Regulatory requirements to adopt IFRS and to disclose audit fees make it possible to examine association between audit fees and proportion of fair-valued assets among firms in Taiwan. A voluntary choice of adding audit committee in the firm for monitoring purpose also helps to examine the association further. Empirical results indicate that lower audit fees is related to higher proportion of (Level 2) fair-valued assets, a finding consistent to Goncharov et al.'s (2014) suggestion that firms pay lower audit fees with fair-value model than with cost model. Insignificant association is found for proportion of Level 3 fair-valued assets, which is similar to Glover et al. 's (2014) suggestion that firm's reluctant attitude in adopting Level 3 assets. Last of all, when audit committee is added, firm's audit fees is negatively associated with Level 1 and 2 fair-valued assets, implying audit committee's role of monitoring and further reducing audit risk and audit fees among Taiwanese firms.
Air pollution prediction is an important issue for regulators and practitioners in a sustainable era. Air pollution, especially PM2.5 resulting from industrialization, has fostered a wave of global weather migration and jeopardized human health in the past three decades. Taiwan has evolved as a highly developed economy and has a severe PM2.5 pollution problem. Thus, the control of PM2.5 is a critical issue for regulators, practitioners and academics. More recently, GA-SVM, an artificial-intelligence-based approach, has become a preferred prediction model, attributed to the advances in computer technology. However, hourly observation of PM2.5 concentration tends to present the GARCH effect. The objective of this study is to explore whether the integration of GA-SVM with the GARCH model can build a more accurate air pollution prediction model. The study adopts central Taiwan, the region with the worst level of PM2.5, as the source of observations. The empirical implementation of this study took a two-step approach; first, we examined the potential existence of the GARCH effect on the observed PM2.5 data. Second, we built a GA-SVM model integrated with the GARCH framework to predict the 8 h PM2.5 concentration of the sample region. The empirical results indicate that the prediction performance of our proposed alternative model outperformed the traditional SVM and GA-SVM models in terms of both MAPE and RMSE. The findings in this study provide evidence to support our expectation that adopting the SVM-based approach model for PM2.5 prediction is appropriate, and that prediction performance can be improved by integrating the GARCH model. Moreover, consistent with our prior expectation, the evidence further supports that taking the GARCH effect into account in the GA-SVM model significantly improves the accuracy of prediction. To the knowledge of the authors, this study is the first to attempt to integrate the GARCH effect into the GA-SVM model in the prediction of PM2.5. In summary, with regard to the development of sustainability for both regulators and practitioners, our results strongly encourage them to take the GARCH effect into consideration in air pollution prediction if a regression-based model is to be adopted. Furthermore, this study may shed light on the application of the GARCH model and SVM models in the air pollution prediction literature.
The purpose of this paper is to propose a new theory regarding the heterogeneity of trading information and price-volume relationship. Basically, the heterogeneity of trading information influences the market demand and supply curves of a stock (or equity index), which in turn affects the price-volume relationship for that stock (or index). This theoretical framework helps resolve existing issues regarding price-volume relationships for equities. For example, empirical experience demonstrates that stock price reversals from tops or rebounds from bottoms are often accompanied with extremely large trading volume; however, an abnormal large volume is not always, but more likely, to lead a price reversal (or rebound). This is due to the greatest extent of heterogeneity of trading information among traders at the time of price reversals (or rebounds). Empirically, this investigation focuses on the price-volume relationship surrounding stock price reversals (or rebounds), which clarify the role of information. The results strongly support the proposed framework.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.