Annual report narrative disclosures, information asymmetry and future firm performance: evidence from Vietnam

Abstract

Purpose: This paper examines the role of the annual report’s linguistic tone in predicting future firm performance in an emerging market, Vietnam. Design/methodology/approach: Both manual coding approach and the naïve Bayesian algorithm are employed to determine the annual report tone, which is then used to investigate its impact on future firm performance. Findings: The study finds that tone can predict firm performance one year ahead. The predictability of tone is strengthened for firms that have a high degree of information asymmetry. Besides, the government’s regulatory reforms on corporate disclosures enhance the predictive ability of tone. Research limitations/implications: The study suggests the naïve Bayesian algorithm as a cost-efficient alternative for human coding in textual analysis. Also, information asymmetry and regulation changes should be modeled in future research on narrative disclosures. Practical implications: The study sends messages to both investors and policymakers in emerging markets. Investors should pay more attention to the tone of annual reports for improving the accuracy of future firm performance prediction. Policymakers should regularly revise and update regulations on qualitative disclosure to reduce information asymmetry. Originality/value: This study enhances understanding of the annual report’s role in a non-Western country that has been under-investigated. The research also provides original evidence of the link between annual report tone and future firm performance under different information asymmetry degrees. Furthermore, this study justifies the effectiveness of the governments’ regulatory reforms on corporate disclosure in developing countries. Finally, by applying both the human coding and machine learning approach, this research contributes to the literature on textual analysis methodology.