Conciseness, Financial Disclosure, and Market Reaction: A Textual Analysis of Annual Reports in Listed Chinese Companies

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Abstract
The purpose of this study was to examine the relationship between the conciseness and complexity of financial disclosures and market reactions, using the annual reports of Chinese-listed B-share companies over the period 2006–2018. We employed a set of statistical methods that were derived from other fields, such as computational and event studies, in order to derive the English annual reports of Chinese-listed companies, as well as to obtain other key financial indicators from the CSMAR database. Markets react significantly to increased report length, which means that managers that present poor returns with manipulated financial reports could be hiding poor returns. Additionally, the findings of this study are robust to additional tests that use alternative proxies. Furthermore, the results of this paper reinforce the hypothesis that the readability of financial reports affects financial market response. The results indicate that more complex financial reports are correlated with lower current returns, and negatively affect the expectations of future returns. For the purposes of avoiding the effects of the coronavirus pandemic on the results, we utilized data up to 2018. In light of this circumstance, we recommend that future research be conducted that compares results from before and after the coronavirus pandemic. The findings of our study have important implications for regulators, managers, and investors. Investors should obtain relevant information through annual reports; therefore, the importance of style is less relevant. Managers should be encouraged to write their annual reports more concisely. This study concluded that these reports are significant outputs of firms, and are widely read by investors. The study also provides empirical evidence of market reactions that are associated with readability and earnings, as well as with surprise earnings; thus, the complexity of annual reports provided by a variety of investors, using computational and event analysis, should be reduced.

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