This study establishes a combination method-based prediction model for the CSI 300 stock index price embedded with options market information. Firstly, utilizing options and spot market information, a BP neural network is employed to predict the CSI...
With the outbreak and evolution of the pandemic worldwide, the financial market has experienced unprecedented shocks and adjustments, and the volatility and correlation of the stock market, as an important indicator of economic activities, have shown...
In this study, neural networks are utilized to develop a stock price prediction model based on the constituent stocks of the China Securities Index 300 (CSI300). This research investigates various prediction methods and models through experiments, co...
Combined with the B-P (breakpoint) test and VAR–DCC–GARCH model, the relationship between WTI crude oil futures and S&P 500 index futures or CSI 300 index futures was investigated and compared. The results show that breakpoints exist in the relat...
Due to the heterogeneity of investor structure between the Chinese mainland stock market (A-share market) and the Hong Kong stock market (H-share market) as well as the limitations on arbitrage activities, most cross-listed stocks in the two markets...
This paper examines the daily return series of four main indices, including Shanghai Stock Exchange Composite Index (SSE), Shenzhen Stock Exchange Component Index (SZSE), Shanghai Shenzhen 300 Index (SHSE-SZSE300), and CSI Smallcap 500 index (CSI500)...
Predicting stock prices is a popular area of study within the realms of data mining and machine learning. Precise forecasting can assist investors in mitigating the risks associated with their investments. Given the unpredictable nature of the stock...
This paper evaluates the impact of retail investors’ bullish sentiment in comparison to that of financial institutions on the return of Chinese CSI 300 index stocks over the period of 2015 to 2023. We document several regularities. First, the s...
We collected online public opinions on the CSI 300 index constituents and investigated the different impacts of online public opinion divergence on trading volume. Here, we find that online public opinions are helpful in improving the trading volume,...
It is meaningful to analyze the market correlations for stock selection in the field of financial investment. Since it is difficult for existing deep clustering methods to mine the complex and nonlinear features contained in financial time series, in...
Stock price prediction has long been a topic of interest in academia and the financial industry. Numerous factors influence stock prices, such as a company’s performance, industry development, national policies, and other macroeconomic factors....
The inherent challenges of financial time series forecasting demand advanced modeling techniques for reliable predictions. Effective financial time series forecasting is crucial for financial risk management and the formulation of investment decision...
This paper investigates the predictability of stock returns in the Chinese market through the lens of consumption–wealth dynamics within a broader financial system. We focus on two key state variables derived from modern consumption-based asset...
Forecasting financial time series is challenging due to their intrinsic nonlinearity, high volatility, and complex dependencies across temporal scales. This study introduces MSGformer, a novel hybrid architecture that integrates multi-scale graph neu...
Recently, deep-learning-based quantitative investment is playing an increasingly important role in the field of finance. However, due to the complexity of the stock market, establishing effective quantitative investment methods is facing challenges f...
The stock market plays a crucial role in the financial system, with its price movements reflecting macroeconomic trends. Due to the influence of multifaceted factors such as policy shifts and corporate performance, stock prices exhibit nonlinearity,...
With the development of quantitative finance, machine learning methods used in the financial fields have been given significant attention among researchers, investors, and traders. However, in the field of stock index spot–futures arbitrage, re...
This paper proposes a novel multiscale random forest model for stock index trend prediction, incorporating statistical inference principles to improve classification confidence. Traditional random forest classifiers rely on majority voting, which can...
As a popular passive investment strategy, a sparse index tracking strategy has advantages over a full index replication strategy because of higher liquidity and lower transaction costs. Sparsity and nonnegativity constraints are usually assumed in th...
In the data-driven era, the mining of financial asset information and the selection of appropriate assets are crucial for stable returns and risk control. Multifactor quantitative models are a common method for stock selection in financial assets, so...
Technical analysis has been proved to be capable of exploiting short-term fluctuations in financial markets. Recent results indicate that the market timing approach beats many traditional buy-and-hold approaches in most of the short-term trading peri...
Local character can shape corporate resource accumulation and utilization, especially in emerging economies, and accordingly plays an important role in affecting the performance results of corporate social responsibility (CSR) practices. This paper t...