This study aims to address the limited understanding of how linguistic signals in corporate disclosures influence firm performance in emerging economies, where unique ownership structures, market concentration and policy uncertainties shape disclosure practices. By exploring these dynamics, the study provides insights into the complex interplay between communication strategies and financial outcomes.
The study uses annual reports to extract the embedded tones/signals using natural language processing (NLP) techniques. Specifically, this paper performs the sentence segmentation, preprocessing and parsing of textual content of the annual reports using Gensim, Spacy and the Regular Expression package in Python at several phases of parsing the text. Furthermore, we test our proposed hypotheses using a panel data regression approach.
The findings report that uncertain, litigious and financial constraint signals have a negative and significant effect on the firm’s financial outcomes. It highlights that the uncertain or litigation-related content in the annual report disclosure deteriorates the financial outcomes. Most importantly, the study reveals that promoter ownership and the Herfindahl index positively moderate the relationship between embedded signals and financial performance. However, policy uncertainties and business group (BG) affiliation negatively moderate the relationship between embedded signals and financial performance.
This study uniquely contributes to the literature by employing advanced NLP techniques to decode embedded signals in corporate disclosures and assess their impact on financial performance. It provides novel insights into the moderating roles of promoter ownership, market concentration, EPU and BG – factors that are especially pertinent in the context of emerging economies.
