• 산경연구논집
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• 제11권12호
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• pp.39-45
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• 2020

Purpose: The stock price delay phenomenon refers to a phenomenon in which stock prices do not immediately reflect corporate information and the reflection is delayed. A prior study reported that the stock price delay phenomenon appears strongly when the quality of corporate information is low (Callen, Khan, & Lu, 2013). The purpose of the internal accounting control system is to improve the reliability of accounting information. Specifically, the more professionals such as certified public accountants are placed in the internal accounting control system, the more information is prevented from being distorted, so the occurrence of stock price delay will decrease. Research design, data and methodology: In this study, companies listed on the securities market from 2012 to 2016 were selected as a sample to analyze whether the stock price delay phenomenon is alleviated as accounting experts are assigned to the internal accounting control system. The internal control personnel data were collected in the "Internal Accounting Control System Operation Report" attached to the business report of each company of the Financial Supervisory Service's Electronic Disclosure System(DART). The measurement method of the stock price delay phenomenon was referred to the study of Hou and Moskowitz (2005). The final sample used in the study is 2,641 firm-years. Results: It was found that companies with certified accountants in the internal accounting control system alleviate the stock price delay phenomenon. This result can be interpreted as increasing the speed at which corporate information is reflected in the stock price by improving the reliability of information disclosed in the market by the placement of experts in the system. Conclusions: The results of this study suggest that accounting professionals assigned to the internal accounting control system are playing a positive role in providing high-quality information to the market. In this study, focusing on the fact that the speed at which corporate information is reflected in the stock price is very important for the stakeholders in the capital market, we find that having a certified public accountant in the internal accounting control system alleviates the stock price delay phenomenon.

• 한국컴퓨터정보학회논문지
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• 제19권12호
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• pp.339-348
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• 2014

본 연구는 2011년부터 우리나라에서 시행하고 있는 K-IFRS하에서 기업회계의 투명성과 신뢰성 및 회계감사의 신뢰성을 확보하기 위한 외부회계감사제도의 문제점을 살펴보고 IFRS도입 이후의 국제회계환경에 맞는 회계감사의 개선방안을 모색하고자 한다. K-GAAP에서 지적되는 회계감사제도의 문제점에 대한 개선방안으로는 첫째, 기업의 분식회계와 감사인의 책임성 결여, 범법행위 미인식과 독립성 결여에 대해서는 외부회계감사인의 직무수행의 자질보장이 필요하다. 둘째, 관행화된 감사인 수임제도를 자유수임제와 지정제를 차별적으로 채택하여야 한다. 셋째, 감사인선임위원회 사외이사의 독립적 권한을 부여하여야 한다. 넷째, 외부감사인은 시험제도의 개선 측면에서 감사의 품질을 위해 공인회계사 수를 늘리고 감사보수료도 감사투입시간 만큼 늘려야 한다. 다섯째, 감사인의 피감사기업에 대한 주식의 보유를 제한하여야 한다. 여섯째, 외부감사인의 감리를 위한 인원을 확충하고, 감리비율을 높여야 한다. 마지막으로 한미 FTA협상 결과에 따른 회계서비스 시장이 개방되어 해외 회계법인의 한국 진출에 대해서 당분간 제한하여 경쟁력을 쌓아야 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3218-3241
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• 2023

Financial fraud undermines the sustainable development of financial markets. Financial statements can be regarded as the key source of information to obtain the operating conditions of listed companies. Current research focuses more on mining financial digital data instead of looking into text data. However, text data can reveal emotional information, which is an important basis for detecting financial fraud. The audit opinion of the financial statement is especially the fair opinion of a certified public accountant on the quality of enterprise financial reports. Therefore, this research was carried out by using the data features of 4,153 listed companies' financial annual reports and audits of text opinions in the past six years, and the paper puts forward a financial fraud detection model integrating audit opinions. First, the financial data index database and audit opinion text database were built. Second, digitized audit opinions with deep learning Bert model was employed. Finally, both the extracted audit numerical characteristics and the financial numerical indicators were used as the training data of the LightGBM model. What is worth paying attention to is that the imbalanced distribution of sample labels is also one of the focuses of financial fraud research. To solve this problem, data enhancement and Focal Loss feature learning functions were used in data processing and model training respectively. The experimental results show that compared with the conventional financial fraud detection model, the performance of the proposed model is improved greatly, with Area Under the Curve (AUC) and Accuracy reaching 81.42% and 78.15%, respectively.