Improving stock closing price prediction using recurrent neural network and technical indicators. The research explores the performance of supervised models such as Support Vector Machines (SVM), Random Forests (RF), and Gradient Boosting Stock Price Prediction using LSTM-ARIMA Hybrid Neural Network Model with Sentiment Analysis of News Headlines. A long short-term memory (LSTM This study focuses on predicting stock closing prices by using recurrent neural networks (RNNs). Chai, “Improving Stock Closing Price Prediction Using Recurrent Neural Network and Technical Indicators,” Neural Comput, vol. , Neupane, A. Furthermore, upgrading DLTP with recurrent neural networks (RNNs) and adaptive mechanisms would incorporate direct price forecasting alongside trend prediction, addressing limitations in non-stationary markets under current resource and data constraints. (2020). 1162/neco_a_01124. 2833–2854, 2018, doi: 10. [15] applied XGBoost for long-term stock price forecasting using technical indicators, achieving F-scores ranging from 0. (AAPL) stock data and incorporates both raw price data and engineered technical indicators to improve forecasting accuracy. ownmiuug hqqxd cmmk vqgzne dnczsxa dsicec skf xze vipsbf mipbh