• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.594-606
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• 2018

The purpose of this study is to examine the mediating effect of positive psychological capital as an protective factor and mental health as an risk factor on the relationship between smart phone addiction and self life management of university students. The following are the results of the analysis on the measures of smart-phone addiction, positive psychological capital, mental health and self life management among the 266 college students. First, the analysis of the correlation between relevant factors, smart-phone addiction showed a significant amulet correlation between self life management and positive psychological capital and a significant static correlation between mental health. Second, the positive psychological capital showed a mediate effect as a result of the analysis of university students' smart-phone addiction and self life management. Third, the results of a regression analysis between positive psychological capital and psychosomatic health sub-factors, optimism and resilience in the positive psychological capital and Depression and Impulsion in mental health, had a significant impact on managing self-life management Based on the results of this study, we discussed educational implications for the management of university students ' lives and suggestions for further research.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.204-212
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• 2022

Recently, the Korean government has been actively promoting the smart city as their strategic agenda. However, to build smart cities that are greener, the authors believe it is essential to rapidly transit conventional energy sources such as fossil fuels to renewable energy. Although there is a big potential for Offshore wind in Korea, there has not been a full-scale commercial offshore wind farm until today. Since Korea is relatively a new market compared to the EU, it can be risky for developers. The authors will introduce risk management best practices in the offshore wind industry applicable to the Korean environment. This paper will mainly introduce an offshore wind project size of 99 MW. The project is expecting a Finance Close (FC) in Q3 2022, so the project team has prepared a risk register with over 150 risks and levers throughout the project lifecycle. Overall risks include risks with Development Expenditure (DEVEX) impact, a Capital Expenditure (CAPEX) impact, and an Operating Expenditure (OPEX) impact. Based on the identified risks, a more qualitative assessment of Cost and Schedule Impact was conducted. In conclusion, a Monte Carlo simulation was performed to propose a quantitative risk assessment to evaluate a benchmark contingency of the project cost.

• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.64-73
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• 2020

The government announced a plan for fund support to the enterprises with high possibility of recovery and early restructuring for the enterprises with low recovery by objectifying credit assessment system. Such announcement of government could be extended to restructuring risk of middle standing enterprises with low financial soundness by establishing the basis to prepare prompt restructuring by reinforcing the basis for restructuring through capital market. This research analyzed financial soundness based on the financial evaluation of bank by selecting 10 middle standing construction companies which focused on housing business in 2019, based on such analysis result, it was confirmed that there was a high possibility of restructuring risk. This research determined that there would be a decrease in growth rate of construction industry on the whole in 2020 due to fall of economic growth rate and reinforced real estate regulation, accordingly, there's a big possibility for middle standing construction companies with paid-in capital ratio due to its low possibility of maintenance of stable credit rating. This research established KCSI assessment model by utilizing the material of a reliable research institute in order for middle standing construction companies to evade restructuring risk, and indicated risk ratio differentiated per each item through a working-level expert survey. Such research result could suggest credit risk reduction method to middle standing construction company management staffs, and prepare a basis to evade restructuring risk.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Atmosphere
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• v.34 no.2
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• pp.107-121
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• 2024

In this study, we empirically analyzed the impact of physical risks due to climate change on the soundness and operational performance of the financial industry by combining economics and climatology. Particularly, unlike previous studies, we employed the Seasonal-Trend decomposition using LOESS (STL) method to extract trends of climate-related risk variables and economic-financial variables, conducting a two-stage empirical analysis. In the first stage estimation, we found that the delinquency rate and the Bank for International Settlement (BIS) ratio of commercial banks have significant negative effects on the damage caused by natural disasters, frequency of heavy rainfall, average temperature, and number of typhoons. On the other hand, for insurance companies, the damage from natural disasters, frequency of heavy rainfall, frequency of heavy snowfall, and annual average temperature have significant negative effects on return on assets (ROA) and the risk-based capital ratio (RBC). In the second stage estimation, based on the first stage results, we predicted the soundness and operational performance indicators of commercial banks and insurance companies until 2035. According to the forecast results, the delinquency rate of commercial banks is expected to increase steadily until 2035 under assumption that recent years' trend continues until 2035. It indicates that banks' managerial risk can be seriously worsened from climate change. Also the BIS ratio is expected to decrease which also indicates weakening safety buffer against climate risks over time. Additionally, the ROA of insurance companies is expected to decrease, followed by an increase in the RBC, and then a subsequent decrease.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.107-117
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• 2008

Private financing is playing an increasing role in public infrastructure construction projects worldwide. However, private investors/operators are exposed to the financial risk of low profitability due to the inaccurate estimation of facility demand, operation income, maintenance costs, etc. From the operator's perspective, a sound and thorough financial feasibility study is required to establish the appropriate capital structure of a project. Operators tend to reduce the equity amount to minimize the level of risk exposure, while creditors persist to raise it, in an attempt to secure a sufficient level of financial involvement from the operators. Therefore, it is important for creditors and operators to reach an agreement for a balanced capital structure that synthetically considers both profitability and repayment capacity. This paper presents an optimal capital structure model for successful private infrastructure investment. This model finds the optimized point where the profitability is balanced with the repayment capacity, with the use of the concept of utility function and multi-objective GA (Generic Algorithm)-based optimization. A case study is presented to show the validity of the model and its verification. The research conclusions provide a proper capital structure for privately-financed infrastructure projects through a proposed multi-objective model.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.588-597
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• 2021

In Korea, the standard for calculating the profit of a general insurance, which constitutes the loading in the premium, is not specified, and most of the non-life insurance companies reflect 2~5% of the premium as profit margin. Although the transparency of pricing is required due to the nature of insurance products, there are insufficient standards and empirical studies on the determination of insurance price factors in the domestic insurance industry. In this study, we propose a method of calculating the expected profit margin of general insurance. A way for calculating the expected profit margin of the general insurance is to reflect the shareholder demand on the capital that the insurance company should secure against the risk of loss due to the profit/loss volatility, as a ratio to the insurance premium. Shareholders should be compensated for the risks associated with their insurance operations, and the opportunity cost of these shareholders is to be reflected in premiums. In this study, we calculate the amount of capital that the company should accumulate to prepare insurance risk for each product, and insurance risk is defined as the volatility of insurance operating profit/loss. And insurance risk is calculated using stochastic simulation based on Dynamic Financial Analysis (DFA) methodology. Finally, we calculate the expected profit margins for 25 products and analyzed the difference between those and the profit ratio of domestic general insurance.

• Asian Journal of Innovation and Policy
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• v.7 no.1
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• pp.150-177
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• 2018

We review two specific risk management strategies of venture capitalists (VCs): deal syndication and domain specialization with respect to their explicit role in adjudging and managing the overall magnitude of information asymmetry risks. These are analyzed for three distinct categories of VC firms as classified by their funding stage focus (early vs. late), ownership type (foreign vs. domestic) and the human capital composition of the core VC team (entrepreneurial vs. investor). The analysis is based on both secondary data and primary data for active 72 VC firms in India. Syndication is moderately important for entrepreneurial VC firms, but not at all important for early-stage focused and foreign VC firms. This finding is distinctly different from what has been conventionally observed in the literature. Among the various arenas of domain specialization, high-technology focus is important for all segments of VC firms. In the context of investment-stage focus, foreign VC firms exhibit growth-stage specialization, while entrepreneurial VC firms concentrate on earlier investment stages.

• The Journal of Economics, Marketing and Management
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• v.8 no.1
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• pp.12-19
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• 2020

Purpose - Strategic causes for the failure include unrealistic growth, overexpansion, unfamiliar new markets, volume obsession, unrealistic promises and poor project selection. Organizational causes are insufficient capital and profits, lack of business knowledge, poor financial management, poor sales skills, inadequate marketing, poor leadership, poor leadership transfer, project losses, poor field performance and owner bankruptcy. Uncontrollable causes include industry and economic weakness and banking and surety changes. While helpful, the list provides insufficient clarity regarding the causal roots of failure. Research design, data and methodology - The research framework to organize the information involved with many of the recent and large failures in the industry. Results - This research then identified five dominant root causes - excessive egoism, poor strategic leadership, too much change, loss of discipline and inadequate capitalization. Conclusion - Finally, additional input from external forces may accelerate the firm's pace to failure. It is important on the development of diagnostic tools that are based on this model and that will provide new ways to assess a conglomerate's level of risk for incurring a financial crisis.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.2
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• pp.166-191
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• 2012

This paper investigates how the agent specificity of economic infrastructure-related public company proceeds in overseas investments. At first this paper examines general features focused on investment region, project type, starting year, and then analyzes overseas investments based on specific cases with three classifications which are composed of special consulting type that do not accompany capital investment, FDI type, and type of overseas energy resources development that go with capital investment and business risk. Finally nationality issues that is generated by agent characteristics of infrastructure public company are interpreted in perspective of duality which is financial pressure and dependency of host county's position, and relationship with private capital of home country.