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Sains Malaysiana 52(3)(2023): 993-1009
http://doi.org/10.17576/jsm-2023-5203-22
A Comparative Study of Deep
Learning Algorithms in Univariate and Multivariate Forecasting of the Malaysian
Stock Market
(Kajian Perbandingan
Algoritma Pembelajaran Mendalam dalam Peramalan Univariat dan Multivariat
Pasaran Saham Malaysia)
1Malaysian Administrative Modernisation and Management
Planning Unit (MAMPU), Federal Government Administrative Centre, 62502
Putrajaya, Federal Territory, Malaysia
2Faculty of Information Science and Technology,
Universiti Kebangsaan Malaysia 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia
Received: 8 August 2022/Accepted: 28 December 2022
Abstract
As part of a
financial institution, the stock market has been an essential factor in the
growth and stability of the national economy. Investment in the stock market is
risky because of its price complexity and unpredictable nature. Deep learning
is an emerging approach in stock market prediction modeling that can learn the
non-linearity and complexity of stock market data. To date, not much study on
stock market prediction in Malaysia employs the deep learning prediction model,
especially in handling univariate and multivariate data. This study aims to
develop a univariate and multivariate stock market forecasting model using
three deep learning algorithms and compare the performance of those models. The
algorithm intends to predict the close price of the Malaysian stock market
using the Axiata Group Berhad and Petronas Gas Berhad from Bursa Malaysia,
listed in Kuala Lumpur Composite Index (KLCI) datasets. Three deep learning
algorithms, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN),
and Long Short-Term Memory (LSTM), are used to develop the prediction model.
The deep learning models achieved the highest accuracy and outperformed the
baseline models in short and long-term forecasts. It also shows that LSTM
possessed the best deep learning model for the Malaysian stock market,
achieving the lowest prediction error among the other models. Deep learning
demonstrates the ability to handle univariate and multivariate data in
preserving important information, thus forecasting the stock market. This
finding is relatively significant as deep learning works well with
high-dimensional datasets.
Keywords: CNN;
deep learning; LSTM; MLP; multivariate; stock forecasting; time series;
univariate
Abstrak
Pasaran saham merupakan sebahagian daripada institusi kewangan yang menjadi faktor penting dalam pertumbuhan dan kestabilan sesebuah ekonomi negara. Pelaburan dalam pasaran saham adalah sangat berisiko disebabkan oleh perubahan harganya yang rumit dan sifatnya yang sukar untuk diramal. Pembelajaran mendalam adalah satu pendekatan baharu yang semakin menonjol dalam ramalan pasaran saham kerana ia mampu mempelajari data pasaran saham yang tidak linear dan rumit. Sehingga kini, tidak banyak kajian yang dilakukan mengenai ramalan pasaran saham di Malaysia menggunakan pendekatan pembelajaran mendalam khususnya yang melibatkan pendekatan data univariat dan multivariat. Penyelidikan ini dijalankan untuk membangunkan model ramalan pasaran saham univariat dan multivariat menggunakan tiga algoritma pembelajaran mendalam dan seterusnya membuat perbandingan prestasi antara model tersebut. Ia akan meramal harga tutup di pasaran saham Malaysia menggunakan data saham Axiata Group Berhad dan Petronas Gas Berhad dari Bursa Malaysia dan turut tersenarai di dalam Indeks Komposit Kuala Lumpur (KLCI). Tiga algoritma pembelajaran mendalam iaitu Multilayer Perceptron (MLP), Convolutional
Neural Network (CNN) dan Long Short-Term
Memory (LSTM) digunakan untuk membangunkan model ramalan. Hasil uji kaji menunjukkan model pembelajaran mendalam mencapai ketepatan yang tinggi dan mengatasi kesemua model dasar bagi ramalan untuk tempoh jangka pendek dan panjang. Ia juga menunjukkan LSTM merupakan model pembelajaran mendalam yang terbaik untuk pasaran saham Malaysia dengan ralat ramalan yang paling rendah berbanding kesemua model lain. Pembelajaran mendalam menunjukkan keupayaan yang ketara dalam membuat ramalan pasaran saham menggunakan data univariat dan multivariat. Penemuan ini adalah signifikan dengan keupayaan pembelajaran mendalam terutamanya dalam mempelajari set data yang bersifat multidimensi dan mempunyai fitur yang banyak.
Kata kunci: CNN;
LSTM; MLP; pembelajaran dalam; multivariat; ramalan saham; siri masa; univariat
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*Corresponding author; email: azuraliza@ukm.edu.my
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