Posted: Feb 25, 2018
The composition and expertise of corporate audit committees have long been recognized as critical factors in financial reporting quality, yet the precise mechanisms through which expertise influences disclosure practices remain inadequately understood. Traditional research in this domain has predominantly relied on binary classifications of financial expertise and manual content analysis of disclosures, approaches that suffer from significant limitations in capturing the multidimensional nature of expertise and the nuanced quality of financial communications. This study addresses these limitations by developing and applying computational linguistics and machine learning techniques to create a comprehensive framework for quantifying both audit committee expertise and financial statement disclosure quality.
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