Cited by 97Stock Market Forecasting MethodsTime Series Analysis and ForecastingFinancial Markets and Investment Strategies

Predicting the Trend of Stock Market Index Using the Hybrid Neural Network Based on Multiple Time Scale Feature Learning

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Hao, Yaping, Gao, Qiang·2020·Applied Sciences

Abstract

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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Crossref · 10.3390/app10113961

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Authors
Hao, Y., & Gao, Q.
Affiliations
Beihang University (CN)
Corresponding author
Hao, Yaping
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
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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