Pankaj Trivedi, T. Berger, S. Parmar
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摘要

本文着眼于印度和德国年度报告中披露的风险信息。这种年度报告是通过获取上市公司业绩和相关风险的信息,来克服投资者和管理者之间的信息不对称的一种方法。对于分析师和管理人员来说,作为他们自己的报告和(财务)分析的参考,它们也很有价值。因此,我们分析了印度SENSEX指数中公司的年度报告,并将其与德国DAX指数中的公司进行了比较,同时牢记其运营的监管框架。这是在评估方案的帮助下完成的,作为定性内容分析的指导方针。由于披露要求在强制性和自愿性之间摇摆不定,我们发现,虽然这两个指数的公司都遵守了强制性要求,但自愿性的信息,特别是具体的数字风险报告,可以得到改善,在印度的情况下更是如此。此外,两个经济体中的一些公司已经展示了对风险相关信息进行全面分类的方法,这些信息可以在可能的情况下被其他公司采用在各自的指数中。总体而言,我们发现(1)公司发布的具体风险信息很少,(2)两个样本之间的信息质量差异具有统计学意义,(3)规模和杠杆可以解释样本内的部分方差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Risk Information Practices in Annual Reports: An Indo-German Collaborative Comparison
This paper looks at the information given on risks as disclosed in Indian and German annual reports. Such annual reports are a method for overcoming the information asymmetry between investors and managers by gaining information on the performance and the associated risks of listed companies. They can also be of valuable for analysts and managers as a reference for their own reporting and (financial) analysis. We therefore analysed the annual reports of the companies in the Indian SENSEX index and compared them with the companies included in the German DAX index keeping in mind the regulatory framework of their operations. This was done with the help of an evaluation scheme, acting as a guideline for the qualitative content analysis. With disclosure requirements oscillating between mandatory and voluntary, we found that while companies of both indices comply with the mandatory requirements, information on voluntary basis, especially on numerical concrete risk reporting, can be improved, more so in the Indian context. Also, some companies in both economies have shown the way in comprehensively categorizing risk related information which can be adopted, where possible, by other companies in their respective indices. Overall, we found that (1) companies issue only few information on concrete risks, (2) the difference in information quality between the two samples is statistically significant, and (3) size and leverage can explain part of the variance within the samples.
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