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Abstract: As the origin center of apricots, China has the richest apricot germplasm resources. The establishment of core collections is very important for a better evaluation and utilization of apricot germplasm. In this study, 1501 apricot accessions from the initial collections in China were used to sample and establish a primary core collection. Data of 18 traits including both quantitative and qualitative were collected and used to design sampling strategy. The overall sampling strategy includes principles for grouping, sampling proportion within group and sampling method from each group. Three sampling proportions 5%, 10% and 15% were used in the study. Our results suggest that 10% was the best entire sampling ratio for primary core collection of apricots. With 10% entire sampling ratio, the optimal sampling strategy was to group samples based on their growing regions, in combination with logarithmic sampling proportion within each group. Using this sampling strategy, we have established a primary core collection with 150 accessions, and the primary core collection can well represent the genetic diversities of the entire collection. The genetic diversity of the apricot primary core collection was further analyzed using Simple Sequence Repeats (SSRs) molecular marker technique. A total of 22 polymorphic specific primers were developed and 196 alleles were detected. The average number of alleles in each locus was 8.9, suggesting that there were very rich genetic variances among the prime core collection. This also confirms that our primary core collection is a good representation of the genetic diversities of the whole collections at DNA level. [Copyright &y& Elsevier]

In this paper, we analyze and compare long-range power-law correlations of returns, absolute returns, squared returns, cubed returns and square waved returns for sixteen individual stocks from the block of energy sources of Chinese stock market and five stock indices (Shanghai Composite Index, Shenzhen Component Index, Dow Jones Industrial Average index, Nasdaq Composite Index, the Standard and Poor's 500 Index) by using a detrended fluctuation analysis approach. The empirical evidence suggests that Shanghai Composite Index is very close to Shenzhen Component Index and Nasdaq, DJIA is very close to S&P 500 in all cases. And the exponent trends of the returns are close to that of square waved returns. Also, five indices deviate from other sixteen individual energy stocks in all cases except square waved returns. Further, there are long-range correlations and persistence in volatility series of absolute returns and squared returns. Moreover, we investigate the long-term memory of these returns by applying Lo's modified rescaled range statistic. We find that the China energy market exhibits fractal and persistence properties. [ABSTRACT FROM AUTHOR]

Abstract This paper studies the systemic risk of China's stock market during crashes in 2008 and 2015 using the 5-minute intraday transaction data. The results show that liquidity contracted significantly after the downtrend. The systemic risk was magnified during the crash in 2008 while the system risk increased to an abnormal level before the crash in 2015. The volatility of systemic risk rose in 2015 compared to the one in 2008. Moreover, the Johansen co-integration test proves that there is a long-run equilibrium relationship between security margin trading and systemic risk volatility. Granger causality test indicates that margin financing is the Granger cause of the volatility of systemic risk in a bear market. This shows that the government response may impose negative effects on the systemic risk of China's stock market. It helps us better to understand features of systemic risk in China's stock market, and offer new ideas on how to reduce and stabilize the systemic risk. Highlights • The 5-minute intraday transaction data is used to study the systemic risk of China's stock market. • The system risk increased to an abnormal level before the crash in 2015. • There is a long-run equilibrium relationship between systemic risk volatility and margin trading. • The most risky industry changed from manufacturing to finance & insurance. • Government response imposes negative effects on the systemic risk of China's stock market. [ABSTRACT FROM AUTHOR]

Abstract The trading volume in stock markets is known as an important variable which reflects the liquidity of the financial markets and therefore is regarded to be greatly important for the measurement of market liquidity risk. In this work, a new concept called recurrence duration is introduced for study of daily trading volumes, which is inspired by idea of the volatility duration that was proposed and studied in our previous work. The recurrence duration is thought as the shortest passing time that the following days' trading volume takes to exceed or go below the current trading volume which is time-varying. Similar to the volatility duration distribution of the price returns, the power-law function could describe the empirical probability distribution of recurrence durations of trading volumes, and their tail distributions can be fitted by two stretched exponential functions. Further, the correlation relationships of trading volumes between Chinese stock indices as well as the correlations of recurrence durations are investigated. One approach employed is a recently proposed method, time-dependent intrinsic correlation (TDIC), which is based on the empirical mode decomposition (EMD) to decompose nonlinear and nonstationary signals into the intrinsic mode functions (IMFs), the instantaneous periods of which are used then in determination of the sizes of sliding windows to compute the running correlation coefficients for the multiscale signals. The empirical results reveal rich patterns of correlations for both trading volumes and recurrence durations at different scales for different modes. Another approach is the widely-used DCCA cross-correlation coefficient, by which the level of cross-correlation is measured for both original series and IMF modes of the stock indices. Highlights • A concept called recurrence duration is introduced for study of stock trading volumes. • Prob-distributions of recurrence durations for Chinese stock indices are analyzed. • Relationships between Chinese stock indices are investigated from trading volumes and recurrence durations aspects. • The TDIC method and DCCA cross-correlation coefficient are observed a well practical application. [ABSTRACT FROM AUTHOR]

The impacts of a CEO's social connections to a firm have received much attention. However, less is known on between-CEOs differences in building connections. This paper is a step towards filling this gap by investigating whether CEOs social connections are influenced by China's local alcohol drinking culture. We use the sample of Chinese listed companies to test our hypotheses. We find that CEOs whose firms are located in regions with strong alcohol drinking culture have more social connections, both inside and outside the firms. We further find that the drinking culture only significantly affects the social connections of male CEOs, but not female CEOs. These findings remain robust after accounting for endogeneity by instrumental variable approach and matched sample analysis. Besides, we find CEO's inter-firm but not intra-firm social connections established from alcohol drinking can help firms acquire more trade credit. [ABSTRACT FROM AUTHOR]

• A new stock selection algorithm is proposed. • Grey wolf optimizer is innovatively hybridized with support vector regression. • The performance of hybridizing different meta-heuristics is compared. • The new algorithm can achieve excess returns in American and Chinese markets. • The effectiveness and superiority of the new algorithm are empirically examined. Artificial intelligence remarkably facilitates quantitative investment. A latest intelligent search algorithm, grey wolf optimizer, is well integrated with support vector regression machine to obtain the optimal portfolio. The performance of the hybrid algorithm is empirically investigated through transactional and financial data from stock markets of America and China. The experimental results indicate that (i) the proposed algorithm is able to stably achieve excess returns; (ii) compared with genetic algorithm, particle swarm optimization, gravitational search algorithm and harmony search, the enhanced grey wolf optimizer significantly boots the predictive performance of support vector regression machine; (iii) the proposed algorithm can achieve the better profitability and the higher reliability in Chinese A-share market. [ABSTRACT FROM AUTHOR]

This paper presents a Long Short Term Memory Recurrent Neural Network and Hidden Markov Model (LSTM-HMM) to predict China's Gross Domestic Product (GDP) fluctuation state within a rolling time window. We compare the predictive power of LSTM-HMM with other dynamic forecast systems within different time windows, which involves the Hidden Markov Model (HMM), Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and LSTM-HMM with an input of monthly Consumer Price Index (CPI) or quarterly CPI within 4-year, 6-year, 8-year and 10-year time window. These forecasting models employed in our empirical analysis share the basic HMM structure but differ in the generation of observable CPI fluctuation states. Our forecasting results suggest that (1) among all the models, LSTM-HMM generally performs better than the other models; (2) the model performance can be improved when model input transforms from quarterly to monthly; (3) among all the time windows, models within 10-year time window have better overall performance; (4) within 10-year time window, the LSTM-HMM, with either quarterly or monthly input, has the best accuracy and consistency., Competing Interests: The authors have declared that no competing interests exist.