Predicting the Trend of Stock Market Index Using the Hybrid Neural Network Based on Multiple Time Scale Feature Learning
Open accessAbstract
In the stock market, predicting the trend of price series is one of the most widely investigated and challenging problems for investors and researchers. There are multiple time scale features in financial time series due to different durations of impact factors and traders’ trading behaviors. In this paper, we propose a novel end-to-end hybrid neural network, a model based on multiple time scale feature learning to predict the price trend of the stock market index. Firstly, the hybrid neural network extracts two types of features on different time scales through the first and second layers of the convolutional neural network (CNN), together with the raw daily price series, reflect relatively short-, medium- and long-term features in the price sequence. Secondly, considering time dependencies existing in the three kinds of features, the proposed hybrid neural network leverages three long short-term memory (LSTM) recurrent neural networks to capture such dependencies, respectively. Finally, fully connected layers are used to learn joint representations for predicting the price trend. The proposed hybrid neural network demonstrates its effectiveness by outperforming benchmark models on the real dataset.
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Supervised learning approaches for automated stock trading had many limitations in achieving the optimal point.
“Although these approaches were impressive, they had many limitations in achieving the optimal point.”
From A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks (2025) · cites this paper as [2]
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- Authors
- Hao, Y., & Gao, Q.
- Affiliations
- Beihang University (CN)
- Corresponding author
- Hao, Yaping
- ORCID
- Gao, Qiang
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2020-06-07
- Volume
- 10
- Issue
- 11
- Pages
- 3961
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3033899148
- Cited by
- 95
- References
- 28
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 9 · 2023: 22 · 2022: 18 · 2021: 19 · 2020: 6
- FWCI
- 10.34
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Keywords
- stock index forecasting, price trend forecasting, hybrid neural network, convolutional neural networks, long short-term memory, financial time series, time dependence, joint representation learning
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