The interplay between corporate financial specification, macro-risk, and earnings information of large firms: Evidence from Indonesia

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Type of the article: Research Article

Abstract
The purpose of this study is to investigate how corporate specification, ownership, and macro-risk affect earnings information, with firm size functioning as a moderator in one of the world’s most dynamic economies. According to the research criteria, quantitative research is employed using secondary data with a supportive sample. The population of this study comprises the firms listed as members of the Kompas-100 index during the 2013–2023 period at IDX. This study employs panel data analysis to examine causal relationships and conduct in-depth hypothesis testing, thereby enhancing scientific credibility. The findings of this study indicate that the relationship between leverage and profitability yields two results: DAR and DER have positive and negative effects, respectively, at the 1% significance level. This finding is noteworthy because it reveals a difference in the impact of the two types of leverage. Other corporate specifications that significantly influence earnings information are the market ratio, firm size, and business prospects at the 10% level. However, tangibility and ownership do not affect substantially earnings information, which is primarily influenced by macroeconomic risk and the interaction with firm size. Further analysis reveals that, although certain corporate specifications have a short-term effect, the interaction of firm size as a moderating role confirms the impact of corporate specifications on earnings information, suggesting managerial opportunism in long-term decisions. Generally, this study demonstrates that the quality of corporate specification plays a critical role in discouraging ownership and macro-risk-manipulated management behavior on earnings information for the market.

Acknowledgment
Thanks to all the colleagues and reviewers who provided valuable input, criticism, and feedback on this article.

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    • Table 1. Variables and measurement
    • Table 2. Descriptive statistics
    • Table 3. Panel data estimation
    • Conceptualization
      Harry Budiantoro, Perdana Wahyu Santosa
    • Data curation
      Harry Budiantoro, Perdana Wahyu Santosa
    • Formal Analysis
      Harry Budiantoro, Perdana Wahyu Santosa
    • Funding acquisition
      Harry Budiantoro
    • Investigation
      Harry Budiantoro
    • Methodology
      Harry Budiantoro, Perdana Wahyu Santosa
    • Project administration
      Harry Budiantoro
    • Resources
      Harry Budiantoro, Perdana Wahyu Santosa
    • Software
      Harry Budiantoro
    • Supervision
      Harry Budiantoro
    • Writing – original draft
      Harry Budiantoro
    • Writing – review & editing
      Harry Budiantoro, Perdana Wahyu Santosa
    • Validation
      Perdana Wahyu Santosa
    • Visualization
      Perdana Wahyu Santosa