A prediction model for stock market based on the integration of independent component analysis and Multi-LSTM

He, Hongzeng; Dai, Sheng

doi:10.3934/era.2022196

era

2022

DOI: 10.3934/era.2022196

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A prediction model for stock market based on the integration of independent component analysis and Multi-LSTM

Hongzeng He

Sheng Dai

Abstract: <abstract><p>In this paper, we investigate the statistical behaviors of the stock market complex network. A hybrid model is proposed to predict the variations of five stock prices in the securities plate sub-network. This model integrates independent component analysis (ICA) and multivariate long short-term memory (Multi-LSTM) neural network to analyze the trading noise and improve the prediction accuracy of stock prices in the sub-network. Firstly, we apply ICA to deconstruct the original dataset … Show more

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Cited by 4 publications

References 32 publications

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PCA-ICA-LSTM: A Hybrid Deep Learning Model Based on Dimension Reduction Methods to Predict S&P 500 Index Price

Sarıkoç,

Celik

2024

Comput Econ

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In this paper, we propose a new hybrid model based on a deep learning network to predict the prices of financial assets. The study addresses two key limitations in existing research: (1) the lack of standardized datasets, time scales, and evaluation metrics, and (2) the focus on prediction return. The proposed model employs a two-stage preprocessing approach utilizing Principal Component Analysis (PCA) for dimensionality reduction and de-noising, followed by Independent Component Analysis (ICA) for feature extraction. A Long Short-Term Memory (LSTM) network with five layers is fed with this preprocessed data to predict the price of the next day using a 5 day time horizon. To ensure comparability with existing literature, experiments employ an 18 year dataset of the Standard & Poor's 500 (S&P500) index and include over 40 technical indicators. Performance evaluation encompasses six metrics, highlighting the model's superiority in accuracy and return rates. Comparative analyses demonstrate the superiority of the proposed PCA-ICA-LSTM model over single-stage statistical methods and other deep learning architectures, achieving notable improvements in evaluation metrics. Evaluation against previous studies using similar datasets corroborates the model's superior performance. Moreover, extensions to the study include adjustments to dataset parameters to account for the COVID-19 pandemic, resulting in improved return rates surpassing traditional trading strategies. PCA-ICA-LSTM achieves a 220% higher return compared to the “hold and wait” strategy in the extended S&P500 dataset, along with a 260% higher return than its closest competitor in the comparison. Furthermore, it outperformed other models in additional case studies. Graphical Abstract

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PCA-ICA-LSTM: A Hybrid Deep Learning Model Based on Dimension Reduction Methods to Predict S&P 500 Index Price

Sarıkoç,

Celik

2024

Comput Econ

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Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction

Zhao

Liu

et al. 2023

Mathematics

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The ability to predict stock prices is essential for informing investment decisions in the stock market. However, the complexity of various factors influencing stock prices has been widely studied. Traditional methods, which rely on time-series information for a single stock, are incomplete as they lack a holistic perspective. The linkage effect in the stock market, where stock prices are influenced by those of associated stocks, necessitates the use of more comprehensive data. Currently, stock relationship information is mainly obtained through industry classification data from third-party platforms, but these data are often approximate and subject to time lag. To address this, this paper proposes a time series relational model (TSRM) that integrates time and relationship information. The TSRM utilizes transaction data of stocks to automatically obtain stock classification through a K-means model and derives stock relationships. The time series information, extracted using long short-term memory (LSTM), and relationship information, extracted with a graph convolutional network (GCN), are integrated to predict stock prices. The TSRM was tested in the Chinese Shanghai and Shenzhen stock markets, with results showing an improvement in cumulative returns by 44% and 41%, respectively, compared to the baseline, and a reduction in maximum drawdown by 4.9% and 6.6%, respectively.

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Transmission effect of extreme risks in China's financial sectors at major emergencies: Empirical study based on the GPD-CAViaR and TVP-SV-VAR approach

Xie

et al. 2022

era

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<abstract> <p>Major emergencies cause massive financial risk and economic loss. In the context of major emergencies, we propose the GPD-CAViaR model to depict the extreme risks of financial sectors, and utilize the TVP-SV-VAR model to analyze their transmission effect. We find that (ⅰ) the securities sector has the highest extreme risks among the four financial sectors; (ⅱ) when major emergencies occur, the extreme risks of various financial sectors increase rapidly; (ⅲ) the transmission effect in short term is stronger than that in medium and long term; and (ⅳ) the transmission effects at different time points are relatively consistent.</p> </abstract>

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A prediction model for stock market based on the integration of independent component analysis and Multi-LSTM

Cited by 4 publications

References 32 publications

PCA-ICA-LSTM: A Hybrid Deep Learning Model Based on Dimension Reduction Methods to Predict S&P 500 Index Price

PCA-ICA-LSTM: A Hybrid Deep Learning Model Based on Dimension Reduction Methods to Predict S&P 500 Index Price

Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction

Transmission effect of extreme risks in China's financial sectors at major emergencies: Empirical study based on the GPD-CAViaR and TVP-SV-VAR approach

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