Hybrid deep learning models with multi-classification investor sentiment to forecast the prices of China's leading stocks.

The prediction of stock prices has long been a captivating subject in academic research. This study aims to forecast the prices of prominent stocks in five key industries of the Chinese A-share market by leveraging the synergistic power of deep learning techniques and investor sentiment analysis. To achieve this, a sentiment multi-classification dataset is for the first time constructed for China's stock market, based on four types of sentiments in modern psychology. The significant heterogeneity of sentiment changes in the sectors' leading stock markets is trained and mined using the Bi-LSTM-ATT model. The impact of multi-classification investor sentiment on stock price prediction was analyzed using the CNN-Bi-LSTM-ATT model. It finds that integrating sentiment indicators into the prediction of industry leading stock prices can enhance the accuracy of the model. Drawing upon four fundamental sentiment types derived from modern psychology, our dataset provides a comprehensive framework for analyzing investor sentiment and its impact on forecasting the stock prices of China's A-share market.

The Risk Contagion between Chinese and Mature Stock Markets: Evidence from a Markov-Switching Mixed-Clayton Copula Model.

Exploring the risk spillover between Chinese and mature stock markets is a promising topic. In this study, we propose a Markov-switching mixed-Clayton (Ms-M-Clayton) copula model that combines a state transition mechanism with a weighted mixed-Clayton copula. It is applied to investigate the dynamic risk dependence between Chinese and mature stock markets in the Americas, Europe, and Asia-Oceania regions. Additionally, the conditional value at risk (CoVaR) is applied to analyze the risk spillovers between these markets. The empirical results demonstrate that there is mainly a time-varying but stable positive risk dependence structure between Chinese and mature stock markets, where the upside and downside risk correlations are asymmetric. Moreover, the risk contagion primarily spills over from mature stock markets to the Chinese stock market, and the downside effect is stronger. Finally, the risk contagion from Asia-Oceania to China is weaker than that from Europe and the Americas. The study provides insights into the risk association between emerging markets, represented by China, and mature stock markets in major regions. It is significant for investors and risk managers, enabling them to avoid investment risks and prevent risk contagion.

Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine.

In view of the important position of crude oil in the national economy and its contribution to various economic sectors, crude oil price and volatility prediction have become an increasingly hot issue that is concerned by practitioners and researchers. In this paper, a new hybrid forecasting model based on variational mode decomposition (VMD) and kernel extreme learning machine (KELM) is proposed to forecast the daily prices and 7-day volatility of Brent and WTI crude oil. The KELM has the advantage of less time consuming and lower parameter-sensitivity, thus showing fine prediction ability. The effectiveness of VMD-KELM model is verified by a comparative study with other hybrid models and their single models. Except various commonly used evaluation criteria, a recently-developed multi-scale composite complexity synchronization (MCCS) statistic is also utilized to evaluate the synchrony degree between the predictive and the actual values. The empirical results verify that 1) KELM model holds better performance than ELM and BP in crude oil and volatility forecasting; 2) VMD-based model outperforms the EEMD-based model; 3) The developed VMD-KELM model exhibits great superiority compared with other popular models not only for crude oil price, but also for volatility prediction.

A hybrid model combining variational mode decomposition and an attention-GRU network for stock price index forecasting.

In this paper we introduce a new hybrid model based on variational mode decomposition (VMD) and Gated Recurrent Units (GRU) network improved by attention mechanism to enhance the accuracy of stock price indices forecasting. In the process of establishing the model, VMD is made a use to decompose the primary series into some almost orthogonal subsequences. The attention mechanism is introduced into GRU to assign different weights to the input elements in advance so that better predictive results can be achieved for each component. In empirical experiment, London FTSE Index (FTSE) and Nasdaq Index (IXIC) are adopted to examine the performance of VMD-AttGRU model. Empirical results report that the developed hybrid model outperforms the single models and indeed raises the accuracy of stock price indices forecasting. In addition, the introduction of attention mechanism can increase the level predictive accuracy but decrease the correctness of direction forecasting.

Financial Time Series Prediction Using Elman Recurrent Random Neural Networks.

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.

Nonlinear multi-analysis of agent-based financial market dynamics by epidemic system.

Based on the epidemic dynamical system, we construct a new agent-based financial time series model. In order to check and testify its rationality, we compare the statistical properties of the time series model with the real stock market indices, Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index. For analyzing the statistical properties, we combine the multi-parameter analysis with the tail distribution analysis, the modified rescaled range analysis, and the multifractal detrended fluctuation analysis. For a better perspective, the three-dimensional diagrams are used to present the analysis results. The empirical research in this paper indicates that the long-range dependence property and the multifractal phenomenon exist in the real returns and the proposed model. Therefore, the new agent-based financial model can recurrence some important features of real stock markets.

Differences and determinants in access to essential public health services in China: a case study with hypertension people and under-sixes as target population.

BACKGROUND: Since 2009, health reform had launched in China and essential public health services were provided for all residents to ensure service equity and accessibility, and to achieve sustained population-wide health improvement. This study aimed to investigate the differences and determinants among populations with different characteristics access to essential public health services in China, especially hypertension people and children aged 0-6 years. METHODS: A cross-sectional study with socio-demographic data analysis was undertaken to estimate distribution characteristics of receiving essential public health services of hypertension patients and children. Regular follow-ups and effective blood pressure control reflected the effective management for hypertension patients, and for children, public services provided were vaccination on schedule and regular physical check-up. Logistic regression was used to determine the predictors for effective management. RESULTS: A total of 1 505 hypertension patients and 749 children were involved; 39.14% of hypertension participants could control their blood pressure in the normal range, and the rate in urban areas (43.61%) was higher than that in rural (31.88%). And 34.68% of them could receive more than 4 times follow-ups by the medical technician. Of 754 children, 79.84% could receive the periodic physical examination and 98.40% had vaccinated regularly. Children living in rural areas were more likely to have regular check-ups (83.96%) and regular vaccination (nearly 99%). Overall, geographic location and education level were the determinants of people access to essential public health services. CONCLUSIONS: Implementation of the health reform since 2009 has headed China's public health system in the right direction and promoted the improvement of public health system development. Our study highlights the growing needs for more public health services in China, and China's public health system needs to be greatly improved in terms of its quality and accessibility.