• 디지털융복합연구
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• 제20권5호
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• pp.393-400
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• 2022

본 연구는 기업 회계의 보수주의 수준이 경영자 예측정보에 영향을 미치며, 이것이 경영자의 보상에는 어떠한 영향을 미치는지를 살펴보고자 한다. 본 연구 목적을 분석하기 위한 연구방법으로, 보수주의를 측정하기 위한 여러 국외 선행연구를 참고하였으며, 이익예측정보를 이익예측치 공시여부와 이익예측정확성으로 나누어 살펴보았다. 따라서, 경영자의 성향에 따라 달라질 수 있는 회계보수주의 정도가 경영자의 이익예측정보와 경영자 보상에 미치는 영향에 대한 가설을 설정하였다. 분석 결과, 보수적 성향의 경영자는 이익 예측 공시에 있어서도 보수적이며, 기업의 경영자가 보수성이 높은 경우에는, 보수성이 낮은 기업 경영자보다 이익 예측 공시를 함에 있어 더 자주, 더 정확하게 함으로써 그들의 능력을 보여주며, 이해관계자들의 예측 오류가 줄어들 수 있게 할 것이다. 따라서 기업이 보수적으로 회계처리를 할 때 경영자의 이익예측 또한 보수적으로 측정되며 이것은 경영자의 핵심적인 능력으로, 예측능력이 보상을 결정하는데 있어 중요한 역할을 할 것이라 기대된다. 또한 이해관계자들이 경영자 보상의 지급 근거와 형태를 파악하는데 유용한 정보를 제공할 것으로 기대해본다.

• The Journal of Asian Finance, Economics and Business
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• 제6권4호
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• pp.91-98
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• 2019

Dividend policy is an important business decision and is considered a channel to communicate a firm's performance to shareholders. Given the empirical findings that earnings quality significantly affects financial analysts' forecasting activities, it is predicted that higher earnings quality would positively influence forecast accuracy. Specifically, it is expected that financial analysts would forecast dividends more accurately for firms with higher earning quality. Unlike the research on financial analysts' earnings forecasts was heavily conducted, there is little study about financial analysts' dividend forecasts. This paper examines the effect of earnings quality on financial analysts' dividend forecast accuracy. We use a sample of South Korean firms for the period of 2011-2015 for multivariate regression. Earnings quality is measured by accruals quality and performance-adjusted discretionary accruals followed by prior studies. We first compare the accuracy between dividend forecasts and earnings forecasts using t-test and Wilcoxon singed-rank test. It is confirmed that financial analysts' dividend forecasts are more accurate than earnings forecasts in Korea. We find that financial analysts' dividend forecasts are more accurate for firms with higher earnings quality. We also find that the result is still valid after controlling for the accuracy of financial analysts' earnings forecasts. This confirms that earnings quality positively affects financial analysts' dividend forecasts.

• The Journal of Asian Finance, Economics and Business
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• 제7권1호
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• pp.29-36
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• 2020

Stock price multiple is one of the most well-known equity valuation technique used to forecast equity price. It measures by multiplying "the ratio of stock price to a value driver" by a value driver. The value driver can be earning per share (EPS), sales or other financial measurements. The objective of price multiple technique is to evaluate the value of assets and compare how similar assets are priced in the market. Although stock price multiple technique is common in financial filed, studies on the application of the technique in Thailand is still limited. The present study is conducted to serve three major objectives. The first objective is to apply the technique to measure value of firms in banking sector in the Stock Exchange of Thailand. The second objective is to develop composite price multiple index to forecast equity prices. The third objective is to compare valuation accuracy of different value drivers of price multiple (i.e. EPS, Earnings Growth, Earnings Before Interest Taxes Depreciation and Amortization, Sales, Book Value and Composite Index) in forecasting equity prices. Results indicated that EPS is the most accurate value drivers of price multiple used to forecast equity price of firms in baking sector.

• 대한산업공학회지
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• 제39권6호
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• pp.535-545
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• 2013

The goal of stock investment is earning high rate or return with stability. To accomplish this goal, using a portfolio that distributes stocks with high rate of return with less variability and a stock price prediction model with high accuracy is required. In this paper, three methods are suggested to require these conditions. First of all, in portfolio re-balance part, Max-Return and Min-Risk (MRMR) model is suggested to earn the largest rate of return with stability. Secondly, Entering/Leaving Rule (E/L) is suggested to upgrade portfolio when particular stock's rate of return is low. Finally, to use outstanding stock price prediction model, a model based on Semi-Supervised Learning (SSL) which was suggested in last research was applied. The suggested methods were validated and applied on stocks which are listed in KOSPI200 from January 2007 to August 2008.