Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Quantitative Economics and Management Studies
Hartono Hartono
Universitas Widya Dharma Pontianak, Indonesia
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
BUMI Stock Price Prediction Using Long Short Term Memory (LSTM) with Three Hyperparameter Tuning Regression Hartono Hartono
Quantitative Economics and Management Studies Vol. 3 No. 6 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (602.625 KB) | DOI: 10.35877/454RI.qems1118

Abstract

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.
Co-Authors