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From Human to Machine

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Resumo:In capital markets, earnings calls are central to investment decisions, providing stakeholders with insights into firms’ performance and strategic direction. Held quarterly, earnings calls combine mandatory and voluntary disclosures, reducing information asymmetry, supporting stock price stability, and strengthening investor confidence. Despite their importance, current evaluation approaches often examine isolated elements such as tone or textual content, without capturing the multidimensional nature of these events. The increasing prevalence of videocasts offers new opportunities to analyse verbal and non-verbal cues, such as vocal tone and facial expressions, providing richer evidence of managerial sentiment. This study addresses a key gap by proposing a robust scale for evaluating earnings calls. Integrating dimensions of regulated disclosures, voluntary communication, management forecasts, and AI-driven analytical tools, the research identifies core components of corporate disclosure grounded in financial communication literature. Scale development followed a rigorous process: initial item generation, expert review, pilot testing with financial professionals, and survey data collection from investors and analysts. Exploratory factor analysis refined the structure and validated the instrument. The resulting scale aims to offer a reliable tool to assess earnings call effectiveness and advance research in corporate disclosure, investor relations, and managerial communication.
Autores principais:Maia, Rodrigo dos Reis
Assunto:SDG 8 - Decent Work and Economic Growth SDG 12 - Responsible Consumption and Production
Ano:2025
País:Portugal
Tipo de documento:póster em conferência
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

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