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<p>Seminal papers by Ball and Brown (1968) and Beaver (1968) spawned a vast literature on the role of accounting numbers in capital markets. This literature, often referred to as capital markets research in accounting (CMRA), is now more than a half-century old. In light of numerous changes to the economic and financial reporting environments over this time, we estimate CMRA’s major relations using a comprehensive sample period. We illustrate each relation using plots, allowing us to efficiently present CMRA’s first half-century consistent with the adage “a picture is worth a thousand words.” The aims of our study are to document the extent of time-series variation in CMRA’s major relations and to provide evidence on market-level determinants of that variation. In doing so, our study provides a natural starting point for future research designed to develop and test additional causal explanations for time-series variation in the properties of CMRA’s major relations.</p>

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The first half-century of empirical capital markets research in accounting in pictures

  • 期刊论文

  • 发表日期：

    2025年6月7日

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  • S. P. Kothari,
  • Bryce Schonberger,
  • Charles Wasley,
  • Jason J. Xiao

摘要

Seminal papers by Ball and Brown (1968) and Beaver (1968) spawned a vast literature on the role of accounting numbers in capital markets. This literature, often referred to as capital markets research in accounting (CMRA), is now more than a half-century old. In light of numerous changes to the economic and financial reporting environments over this time, we estimate CMRA’s major relations using a comprehensive sample period. We illustrate each relation using plots, allowing us to efficiently present CMRA’s first half-century consistent with the adage “a picture is worth a thousand words.” The aims of our study are to document the extent of time-series variation in CMRA’s major relations and to provide evidence on market-level determinants of that variation. In doing so, our study provides a natural starting point for future research designed to develop and test additional causal explanations for time-series variation in the properties of CMRA’s major relations.