This study aims to investigate the relationship of firm performance and corporate social responsibility reporting and the moderating role of a firm’s life cycle stages in Chinese listed companies. We used the sample of all A-share listed firms on the Shanghai and Shenzhen stock exchanges for the period 2010 to 2020. The authors used pooled ordinary least squares (OLS) regression as a baseline methodology. Our regression results show that positive Corporate social responsibility (CSR) activity significantly reduces the performance of the firm. In addition, the negative link between positive Corporate social responsibility and a firm’s performance is more pronounced for firms in mature life cycle stages. Our results are robust to alternative proxy measures of ROA for firm performance, corporate social responsibility reporting, and life cycle stages. To control the possible problem of endogeneity, we use a one-year lag and 2SLS least squares regression. We find that firm performance has a statistically significant influence on CSR reporting. Moreover, we see that firms with high performance are more likely to report CSR activities than low-performance firms. Additionally, six out of ten control variables (Independent Director, Board Shares, State Owned Enterprise, Board Meeting, Chief executive officer Duality, and Firm Growth) have positive influences on CSR reporting. These findings hold for a set of robustness tests. Our results have implications for the development of CSR reporting in developing countries such as China. Our research suggests that, in China, firms with better financial performance undertake more CSR reporting. This paper contributes to the existing literature by investigating the effect of firm performance on CSR reporting and the moderating role of a firm’s life cycle stages in Chinese listed companies. Additionally, this paper enriches the current literature on CSR reporting and highlights the importance of a firm’s financial performance for better environmental performance and reporting.
The Sloan Digital Sky Survey (SDSS) comprises about one billion objects classified spectrometrically. Because astronomical datasets are so enormous, manually classifying them is nearly impossible-a huge dataset results in class imbalance and overfitting. We recommend a framework in this research study that overcomes these constraints. The framework uses a hybrid Synthetic Minority Oversampling Technique + Edited Nearest Neighbor (SMOTE + ENN) balancer. The balanced dataset is then used to extract features via a non-linear algorithm using Kernel Principal Component Analysis (KPCA). The features are then passed into the proposed Int-T2-Fuzzy Support Vector Machine classifier, which uses a modified type reducer and inference engine to achieve more precise categorization. Using the Sloan Digital Sky Survey dataset and a number of evaluation metrics, the SMOTE+ENN model's performance is measured. The research shows that the model does a good job.
Energy prices (EPs) play an imperative role in South Asian Country (SAC) Gross Domestic Product (GDP). This research empirically examines the influence of sustainable energy price shocks (EPSs) on macroeconomic indicators. The study is to forecast the impact of EPS on macroeconomic indicators from 1980 to 2020. The analysis is carried out by employing the Vector Auto-Regression (VAR) approach. Impulse Response Functions (IRFs) results indicate that EPS decreases Gross Domestic Product (GDP). They exist in the short run and the long run. This research study’s overall findings suggest that high EPSs have a negative impact on GDP. The study implies that policymakers should develop, adopt, and initiate some imperatives to control the unanticipated volatility and movements in EP. The study highlights that policy should be designed to prevent fluctuations in sustainable EP and plan conservative energy policies that motivate discovering alternative energy sources to meet increasing energy demand and improve economic growth.
Deep Learning (DL) in finance is widely regarded as one of the pillars of financial services sectors since it performs crucial functions such as transaction processing and computation, risk assessment, and even behavior prediction. As a subset of data science, DL can learn and develop from their experience, which does not require constant human interference and programming, implying that the technology will improve quickly. By loading an Ensemble Model (EM), a Deep Sequential Learning (DSL)model, and additional upper-layer EM classifier in the correct order, a new “Contained-In-Between (C-I-B)” composite structured DSL model is recommended in this article. In cases like Fraud Detection System (FDS), where the data flow comprises vectors with complex interconnected characteristics, DL models with this structure have proven to be highly efficient. Finally, by utilizing optimized transaction eigenvectors, a NB classifier is trained. This strategy is more effective than most standard approaches in identifying transaction fraud. The proposed model is evaluated for its accuracy, Recall and F-score, and the results show that the model has better performance against its counterparts.
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