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This article proposes a combination model, which is composed of latent Dirichlet allocation model, TF-IDF feature extraction algorithm and Euclidean distance measurement method, to identify and judge whether the similarities between multiple policy texts exist or not. With the help of actual data result, this will drive the relevant government agencies to figure out problems in a timely manner and provide a decision-making basis for them to formulate and optimise appropriate economic policies. To this end, this article analyses and studies the four types of economic texts that are classified as Insurance, Banking, Tax and Finance from the Central Government of Hebei province and Shijiazhuang city levels. Also, we consider Beijing, Shanghai and Guangdong. Experimental results show that (1) the combination model can quickly and effectively recognise and determine whether there are similarities between multiple economic policy texts; (2) similarities exist or not between the central, provincial and municipal level policy texts depending on the comparison of the distance values across them; (3) the smaller the distance value between economic policy texts of the same kind, the higher the similarity in them; and (4) the distance values between the six policy texts in Finance, Insurance, Bank and Tax categories are ranked from low to high. In terms of similarity, the Finance category is the highest, followed by Insurance and Bank, and the Tax category is the lowest. [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. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal hedging strategy in the presence of market frictions using the Long Short Term Memory Recurrent Neural Network (LSTM-RNN) method, which is a modification of the method proposed in Buehler et al. (Deep hedging. Quant. Finance, 2019, 19(8), 1271–1291). The market frictions are transaction costs, liquidity constraints, trading limits and cost of funds. The loss function is a spectral risk measure. We first make an empirical analysis of the LSTM-RNN model of real option markets, which are the Asian market (domestic market 50 ETF option, Hong Kong Hang Seng Index Option, Nikkei Index Option), the North American market (S&P 500 Index Option) and the European market (FTSE 100 Index Option). The benchmark models are from Leland (Option pricing and replication with transaction costs. J. Finance., 1985, 40(5), 1283–1301), Boyle and Vorst (Option replication in discrete time with transaction costs. J. Finance, 1992, 47(1), 271–293) and Whalley and Wilmott (A hedging strategy and option valuation model with transaction costs. OCIAM Working Paper, Mathematical Institute, Oxford, 1993). Finally, we compare the results from the LSTM-RNN model with benchmark models involving transaction costs for both simulated market data generated by Geometric Brownian Motion (GBM) and the Heston model and real market data. The results show that the LSTM-RNN model outperforms benchmark models for low or medium volatility (<0.8), OTM moneyness and under a certain risk level (<80%) in the GBM market setting. For the 50ETF option market, the LSTM-RNN model outperforms benchmark models for ATM and under a certain risk level (<15%). For the HSI option market, the LSTM-RNN model outperforms benchmark models when transaction costs are smaller than 1.5%. For the Nikkei and S&P 500 option markets, the LSTM-RNN model always outperforms benchmark models. For the FTSE option market, the LSTM-RNN model outperforms benchmark models when moneyness is not too deeply ITM. [ABSTRACT FROM AUTHOR]

Abstract We simulate the asset pricing in the framework of information networks when the number of agents is constant and tends to infinity. When the number of agents is a constant, we find that a higher risk aversion coefficient, a lower information uncertainty, or a higher standard variance of payoff volatility induces a lower asset price; a higher number of agents induces a higher aggregate demand. When the number of agents tends to infinity, we study and simulate the closed form expressions for asset price with risk aversion coefficient. We find that a higher network connectedness or a lower risk aversion coefficient induces a higher information driven volatility component and a lower Sharpe ratio; a higher network connectedness or a lower risk aversion coefficient induces a higher market efficiency. Liquidity driven volatility component, trading profit, price volatility are non-monotonic functions of network connectedness, or risk aversion coefficient. Highlights • We simulate the asset price in the framework of information networks. • A higher risk aversion coefficient, a lower information uncertainty, or a higher standard variance of payoff volatility induces a lower asset price. • A higher number of agents induces a higher aggregate demand. • A higher network connectedness or a lower risk aversion coefficient induces a higher information driven volatility component as well as a higher market efficiency and a lower Sharpe ratio. • Liquidity driven volatility component, trading profit, price volatility are non-monotonic functions of network connectedness or risk aversion coefficient. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of individual rationality of buyers and sellers on continuous double auction market outcomes in terms of the proportion of boundedly-rational buyers and sellers. The individual rationality is discussed in a social network artificial stock market model by embedding network formation and information set. Traders automatically select the most profitable trading strategy based on individual and social learning of the profits and trading strategies of themselves and their neighbors, and submit orders to markets. The results show that (i) a higher proportion of boundedly-rational sellers induces a higher market price, higher sellers’ profits and a higher market efficiency; (ii) a higher proportion of boundedly-rational sellers induces a lower number of trades and lower buyers’ profits; (iii) a higher proportion of boundedly-rational buyers induces a lower market price, a lower number of trades, and lower sellers’ profits; (iv) a higher proportion of boundedly-rational buyers induces higher buyers’ profits and a higher market efficiency. [ABSTRACT FROM AUTHOR]

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: In this paper, we model a new random stock price model for the stock markets based on the finite range contact process, which is a model for epidemic spreading that mimics the interplay of local infections and recovery of individuals, it is a member of a class of stochastic processes known as interacting particle systems. Then, we analyze the statistical behaviors of Shanghai Stock Exchange (SSE) Composite Index, Shenzhen Stock Exchange (SZSE) Composite Index, Dow Jones Industrial Average Index (DJIA), Nasdaq Composite Index (IXIC), the standard and Poor’s 500 Index (S&P500) and the simulative data derived from the finite range contact model by comparison. And six individual Chinese stocks from large-cap, mid-cap and small-cap categories are discussed. Furthermore, we investigate the long range correlations of the returns for these indices and the corresponding simulative data by applying the detrended fluctuation analysis. At last, the positive part of the probability distributions of the logarithmic returns for the actual data and the simulative data are studied by the q-Gaussian dynamic systems. The main objective of this work is to discuss the impact on the returns with the different range financial models. [Copyright &y& Elsevier]

Abstract: Proton pumps play an important role in the physiological activities of plants. Changes in membrane-associated H+- and Ca2+-ATPase activities in heat-shocked plants after heat acclimation (HA) or salicylic acid (SA) pre-treatment in annual young grape plants (Vitis vinifera L. cv. Jingxiu) were investigated. ATPase activity was assayed through biochemical analysis in which two-phase partitioning was used to purify plasma membranes and cerium trichloride was precipitated with an electromicroscopic cytochemical method. The plasmalemma H+- and Ca2+-ATPase activities were higher in HA- or SA-pre-treated plants than in controls. The stability of H+- and Ca2+-ATPase activities in pre-treated plants always remained at higher levels during subsequent heat shock treatment, which was consistent with the observations made using an electromicroscope. A number of cerium phosphate grains representing enzyme activity in HA- or SA-pre-treated plants were observed 6h following heat shock, whereas no grains were found in control plants under the same conditions. These results suggest that the changes in the activities of plasma membrane H+- and Ca2+-ATPase contributed to the thermotolerance induced by either HA or SA pre-treatment in young grape plants and the two pre-treatments may have had the some same regulatory mechanism. [Copyright &y& Elsevier]

Abstract: Grape berries (Vitis vinifera cv. Jingxiu) were pretreated for 10h in air either at 38°C (heat pretreatment) or at 25°C (control) and then transferred to −2°C for 0, 3, 6,12, 24, 48 and 72h, respectively. Compared with the control without heat pretreatment, membrane permeability and malondialdehyde (MDA) contents were reduced and the activities of peroxidase, superoxide dismutase (SOD) and catalase increased in the heat-pretreated berries under chilling stress. Heat pretreatment could protect the ultrastructure of the pericarp cells against subsequent chilling injury. In the control berries, the ultrastructure of pericarp cells was significantly damaged under chilling stress, with disordered stroma lamellae in the chloroplast or plastid, an indistinct nuclear membrane, looser cell walls, disorganized or absent middle lamella and a less identifiable nuclear membrane. After 72h of chilling stress, the cellular organelles could no longer be identified. These results offered cytological evidence that berries subjected to a mild heat stress could acquire tolerance to subsequent lethal low temperatures. The heat pretreatment also induced expression of Hsp70 transcripts. The results suggest that cross-adaptation could be induced in grape berries and the fruit could adapt, at least partly, to low temperature through heat pretreatment-induced tolerance. [Copyright &y& Elsevier]

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]

Hepatic fibrosis (HF) is caused by persistent inflammation, which is closely associated with hepatic oxidative stress. Peroxynitrite (ONOO−) is significantly elevated in HF, which would be regarded as a potential biomarker for the diagnosis of HF. Research has shown that ONOO− in the Golgi apparatus can be overproduced in HF, and it can induce hepatocyte injury by triggering Golgi oxidative stress. Meanwhile, the ONOO− inhibitors could effectively relieve HF by inhibiting Golgi ONOO−, but as yet, no Golgi-targetable fluorescent probe available for diagnosis and assessing treatment response of HF through sensing Golgi ONOO−. To this end, we reported a ratiometric fluorescent probe, Golgi-PER, for diagnosis and assessing treatment response of HF through monitoring the Golgi ONOO−. Golgi-PER displayed satisfactory sensitivity, low detection limit, and exceptional selectivity to ONOO−. Combined with excellent biocompatibility and good Golgi-targeting ability, Golgi-PER was further used for ratiometric monitoring the Golgi ONOO− fluctuations and screening of ONOO− inhibitors from polyphenols in living cells. Meanwhile, using Golgi-PER as a probe, the overexpression of Golgi ONOO− in HF and the treatment response of HF to the screened rosmarinic acid were precisely visualized for the first time. Furthermore, the screened RosA has a remarkable therapeutic effect on HF, which may be a new strategy for HF treatment. These results demonstrated the practicability of Golgi-PER for monitoring the occurrence, development, and personalized treatment response of HF. In this work, we reported a ratiometric fluorescent probe, Golgi-PER, for diagnosis and assessing treatment response of hepatic fibrosis (HF) through monitoring the Golgi ONOO−. Golgi-PER displayed satisfactory sensitivity, low detection limit, and exceptional selectivity to ONOO−. Combined with excellent biocompatibility and good Golgi-targeting ability, Golgi-PER was used for ratiometric monitoring the Golgi ONOO− fluctuations and screening of ONOO− inhibitors from polyphenols in living cells. Furthermore, using Golgi-PER as a probe, the overexpression of Golgi ONOO− in HF and the treatment response of HF to the screened rosmarinic acid were precisely visualized for the first time, indicated that this probe can serve as an effectively useful tool for exploring the occurrence, development, and treatment of HF. [Display omitted] • A Golgi-targetable probe for ratiometric detection of ONOO− was developed. • Ratiometric detection of Golgi ONOO− was achieved in vitro and in vivo. • Monitor Golgi ONOO− fluctuations and screen ONOO− inhibitors was performed in cells. • Diagnosis and monitor treatment response of HF to the screened RosA was achieved. [ABSTRACT FROM AUTHOR]

The price dynamics of the emerging cryptocurrency market become an important factor affecting the current global investment market. To investigate its microscopic nonlinear dynamics mechanism, we develop a novel financial agent-based price model by combining the stochastic interacting system and two opposite directions of random jump processes, in which the attribute interactions between particles in the interacting system are utilized to imitate the influence of information transmission on investor's investment attitude in the cryptocurrency market. Return series of the model, generated by Monte Carlo simulations, can reproduce a number of "stylized facts" of real cryptocurrency data. Additionally, we apply the composite multiscale fuzzy entropy algorithm and the matching energy method to detecting the complexity of price volatility behaviours in both actual and simulated cryptocurrency markets. The similarity of empirical results indicates that the proposed model can replicate the properties of price volatility in actual cryptocurrency market to some extent. [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]

Hierarchically porous carbon (HPC) has shown to be advantageous over microporous carbon when applied as adsorbent of pollutants, but its preparation often involves sophisticated procedures. In this work, a lignin-derived HPC was produced simply by one-step pyrolysis in a mixed air/N 2 gas flow. The influence of treatment temperature and air ratio in gas flow on the porosity of HPC was investigated, and its hierarchical porosity was validated by analyses of pore size distribution, scanning electron microscope (SEM) and transmission electron microscope (TEM). The outperformance of HPC as adsorbent of organic pollutants was manifested by comparison with microporous carbon. Results showed that the most developed mesoporosity of HPC (mesopore surface area = 257 m2∙g−1) was obtained at 700 °C and an air ratio of 30% (by volume) in gas flow. The adsorption experiment showed that the mesopores of HPC dominated adsorption to two antibiotic pollutants (sulfamethazine and ciprofloxacin) of relatively large molecular size, while micropores of HPC also contributed to adsorption of 4-chlorophenol, a smaller molecular organic. The developed mesoporosity of HPC was also favorable for fast adsorption rate according to the adsorption kinetics study. Overall, this research provides a novel and green method for development of efficient carbon adsorbent by a simple pyrolysis process. • HPC was produced from lignin by one-step pyrolysis in a mixed air/N 2 gas flow. • The hierarchical porosity was validated by pore size distribution, SEM and TEM. • The influence of pyrolysis conditions on porosity of HPC was investigated. • HPC showed to be efficient on adsorption of organic pollutants. • The relationship between adsorption capacity and porosity of HPC was probed. [ABSTRACT FROM AUTHOR]

Highlights: [•] We determine distributions of sugars and acids with compartmental analysis method. [•] We examine the alteration of sugars and acids in vacuole, cytoplasm and free space. [•] Membrane permeability to sugars and acids are measured at different growing stages. [•] We analyze distributions of sweetness and sour in different compartments of cells. [ABSTRACT FROM AUTHOR]