Tai-Hsi Wu, Mei-Chen Lin, Pei-Ju Lucy Ting, Jyun Yan Huang
In this study, we investigate the impact of academic directors on a firm's performance and decisions in the Taiwan equity market. We find that firms with more independent directors and board size are more likely to appoint academic directors, and academic directors can improve firm performance. The presence of academic directors positively affects firm performance through channels like more capital expenditure and larger R&D expenses. Academic directors with finance and technology backgrounds positively correlate with both Tobin's Q and ROA. Moreover, the appropriate match of expertise between firms and their academic directors contributes to a better performance. However, corporations with academic directors have a higher compensation gap between top managers and employees.
{"title":"Do academic directors matter? Evidence from Taiwan equity market","authors":"Tai-Hsi Wu, Mei-Chen Lin, Pei-Ju Lucy Ting, Jyun Yan Huang","doi":"10.1111/irfi.12428","DOIUrl":"10.1111/irfi.12428","url":null,"abstract":"<p>In this study, we investigate the impact of academic directors on a firm's performance and decisions in the Taiwan equity market. We find that firms with more independent directors and board size are more likely to appoint academic directors, and academic directors can improve firm performance. The presence of academic directors positively affects firm performance through channels like more capital expenditure and larger R&D expenses. Academic directors with finance and technology backgrounds positively correlate with both Tobin's Q and ROA. Moreover, the appropriate match of expertise between firms and their academic directors contributes to a better performance. However, corporations with academic directors have a higher compensation gap between top managers and employees.</p>","PeriodicalId":46664,"journal":{"name":"International Review of Finance","volume":"24 1","pages":"4-29"},"PeriodicalIF":1.7,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41462314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Expanding on current research, this study finds that firms with better financial report readability demonstrate a stronger relationship between institutional blockholder monitoring and information asymmetry. This result supports our hypothesis that enhanced readability improves firm information and aids the institutional investor monitoring of firms, reducing information asymmetry. By demonstrating that readability amplifies the marginal effect of institutional blockholder monitoring, we highlight the significance and policy implications of better corporate disclosure readability.
{"title":"The effect of corporate annual report quality on the relationship between institutional blockholder monitoring and firm's information environment","authors":"Chune Young Chung, Amirhossein Fard, Hong Kee Sul","doi":"10.1111/irfi.12430","DOIUrl":"10.1111/irfi.12430","url":null,"abstract":"<p>Expanding on current research, this study finds that firms with better financial report readability demonstrate a stronger relationship between institutional blockholder monitoring and information asymmetry. This result supports our hypothesis that enhanced readability improves firm information and aids the institutional investor monitoring of firms, reducing information asymmetry. By demonstrating that readability amplifies the marginal effect of institutional blockholder monitoring, we highlight the significance and policy implications of better corporate disclosure readability.</p>","PeriodicalId":46664,"journal":{"name":"International Review of Finance","volume":"24 1","pages":"139-153"},"PeriodicalIF":1.7,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46690773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article finds evidence of return cross-predictability among trading partners in international financial markets. We show that the predictability of international customers dominates the predictability of domestic customers, and the predictability of international intra-industry customers dominates the predictability of international inter-industry customers. This return cross-predictability decreases with two country characteristics: financial sophistication and size.
{"title":"The cross-predictability of industry returns in international financial markets","authors":"Xin Wang, Haofei Zhang","doi":"10.1111/irfi.12426","DOIUrl":"10.1111/irfi.12426","url":null,"abstract":"<p>This article finds evidence of return cross-predictability among trading partners in international financial markets. We show that the predictability of international customers dominates the predictability of domestic customers, and the predictability of international intra-industry customers dominates the predictability of international inter-industry customers. This return cross-predictability decreases with two country characteristics: financial sophistication and size.</p>","PeriodicalId":46664,"journal":{"name":"International Review of Finance","volume":"23 4","pages":"859-885"},"PeriodicalIF":1.7,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43858411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a novel approach that analyzes topics and tones of analyst reports using a deep neural network in a supervised learning approach. By letting trained classifiers evaluate topics and tones of the reports, we find that incorporation of topic tones significantly enhances the accuracy of predicting cumulative abnormal returns, increasing adjusted