Al DRIVEN STOCK MARKET PRICE TREND FORECASTING USING RECURRENT NEURAL NETWORKS

      In this study, a predictive system based on artificial intelligence to forecast stock market price trend was developed through the Recurrent Neural Network (RNN) architecture. The main purpose of the designed system is to examine the stock market data history and predict the price trends in the future by applying deep learning technology. For training the model, financial indicators such as open price, close price, high price, low price, and volume were used. As a result of effective data preprocessing and sequence generation, the model was able to discover temporal dependencies within the financial time series. Experimental findings show that the model was successful at discovering key patterns in past stock prices and making accurate predictions. Results from training and validation were obtained for the proposed model which exhibited stable behavior during the entire learning process. Predicted errors were low, indicating that the proposed neural network could predict stock prices accurately while maintaining consistency between actual and predicted values. Visual representation of the prediction outcomes revealed that the model was effective in capturing overall trends and fluctuations within the stock market. Despite high volatility and various influences on the stock market, results reveal that the proposed recurrent neural network exhibited excellent capabilities in analyzing financial datasets.

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