Purpose The purpose of this paper is to give a comprehensive review and synthesis of automated textual analysis of corporate disclosure to show how the accuracy of disclosure tone has been incremented with the evolution of developed automated methods that have been used to calculate tone in prior studies. Design/methodology/approach This study have conducted the survey on “automated textual analysis of corporate disclosure and its impact” by searching at Google Scholar and Scopus research database after the year 2000 to prepare the list of papers. After classifying the prior literature into a dictionary-based and machine learning-based approach, this study have again sub-classified those papers according to two other dimensions, namely, information sources of disclosure and the impact of tone on the market. Findings This study found literature on how value relevance of tone is varied with the use of different automated methods and using different information sources. This study also found literature on the impact of such tone on market. These are contributing to help investor’s decision-making and earnings and returns prediction by researchers. The literature survey shows that the research gap lies in the development of methodologies toward the calculation of tone more accurately. This study also mention how different information sources and methodologies can influence the change in disclosure tone for the same firm, which, in turn, may change market performance. The research gap also lies in finding the determinants of disclosure tone with large scale data. Originality/value After reviewing some papers based on automated textual analysis of corporate disclosure, this study shows how the accuracy of the result is incrementing according to the evolution of automated methodology. Apart from the methodological research gaps, this study also identify some other research gaps related to determinants (corporate governance, firm-level, macroeconomic factors, etc.) and transparency or credibility of disclosure which could stimulate new research agendas in the areas of automated textual analysis of corporate disclosure.

Purpose One of the adverse effects of COVID-19 is on poor economic and financial performance. Such economic underperformance, less demand from the consumer side and supply chain disruption is leading to stock market volatility. In such a backdrop, this paper aims to find the impact of COVID-19 on the Indian stock market by analyzing the analyst’s report. Design/methodology/approach The sample includes a cross-sectional data set on selected Indian firms that are indexed in BSE 100. The authors calculate the score of disclosure tone by using a textual analysis tool based on the analyst report of selected BSE 100 firms' approach in tackling COVID-19’s impact. The relationship between the tone of the analyst report and stock market performance is examined. This empirical model also survives robustness analysis to establish the consistency of the findings. This study uses both frequentist statistics and Bayesian statistics approach. Findings The empirical result shows that tone has negative and significant influence on stock market performance. This study indicates that either analysts are not providing value-relevant and incremental information, which can reduce the stock market volatility during this pandemic situation or investors are not able to recognize the optimism of the information. Practical implications This study provides an interesting insight regarding retail investors' stock purchasing behavior during the crisis period. Hence, this study also lays out crucial managerial implications that can be followed by preparers while preparing corporate disclosure. Originality/value In the concern on pandemic and its impact on the stock market, this study sheds light on investors' preferences during the crisis period. This study uniquely focuses on analyst reports and investors' preference which has not been studied widely. To the best of the authors’ knowledge, this is the first study in the Indian context, which aims to understand retail investors’ investment preferences during a pandemic.