• 한국산학경영학회:학술대회논문집
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• 2004.11a
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• pp.55-72
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• 2004

Before 2003 the companies In Korea should capitalize the interest expenses that are attributable to the acquisition, construction or production of a qualifying assets. However, according to the revised standard which should be applied from 2003, the companies can either capitalize the interest expenses or recognize as an expense when they are incurred. Therefore almost all the companies confronted with the decision making of accounting choices on the interest capitalization. This paper empirically examines which financial characteristics of the companies affect the accounting choice by using logistic regression model and reviews the sufficiency of the foot notes disclosures regarding the capitalized interest. The variables of the financial characteristics are change of debt-equity ratio, borrowing ratio, qualifying assets ratio, firm sire and income smoothing. The results of this study are summarized as follows. First, among the financial characteristics, only qualifying asset ratio has the significant difference between capitalized companies and expensing companies. Second, the results of logistic regression indicate that qualifying asset ratio and firm size have the significant influence on the accounting choices. Therefore, I cannot find the evidence supporting that the companies use the accounting choice to manage the financial ratios.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.1-13
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• 2019

As a way to find new and innovative technologies, many companies have invested in and acquired skilled startups. Because startups are usually small in size and have a small history of past business experience, there are many risks involved in acquiring them as they have limited technical skills and business feasibility verification methods. Thus, venture capital plays an important role in discovering and investing competitive startups. While Independent Venture Capital generally values financial returns, Corporate Venture Capital, which plays investment roles in the firm, values business synergies with the parent company from a strategic perspective. In an industry sector where development of technology is rapid and whether new technology is held determines a company's competitiveness, existing companies incorporate startups with innovative technologies into their investment portfolios, collaborate together, and take over for comprehensive cooperation. In addition, new investments and acquisitions are carried out through the management of portfolio companies to obtain and utilize industry information. In this paper, major U.S. companies listed in the U.S. verified their investment activities through corporate venture capital and their impact on parent companies and startups through regression, while the parent company's acquisition performance was analyzed through an event study based on a stock price analysis. The criteria for startup were defined as companies with less than 12 years of experience, and the analysis showed that the parent companies with corporate venture capital with a larger number of investments actively take over startups. In addition, increasing corporate venture capital's financial investment activities shows a negative impact on the parent companies' acquisition activities, and the acquisition performance increased when the parent companies took over startups in its portfolio.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of Popular Narrative
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• v.25 no.3
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• pp.9-40
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• 2019

This study presents an investigation into South Korean films in the 1990s in the aspects of genre change and emotional reorganization. The 1990s witnessed a change of genres and a paradigm shift in the history of Korean films according to the revolutionary changes of the film industry structure and media environment. Believing that these changes had something to do with emotional changes driven by global capitalization symbolized by democratization in 1987 and the foreign currency crisis in 1998, the investigator analyzed the phenomena in film texts and examined the opportunities and context behind them. Unlike previous researches, this study made an approach to the history of Korean films in the 1990s with three points: first, this study focused on why the romantic comedy genre emerged in the 1990s and what stages its formation underwent since there had been no profound discussions about them; secondly, this study analyzed the biggest hits during the transitional period from 1987~1999 to figure out the mainstream genres and emotions during that period since these hits would provide texts to show the genre domain and public taste in a symbolic way; and finally, this study grew out of the separate investigation approach between melodramas and romantic comedies and looked into an emotional structure to encompass both genres to make a more broad and dynamic approach to South Korean films in the 1990s. History flows continuously without severance from previous times. When there is attention paid to inflection points and opportunities in the continuum, it can show the dynamics and structures of changes. This research led to the following conclusions: the mainstream genre of South Korean films had been melodramas until the 1980s. The old convention had been kept to offset or suture contradictions and excessive elements deviant from the structural consistency. Here, the structural consistency refers to no compliance to rational regulations or trade. The process of genre reorganization in the 1990s happened while securing some distance from the convention of making the structural consistency a sacrifice. The direction was to reinforce control through reasonable rationalism and logic of capital. It developed into romance, which would start with comedy to keep distance from the objects through laughter, heighten the level of remarks, and expand criticality, symbolize emotions with taste items, and build through the logic of mutual consensus and practical trade. In the 1990s, the South Korean films thus developed in a direction of moving away from the narrative of urgent pathos based on unconditional familism. It was on the same track as the entry of the South Korean society into the upgraded orbits of democracy and capitalism as the twins of modern rationalism since the latter part of the 1980s.

• Journal of Venture Innovation
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• v.4 no.3
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• pp.141-150
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• 2021

Although management decisions centered on the board of directors and directors must be made in order to effectively promote ESG management, the company's management is not obligated to make decisions considering ESG factors. A Korean corporation(company) is an established organization for commercial or other profit, and the purpose of treating a legal organization as a corporation is to easily handle the legal relationship of a group (corporate's property) and individual property of a group member, but legal person such as rights to "harm public rights" or "defend fraud". Criminal liability for illegal acts of a corporation, but the liability of a corporation (natural person) for illegal acts of a corporation is recognized within a limited range, but the criminal liability of a corporation (natural person) is limited. As the social responsibility of a corporation is great, limiting the responsibility of a corporation-related person (natural person) to civil responsibility will halve its effectiveness if considering the impact on the corporation's national economy. Objective requirements such as the completeness of control, hybridization of property, infringement of creditors' rights, and small-capitalization, and the subjective intention of abusing the company system to avoid legal application to controlling shareholders should be denied. Despite the increasing influence on corporate society, such as large-scale projects and astronomical business profits, corporate officials (natural persons) are forced to be held liable for negligence and intentional liability within a limited range. In such cases, it is necessary to introduce criminal responsibility separately from civil responsibility to legal persons (natural persons) in consideration of the maturity of capitalism in Korean society and the economic status of the world. In Korea, the requirements for recognition of corporate denial are strict, but the United States says that it is sufficient to have control or fraud. Therefore, it is not about civil responsibility, but about criminal responsibility of a legal person (natural person), so if fraud is recognized, it can strengthen the corporate social responsibility.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.