چکیده مقاله
Stock market forecasting in emerging economies is a complicated task with large volatilities, reduced transparency, and the presence of various economic and political variables The paper suggests an innovative hybrid model based on a Recurrent Neural Network RNN integrated with the Horse Herd Optimization Algorithm HHOA for the directional movement forecasting of the Iraqi stock market index RNN extracts temporal dependencies from sequential financial information, and the hyperparameters of the network, i e , learning rate and layer structure, are tuned by the HHOA for the purpose of optimization to gain better predictive accuracy Real life datasets from the Iraq Stock Exchange ISX relating to the banking sector are utilized for the training and validation of the model The suggested framework of the proposed RNN HHOA is compared with the baselines of the kind like the RNN Particle Swarm Optimization PSO and the RNN Genetic Algorithm GA Experimental results confirm that the proposed RNN HHOA model outperforms the baselines consistently in accuracy, precision, recall, and F1 score The paper suggests the scope for combining deep leaming with the computational power of nature inspired optimization algorithms for robust financial forecasting in highly volatile markets
کلیدواژهها
نویسندگان
Abd Kazem
Department of Computer Engineering, Institute of Artificial Intelligence and Social and Advances Technologies, Isf C , Islamic Azad University, Isfahan, Iran
Negar Majma
Department of Computer Engineering, Naghshejahan Higher Education Institute, Isfahan, Iran
شیوه ارجاع
Kazem, Abd and Majma, Negar,1404,Stock Market Index Forecasting in Iraq Using RNN Optimized by Horse Herd Optimization Algorithm,Conference on Artificial Intelligence in Emerging Economies and Digital Businesses (AIEEDB),Yazd
ارائهشده در
مجموعه مقالات کنفرانس هوش مصنوعی در اقتصادهای نوظهور و کسب وکارهای دیجیتال21 آبان 1404 · یزد