• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.275-284
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• 2021

Survival analysis was used to analyze whether there is a difference in the effect of leverage on corporate failure according to the firm size. A total of 25,250 (year-company) companies listed on the Korea Stock Exchange and KOSDAQ market from 1999 to 2019 were analyzed. First, the increase in leverage generally acts as a factor that increases the possibility of corporate failure. On the other hand, the increase in the trade payable ratio lowered the possibility of failure of the company. The increase in corporate trade payable was perceived as a factor in reducing the possibility of corporate failure because it was considered the active development of business activities or active use of interest-free debt rather than leading to an increase in corporate risk. Second, a higher leverage ratio and trade payable ratio in large firms lowered the possibility of corporate failure. In the SMEs, all types of leverage increases are a factor that increases corporate failure. Overall, the effect of leverage on corporate failure differs according to the size of the company.

• Journal of Korea Entertainment Industry Association
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• v.15 no.6
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• pp.55-70
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• 2021

This study investigates the following important financial questions using entertainment enterprises: 1) how does cash reserve affect a firm's financial value? 2) what factors influence the level of cash retention of a firm? For empirical tests, we use accounting and financial data of entertainment companies listed in the KOSPI and KOSDAQ markets for a long-term time period covering from 2000 to 2018. The main findings of this paper are as follows: First, entertainment companies maintain higher level of cash holdings compared to non-entertainment companies. Second, the cash holdings of entertainment enterprises have positive influence on firms' financial value. Third, among various firm characteristics known for affecting the cash holdings level, leverage and profitability exhibit strong relationships in entertainment enterprises. Entertainment firms with lower leverage and higher profitability tend to reserve more cash inside them. These findings suggest that entertainment companies are highly valued by stock market participants as having prospective opportunities, thus, firms with sufficient cash holdings tend to have higher firm value. In addition, these findings imply that cash in entertainment enterprises functions as a substitute for debts and the cash holdings are less likely driven by agency problems.

• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.1-10
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• 2022

The purpose of this study is to establish a foundation for IoT-related industries to secure financial soundness and to dominate the global market after COVID-19. Through this study, the quantitative management status of IoT-related companies was checked. It also was attempted to preemptively prepare for corporate insolvency by examining the relationship between financial ratios in accordance with stock price fluctuations and designation of management items. This study selected 502 companies that were listed on the KOSPI and KOSDAQ in the stock market from 2019 to 2020. For statistical analysis, multiple regression analysis, difference analysis and logistic regression analysis were performed. The research results are as follows. First, it was found that the impact of IoT company accounting information on stock prices differs depending on before and after COVID-19. Second, it was found that there is a difference in the closing stock prices of IoT companies before and after COVID-19. Third, it was found that financial ratios according to stock price fluctuations exist differently after COVID-19. Fourth, it was found that the financial ratios according to the designation of management items after COVID-19 exist differently. Through these studies, some suggestions were made to secure the financial soundness of IoT companies and to lay the groundwork for leaping into the global market after COVID-19. Through the results of this study, it is expected that it will lead the growth of IoT companies and contribute to growth as a decacorn company of the future that can guarantee financial soundness in the changing financial market.

• Journal of Technology Innovation
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• v.24 no.1
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• pp.143-167
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• 2016

The purpose of this study is to analyze changes of innovation activities and their performance in pre-IPO and post-IPO of KOSDAQ IPO listed companies in medical and pharmaceutical fields, which require high R&D investment, from 2000 to 2005 in Korea. The innovation efficiencies of the IPO companies were measured before and after three years based on the DEA model. The financial data and patent information of the listed company during total 6 years, which were 3 years before IPO and 3 years after IPO, were collected. The main results of this research are as follows. First, it took an average 12.86 years until IPO in the start-up of the IPO companies in the pharmaceutical sector, and innovation was on average more active than the IPO before. R&D investment was higher than the IPO before, and the number of the applied patent during 3 years after IPO was 16.67 which was increased from 8.43 during 3 years before IPO. In addition, the average scope of technology of the IPO companies was expanded from 11 to 22 technology fields during previous 3 year and after 3 year each, and financial growth after IPO was lower than the previous IPO. Second, the financial performance of R&D investment and the performance of patent activity were weakened in the efficiency after the IPO, and the integrated performance from the patenting activities and the R&D investment was decreased after the IPO. Finally, the efficiency of the financial performance of the patenting activity was lower than the efficiency of the financial performance of the patent and R&D investment and patent activities under the R&D investment. In particular, the inefficiency of the firms' patenting activities performance after the IPO was caused by the decreasing return to scale, according to the results of this study. This results implicate that the expansion of R&D investments through the IPO had not lead to the financial performance of the market, and that the overall inefficiency since the IPO is due to the inefficiencies at the stage for the outcome of innovation activity rather than the output obtained through the R&D investments that appear to lead the performance of the market.

• 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.

• The Journal of the Korea Contents Association
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• v.12 no.6
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• pp.497-507
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• 2012

This study aims to verify how the characteristics of organizational supporting unit affect the effectiveness of company-wide HRD Education & Training program. To achieve this objective, we performed an empirical analysis, with the characteristics of organizational supporting unit comprising supervisor's support, job support, and company support as independent variables, and with the level of reaction stage, learning stage, transfer stage, and result stage as dependent variables. Empirical data was collected during the period from August 16, 2011 to September 9, 2011 by sending out questionnaires to employees of 5 securities firms listed on KOSDAQ where online and offline education & training program is running year-round with headquarter in Seoul. A total of 340 questionnaires were sent out three times for the survey, and total of 164 questionnaires were sampled for the final analysis. According to the outcome of the analysis, regarding the first hypothesis that tries to reveal how the characteristics affect the level of reaction stage, it is verified that all of supervisor's support, job support and company support have positive impact on the level of reaction stage with p value less than 0.01. In regard to the second hypothesis that tries to see how the characteristics affect the level of learning stage, it is confirmed that supervisor's support, job support and company support have significant impact on the level of learning stage with p value less than 0.05 or 0.01, respectively. Concerning the third hypothesis that aims to investigate how the characteristics affect the level of transfer stage, it is appeared that all of supervisor's support, job support and company support have positive impact on the level of transfer stage. And lastly, as for the fourth hypothesis that tries to see how the characteristics affect the level of result stage, it is analyzed that supervisor's support, job support and company support have positive impact on the level of result stage with p value less than 0.01. This study reconfirm the outcomes of previous research, which is that the effectiveness of company-wide education & training program depends not only on the contents and quality of education & training program, but also more importantly on the role of organizational supporting unit, and the working environment where what is learned in classroom can be applied to real business. Companies or experts that run education & training program in real world should recognize that the performance of training is dependent more significantly on the characteristics of organizational supporting unit rather than the design or features of education & training program.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.3
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• pp.125-146
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• 2013

Nowadays, the performance of the mobile game sales is influencing the ranking of game companies listed on KOSDAQ. In the meantime, venture capital companies had focused on online game. Recently, however, they have great interest in mobile games and mobile game companies. In addition, angel investors and accelerators are increasing investment for the mobile game companies. The most important issues for mobile game investor is how to evaluate the mobile game companies and their contents. Therefore, this study derived the evaluation factors for the mobile game company. And research method converged of the opinions of both supply side and demand side of the game industry. Ten professionals who are responsible for the supply of the game industry and CEO group & development experts of game development company were selected for survey in this study. Also ten professionals who are responsible for the demand of the game industry and the investment company were selected for survey in this study. And Delphi technique was performed according to the survey. Management skills, development capabilities, game play, feasibility, operational capabilities has emerged as five evaluation factors to evaluate the mobile game company. And the 20 sub-factors including CEO's reliability were derived. AHP(Analytic Hierarchy Process) theory is applied to analyze the importance of the qualitative elements which were derived by Delphi technique. As a result, the analysis hierarchy of evaluation factors for the mobile game company was created. Pair-wise comparison for each element was performed to analyze the importance. As a result, 'Core fun of the game' (12,2%), 'Involvement of the game' (10.3%), 'Security Reliability' (8.9%), 'Core developers' ability' (7.6%) appeared in order of importance. The significance of this study is offering more objective methodology for realistic assessment and importance of elements to evaluate mobile game company.

• Management & Information Systems Review
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• v.35 no.1
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• pp.51-68
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• 2016

In this paper, we aim to analyze the effect of accounting policy change subsequent to the adoption of K-IFRS in Korea, whereby the firms are required to recognize impairment losses on goodwill on a periodic basis rather than to amortize over a specific period. As a principle-based accounting standard, the K-IFRS expands the scope of fair value measurement with a view to enhance the relevance and timeliness of accounting information. In the same vein, intangibles with indefinite useful life, of which goodwill is an example, are subject to regulatory impairment tests at least once a year. Related literature on the impact of mandatory change in goodwill policy document that impairment recognition is more likely to be practiced opportunistically, mainly because managers have a greater discretion to conduct the tests under K-IFRS. However, existing literature examined the frequency and/or magnitude of the goodwill impairment before versus after the K-IFRS adoption, failing to notice the impairment symptoms at individual firm level. Borrowing the definition of impairment symptoms suggested by Ramanna and Watts(2012), this study performs a series of tests to determine whether the goodwill impairment recognition achieves the goal of communicating timelier information under the K-IFRS regime. Using 947 firm-year observations from domestic companies listed in KRX and KOSDAQ markets from 2008 to 2011, we document overall delays in recognizing impairment losses on goodwill after the adoption of K-IFRS relative to prior period, based on logistic and OLS regression analyses. The results are qualitatively similar in robustness tests, which use alternative proxy for goodwill impairment symptom. Afore-mentioned results indicate that managers are likely to take advantage of the increased discretion to recognize the impairment losses on goodwill rather than to provide timelier information on impairment, inconsistent with the goal of regulatory authority, which is in line with the improvement of timeliness and relevance of accounting information in conjunction with the full implementation of K-IFRS. This study contributes to the extant literature on goodwill impairment from a methodological viewpoint. We believe that the method employed in this paper potentially diminishes the bias inherent in researches relying on ex post impairment recognition, by conducting tests based on ex ante impairment symptoms, which allows direct examination of the timeliness changes between before and after K-IFRS adoption.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.