Download pdf
Stock price prediction on Australian companies under China’s trade restriction based on the LSTM model
- 1 Maspeth High School
* Author to whom correspondence should be addressed.
Abstract
The volatility of Australian companies' stock prices in 2020, caused by China's trade restrictions, poses a significant challenge for predicting financial gain or loss. This research contributes to future scholarship in predicting stock prices under specific circumstances or during special time periods. The study proposes a novel approach to stock price prediction, incorporating news sentiment analysis into a deep learning model. The research collected news items potentially affecting the stock price, incorporating them into an analysis model to generate a new feature for the Long Short-Term Memory (LSTM) model. The LSTM model used in this study was bidirectional, with two sets of gates per layer, and a three-layer model with different units. Each layer employed a dropout layer and a dense layer in the final stage. The study also utilized the feature engineering of lookback, selecting a window of time in the past to predict the next day's stock prices. Following multiple hyperparameter tunings and feature engineering adjustments, the results and graphs demonstrate a successful prediction for all three of the chosen companies, even during an unstable stock market. The overall trend lines achieve optimal predictions for the stock prices, illustrating both upward and downward trends.
Keywords
stock market prediction, deep learning, recurrent neural networks, and long short-term memory (LSTM)
[1]. Ariyo A A Adewumi A O Ayo C K 2014 Stock price prediction using the ARIMA model 2014 UKSim-AMSS 16th international conference on computer modelling and simulation IEEE 106-112
[2]. Roy S S Mittal D Basu A et al. 2015 Stock market forecasting using LASSO linear regression model Afro-European Conference for Industrial Advancement: Proceedings of the First International Afro-European Conference for Industrial Advancement AECIA Springer International Publishing 371-381
[3]. Vijh M 2020 et al. Stock closing price prediction using machine learning techniques Procedia computer science 167 599-606
[4]. Moghar A and Mhamed H 2020 Stock market prediction using LSTM recurrent neural network Procedia Computer Science 170 1168-1173
[5]. Yu Y Si X Hu C et al. 2019 A review of recurrent neural networks: LSTM cells and network architectures Neural computation 31(7): 1235-1270
[6]. Staudemeyer R C Morris E R 2019 Understanding LSTM--a tutorial into long short-term memory recurrent neural networks arXiv preprint arXiv:1909.09586
[7]. Smagulova K James AP 2019 A survey on LSTM memristive neural network architectures and applications The European Physical Journal Special Topics 228(10): 2313-2324
[8]. Dubey S R Singh S K Chaudhuri B B 2023 AdaNorm: Adaptive Gradient Norm Correction based Optimizer for CNNs Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 5284-5293
[9]. Yu Q Wang J Jin Z et al. 2022 Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training Biomedical Signal Processing and Control 72: 103323
[10]. Dubey S R Singh S K Chaudhuri B B 2023 AdaNorm: Adaptive Gradient Norm Correction based Optimizer for CNNs Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 5284-5293
Cite this article
Lin,X. (2023). Stock price prediction on Australian companies under China’s trade restriction based on the LSTM model. Applied and Computational Engineering,16,1-6.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 5th International Conference on Computing and Data Science
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).