BUMI Stock Price Prediction Using Long Short Term Memory (LSTM) with Three Hyperparameter Tuning Regression - Hartono Hartono (Quantitative Economics and Management Studies, 2022)
Shares is one of investment with moderate to high level of risk profile and return rate, and is a legal ownership proof of a company (Ltd). To obtain an optimum return with possible low risk, price of shares can be predicted or analyzed via fundamental or technical analysis. One of the most popular technical analysis is by employing computer algorithms as a device to help making decision. Long short term memory (LSTM), as a deep improvement of recurrent neural networks (RNN), is a commonly used method to predict shares price. BUMI, as one of shares in the energy sector, is the most concerned shares due to the geopolitical conflict in Europe. This research aims to predict BUMI shares price using LSTM by considering three parameters, i.e. number of hidden layers, epoch, and learning rate. Linear regression is used to obtained optimum values of each parameter whichs further combined to yield the best performance of the algorithm. Based on the analysis, the combination of minimum and optimum parameters results in the best performance by RMSE value.