• Asia-Pacific Journal of Business
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• v.13 no.2
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• pp.107-122
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• 2022

Purpose - This study aims to develop machine learning models to predict administrative issue designation in KOSDAQ Market using financial data. Design/methodology/approach - Employing four classification techniques including logistic regression, support vector machine, random forest, and gradient boosting to a matched sample of five hundred and thirty-six firms over an eight-year period, the authors develop prediction models and explore the practicality of the models. Findings - The resulting four binary selection models reveal overall satisfactory classification performance in terms of various measures including AUC (area under the receiver operating characteristic curve), accuracy, F1-score, and top quartile lift, while the ensemble models (random forest and gradienct boosting) outperform the others in terms of most measures. Research implications or Originality - Although the assessment of administrative issue potential of firms is critical information to investors and financial institutions, detailed empirical investigation has lagged behind. The current research fills this gap in the literature by proposing parsimonious prediction models based on a few financial variables and validating the applicability of the models.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.275-283
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• 2022

This study aims to investigate the influence of internal factors on the bankruptcy risk of an enterprise through a sample of 439 companies listed on the Vietnamese stock exchange. The research collected secondary data from annual audited financial statements from 2008 to 2019 of listing companies. Using two different regression models with two dependent variables, six independent and control variables, we discovered that three of the model's six factors, namely return on total assets, current payment rate, and financial leverage, influence the risk of bankruptcy and account for 86.78% of the variations in firm bankruptcy risk. Financial leverage has the opposite effect on the Z-score index, increasing the risk of bankruptcy of listed firms. Return on total assets and current ratio have a positive impact on the Z-score index, reducing the risk of bankruptcy of listed companies. The findings also revealed that there is no evidence that the size of a corporation, its fixed asset investment ratio, or the size of an auditing firm have an impact on the Z-score index. These findings provide crucial evidence for business owners and managers, as well as shareholders making future capital investment decisions. Our findings can be applied to other businesses in Vietnam and similar jurisdictions.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Korean Business Review
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• v.6
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• pp.1-23
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• 1993

• Proceedings of the Technology Innovation Conference
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• 2006.02a
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• pp.44-77
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• 2006

The technological innovation has been recognized critical factors of productivity of firms and key contribution of improvement of firm performance for a long time. Many researchers have investigated the relationship between R&D investment or patents as a proxy of innovation and productivity. But there were some problem such as lacking of data and ambiguity of innovation definition. So, previous literatures have some difficulty in reliability of analysis for using only survey data. And investigating the relationship R&D and other factors is very difficult. Therefore, this paper tries to investigate and analysis the relationship between innovation and performance of Korean manufacturing firms. Using KIS2002(Korean Innovation survey 2002) which was based on Oslo manual in 2002 and financial data of firms (KISINFO), I will intend to establish the reliability of multiple analysis. In detail, I will investigate whether the innovation input have effect on the innovation output, whether the innovation output have effect on productivity for heterogeneity and what factor of innovation environment have effect on the innovation itself.

• Management Science and Financial Engineering
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• v.11 no.1
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• pp.25-48
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• 2005

Firms often collaborate on building an infrastructure, which benefits all the firms in the industry, although in unequal magnitudes. Then a difficult and tricky issue is concerned with 'free riding.' Should there be only 'common, i.e., industry-wide' benefits in such collaboration, the literature indicated that the free rider problem is unavoidable, In this paper, however, we suggest that while collaborating, the firm also learns firm-specific knowledge, experience, and know-how, which can be directly utilized for its own internal improvement. That is, the collaboration between firms provides them with not only 'industry-wide,' but also 'firm-specific' benefits, Our analysis shows that if there indeed exist two types of benefits simultaneously, depending on the balance between the two, the free rider problem can be mitigated or even eliminated.

• Korean Management Science Review
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• v.32 no.2
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• pp.15-35
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• 2015

Large business groups typically have central firms through which their controlling families establish (or acquire) new firms and maintain control over other member firms. Research on corporate governance has developed metrics to identify those central firms and investigated an impact of the centrality on ownership structure and firm's financial performance. This paper introduces centrality metrics used in social network analysis (SNA) to measure how crucial a role each firm plays in the ownership structure of its business group. Then, the SNA centrality metrics are compared with the metrics developed in corporate governance field. Also, we test the relationship between the SNA centrality metrics and firm's value. Experimental results show that the SNA centrality metrics are closely correlated with the centrality metrics used in corporate governance and are significantly correlated with firm's value.

• Korea Trade Review
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• v.44 no.1
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• pp.253-268
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• 2019

This paper analyses the characteristics of partner countries when multinational firms of Korea, China, and Japan make greenfield FDI in foreign countries. Particularly, this paper applies the gravity model for greenfield FDI flows for the period 2003-2017. This paper finds that multinational firms of Korea, as compared to those of China and Japan, are very significantly and negatively responsive to political risks of partner countries. In contrast, multinational firms of Korea as well as those of China and Japan tend to make greater amounts of greenfield FDI in financially high-risk countries. This result indicates that multinational firms from these three countries should take financial risks of partner countries into more serious consideration.

• Journal of Korea Trade
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• v.23 no.5
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• pp.1-26
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• 2019

Purpose - We examine the disclosures on foreign divestment from China by 77 Korean firms between 2007 and 2016 to identify the effects (and their determinants) on parent firm value. Design/methodology - We analyze how divestment affects firm value by examining the disclosure of divestment from China by Korean firms. Then, we examine the determinants of these disclosure effects using cross-sectional regression analyses. Findings - We find negative effects on parent firm value in the short and medium term, and both the KOSPI and KOSDAQ stock markets show negative correlations between foreign divestment and firm value. The parent firm's financial condition and profitability and the reason for divesting are statistically significant determinants. Practical implications - Most Korean firms in China belong to the manufacturing industry. As a result, divestment signifies a loss of important manufacturing bases and assets. Originality/value - We analyze foreign direct divestment, which has not been studied in detail previously owing to a lack of data. In addition, this research is the first to compare the disclosure effects in the KOSPI market with those in the KOSDAQ market for the same period.

• Asia pacific journal of information systems
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• v.32 no.3
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• pp.564-579
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• 2022

The use of on-premises technology in the business environment to create a competitive advantage is ushering in a new era known as digital transformation. As the foundation of digital transformation of enterprises, information technology still has a paradoxical effect on enterprises. This paper documents the effect of investments in IT on a firm's long-term profitability performance measures as return on assets (ROA), as well as tests whether the earlier entrant and the later entrant are different in IT investment performance. Using a sample of China's public firms IT investment data between 2016 and 2019, the result indicates that IT investment in firms have a positive effect on firm performance in full sample, but not in the financial industry firms. When it comes to the different investment time, the result shows no significant difference between the earlier entrant firm and the later entrant firm in the full sample, but not in the case of software industry sample. This should help alleviate the concerns that some have expressed about the viability of digital transformation given the highly publicized IT investment and implementation problems at some firms.