Identification of Financial Reporting Readability Measurement Metrics and Factors Affecting the Cost of Capital Based on Knowledge Domain Analysis Using Content Analysis

Abstract

Objective: This study aims to examine the relationships between financial reporting readability and the cost of capital of companies listed in the Iranian capital market using econometric and machine learning (ML) approaches.   Methodology: The present study is applied in terms of purpose and descriptive-analytical in terms of methodology. The research data were collected from two sources: experts and companies listed in the Iranian capital market. A non-random snowball sampling method was used to select experts, while a systematic elimination method was applied to select companies. Data were analyzed using content analysis, thematic analysis, machine learning-based modeling, and econometrics. To validate the findings, the Delphi method and fuzzy logic were employed to rank readability metrics and factors affecting the cost of capital.   Findings: The results indicated that financial reporting readability metrics and cost of capital factors were classified into seven main themes and fifty-one sub-themes. The most significant readability indices included Flesch Reading Ease, Fog Index, report length, Flesch-Kincaid Index, and the Fog Index. Additionally, ten key factors influencing the cost of capital were identified, with the most important being return on equity (ROE), earnings volatility, return on assets (ROA), profitability, return on investment (ROI), and information quality.   Conclusion: The study revealed that financial reporting readability directly impacts the cost of capital, and the use of machine learning approaches can help identify and analyze this effect more precisely.