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SYNOPSIS: Extant accounting research based on data from the United States, Europe, and Australia finds that increases in carbon emissions are associated with lower firm value. However, recent research indicates that investor perspectives on corporate social responsibility (CSR) in other parts of the world differ from those of Western investors. This study investigates whether increases in carbon emissions, as one indicator of poor CSR performance, are also associated with a lower firm value in Taiwan. Using carbon disclosure data from Taiwanese-listed companies between 2012 and 2016, we find that firm value is positively associated with carbon emissions, which contrasts with the findings of extant research. Likewise, firm value is not associated with a combined measure of financial information and carbon emissions in Taiwan. Our findings suggest that international generalizations of the findings of the extant research on the relationship between carbon emissions and firm value should be undertaken with caution. JEL Classifications: G32; M4; M48; Q56. [ABSTRACT FROM AUTHOR]

This study examines the association between the new drug approvals released by the US Food and Drug Administration (FDA) and subsequent market stock price reactions for listed pharmaceutical and biotech companies in Taiwan. We find that new drug approvals released by the US FDA lead to significantly positive stock price cumulative abnormal returns (CARs) in most stages of the new drug approval process, in particular, for the phase 3 stage. This implies that public investors are more willing to invest in the later stages of the US FDA approval process due to the high failure rate of drug verification. We also find that research and development (R&D) investment has significantly positive impacts on CARs. Since R&D investments may substantially reduce the taxes and operating costs of pharmaceutical and biotech companies, they are positively related to stock price returns and serve as an important indicator for market investors. Finally, we find that the effect of R&D investment on CARs associated with FDA approvals is more prominent in the period after the passage of the 'Taiwanese Biotech and New Pharmaceutical Development Act', indicating that tax incentives or policy effects indeed influence market responses. [ABSTRACT FROM AUTHOR]

Using data from Taiwan listed firms required to voluntarily disclose not only executive but also non-executive compensation adjustments, this study reveals that a higher attendance rate of compensation committee relates to a higher possibility of voluntary disclosure. In addition, our result implies that a higher meeting frequency may represent more arguments among compensation committee members in terms of determining whether or not to make such disclosures. We also find evidence implying that busy members are likely to be ceremonial and unfavourable towards compensation adjustments for non-executive employees, which might have practical implications for policy makers. [ABSTRACT FROM AUTHOR]

Financial forecasting is based on the use of past and present financial information to make the best prediction of the future financial situation, to avoid high-risk situations, and to increase benefits. Such forecasts are of interest to anyone who wants to know the state of possible finances in the future, including investors and decision-makers. However, the complex nature of financial data makes it difficult to get accurate forecasts. Artificial intelligence, which has been shown to be suitable for analyzing very complex problems, can be applied to financial forecasting. Financial data is both nonlinear and nonstationary, with broadband frequency features. In other words, there is a large range of fluctuation, meaning that predictions made only using long short-term memory (LSTM) are not enough to ensure accuracy. This study uses an LSTM model for analysis of financial data, followed by a comparison of the analytical results with the actual data to see which has a larger root-mean-square-error (RMSE). The proposed method combines deep learning with empirical mode decomposition (EMD) to understand and predict financial trends from financial data. The financial data for this study are from the Taiwan corporate social responsibility (CSR) index. First, the EMD method is used to transform the CSR index data into a limited number of intrinsic mode functions (IMF). The bandwidth of these IMFs becomes narrower, with regular cyclic, periodic, or seasonal components in the time domain. In other words, the range of fluctuation is small. LSTM is a good way to forecast cyclic or seasonal data. The forecast result is obtained by adding all the IMFs together. It has been verified in past studies that only the LSTM and LSTM combined with the EMD can be used. The analytical results show that smaller RMSEs can be obtained using the LSTM combined with EMD compared to real data. [ABSTRACT FROM AUTHOR]