Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3534 2018 2018 Department of Economics and Management, Khorezm University of Economics, Urgench, 220100, Uzbekistan Denis Denis Department of Economics, Urgench State University, Urgench, 220100, Uzbekistan Inomjon Yusubov Department of Economics, Mamun University, Khiva, 220900, Uzbekistan Sanat Yakubov Kursk Branch, Financial University under the Government of the Russian Federation, Moscow, 125167, Russia Aleksey Ilyin Department of Business Management, Azerbaijan State University of Economics (UNEC), Baku, AZ1001, Republic of Azerbaijan Emil Hajiyev Department of Money Circulation and Credit, Kuban State Agrarian University named after I.T. Trubilin, Krasnodar, 350044, Russia Tatyana Khorolskaya Prediction of time series is a vital issue related to an extensive array of financial, and social applications, and engineering. The main challenge arises from the intricacy due to the temporal assets of time series and the unavoidable weakening function of analytical systems. Therefore, it is usually problematic to precisely forecast values, particularly in a multi-step ahead situation. Multi-step financial stock price forecast over a lasting perspective is vital for predicting its instability, letting economic organizations charge and evade derivatives, and banks to measure the hazard. Recently, Deep learning systems have been capable to perceive and analyze intricate patterns and connections in the data automatically and haste up the trading procedure. This manuscript designs and develops a Multi-Step Financial Stock Index Forecasting Model Using a Convolutional Neural Network with Gated Recurrent Unit (MFSIFM-CNNGRU) model. The proposed MFSIFM-CNNGRU model relies on enhancing the predicting model for the financial stock index. To accomplish that, the data normalization stage is initially performed by employing z-score normalization to convert input data into a suitable format. Next, the proposed MFSIFM-CNNGRU model designs a hybrid of convolutional neural network and gated recurrent unit (CNN-GRU) technique for the prediction model. Eventually, the hyperparameter selection of the CNN-GRU model can be implemented by the design of the improved whale optimization algorithm (IWOA). The efficiency of the MFSIFM-CNNGRU method has been validated by comprehensive studies using the benchmark dataset. The numerical result shows that the MFSIFM-CNNGRU method has better performance and scalability under various measures over the recent techniques 2025 2025 100 109 10.54216/FPA.180208 https://www.americaspg.com/articleinfo/3/show/3534