Abstract
Annual reports are the primary source of information for investors. It contains all the details about the company’s activities in the preceding financial year. About 80% of an annual report is text (Cheng et al. 2018); hence proper comprehension and readability enhance the usefulness of the information. Earlier researches prove the positive relationship between firm performance and annual report readability (Li 2008). Studies also find that complexity is a deliberate attempt by companies to obfuscate earnings management and poor performance (Bloomfield 2008). Against this backdrop, we investigate the relationship between firm value, financial performance and annual report readability in the Indian context. For this purpose, we use Gunning Fog Index (Flesch Kincaid index, Smog index, and File size as robustness checks) to measure the annual report readability of NSE 500 firms for the period 2016 to 2020. We find a positive relationship between firm performance (Tobin’s Q, ROA, and ROCE) and annual report readability. Further, this paper finds a positive relationship between the annual report readability and firm value (Tobin’s Q and ROA) in the next year. The findings will be helpful for the investors as it shows how vital is annual report readability in signaling future firm value. It shows the readability of the narrative content as an important signal about a firm’s current performance and prospects. This paper recommends that the management be more cautious while preparing financial disclosures because complex disclosures may send negative signals to the investors about the firm’s prospects.
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Notes
National Stock Exchange.
When there is suspected endogeneity among the independent variables, then estimators of OLS or Random Effects may be biased and lead to misleading inferences (Baltagi et al. 2003). HT Model has been widely used as a solution for this problem. HT estimator handles the data setting properly, when some of the independent variables are correlated with the individual effects. The estimation is normally based on the IV methods, and the instruments will be derived from internal data transformations of the variables in the model. So external information is not required for the model estimation. HT Model avoids the “all or nothing” assumption concerning the correlation between error terms and independent variables, which is the usual assumption made in the standard FE and RE Model approaches, respectively. To implement the HT Model, one needs to classify the variables as correlated and uncorrelated with the individual effects (Mitze 2009).
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Gangadharan, V., Padmakumari, L. Fogging the firm performance: an empirical examination of the annual report readability in India. Int J Discl Gov (2023). https://doi.org/10.1057/s41310-023-00195-3
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DOI: https://doi.org/10.1057/s41310-023-00195-3