This study examines the effect of chief financial officers (CFOs) gender on firms' financial statement comparability. Using a sample of Chinese listed firms, we find that firms with female CFOs exhibit higher comparability relative to firms with male CFOs. We further find that the positive relation between having a female CFO and comparability disappears in the industries that are dominated with male CFOs. In additional analyses, we fail to find any significant effects of having female chief executive officers (CEOs) or other female executives on comparability, highlighting the importance of CFOs in the domain of financial reporting decisions. Finally, we show that only in non-male CFO dominated industries, greater comparability is associated with an improvement in analysts' forecast accuracy and dispersion. Overall, our findings are consistent with the view that, due to their innate personality and behavioral differences, female CFOs are likely to exhibit stronger incentives to comply with accounting rules and standards, which in turn improves comparability. We also highlight the uniqueness of comparability by showing that it does not always co-move with reporting quality since it is an inter-firm attribute rather than a firm's own reporting characteristic. 

CFO gender and financial statement comparability

Globisporangium, especially G. sylvaticum, causes devastating root rot, blight, and other diseases in various species of cash crops. To investigate the distribution and host range of G. sylvaticum in Guizhou, a suitable habitat for this pathogen, we collected 156 root-diseased samples, isolated the pathogens, and found that G. sylvaticum is widespread and has eleven host plants, including four novel hosts. Furthermore, to effectively identify G. sylvaticum, we developed a simple and dependable method based on loop-mediated isothermal amplification (LAMP), which used a primer set designed from the internal transcribed spacer sequences with high specificity and sensitivity of 1 pg/μL. Additionally, to perform field identification, we used the “Plant-LAMP” method with crude DNA extraction to detect the pathogen in 45 root samples from nine species of plants. Our results showed that this method could effectively detect G. sylvaticum in diseased roots. Therefore, our findings not only enrich existing research on the diversity of pathogenic Globisporangium in Guizhou but also present an efficient LAMP field detection method that could significantly contribute to plant disease management and prevention.

Host Range and Loop-Mediated Isothermal Amplification Detection of Globisporangium sylvaticum from Guizhou, China

This paper studies whether climate transition risks affect manufacturing firms' real investment decisions by exploiting the state-level adoption of the Renewable Portfolio Standards (RPS), a set of prominent energy and environmental regulations in the U.S. from 1984–2022. Using a staggered difference-in-differences analysis, we show that while these programs impose substantial compliance costs on emission sources and result in a marked increase in electricity prices, they do not alter manufacturers' capital expenditures, mergers and acquisitions, and alliance formation. This finding survives a battery of robustness checks. It broadly persists in cross-sectional tests of firms with varied exposure to RPS, in different size groups, and with varying degrees of financial constraints, operating leverage, market competition, and corporate governance quality. Only manufacturing firms from more competitive markets reduce their capital expenditures after RPS' adoption. Furthermore, our results show that RPS do not influence other firm decisions and outcomes, such as employment growth, innovation, and firm performance. Our study documents novel evidence of manufacturers' notable resilience to transition risks associated with energy and environmental policies. Our findings provide implications for the debate on the benefits and costs of regulations tackling climate change and call for further research on firms' climate adaptation strategies. 

Do environmental regulations affect firms’ investment decisions? Evidence from renewable energy policy

Aconitum carmichaelii is a herbaceous herb indigenous to China that has been cultivated for traditional medicine for centuries. Virus-like symptoms of A. carmichaelii plants were observed on leaves in some A. carmichaelii plantations in Zhanyi and Wuding Counties, Yunnan Province, southwest China. High-throughput sequencing (HTS) was performed on 28 symptomatic plants, and the results revealed infection with 11 viruses, including 2 novel viruses and 9 previously described viruses: Aconitum amalgavirus 1 (AcoAV-1), aconite virus A (AcVA), cucumber mosaic virus (CMV), currant latent virus (CuLV), apple stem grooving virus (ASGV), chilli veinal mottle virus (ChiVMV), tomato spotted wilt orthotospovirus (TSWV), tobacco vein distorting virus (TVDV), and potato leafroll virus (PLRV). Two novel viruses tentatively named Aconitum potyvirus 1 and Aconitum betapartitivirus 1, were supported by sequence and phylogenetic analysis results of their genomes. We proposed the names Potyvirus aconiti and Betapartitivirus aconiti. RT-PCR assays of 142 plants revealed the predominance and widespread distribution of CMV, AcVA, and AcoPV-1 in plantations. The detection of isolates of CuLV, ASGV, ChiVMV, TSWV, TVDV, and PLRV infections for the first time in A. carmichaelii expands their known host ranges.

Virome Analysis of Aconitum carmichaelii Reveals Infection by Eleven Viruses, including Two Potentially New Species

Purpose This study is based on the enforcement record from Canada’s natural mutual fund regulator. This record documented a small subset of mutual fund dealers who had been disciplined for their misconduct from 2007 to 2014. The purpose of this paper is to determine what factors contribute to mutual fund dealers’ time to first financial fraud offense. The longer the time to fraud, the healthier the mutual fund industry and the better a mutual fund dealer’s career. Design/methodology/approach Based on the belief that adversity reveals true character, the study approaches a mutual dealer’s career success from human capital, socio-demographic and organizational sponsorship points of view by measuring dealers’ success as their time from career beginning to first instance of financial fraud. Ordinary least square regression analysis was used to identify if those factors, including provision of supervisor reminders, gender, position and penalties, are related to career success within the Canadian mutual fund regulatory framework. The research is based on a small sample of mutual fund dealers who had been disciplined for their misconduct from 2007 to 2014. Findings The study finds that a supervisor’s reminders positively contribute to the career success of a mutual fund dealer in the form of extending their time to fraud. As well, being female is an adverse factor to career success even when both female and male dealers received about the same level of supervisor reminders. It also finds that being in a management position has no association with time to fraud. Originality/value The study establishes the statistically significant positive relationship between time to fraud and supervisor’s reminders for mutual fund dealers. At the same time, it shows that human capital and access to organizational resources, measured by being in a management position, have no significant relation to when fraud is committed. This result indicates the value of continuing education for all mutual fund dealers, both inexperienced and experienced. 

Can supervisor reminders help prevent fraud in the mutual funds sector

Aconitum carmichaelii Debeaux is a traditional Chinese medicinal herb that has been utilized for approximately two thousand years. However, as cultivation has increased, there have been more reports of A. carmichaelii infections caused by four major pathogenic fungal species, Fusarium oxysporum, F. solani, Mucor circinelloides, and Sclerotium rolfsii, resulting in increased disease incidences and limited production and quality. To detect these infections, we developed a LAMP-based toolbox in this study. The cytochrome c oxidase subunit 1 (cox1) gene, translation elongation factor-1α (EF-1α), internal transcribed spacer (ITS) regions of rDNA, and alcohol dehydrogenase 1 (ADH1) gene, respectively, were used to design species-specific LAMP primer sets for F. oxysporum, F. solani, S. rolfsii, and M. circinelloides. The results showed that the LAMP-based toolbox was effective at detecting pathogens in soil and plant materials. We also used this toolbox to investigate pathogen infection in the main planting regions of A. carmichaelii. Before harvesting, F. oxysporum, M. circinelloides, and S. rolfsii, were commonly found in the planting fields and in infected A. carmichaelii plants. Therefore, the toolbox we developed will be useful for tracking these infections, as well as for disease control in A. carmichaelii.

A LAMP-based toolbox developed for detecting the major pathogens affecting the production and quality of the Chinese medicinal crop Aconitum carmichaelii Debx.

Carbon Emissions and Assets Pricing——Evidence from Chinese Listed Firms

Agency conflicts in co-regulation: Evidence from IPO application screening in China

Penalty prediction is one of the main tracks of legal judgement prediction (LJP) which is to apply artificial intelligence methods to predicting the court’s judgement based on case descriptions. It is still far from effective, as the elements affecting the penalty are numerous but sparsely distributed in case descriptions, which may make them covered by noise in contexts. Allocating reasonable attention to these elements related to the penalty is the key to improving the effect of the penalty prediction. To this end, we propose a novel model called MHA-AR which learns to focus on the key elements by a modulated hierarchical attention mechanism and a legal attribute recognition subtask. Besides benefiting the prediction accuracy, it also improves the reliability and credibility of predictions for users by providing comprehensible legal attributes related to the penalty. A series of experiments conducted on the real-world datasets demonstrate the correctness of our assumptions and the superiority of MHA-AR. The implementation of our proposed model will be available at https://github.com/realcatking/penaltyprediction.

Penalty Prediction Based on Modulated Hierarchical Attention Coupled with Legal Attribute Recognition

Real Effects of Carbon Emission Trading System: Evidence from Tradable Performance Standard

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