisting in decision making process. The ICA concept is applied due to its ability to facilitate data driven operation, which is important in markets, where decision-making is implemented in milliseconds and transparency allows to efficiently change the structure of a model in accordance with the emerging information. The proposed solution combines the information gathering, selection and modification modules based on ICA with the predictive ANN module, working with the selected features. Thirteen decision support tasks have been formally defined and tested on five minutes long foreign exchange time series with respect to accuracy, RMSE, MAE, AUC, precision, recall and Sharpe ratio metrics. In the empirical case study, the proposed ICA-ANN solution achieved directional accuracy equal to 0.612 and decreased RMSE approximately by 19%, compared to ANN only.
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