Forecasting the Dynamic Correlation of Stock Indices Based on Deep Learning Method

Ni, Jian; Xu, Yue

doi:10.1007/s10614-021-10198-3

Cited by 14 publications

(2 citation statements)

References 37 publications

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“…For the stock price prediction method based on statistics, in [22], a news augmented generalized autoregressive conditional heterscedasticity (NA-GARCH) model was proposed to use quantitative news sentiment and its impact on asset price movement as the second information source to predict the fluctuation of asset price return together with asset time series data. In [23], the authors introduced deep learning method into the study of stock market correlation. Based on recurrent deep neural network and GARCH model, a hybrid model was proposed for stock price prediction.…”

Section: Related Workmentioning

confidence: 99%

Research on HMM-Based Efficient Stock Price Prediction

2022

Mobile Information Systems

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Stock market is one of the most important parts of the investment market. Compared with other industries, the stock market not only has a higher rate of return on investment but also has a higher risk, and stock price prediction has always been a close concern of investors. Therefore, the research on stock price prediction methods and how to reduce the error of stock price prediction has become a hot topic for many scholars at home and abroad. In recent years, the development of computer technology such as machine learning and econometric method makes the stock price prediction more reliable. Due to the hidden Markov nature of stock price, this paper proposes a stock price prediction method based on hidden Markov model (HMM). To be specific, since the data of stock price have continuity in time series, it is necessary to extend the discrete HMM to the continuous HMM, and then put forward the up and down trend prediction model based on the continuous HMM. The first-order continuous HMM is extended to the second-order continuous HMM, and the stock price is predicted by combining the prediction method of fluctuation range. As a result, the proposed second-order continuous HMM-based stock price prediction model is simulated on Hang Seng Index (HSI), one of the earliest stock market indexes in Hong Kong. The evaluation results on six months HSI show that the predicted value of the proposed model is very close to the actual value and outperforms three benchmarks in terms of RMSE, MAE, and R2.

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Section: Related Workmentioning

confidence: 99%

Research on HMM-Based Efficient Stock Price Prediction

2022

Mobile Information Systems

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“…need for asset risk diversification and optimal asset allocation. In accordance with the well-known Markowitz portfolio theory, the construction of an asset portfolio depends primarily on the expected returns and covariances of the underlying assets, while the correlation coefficients between the assets are the key determinants of the systematic risk exposure generated by the portfolio as a whole [1]. Improving the predictive accuracy of asset correlations has therefore become an important aspect of portfolio management and asset allocation, as it is considered important for diversifying investment risk.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Stock Correlation Forecasting Based on CNN-BiLSTM Enhanced by Attention Mechanism

Luo,

Zhong,

Wang

et al. 2024

IEEE Access

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This study utilizes a new approach for short-term stock correlation forecasting using a combination of convolutional neural networks (CNN), bi-directional long and short-term memory (BiLSTM), and attention mechanisms to address the issue of information loss due to excessively long input time series data. The objective is to improve the accuracy of short-term equity correlation forecasts, which is essential for the efficient management of financial asset portfolios. The existing time series models usually only take into account the effect of stock historical data characteristics on stock correlation but ignore the fact that stock correlation is affected by multiple factors and undergoes an unknown unstable trend. More efficient methods or algorithms need to be devised to deal with financial data. This paper explores stock metrics and introduces a multi-factor model to quantify the factors affecting stock returns in order to calculate the correlation between stocks. Meanwhile, to cope with the problem of incomplete input data, this paper simultaneously incorporates stock return data that is directly related to the correlation data. In the CNN-BiLSTM-Attention (CLATT) model, the role of the CNN module is to obtain high-dimensional features from the input time series data, and the role of the BiLSTM module is to process the time series information to capture the time-series features in the stock return data and correlation data in the sample. In order to acquire the inherent correlation between the input stock return data and correlation data and to enhance the prediction accuracy of the model, the attention mechanism is finally implemented and used to optimize the weights of the BiLSTM output. In this paper, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) metrics, mean absolute percentage of error (MAPE), and the coefficient of determination R 2 , as well as four benchmark models, are used to evaluate the performance of the proposed model. The result shows that the CLATT model has 57.32% and 33% improvement in the correlation prediction accuracy of different stock portfolios compared to the single LSTM model, and the model prediction accuracy has been improved. Compared with the benchmark CNN-BiLSTM, it also has 56.06% and 28.87% improvement.

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Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition

Liao

Chen

et al. 2023

Comput Econ

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Forecasting the Dynamic Correlation of Stock Indices Based on Deep Learning Method

Cited by 14 publications

References 37 publications

Research on HMM-Based Efficient Stock Price Prediction

Research on HMM-Based Efficient Stock Price Prediction

Short-Term Stock Correlation Forecasting Based on CNN-BiLSTM Enhanced by Attention Mechanism

Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition

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