• Journal of Digital Convergence
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• v.20 no.5
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• pp.393-400
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• 2022

In a situation where the company handles accounting conservatively, the management's earnings forecasting information will be more conservative, and the conservativeness of this earnings forecasting information will have a differential effect in evaluating the performance of managers and paying compensation. This study aims to examine how the level of corporate accounting conservatism affects the forecast information of managers and how this affects the compensation of managers. This study establishes a hypothesis on the effect of the level of accounting conservatism on the earnings forecasting information and compensation of managers, and examines the relationship between managerial profit forecasting information & manager compensation according of conservatism in corporate accounting that can vary depending on the manager's disposition. As a result of the analysis, conservative managers are also conservative in earnings forecasting disclosure, and when corporate managers are highly conservative, they show their ability by making earnings forecasts disclosures more frequently and more accurately than corporate managers with low conservatism. It will help reduce the forecasting errors of stakeholders. Therefore, it is expected that this will play an important role in judging the manager's ability and determining compensation. Therefore, when a company handles accounting conservatively, management's earnings forecasts are also measured conservatively, which is expected to provide useful information on the basis and form of management's compensation to stakeholders.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.81-101
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• 2003

The financial statements purpose to provide useful information to decision-making process of business managers. The value-relevant information, however, embedded in the financial statement has been often overlooked in Korea. In fact, the financial statements in Korea have been utilized for nothing but account reports to Security Supervision Boards (SSB). The objective of this study is to develop earnings forecasting models through financial statement analysis using artificial intelligence (AI). AI methods are employed in forecasting earnings: artificial neural networks (ANN) for manufacturing industry and case~based reasoning (CBR) for banking industry. The experimental results using such AI methods are as follows. Using ANN for manufacturing industry records 63.2% of hit ratio for out-of-sample, which outperforms the logistic regression by around 4%. The experiment through CBR for banking industry shows 65.0% of hit ratio that beats the statistical method by 13.2% in holdout sample. Finally, the prediction results for manufacturing industry are validated through monitoring the shift in cumulative returns of portfolios based on the earning prediction. The portfolio with the firms whose earnings are predicted to increase is designated as best portfolio and the portfolio with the earnings-decreasing firms as worst portfolio. The difference between two portfolios is about 3% of cumulative abnormal return on average. Consequently, this result showed that the financial statements in Korea contain the value-relevant information that is not reflected in stock prices.