Enhancing stock market predictions via hybrid external trend and internal components analysis and long short term memory model

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-12-01 DOI:10.1016/j.jksuci.2024.102252
Fatene Dioubi , Negalign Wake Hundera , Huiying Xu , Xinzhong Zhu
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Abstract

When it comes to financial decision-making, stock market predictability is extremely important since it offers valuable information that may guide investment strategies, risk management, and portfolio allocation overall. Traditional methods often fail to accurately predict stock prices due to their complexity and inability to handle non-linear and non-stationary patterns in market data. To address these issues, this study introduces an innovative model that combines the External Trend and Internal Components Analysis decomposition method (ETICA) with the Long Short-Term Memory (LSTM) model, aiming to enhance stock market predictions for S&P 500, NASDAQ, Dow Jones, SSE and SZSE indices. Through rigorous testing across various training data proportions and epoch settings, our findings reveal that the proposed hybrid model outperforms the single LSTM model, delivering significantly lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. This enhanced precision reduces prediction errors, underscoring the model’s robustness and reliability. The superior performance of the ETICA-LSTM model highlights its potential as a powerful financial forecasting tool, promising to transform investment strategies, optimize risk management, and enhance portfolio performance.
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在金融决策方面,股票市场的可预测性极为重要,因为它提供了宝贵的信息,可以指导投资策略、风险管理和投资组合的整体配置。传统方法由于其复杂性以及无法处理市场数据中的非线性和非平稳模式,往往无法准确预测股票价格。为了解决这些问题,本研究引入了一个创新模型,将外部趋势和内部成分分析分解法(ETICA)与长短期记忆(LSTM)模型相结合,旨在提高对 S&P 500、纳斯达克、道琼斯、上证和深证指数的股市预测能力。通过对各种训练数据比例和历时设置进行严格测试,我们的研究结果表明,所提出的混合模型优于单一的 LSTM 模型,其均方根误差(RMSE)和平均绝对误差(MAE)值明显降低。精度的提高减少了预测误差,凸显了模型的鲁棒性和可靠性。ETICA-LSTM 模型的卓越性能彰显了其作为强大的金融预测工具的潜力,有望改变投资策略、优化风险管理并提高投资组合绩效。
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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