• Management & Information Systems Review
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• v.36 no.3
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• pp.151-165
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• 2017

This study investigates the effect of revenue recognition by percentage of completion method on financial analysts' earnings forecasting information in order industry. Specifically, we examines how the analysts' earnings forecast errors and biases differ according to whether or not to report the unbilled revenue account balance and the level of unbilled revenue account balance. The sample consists of 453 firm-years listed in Korea Stock Exchange during the period from 2010 to 2014 since the information on unbilled revenue accounts can be obtained after the adoption of K-IFRS. The results are as follows. First, we find that the firms with unbilled revenue account balances have lower analysts' earnings forecast accuracy than the firms who do not report unbilled revue account balances. In addition, we find that the accuracy of analysts' earnings forecasts decreases as the amount of unbilled revenue increases. Unbilled revenue account balances occur when the revenue recognition of the contractor is faster than the client. There is a possibility that managerial discretionary judgment and estimation may intervene when the contractor calculates the progress rate. The difference between the actual progress of the construction and the progress recognized by the company lowers the predictive value of financial statements. Our results suggest that the analysts' earnings forecasts may be more difficult for the firms that report unbilled revenue balances as applying the revenue recognition method based on the progress criteria. Second, we find that the firms reporting unbilled revenue account balances tend to have higher the optimistic biases in analysts' earnings forecast than the firms who do not report unbilled revenue account balances. And we find that the analysts' earnings forecast biases are increases as the amount of unbilled revenue increases. This study suggests an effort to reduce the arbitrary adjustment and estimation in the measurement of the progress as well as the introduction of the progress measurement method which can reflect the actual progress. Investors are encouraged to invest and analyze the characteristics of the order-based industry accounting standards. In addition, the results of this study empower the accounting transparency enhancement plan for order industry proposed by the policy authorities.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.239-276
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• 2009

The paper investigates the possible relationship between earnings prediction by security analysts and special ownership ties that link security companies those analysts belong to and firms under analysis. "Security analysts" are known best for their role as information producers in stock markets where imperfect information is prevalent and transaction costs are high. In such a market, changes in the fundamental value of a company are not spontaneously reflected in the stock price, and the security analysts actively produce and distribute the relevant information crucial for the price mechanism to operate efficiently. Therefore, securing the fairness and accuracy of information they provide is very important for efficiencyof resource allocation as well as protection of investors who are excluded from the special relationship. Evidence of systematic distortion of information by the special tie naturally calls for regulatory intervention, if found. However, one cannot presuppose the existence of distorted information based on the common ownership between the appraiser and the appraisee. Reputation effect is especially cherished by security firms and among analysts as indispensable intangible asset in the industry, and the incentive to maintain good reputation by providing accurate earnings prediction may overweigh the incentive to offer favorable rating or stock recommendation for the firms that are affiliated by common ownership. This study shares the theme of existing literature concerning the effect of conflict of interests on the accuracy of analyst's predictions. This study, however, focuses on the potential conflict of interest situation that may originate from the Korea-specific ownership structure of large conglomerates. Utilizing an extensive database of analysts' reports provided by WiseFn(R) in Korea, we perform empirical analysis of potential relationship between earnings prediction and common ownership. We first analyzed the prediction bias index which tells how optimistic or friendly the analyst's prediction is compared to the realized earnings. It is shown that there exists no statistically significant relationship between the prediction bias and common ownership. This is a rather surprising result since it is observed that the frequency of positive prediction bias is higher with such ownership tie. Next, we analyzed the prediction accuracy index which shows how accurate the analyst's prediction is compared to the realized earnings regardless of its sign. It is also concluded that there is no significant association between the accuracy ofearnings prediction and special relationship. We interpret the results implying that market discipline based on reputation effect is working in Korean stock market in the sense that security companies do not seem to be influenced by an incentive to offer distorted information on affiliated firms. While many of the existing studies confirm the relationship between the ability of the analystand the accuracy of the analyst's prediction, these factors cannot be controlled in the above analysis due to the lack of relevant data. As an indirect way to examine the possibility that such relationship might have distorted the result, we perform an additional but identical analysis based on a sub-sample consisting only of reports by best analysts. The result also confirms the earlier conclusion that the common ownership structure does not affect the accuracy and bias of earnings prediction by the analyst.

• 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 Digital Convergence
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• v.17 no.4
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• pp.107-114
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• 2019

This study analyzes the effect of the accuracy of future management performance, which managers voluntarily announce in the previous year's disclosure, on managers compensation. In the case of a company that disclosed the bad news in the previous year, the ability to predict uncertain future will be more important, and expects executives with better predictability to receive more compensation. The results of this study show that there is a significant negative(-) relationship between the accuracy of the manager's earnings forecast and the performance - compensation of the firms that disclosed the bad news in the previous year. The accuracy of the manager's disclosure is important, and it is confirmed that the manager's compensation increases as the incentive of the manager's effort to reduce future uncertainty. The results of this study are as follows: there is a positive relationship between the managerial performance and the managerial competence of managers. It is important to note that there is a difference and that we have identified additional determinants of the manager compensation contract.

• Asia Marketing Journal
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• v.15 no.3
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• pp.53-77
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• 2013

As advertising and promotions are categorized as operating expenses, managers tend to reduce marketing budget to improve their short term profitability. Gauging the value and accountability of marketing spending is therefore considered as a major research priority in marketing. To respond this call, recent studies have documented that financial market reacts positively to a firm's marketing activity or marketing related outcomes such as brand equity and customer satisfaction. However, prior studies focus on the relation of marketing variable and financial market variables. This study suggests a channel about how marketing activity increases firm valuation. Specifically, we propose that a firm's marketing activity increases the level of the firm's product market information and thereby the dispersion in financial analysts' earnings forecasts decreases. With less uncertainty about the firm's future prospect, the firm's managers and shareholders have less information asymmetry, which reduces the firm's cost of capital and thereby increases the valuation of the firm. To our knowledge, this is the first paper to examine how informational benefits can mediate the effect of marketing activity on firm value. To test whether marketing activity contributes to increase in firm value by mitigating information asymmetry, this study employs a longitudinal data which contains 12,824 firm-year observations with 2,337 distinct firms from 1981 to 2006. Firm value is measured by Tobin's Q and one-year-ahead buy-and-hold abnormal return (BHAR). Following prior literature, dispersion in analysts' earnings forecasts is used as a proxy for the information gap between management and shareholders. For model specification, to identify mediating effect, the three-step regression approach is adopted. All models are estimated using Markov chain Monte Carlo (MCMC) methods to test the statistical significance of the mediating effect. The analysis shows that marketing intensity has a significant negative relationship with dispersion in analysts' earnings forecasts. After including the mediator variable about analyst dispersion, the effect of marketing intensity on firm value drops from 1.199 (p < .01) to 1.130 (p < .01) in Tobin's Q model and the same effect drops from .192 (p < .01) to .188 (p < .01) in BHAR model. The results suggest that analysts' forecast dispersion partially accounts for the positive effect of marketing on firm valuation. Additionally, the same analysis was conducted with an alternative dependent variable (forecast accuracy) and a marketing metric (advertising intensity). The analysis supports the robustness of the main results. In sum, the results provide empirical evidence that marketing activity can increase shareholder value by mitigating problem of information asymmetry in the capital market. The findings have important implications for managers. First, managers should be cognizant of the role of marketing activity in providing information to the financial market as well as to the consumer market. Thus, managers should take into account investors' reaction when they design marketing communication messages for reducing the cost of capital. Second, this study shows a channel on how marketing creates shareholder value and highlights the accountability of marketing. In addition to the direct impact of marketing on firm value, an indirect channel by reducing information asymmetry should be considered. Potentially, marketing managers can justify their spending from the perspective of increasing long-term shareholder value.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.287-295
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• 2017

Since Google's AlphaGo defeated a world champion of Go players in 2016, there have been many interests in the deep learning. In the financial sector, a Robo-Advisor using deep learning gains a significant attention, which builds and manages portfolios of financial instruments for investors.In this paper, we have proposed the a deep learning algorithm geared toward identification and forecast of the KOSPI index direction,and we also have compared the accuracy of the prediction.In an application of forecasting the financial market index direction, we have shown that the Robo-Advisor using deep learning has a significant effect on finance industry. The Robo-Advisor collects a massive data such as earnings statements, news reports and regulatory filings, analyzes those and recommends investors how to view market trends and identify the best time to purchase financial assets. On the other hand, the Robo-Advisor allows businesses to learn more about their customers, develop better marketing strategies, increase sales and decrease costs.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.