Search

Corporate innovation legitimacy is the key guarantee for listed companies to obtain returns from innovation activities. The purpose of this paper is to investigate the effect of analyst coverage on corporate innovation legitimacy based on effective supervision hypothesis and market pressure hypothesis. Moreover, it examines the moderating effect of analyst coverage characteristics, individual corporate behavior, external governance mechanism on the relationship between analyst coverage and corporate innovation legitimacy. The hypotheses are tested using a data set of Shanghai and Shenzhen A-share listed companies from China Research Data Service Platform (CNRDS) database, Hexun.com and CSMAR database. To overcome sample selection bias and reverse causality, this paper conduct robustness test using propensity score matching, lag variable, substitution variable and sample reduction to ensure the reliability of the research conclusions. Findings show that analyst coverage can effectively improve the innovation legitimacy of listed companies. In addition, based on the heterogeneity of the above relationship in different contexts, further research finds that the depth of analyst tracking, and the assessment of analyst optimism strengthen the positive impact of analyst coverage on innovation legitimacy. The implementation of green social responsibility brings a good reputation for the company, thereby helping analysts to focus on improving the legitimacy of innovation; In the market environment with relatively perfect external governance mechanism, the stronger the effect of analyst coverage on corporate innovation legitimacy. Prior studies mostly considered the R&D investment and other factors to overlook how other dimensions influence companies from the perspective of quantity. The research conclusion confirms the significance of analyst coverage as an external corporate governance mechanism, further enriches the theoretical research framework of innovation legitimacy, and has certain reference value for the improvement of innovation quality of listed companies. [ABSTRACT FROM AUTHOR]

Probabilistic site characterization is the cornerstone of data-centric geotechnics, which underpins the digitalization in geotechnical engineering. The essential aspect of probabilistic site characterization is to reasonably infer the probability density distributions of geotechnical parameters, based on field and laboratory data. In this paper, a clay property database, SZ-CLAY/11/5130, was established by collecting geotechnical investigation reports from 52 engineering projects in Shenzhen city. This SZ-CLAY/11/5130 database was first compared with the global clay database CLAY/10/7490 and the Shanghai clay database SH-CLAY/11/4051, in terms of their sample ranges, correlations, and scatter plots. Then, the multivariate probability distribution of eight clay parameters was established using the training records selected from SZ-CLAY/11/5130, to characterize the generic correlations between multiple clay parameters. Based on Bayesian theory, the posterior distributions of clay mechanical parameters from a construction site in Shenzhen city were updated using both the site-specific correlations and the generic correlations given by SZ-CLAY/11/5130. The constructed multivariate probability density distribution is necessary for this updating step. [ABSTRACT FROM AUTHOR]

To achieve its carbon peak and neutrality targets, China is in need of balancing environmental and economic sustainability; In addition, whether environmental regulations can gain economic dividends while protecting the environment is particularly important. Based on the data of A-share listed companies in Shanghai and Shenzhen from 2012 to 2021, this study takes China's environmental fee-to-tax as a quasi-natural experiment, and adopts the difference-in-difference-in-differences model to evaluate the impact of environmental tax on enterprises' total factor productivity (ETFP). The results show that the environmental fee-to-tax significantly hinders the growth of ETFP. Further, this negative impact mainly stems from the fact that the reform prompts enterprises to choose strategic innovation and conduct greenwashing behavior. Furthermore, when considering enterprises' heterogeneity, we find that enterprises with large scale, high investment efficiency, state ownership, high market competitiveness and located in western regions are more resistant to policy shocks. This paper provides reliable empirical evidence and useful policy implications for further formulating environmental tax policies to accomplish the "win-win" goal for both the environment and the economy. [ABSTRACT FROM AUTHOR]

Stimulating enterprises to implement environmental management system certification (EMSC, that is to say ISO14001 certification) to the greatest extent and resolving their obstacles in time are the basis for promoting green development of enterprises. Using the full samples of Shanghai and Shenzhen A-share listed industrial enterprises from 2010 to 2019, and employing the Logit model and Propensity Score Matching method (PSM), this paper empirically analyzes the motivations and obstacles for Chinese enterprises to implement EMSC certification, further explores the transformation between advantages and disadvantages under certain conditions, and the differentiation characteristics among enterprises under different social responsibility, property rights and enterprise size. The results show that: ① There is a "U"-shaped relationship between corporate social responsibility and EMSC, and the social responsibility of non-state-owned enterprises could realize the transformation from obstacle to motivation earlier than that of state-owned enterprises. ② There is an inverted "U"-shaped relationship between environmental regulation and EMSC, precisely speaking, this inverted "U"-shaped relationship only exists in non-state-owned enterprises, large-scale enterprises and enterprises with high social responsibility. ③ The public environmental awareness could promote enterprises to implement EMSC, and would strengthen the impact of environmental regulations on enterprises' implementation of EMSC. ④ Based on Propensity Score Matching method, it is concluded that Chinese enterprises indeed improve their environmental performance rather than their financial performance by EMSC. The conclusions provide helpful practical guidance for comprehensively revealing the motivations and obstacles for China's enterprises to implement EMSC. [ABSTRACT FROM AUTHOR]

Intelligent forecasting model of stock price is an effective way to obtain ideal investment returns. Due to the impact of quantitative transactions, traditional forecasting methods face challenges and pressures. How to find a reliable forecasting model and improve the forecasting accuracy will be a scientific problem worthy of further discussion. This paper proposes an intelligent forecasting model of stock price based on neighborhood rough set (NRS) and multivariate empirical mode decomposition (MEMD) by means of the ideal of "granular computing" and "decomposition ensemble". Firstly, the multiscale permutation entropy (MPE) method is conducted to determine decision features, which can improve the stability of permutation entropy. Then, a new feature selection method, fusing mutual information (MI) and the NRS, is proposed to automatically capture and select the condition features from the stock market. Subsequently, aiming on revealing the more detailed feature information and maintaining relevance between features, the MEMD is utilized to simultaneously decompose all features. Finally, the decomposed features are inputted into the long short-term memory (LSTM) network to train the intelligent forecasting model and provide the forecasting results of stock price. The validity of our proposed model is assessed through the stocks of Shanghai and Shenzhen markets. The results show that our proposed intelligent forecasting model of stock price makes a beneficial attempt and discussion for the integration of "granular computing" and "decomposition ensemble". And it will provide scientific support and reference for investors' actual investment decisions. [ABSTRACT FROM AUTHOR]

• Introduces a hybrid forecasting framework to identify enterprise financing risk. • Develops a feature selection module to identify the optimal input variables. • Proposes an improved ELM prediction model for enterprise financing risk. • Demonstrates the framework's reliability in forecasting enterprise financing risk. A scientific framework that can effectively forecast enterprise financing risks can both promote enterprise management and reduce the cost of risk for financial institutions. This study constructs a novel hybrid forecasting framework for enterprise financing risk incorporating modules for data preprocessing, feature selection, forecasting, and evaluation. Specifically, the data preprocessing module mainly realizes the prescreen financing risk indicators and solves the forecasting challenge created by imbalanced data; The feature selection module based on binary grey wolf optimization is designed to intelligently identify optimal financing risk indicators; The forecasting module based on the improved extreme learning machine model established in this paper achieves higher forecasting accuracy; and the evaluation module provides reasonable and scientific evaluations of the proposed hybrid forecasting framework by using the data from small and medium-sized enterprises (SMEs) in China and all listed enterprises with Shanghai and Shenzhen A-shares. Using the SMEs dataset as an example, the Type-2 error value of the developed hybrid forecasting framework is 0.1765, which is 70.24% lower than the average result of the other models; the G-mean value of the framework is 0.8566, which is 40.56% higher than the average result of the other models. Based on the results, the proposed hybrid forecasting framework outperforms other comparative models and is a reliable tool for forecasting enterprise financing risk. [ABSTRACT FROM AUTHOR]