• Management & Information Systems Review
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• v.6
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• pp.137-162
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• 2001

Our business conglomerates are sharing their fates economically because of mutual debt warranty among their own affiliates and excessive financial loans. For this reason, it is inevitably restrictive to obtain the whole information on such conglomerates by individual and consolidated financial statements. To solve this problem, the system of combined financial statement was introduced through modifications of the Act of the outside audit of corporations in 1988. As a result, 15 out of this nation's 30 major business conglomerates prepared and submitted their own combined financial statements. In this paper, all financial statements are grouped into financial and non-financial parts, based on characteristics of business control and combined financial statement. Then the business size, financial rate and internal transactions for each of the conglomerates are analyzed, based on which problems of the combined financial statement as announced publicly are clarified. For the system, this study suggests improvement points such as a sufficient publication of any possible situations and interest coordination caused between the date of business settlement and that of preparing combined financial statements by applying principles of sharing to the evaluation of valuable instrument papers for investment and by determining the amount, 5% accounting for the total amount of debt warranty, foreign exchange assets and debts.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.209-216
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• 1998

The Korean economy has been under the IMF-led rescue programs since the late 1997. For the transparency of financial currency, Korean government had made the Korean firms which are depend on each other financially compose the consolidated financial statements since 1985, because South Korea's financial crisis has been caused by a string of corporate failures that have put enormous strain on the banking system at a time when the won was plunging against the dollar. But there are many problems to get the transparency in the actions and accountability of both public and private institutions, because of the domestic corporations' structural characteristics and the law of accounting. This paper reviewed, therefore, some problems on the consolidated financial statements and advantages to introducing the combined financial statements through comparing the contents of those statements

• The Journal of the Korea Contents Association
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• v.8 no.3
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• pp.215-224
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• 2008

For global companies that are going to raise funds from international capital markets or to which lots of foreign investment are being given, it is necessary to prepare consolidated financial statements. This study provides the results of some analyses in accordance with the system of consolidated financial statements disclosure. First, disclosing those statements with audit opinion reflected would provide useful reliable and financial information. Second, such disclosure as foresaid would meet international standards that consolidated financial statements are used as main financial statements and separate financial statements as auxiliary ones. The disclosure system would make it possible for domestic corporations to introduce international accounting standards earlier, especially from 2009, in accordance with the road map of those standards. Third, the system of consolidated financial statements disclosure would play an important role in promoting the implementation of consolidated taxation systems under which corporate tax is levied in accordance with consolidated balance sheet.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.659-666
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• 2023

Characterized by boom-and-bust cycles, low entry barriers, and an almost perfectly competitive structure, the construction industry presents a unique challenge for the survival and growth of its constituent companies. A crucial aspect of this challenge is the ongoing monitoring of their financial health and business performance. To understand the typical financial and operational status of construction companies, this study analyzes the financial statements of 6,252 such companies, all of which have undergone at least one external audit between 2000 and 2019. These statements were used to develop combined financial profiles and derive industry averages. The findings indicate that the construction industry experiences limited growth in sales and profitability. High leverage ratios can jeopardize financial stability, pushing companies to seek production efficiency, such as enhancing gross asset turnover. This tendency has been particularly noticeable in the aftermath of the global financial crisis in 2008.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.637-649
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• 2022

Investors aim to increase their profitability by investing in the stock market. An adroit strategy for minimizing related risk lies through diversifying portfolio operationalization. In this paper, we propose a six-step stocks portfolio selection model. This model is based on data mining clustering techniques that reflect the ensuing impact of the political, economic, legal, and corporate governance in Mongolia. As a dataset, we have selected stock exchange trading price, financial statements, and operational reports of top-20 highly capitalized stocks that were traded at the Mongolian Stock Exchange from 2013 to 2017. In order to cluster the stock returns and risks, we have used k-means clustering techniques. We have combined both k-means clustering with Markowitz's portfolio theory to create an optimal and efficient portfolio. We constructed an efficient frontier, creating 15 portfolios, and computed the weight of stocks in each portfolio. From these portfolio options, the investor is given a choice to choose any one option.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.8
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• pp.99-108
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• 2022

This study aims to examine the influence of capital structure on the business efficiency of joint stock commercial banks listed on the Vietnamese stock market. The article uses data collected from the financial statements of 15 prominent joint-stock commercial banks out of 27 joint-stock commercial banks listed in Vietnam from 2011 to 2021. The research uses E-view software in quantitative analysis to build regression models to determine the relationship and the impact of capital structure factors on the business efficiency of listed joint stock commercial banks. Research results show that ROA is affected by 2 variables of capital structure. It is the sum of customer deposits to total assets and total liabilities to total equity. Total debt to total equity and total customer deposits to total assets both have a negative effect on ROA. For the regression results of ROA with all control variables, the control variables have a positive relationship with the dependent variable. The article has provided recommendations based on the research findings to determine the proper capital structure. Managers must solve the outstanding amount of mobilized capital in previous years, combined with the bad debt handling activities that have arisen.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.67-75
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• 2023

Stock price prediction is an important topic extensively discussed in the financial market, but it is considered a challenging subject due to numerous factors that can influence it. In this research, performance was compared and analyzed by applying time series prediction models (LSTM, GRU) and non-time series prediction models (RF, SVR, KNN, LGBM) that do not take into account the temporal dependence of data into stock price prediction. In addition, various data such as stock price data, technical indicators, financial statements indicators, buy sell indicators, short selling, and foreign indicators were combined to find optimal predictors and analyze major factors affecting stock price prediction by industry. Through the hyperparameter optimization process, the process of improving the prediction performance for each algorithm was also conducted to analyze the factors affecting the performance. As a result of feature selection and hyperparameter optimization, it was found that the forecast accuracy of the time series prediction algorithm GRU and LSTM+GRU was the highest.

• Journal of East Asia Management
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• v.4 no.1
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• pp.23-51
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• 2023

We investigate whether the association between management overconfident tone and the level of audit effort measured by audit fees and hours differs depending on the level of earnings management. Prior studies suggest that firms led by overconfident managers are likely to initiate risky investments, report low quality financial statements, and have material weaknesses in internal control system. These characteristics, combined together, result in higher audit risk. At the same time, auditors assess audit risk based on the quality of financial reporting, measured by level of earnings management. As a result, the assess audit risk is likely to reflect the combined effect of management overconfidence and the level of earnings management. In this paper, we investigate whether auditors differentiate the effects of real earnings management (REM) and accrual-based earnings management (AEM) when they assess the audit risk related management overconfident. Using the CEO's letter published in 2018, we measure the CEO's tone representing the degree of overconfidence (i.e., activity). Based on this measure, we find that the positive association between managerial overconfident tone and audit effort is more pronounced as the level of REM is higher. However, we find that the baseline association does not vary depending on the level of AEM. These results suggest that auditors consider the managerial overconfident severer when such characteristic accompany the higher level of REM, which can be outcome of aggressive business decisions possibly leading to the higher audit risks. We further find that these results are stronger for Big 4 auditors and continuing auditors. This paper contributes to the literature and practice as follows. First, we provide contextual evidence on how auditors reflect managerial characteristics in the audit process by documenting that auditors actively increase their audit efforts only when overconfident managerial characteristics are highly likely to lead to audit risk. This result suggests that auditors conduct external auditing considering both the efficiency and effectiveness of the audit process. Second, we suggest that auditors use information obtained from a wide range of sources to identify audit risks. Our results provide evidence of how the auditing standards, which do not provide detailed guidelines for audit risk assessment, are being applied in practice. Finally, our results also enhance the understanding of how audit fees are determined. Combined with the studies related to audit pricing, we provide the important reference for discussion between the auditor and the auditee about the audit fee that has created acute tension after the enforcement of the new External Audit Act.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Management & Information Systems Review
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• v.35 no.4
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• pp.169-184
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• 2016

Deferred tax assets (liability) in a company's financial statements are to reflect the temporary difference between taxable income and accounting income and therefore can provide useful information as a proxy for discretionary accruals. In addition, deferred tax assets allow a company to manage its earnings by reviewing the feasibility of the assets' recognition. As such, this study focused on deferred tax assets to examine their relationship with discretionary accruals, which were measured by a modified Jones model (Dechow et al. 1995), and investigated the impact of audit quality on this relationship. In order to control for the effects of tax rate change and measurement credibility, deferred tax assets of 2,670 non-financial firms from 2009 to 2010 were collected as samples for the study. The results of the empirical analysis are as follows. First, the samples as a whole indicated that deferred tax assets have a negative relationship with discretionary accruals in a general sense, but a high-quality audit did not reveal a significant relationship between them. Second, the 1,379 samples with negative discretionary accruals did not reveal a significant relationship between deferred tax assets and discretionary accruals; however, the result showed a significant negative relationship under a high-quality audit. These findings suggest that in the case of negative discretionary accruals, a high-quality audit restricts an earnings management technique that utilizes deferred tax assets and that the assets can be a useful tool for detecting discretionary accruals. The present study is meaningful in that, unlike previous research, it combined the two contrasting roles of deferred tax assets-that of an earnings management detector and an earnings management tool-to examine their general relationship. The study also suggested that audit quality could influence the usefulness of deferred tax assets in providing information on discretionary accruals.