| Abstract: |
Official meetings of the International Accounting Standards Board (IASB) are crucial to shaping accounting standards globally. Researchers examining these meetings depend on voice recordings from the IASB website. This study processes all available recordings of board meetings, identifies speakers using voice printing technology, and applies natural language processing techniques for transcription. We intend to create a publicly available, fully indexed dataset organizing each participant’s contributions by meeting and topic. Using textual analysis, we will connect these discussions to stakeholder comment letters and final accounting standard texts. Our granular data can provide new evidence on major unresolved issues in the political process of accounting standard setting, such as how ideological is standard-setting and are accounting standard-setters captured by interest groups?
Keywords: Accounting standard-setting; IASB; Natural language processing; Text analysis; Speech recognition; Machine learning
JEL Codes: M41, M48, D72, C55
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