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학술논문대한경영학회지2024.02 발행KCI 피인용 2

Does ESG Information Mitigate Accounting-based Market Anomaly? Evidence Based on Post Earnings Announcement Drift

Does ESG Information Mitigate Accounting-based Market Anomaly? Evidence Based on Post Earnings Announcement Drift

최수영(인하대학교 경영학부); 신희정(동의대학교 회계학과)

37권 2호, 323~342쪽

초록

This study investigates the impact of ESG ratings information, which provides information on environmental, social, and governance aspects, on the magnitude of post-earnings announcement drift (hereafter, PEAD). PEAD refers to the phenomenon where stock prices continue to move in the direction of unexpected earnings for a certain period even after the disclosure of accounting earnings, indicating a market anomaly arising from the insufficient reflection of accounting earnings information in stock prices. In tradition, accounting earnings information and earnings persistence predictions have been considered crucial factors in evaluating corporate value. However, with the increasing need for non-financial information to complement the limitations of financial information, ESG reporting has also become important. This study analyzes the relationship between ESG ratings and PEAD to examine whether ESG information, as non-financial information, contributes to the efficiency of corporate valuation. The analysis, conducted through univariate and multiple regression analyses on KSE-listed firms from 2011 to 2019, reveals that firms with higher ESG ratings show a significant decrease in the magnitude of PEAD. Furthermore, this effect is more pronounced for firms with positive unexpected earnings. This suggests that high-quality non-financial information provided by firms with high ESG ratings contributes to reducing PEAD by improving the interpretation and response to financial information, particularly accounting information, in the market. This study demonstrates the potential significant role of ESG information in enhancing the efficiency of capital markets, providing important insights into understanding and mitigating accounting-based market anomalies.

Abstract

This study investigates the impact of ESG ratings information, which provides information on environmental, social, and governance aspects, on the magnitude of post-earnings announcement drift (hereafter, PEAD). PEAD refers to the phenomenon where stock prices continue to move in the direction of unexpected earnings for a certain period even after the disclosure of accounting earnings, indicating a market anomaly arising from the insufficient reflection of accounting earnings information in stock prices. In tradition, accounting earnings information and earnings persistence predictions have been considered crucial factors in evaluating corporate value. However, with the increasing need for non-financial information to complement the limitations of financial information, ESG reporting has also become important. This study analyzes the relationship between ESG ratings and PEAD to examine whether ESG information, as non-financial information, contributes to the efficiency of corporate valuation. The analysis, conducted through univariate and multiple regression analyses on KSE-listed firms from 2011 to 2019, reveals that firms with higher ESG ratings show a significant decrease in the magnitude of PEAD. Furthermore, this effect is more pronounced for firms with positive unexpected earnings. This suggests that high-quality non-financial information provided by firms with high ESG ratings contributes to reducing PEAD by improving the interpretation and response to financial information, particularly accounting information, in the market. This study demonstrates the potential significant role of ESG information in enhancing the efficiency of capital markets, providing important insights into understanding and mitigating accounting-based market anomalies.

발행기관:
대한경영학회
DOI:
http://dx.doi.org/10.18032/kaaba.2024.37.2.323
분류:
경영학

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Does ESG Information Mitigate Accounting-based Market Anomaly? Evidence Based on Post Earnings Announcement Drift | 대한경영학회지 2024 | AskLaw | 애스크로 AI