Machine Learning Techniques for Predicting Stock Closing Prices

Due to how unpredictable and non-linear financial markets are, it is challenging to predict stock market returns with accuracy. Programmed methods, however, have shown to be increasingly effective at forecasting stock values as artificial intelligence and processing power have advanced, This study used Support Vector Machine (SVM), Random Forest (RF), Long Short-Term Memory (LSTM), K-Nearest Neighbors (KNN), and Gated Recurrent Unit (GRU) approaches to estimate the closing price of five businesses from distinct sectors for the following day. The models' new variables were created by using financial data, such as Open, High, Low, and Close prices, as inputs. The models were assessed using common strategic metrics, including RMSE, MSE, and MAE, where low values signify that the models were successful at predicting stock values.