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The paper reports new evidence of herding in the Chinese A-type and B-type markets by employing nonparametric kernel regression. We find statistically significant evidence of herding in A-type market under both extreme high and low market returns. Herding in B-type market, which predominantly consists of foreign investors, indicates only weak evidence of herding. We do not find any statistically significant evidence of herding in the pre-2001 sample of B-type market, when only foreign investors could do the trading. Lack of knowledge and experience of local investors may be attributed to the presence of herd behaviour in the Chinese markets. [ABSTRACT FROM AUTHOR]

Since the establishment of the Shanghai Stock Exchange (SHSE) in 1990 and the Shenzhen Stock Exchange (SZSE) in 1991, China's stock markets have expanded rapidly. Although this rapid growth has attracted considerable academic interest, few studies have examined the ability of conventional financial models to predict the share price movements of Chinese stock. This gap in the literature is significant, given the volatility of the Chinese stock markets and the added risk that arises from the Chinese legal and regulatory environment. In this paper we address this research gap by examining the predictive ability of several well-established forecasting models, including dynamic versions of a single-factor CAPM-based model and Fama and French's three-factor model. In addition, we compare the forecasting ability of each of these models with that of an artificial neural network (ANN) model that contains the same predictor variables but relaxes the assumption of model linearity. Surprisingly, we find no statistical differences in the forecasting accuracy of the CAPM and three-factor model, a result that may reflect the emerging nature of the Chinese stock markets. We also find that each ANN model outperforms the corresponding linear model, indicating that neural networks may be a useful tool for stock price prediction in emerging markets. [ABSTRACT FROM AUTHOR]

This paper concentrate on related theories based on moral hazard arid agency costs to divide the stakeholder into two categories. By making several assumptions, we establish the overlapping-generations model based on genetic replication equation and analyze the stakeholder in stock market with evolutionary game and then conclude the evolutionary equilibrium based on data of Chinese stock market. [ABSTRACT FROM AUTHOR]

CSI 800 index consists of CSI 500 index and CSI 300 index, aiming to reflect the performance of stocks with large, mid and small size of China A share market. In this paper we analyze the multifractal structure of Chinese stock market in the CSI 800 index based on the multifractal detrended fluctuation analysis (MF-DFA) method. We find that the fluctuation of the closing logarithmic returns have multifractal properties, the shape and width of multifractal spectrum are depended on the weighing order q . More interestingly, we observe a bigger market crash in June–August 2015 than the one in 2008 based on the local Hurst exponents. The result provides important information for further study on dynamic mechanism of return fluctuation and whether it would trigger a new financial crisis. [ABSTRACT FROM AUTHOR]

We examine the different effects of monetary policy actions and central bank communication on China’s stock market bubbles with a Time-varying Parameter SVAR model. We find that with negative responses of fundamental component and positive responses of bubble component of asset prices, contractionary monetary policy induces the observed stock prices to rise during periods of large bubbles. By contrast, central bank communication acts on the market through expectation guidance and has more significant effects on stock prices in the long run, which implies that central bank communication be used as an effective long-term instrument for the central bank’s policymaking. [ABSTRACT FROM AUTHOR]

Stock index forecasting is an important tool for both the investors and the government organizations. However, due to the inherent large volatility, high noise, and nonlinearity of the stock index, stock index forecasting has been a challenging task for a long time. This paper aims to develop a novel hybrid stock index forecasting model named BSO-GNN based on the brain storm optimization (BSO) approach and the grey neural network (GNN) model by taking full advantage of the grey model in dealing with data with small samples and the neural network in handling nonlinear fitting problems. Moreover, the new developed BSOGNN, which initializes the parameters in grey neural network with the BSO algorithm, has great capability in overcoming the deficiencies of the traditional GNN model with randomly initialized parameters through solving the local optimum and low forecasting accuracy problems. The performance of the proposed BSO-GNN model is evaluated under the normalization and nonnormalization preprocessing situations. Experimental results from the Shanghai Stock Exchange (SSE) Composite Index, the Shenzhen Composite Index, and the HuShen 300 Index opening price forecasting show that the proposed BSO-GNN model is effective and robust in the stock index forecasting and superior to the individual GNN model. [ABSTRACT FROM AUTHOR]

Financial time series forecasting has become a challenge because of its long-memory, thick tails and volatility persistence. Multifractal process has recently been proposed as a new formalism for this problem. An iterative Markov-Switching Multifractal (MSM) model was introduced to the literature. It is able to capture many of the important stylized features of the financial time series, including long-memory in volatility, volatility clustering, and return outliers. The model delivers stronger performance both in- and out-of-sample than GARCH-type models in long-term forecasts. To enhance MSM's short-term prediction accuracy, this paper proposes a support vector machine (SVM) based MSM approach which exploits MSM model to forecast volatility and SVM to model the innovations. To verify the effectiveness of the proposed approach, two stock indexes in the Chinese A-share market are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results. It indicates that the proposed model provides a promising alternative to financial short-term volatility prediction. [ABSTRACT FROM AUTHOR]

Many published studies have considered information asymmetry between domestic and foreign investors about local assets in the stock market, particularly in developed markets. The present study proposes a new perspective to address the issue in the case of China's forward exchange rate market. Following the framework of Clarida and Taylor (1997), the term structures of exchange rates in the domestic forward and the non-deliverable forward markets are constructed and then applied to predict future spot exchange rates based on a vector equilibrium correction model. By comparing the forecast accuracy on the basis of the root mean square error and the mean absolute error, it is shown that dynamic out-of-sample forecasts of the domestic forward market are superior to those of the non-deliverable forward market, suggesting that domestic investors are better informed than foreign investors. The result has several important policy implications, especially for exchange rate determination. [ABSTRACT FROM AUTHOR]

The Chinese stock market has a large ratio of retail investors, which is significantly different from the stock markets in the US and Europe. We have known that momentum profits exist in the latter by applying Jegadeesh and Titman's (J Financ 48:65-91, 1993) model with 6-month formation and holding periods. However, there are only a few studies on momentum profits in China. Therefore, this study examines whether the Shanghai and Shenzhen stock markets produce momentum profits. We find that these two markets have significant contrarian but not momentum profits. We also create an 'artificial momentum' portfolio and follow Bhattacharya et al. (Account Rev 78:641-678, 2003) to compute the transparency indices. Our outcomes show that the corporate transparencies of the winners (losers) in the artificial momentum portfolios are close to those in the commonly-defined momentum portfolios. The averages of the decile transparencies are between 4.5 and 6.5, not only for the top 10% of winners but also for the bottom 10% of losers. According to these results, we suggest that financial transparency is irrelevant to the inertia and reversal of stock prices in the Shanghai and Shenzhen stock markets. [ABSTRACT FROM AUTHOR]

Forecasting extreme volatility is a central issue in financial risk management. We present a large volatility predicting method based on the distribution of recurrence intervals between successive volatilities exceeding a certain thresholdQ, which has a one-to-one correspondence with the expected recurrence time. We find that the recurrence intervals with largeare well approximated by the stretched exponential distribution for all stocks. Thus, an analytical formula for determining the hazard probabilitythat a volatility aboveQwill occur within a short intervalif the last volatility exceedingQhappenedtperiods ago can be directly derived from the stretched exponential distribution, which is found to be in good agreement with the empirical hazard probability from real stock data. Using these results, we adopt a decision-making algorithm for triggering the alarm of the occurrence of the next volatility aboveQbased on the hazard probability. Using the ‘receiver operator characteristic’ analysis, we find that this prediction method efficiently forecasts the occurrence of large volatility events in real stock data. Our analysis may help us better understand reoccurring large volatilities and quantify more accurately financial risks in stock markets. [ABSTRACT FROM PUBLISHER]

This study revisits the risk-return relationships in the Hong Kong stock market using a conditional model based on up and down markets. Beta is found significantly and positively (negatively) related to realized returns when the market excess returns are positive (negative). The same results are found for unsystematic risk, total risk and kurtosis of stock returns during up and down markets when they are added to the model. Furthermore, skewness is significantly but negatively (positively) related to realized returns during up (down) markets. These results indicate that other risk measures in addition to beta are also important in pricing risky assets and investors do not hold diversified portfolios in this market. Moreover, the results support investors’ preference that they prefer positive skewness but dislike kurtosis. [ABSTRACT FROM AUTHOR]