Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3564 2018 2018 School of Technology, Pandit Deendayal Energy University, India Thippa Thippa Department of Computer Engineering, SVM Institute of Technology, India Rutvij H. Jhaveri Department of Computer Science, Prince Sattam Bin Abdulaziz University, Al-Kharj, Riyadh 16278, Saudi Arabia Faisal Mohammed alotaibi The College of Mathematics and Computer Science, Zhejiang A&F University, China; The Division of Research and Development, Lovely Professional University, India; The Center of Research Impact and Outcome, Chitkara University, India Thippa Reddy Gadekallu Stock price forecasting has oftentimes interested several researchers around the world. Making predictions for the future largely depends on the data that will be used to train the model. In general, historical data are used to train models, which contain a features of different types, out of which, not all are necessarily helpful in making predictions. It is, hence, crucial to select the features that can be most useful to make precise predictions. This article proposes a feature selection approach based on the K-means clustering algorithm and elastic net regularization. We have used the K-means algorithm to cluster all the correlated features together and apply elastic net regularization to select the most predictive features within each cluster. We use the selected features to train an LSTM model which predicts the future closing price of a stock for the upcoming trading day. We evaluate the performance of our proposed approach in comparison to the existing approach and observe performance improvement. 2025 2025 251 261 10.54216/FPA.180218 https://www.americaspg.com/articleinfo/3/show/3564