Geography and distance impact financial dynamics in Chinese stock markets.
problem Investigate the impact of geography and distance on financial dynamics in Chinese stock markets.
method Daily data analysis of individual stocks in Shanghai and Shenzhen stock markets, focusing on geographical correlation and distance effect.
result Stock location impacts financial dynamics, except during financial crises. Short distance has higher probability than long distance, and correlation weakly decays with distance in Shanghai but remains stable in Shenzhen.
China's QFII and RQFII programs expand foreign investment in Chinese markets.
problem Foreign investment restrictions in China's capital markets.
method Comparative analysis of globalization processes in Taiwan, Korea, and India.
result China's market openness increases as foreign demand grows.
SHHK Stock Connect increases A-H share price premium, more for less efficient markets.
problem Impact of financial liberalization on cross-market pricing efficiency.
method Monthly data for 67 A-H dual-listed firms, system GMM dynamic models.
result Heterogeneous effect of SHHK Stock Connect on A-H price premium, more pronounced for less efficient markets.
AI predicts stock prices with neural networks.
problem Predicting exact stock prices at the stock market.
method Feedforward multi-layer perceptron with error backpropagation, using 5:21:21:1 architecture.
result Simple neural networks can predict stock prices effectively.
Study reveals multifractal nature in Chinese stock markets and predicts future returns.
problem Predicting future stock market returns using multifractal characteristics.
method Multifractal detrended fluctuation analysis (MF-DFA) on high-frequency stock data.
result Spectral width Δα is a significant predictor of future stock returns.
Research evaluates ANN stock price prediction system for Shanghai Stock Exchange.
problem Predicting exact stock prices in the Shanghai Stock Exchange.
method Feedforward multi-layer perceptron with error backpropagation, using 5:21:21:1 configuration with 80% training data.
result Neural networks can predict stock prices with low mean absolute percentage errors (1.95%).
We investigate the behavior of the Shanghai Stock Exchange Composite (SSEC) index for the period from 1990:12 to 2007:06 using an unconstrained two-regime threshold autoregressive (TAR) model with an unit root developed by Caner and Hansen. The method allows us to simultaneously consider non-stationarity and nonlineari…
The study finds solar terms significantly impact China's stock market returns and volatility.
problem Investigating the effect of solar terms on China's stock market.
method Regression framework, analyzing multiple solar terms and their impact on return and volatility.
result Solar terms 1, 3, and 4 cause significant positive returns, while 8, 11, and 14 bring high volatility.
Deep learning model predicts stock market direction.
problem Forecasting stock market direction in finance.
method Stacked Denoising Autoencoder (SDAE) applied to financial prediction.
result Deep learning model outperforms traditional methods in predicting CSI 300 index.
We use high-frequency data of 1364 Chinese A-share stocks traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange to investigate the intraday patterns in the bid-ask spreads. The daily periodicity in the spread time series is confirmed by Lomb analysis and the intraday bid-ask spreads are found to exhibit L…
Taylor's law found in stock market illiquidity, with varying exponents.
problem Understanding the temporal fluctuation scaling in stock illiquidity.
method Investigation of high-frequency illiquidity data from multiple exchanges and industries.
result Taylor's law holds with varying exponents (b > 2 for A-shares, b < 2 for B-shares) across different markets and sectors.
We investigate the behavior of stocks in daily price-limited stock markets by purposing a quantum spatial-periodic harmonic model. The stock price is presumed to oscillate and damp in a quantum spatial-periodic harmonic oscillator potential well. Complicated non-linear relations including inter-band positive correlatio…
Article proposes a profitable intraday trading strategy for Chinese stocks.
problem Intraday trading opportunities in Chinese stock market.
method Markowitz optimization and Multilayer Perceptron (MLP) for stock price prediction.
result Validation of Markowitz portfolio optimization and MLP for intraday stock price prediction.
This study analyzes factors affecting China's stock market volatility.
problem Investigating factors influencing China's stock market volatility.
method Used ARDL model and principal component analysis on daily data from 2010-2024.
result Exchange rate and bond yields significantly impact stock market volatility.
A new concept, called balanced estimator of diffusion entropy, is proposed to detect scalings in short time series. The effectiveness of the method is verified by means of a large number of artificial fractional Brownian motions. It is used also to detect scaling properties and structural breaks in stock price series o…
Market liquidity plays a vital role in the field of market micro-structure, because it is the vigor of the financial market. This paper uses a variable called convexity to measure the potential liquidity provided by order-book. Based on the high-frequency data of each stock included in the SSE (Shanghai Stock Exchange)…
Paper uses clustering to diversify stocks, reducing risk.
problem Diversifying stock portfolios to reduce risk.
method Applied correlation clustering to a stock correlation matrix.
result Portfolio diversification using clustering reduces risk.
It is believed by the majority today that the efficient market hypothesis is imperfect because of market irrationality. Using the physical concepts and mathematical structures of quantum mechanics, we construct an econophysics framework for the stock market, based on which we analogously map massive numbers of single s…
Study shows COVID-19 increases stock market crash risk in China.
problem Impact of COVID-19 on stock market crash risk in China.
method Estimated conditional skewness using GARCH-S model and constructed fear index from Baidu Index data.
result Conditional skewness reacts negatively to daily growth in total confirmed cases, indicating increased crash risk.
Study finds inefficiency in Brazilian stock market through correlations and fat-tailed returns.
problem Inefficiency of the Brazilian stock market, particularly the IBOVESPA future contracts.
method Analysis of cross-correlations with foreign markets, examination of log-return distribution, and neural network forecasting.
result Strong dependence on foreign markets and fat-tailed returns indicate inefficiency.
Study uses Hawkes processes to analyze stock market contagion in China.
problem Understanding contagion in Chinese stock market.
method Fitting Hawkes processes to daily returns and sector indices.
result Identifies long-term dependencies and trending patterns in sector indices.
Study examines how COVID-19 affected stock and crypto market efficiency.
problem Impact of COVID-19 on market efficiency of different asset classes.
method Analysis of price returns, absolute returns, and volatility increments in stock and cryptocurrency markets.
result Market efficiency varied by asset class and market, with some time series showing gradual decline over time.
In this paper, we study the dynamics of absolute return, trading volume and bid-ask spread after the trading halts using high-frequency data from the Shanghai Stock Exchange. We deal with all three types of trading halts, namely intraday halts, one-day halts and inter-day halts, of 203 stocks in Shanghai Stock Exchange…
We investigate the spatial and temporal structures of four financial markets in Greater China. In particular, we uncover different characteristics of the four markets by analyzing the sector and subsector structures which are detected through the random matrix theory. Meanwhile, we observe that the Taiwan and Hongkong …
Study examines changes in Chinese stock market correlation structure around 2008 crisis.
problem Analyzing changes in market correlation structure around the 2008 crisis.
method Comparative analysis of high-frequency stock returns using random matrix analysis.
result Stronger average correlation and partial correlation in 2008 compared to 2007, with significant market effect.
We study the dynamic evolution of cross-correlations in the Chinese stock market mainly based on the random matrix theory (RMT). The correlation matrices constructed from the return series of 367 A-share stocks traded on the Shanghai Stock Exchange from January 4, 1999 to December 30, 2011 are calculated over a moving …
Price limit trading rules are adopted in some stock markets (especially emerging markets) trying to cool off traders' short-term trading mania on individual stocks and increase market efficiency. Under such a microstructure, stocks may hit their up-limits and down-limits from time to time. However, the behaviors of pri…
Study examines trading costs after Hong Kong-Shanghai Connect.
problem Estimating trading costs in China's securities market.
method Developed a novel methodology to compensate for lack of data.
result Trading costs on Shanghai may have increased after Connect.
A new stock index model simplifies high-dimensional stock data.
problem Reflecting the overall stock market activity in high-dimensional data.
method Manifold learning and feature detection on discrete Laplace-Beltrami operator.
result The MF index series approximates the stock market better and has lower risk.
This study uses local Gaussian correlation to analyze stock return tails, revealing more sensitive network properties.
problem Misleading results from Pearson correlation in financial networks.
method Local Gaussian correlation coefficient for capturing nonlinear dependence and heavy-tailed distributions.
result Local Gaussian correlation network among negative tails is more sensitive to stock market risks.
An active margin system for margin loans is proposed for Chinese margin lending market, which uses cash and randomly selected stock as collateral. The conditional probability of negative return(CPNR) after a forced sale of securities from under-margined account in a falling market is used to measure the risk faced by t…
This paper introduces anti-correlation networks to study China's stock market.
problem Previous studies ignored anti-correlation in financial networks.
method Constructed weighted temporal anti-correlation and positive correlation networks.
result Unveiled differences in topological measurements between anti-correlation and positive correlation networks.
The paper examines market efficiency in China and India post financial crises.
problem Testing market efficiency in emerging markets post crises.
method Examined daily returns using auto correlation tests, runs tests, and unit root tests.
result Both Chinese and Indian stock markets do not exhibit weak form of market efficiency.
In order to protect brokers from customer defaults in a volatile market, an active margin system is proposed for the transactions of margin lending in China. The probability of negative return under the condition that collaterals are liquidated in a falling market is used to measure the risk associated with margin loan…
New method selects edges in stock networks using multiple threshold values.
problem Balancing prominent correlations and network connectivity in stock networks.
method Uses multiple distributions in a maximum likelihood estimator for selecting threshold values.
result Proposed method develops networks with appropriate connectivities.
Study community structures in Chinese stock markets using returns and turnover rates.
problem Detecting community structure in stock markets for financial dynamics and risk estimation.
method Used PMFG and infomap methods on a distance matrix to analyze community structures in both price returns and turnover rates.
result Community structures for turnover rates are more complex and sector effects are weaker compared to returns.
TGARCH model shows CSI-300 futures reduce spot price volatility.
problem Impact of CSI-300 futures trading on spot price volatility.
method TGARCH model applied to CSI-300 index data.
result CSI-300 futures trading significantly reduces spot price volatility.
The paper analyzes return distribution of Chinese stock market indices over various time scales.
problem Understanding return distribution properties of Chinese stock markets.
method Systematic analysis of 1-min to 4000-min composite index datasets from 2005-2021.
result Return distribution properties are similar to mature markets, with distinct behavior at different time scales.
Graph-based approach predicts stock trends using dynamic multi-relational graphs.
problem Predicting future stock movements in complex, time-evolving stock relationships.
method Dynamic multi-relational stock graphs, stochastic diffusion process, parallel retention.
result Outperforms state-of-the-art baselines in stock trend forecasting.
Hidden Markov model predicts profitable statistical arbitrage in Shanghai crude oil futures.
problem Statistical arbitrage opportunities in international crude oil futures markets.
method Hidden Markov model for cointegration spread, mean-reverting regime-switching process.
result Statistical arbitrage strategies involving Shanghai crude oil futures are profitable.
Contrarian strategies show profitability in Chinese stock market over long horizons.
problem Evaluating the profitability of contrarian strategies in the Chinese stock market.
method Monthly data of all stocks traded on the Shanghai and Shenzhen Stock Exchanges from 1997 to 2012.
result Contrarian portfolios show long-term profitability with increasing returns as estimation and holding horizons increase.
Study shows price limits in Chinese stock markets reduce trading activity before hitting limits.
problem Maintaining stability in Chinese stock markets during price limit periods.
method Dynamic and probabilistic analysis of high-frequency data from 2000 to 2011.
result Price limits reduce trading activity before hitting limits, with stronger cooling-off effect for down-limit hits.
Study detects Chinese stock market bubbles using LPPLS confidence indicator.
problem Early detection of stock market bubbles in China.
method LPPLS confidence indicator applied to CSI 300 index data.
result LPPLS detects positive and negative bubbles with high accuracy.
Neural networks for stock price prediction often misrepresent model performance due to flawed error metrics.
problem Flawed prediction error metrics lead to unreliable model evaluations in the securities market.
method Used data from 20 stock datasets across multiple markets and evaluated with four prediction error measures.
result Prediction error value only partially reflects model accuracy and fails to represent stock price direction.
Digitwashing gap boosts stock crash risk, study finds.
problem The gap between companies' digital promises and actual performance increases stock crash risk.
method Empirical analysis of Shanghai and Shenzhen A-share companies from 2010 to 2021, robustness tests conducted.
result GDT significantly increases stock price crash risk, confirmed by robust tests.
Margin system for margin loans using cash and stock as collateral is considered in this paper, which is the line of defence for brokers against risk associated with margin trading. The conditional probability of negative return is used as risk measure, and a recursive algorithm is proposed to realize this measure under…
A new dynamic portfolio strategy using MST network clustering for Chinese stock markets.
problem Portfolio optimization in asset management.
method Clustering approach based on MST networks and market conditions.
result Central portfolios outperform peripheral portfolios under different market conditions.
We present a relatively detailed analysis of the persistence probability distributions in financial dynamics. Compared with the auto-correlation function, the persistence probability distributions describe dynamic correlations non-local in time. Universal and non-universal behaviors of the German DAX and Shanghai Index…