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This paper develops a depth-gated recurrent neural network (DGRNN) with self-attention mechanism (SAM) based on long-short-term memory (LSTM)\gated recurrent unit (GRU) \Just Another NETwork (JANET) neural network to improve the accuracy of credit spread prediction. The empirical results of the U.S. bond market indicate that the DGRNN model is more effective than traditional machine learning methods. Besides, we discovered that the Depth-JANET model with one gated unit performs better than Depth-GRU and Depth-LSTM models with more gated units. Furthermore, comparative analyses reveal that SAM significantly improves DGRNN’s prediction performance. The results show that Depth-JANET neural network with SAM outperforms most other methods in credit spread prediction.

This paper attempts to evaluate the coordinated development state of the subsystems within the internet financial ecosystem in China from 2011 to 2016. Focusing on the main business modes, technological innovation, and the external environment, we select 29 indicators to construct an index system and adopt a coupling coordination degree model for evaluation. Furthermore, we use two weight calculation methods, entropy weight and principal component analysis, to ensure the robustness of the results. The empirical results show that China’s internet financial ecosystem experienced five development stages from 2011 to 2016, which are moderate disorder, near disorder, weak coordination, intermediate coordination, and good coordination. Different methods of obtaining weights have little effect on the empirical results. These findings suggest that at the beginning, the coordinated development of China’s internet financial ecosystem was hindered by factors including the scarcity of main business modes and the defect of technological innovation; then, with the rapid development of China’s internet industry, the external environment became another drawback in coordinated development. Finally, based on the findings, we give some policy recommendations from a global perspective to achieve a sustainable internet financial ecosystem.

From the perspective of financing cost, this article investigates the benefits of green bonds to the issuer. Based on 227 green bonds and 405 conventional bonds selected from China’s bond market, we find that (1) green bonds can decrease financing cost by at least 15 bps in the primary market, which is more significant than the effect in the secondary market; (2) third-party certification can strengthen the ‘greenium’ of green bonds in both the primary and secondary markets; and (3) there is no ‘greenium’ effect for financial green bonds in either primary or secondary markets in China, even for green bonds with third-party certification.

With the exponential development of an ecological and sustainable economy and society, the concept and practice of environmental, social, and governance (ESG) investments are being popularized in the capital market of China. ESG disclosure is an important supplement to financial disclosure and plays an increasingly significant role in asset pricing. In this paper, we selected corporate bond data in China’s secondary bond market from 2015 to 2020, and introduced the Nelson–Siegel model to study the influence of ESG disclosure on corporate bond credit spreads in the secondary market. This model passed robustness tests when we used alternative data fitted by the modified Nelson–Siegel model. Results show that ESG disclosure significantly reduces credit spreads on corporate bonds in the secondary market. State ownership and industry play significant roles in moderating the impact of ESG disclosure on corporate bond credit spreads. Specifically, the ESG disclosure of non-state-owned companies and companies in non-high-pollution and -energy-consumption industries has a greater impact on reducing corporate bond credit spreads. Therefore, we urge regulatory departments to establish a sound ESG disclosure evaluation system, and the issue companies to improve the quality of their ESG disclosure, especially non-state-owned companies, and those in non-high-pollution and -energy-consumption industries. Corporate bond investors would benefit from integrating ESG information into their investment decision-making process.

This paper develops the interval maximum entropy model for the interval European option valuation by estimating an underlying asset distribution. The refined solution for the model is obtained by the Lagrange multiplier. The particle swarm optimization algorithm is applied to calculate the density function of the underlying asset, which can be utilized to price the Shanghai Stock Exchange (SSE) 50 Exchange Trades Funds (ETF) option of China and the Boeing stock option of the United States. Results show that maximum entropy distribution provides precise estimations for the underlying asset of interval number situations. In this way, we can get the distribution of the underlying assets and apply it to the interval European option pricing in the financial market.

Capturing determinants of bond default risks has aroused heated discussions ever since the &ldquo, rigid payment&rdquo, system collapsed in China. Within this context, this paper aims to clarify the relation between an issuer&rsquo, s environmental, social, and corporate (ESG) performance and its bond default rate. We developed an ESG factors-embedded Logistic Regression model to empirically examine Chinese default bonds and outstanding industrial bonds from 2014 to 2019. Results indicate that the bond default rate is positively correlated with the company&rsquo, s energy consumption and negatively correlated with its attention to social responsibilities, and corporate governance, in addition to its financial performances. In conclusion, to fully take ESG factors into consideration during the decision-making process and daily operations might improve stability and credibility of corporations in modern Chinese national context.

This paper systematically investigates the properties of six kinds of entropy-based risk measures: Information Entropy and Cumulative Residual Entropy in the probability space, Fuzzy Entropy, Credibility Entropy and Sine Entropy in the fuzzy space, and Hybrid Entropy in the hybridized uncertainty of both fuzziness and randomness. We discover that none of the risk measures satisfy all six of the following properties, which various scholars have associated with effective risk measures: Monotonicity, Translation Invariance, Sub-additivity, Positive Homogeneity, Consistency and Convexity. Measures based on Fuzzy Entropy, Credibility Entropy, and Sine Entropy all exhibit the same properties: Sub-additivity, Positive Homogeneity, Consistency, and Convexity. These measures based on Information Entropy and Hybrid Entropy, meanwhile, only exhibit Sub-additivity and Consistency. Cumulative Residual Entropy satisfies just Sub-additivity, Positive Homogeneity, and Convexity. After identifying these properties, we develop seven portfolio models based on different risk measures and made empirical comparisons using samples from both the Shenzhen Stock Exchange of China and the New York Stock Exchange of America. The comparisons show that the Mean Fuzzy Entropy Model performs the best among the seven models with respect to both daily returns and relative cumulative returns. Overall, these results could provide an important reference for both constructing effective risk measures and rationally selecting the appropriate risk measure under different portfolio selection conditions.

This paper investigates the mean-reversion and volatile of credit spread time series by using regression and time series analysis in Chinese bond market. Then the Longstaff-Schwartz model and GARCH model are applied to price credit spread put option. The authors compare the features of these two models by employing daily bond prices of government bonds and corporate bonds for the period 2010–2012 in Chinese bond market. The proposed results show that the higher the credit ratings of the corporate bonds are, the lower the prices of the credit spread options are.

In this paper, we define the portfolio return as fuzzy average yield and risk as hybrid-entropy and variance to deal with the portfolio selection problem with both random uncertainty and fuzzy uncertainty, and propose a mean-variance hybrid-entropy model (MVHEM). A multi-objective genetic algorithm named Non-dominated Sorting Genetic Algorithm II (NSGA-II) is introduced to solve the model. We make empirical comparisons by using the data from the Shanghai and Shenzhen stock exchanges in China. The results show that the MVHEM generally performs better than the traditional portfolio selection models.

In the security market, there are some bonds with the same structure and properties, namely the same duration and credit level, but their prices are in distinct difierence. In this paper, we flrstly explore information entropy to measure the risk of such kind of bonds, and discuss the difierence of the four kinds of methods, namely entropy, convexity, variance and VaR, then make empirical comparisons among them by using the bond data from Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange (SZSE) in China. The results show that the information entropy method can be a better risk measurement of such bonds than other methods, and the bond risk in SSE is lower than that in SZSE in China.

This article establishes an original methodology to forecast macroeconomy based on the term structure of credit spreads. It combines the traditional Svensson model with genetic algorithms to obtain the interest rate term structures of government bonds and corporate bonds and calculates credit spreads as their differences. And this article defines three factors of the term structure of credit spreads: level, slope and curvature. Based on these three factors and several macroeconomic variables, VAR models are developed and tested to forecast macroeconomic variables. The empirical results confirm that VAR models can predict the changes of China's macroeconomy well, which indicates that the term structure of credit spreads contains information of future changes of macroeconomic variables. We believe this result has significant implications for macroeconomy policy-makers.

Rongxi Zhou, Xiuguo Wang, Xuefan Dong, Ze Zong 1, first School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China, zhourx@buct.edu.cn 2 School of Banking and Finance, University of New South Wales, Sydney, NSW 2052, Australia, zhourx@buct.edu.cn School of Applied Mathematics, Central University of Finance and Economics, Beijing 100081, China 4, Corresponding Author School of Business, University of Nottingham Ningbo China, Ningbo 315100, China 5, School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China

In this paper, we develop a new term structure model of interest rates with combinatorial optimization method based on four classical models: polynomial spline model, exponential spline model, Nelson-Siegel model and Svensson model. Genetic algorithms are employed to solve the combinatorial optimization model. Then, we make some empirical comparisons of five models using daily bond data from Shanghai Stock Exchange in China covering the years from 2004 to 2009. The results show that the combinatorial optimization model outperforms the other models in most of the statistical indicators. Besides, the combinatorial model has good adaptability and robustness which are applicable in Chinese bond market.

In this paper, a forecasting-mean-correlation-entropy portfolio optimization model (FMCE) is developed by using the fuzzy time series techniques to predict securities' future returns distribution and employing entropy as risk measurement. Traditional portfolio models such as MV model have stringent conditions to returns distribution, while entropy as a new risk measurement is free from these restrictions. Besides, traditional models assume securities' future returns distribution the same with their historical distribution which may be not suitable for the complex market. Therefore, a forecasting method is applied in FMCE. Based on the historical data of the Shanghai Stock Exchange and Shenzhen Stock Exchange in China, a comparison among the proposed model, hybrid models and traditional ones which are the mean-variance model, absolute deviation model and maximum absolute deviation model is made. The empirical results show that the proposed FMCE model is an effective tool in portfolio selection.

This paper selects the yield difference between the enterprise bonds and national debts circulated in Shanghai Stock Market as the credit spreads. Multiple regression model and time series model are separately used to analyze the key factors affecting credit spreads of our enterprise bonds both in static and dynamic status by quantitative and qualitative methods. And im-pulse response function is applied into testing the degree of influence affected by various factors. The result shows that some factors, such as CPP, GDP, Short term interest rate, Long term interest rate, Stock market returns and volatility, have some important influence on the research about the variety of the credit spreads of our enterprise bonds. The conclusions reveal the mechanism of changes of credit spreads, which could provide the decision support for the investors and managers.

In present study, We made a plant expression construct that ligated GBSS I cloned from common wheat and endosperm specific promoter HorD from barley into pCAMBIA3301 by using the two bridge vector pGM-T-HorD and pGM-T-GBSSI , then the construct named pHorD-GBSSI was transformed into EHA105. Somatic embryogenesis of inbred line Y423 was used to be transformed by Agrobacterium tumefaciens mediation. Regenerated plants were harvested to be genetically detected and total of 35 palnrts were positive by PCR accreditation, accounting for 15% . We thought this study might provide a molecular way to improve the quality of maize starch.

In this paper, Least Squares Support Vector Regression (LSSVR) method is improved to have a sparse, and enhance the generalization ability of the method. In LSSVR model, an increase of three indexes set down by certain criteria to determine the selection and support vector. Then two models were selected to fit the sample data on the bond spot yield curve. We can derived from analysis and comparison revealed that the improved model fit residuals and LSSVR model results broadly consistent with their training time and prediction time of a relatively large Shortened. Thus, we can set through the addition of three indexes to increase the generalization ability and reduce the computing time when using support vector machine to fit the term structure of interest rates.

In this paper, a three-factor affine term structure model which has the square-root diffuse properties is adopted. By using the Kalman filter method, the parameters of the model are estimated, then the prices of coupon bond options are analyzed by Monte Carlo stimulation and the elements which affect the option pricing are also discussed in this paper. The results show that the price of bond option has a positive correlation with the maturity of the option and the price of bond, it has a negative correlation with the strike price of option. The results also show that value-added option price of the adjacent maturity of the option is a constant approximately and it has a negative correlation with the maturity of the bond.

Probability theory is used to construct a new type of binomial parameter model, which can avoid negative probability. A detailed proof is given for the generalized binomial model where an extended parameter is added to. The European options and Asian options are priced respectively by numerical examples. The results show that all the binomial models with different parameters have excellent convergence. In addition, the convergence of the generalized binomial model is more stable.

In order to improve the smoothness of curve fitted by the interest rate term structure model of polynomial spline functions, the adaptive semi parametric regression with a penalized item is introduced to estimate the unknown parameters. The generalized cross-validation method is discussed to select the smoothing parameter, and genetic algorithm is applied to search the optimal smoothing parameter. Then, the empirical results show that this model with penalty function is relatively effective in China. However, the curve fitting smoothness is improved to some extend at the expense of fitting accuracy.

Term structure of interest rates has played an important role in pricing of fixed-income securities. In this paper, the prices of Chinese Government Bond (CGB) are analyzed firstly based on the famous two-factor affine term structure model, namely Longstaff-Schwartz model. First, by using the Kalman filter method, we estimate the parameters of the model, and obtain the price of CGB by Monte Carlo stimulation. Finally, the pricing results of CGB are compared with Vasicek model and Cox, Ingersoll and Ross model. The results show the two-factor affine model has the high precision.

Radial Basis Function (RBF, for short) neural networks are widely applied for their strong abilities in nonlinear mapping, fast learning, good generalization performance and great accuracy in numerical approximation. In this paper, a RBF neural network combined with time series on C0 2 emissions is proposed by using the characteristics. It examines the rationality and flexibility of the RBF neural network used in prediction of C0 2 emissions in China. The empirical result indicates that the RBF neural network improves the overall reliability of time series forecasting and has a high precision, meanwhile it is a baton to the next phase of the “energy saving and greenhouse gas emissions reduction” project, which is of practical and potential value in China.

For an uncertain decision process, how to combine the subjective preference of decision maker with objective information is a very significant issue. This paper introduces the measure of decision-maker's optimism to describe the situation of the interval number in uncertain multiple attribute decision-making. Based on the method of determining interval entropy weight, the optimization model which can reflect the measure of decision maker's optimism and make full advantage of existing objective information is developed. Then a new method of decision in interval entropy weight is given on the basis of the subjective preference of decision-maker and objective information. Finally, the example of risk decision for the development of the aerospace project is presented to show the feasibility and effectiveness of this method.