• International conference on construction engineering and project management
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• 2015.10a
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• pp.477-480
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• 2015

Construction projects are high-risk activities. When undertaking such projects in an international setting, it can be complicated by foreign exchange (FOREX) fluctuation risk. This affects the construction business performance in various ways, namely its progress due to delays, which in turn create further problems, specifically cost overruns as a result of price increase in raw materials, disputes, arbitration, litigation and even, total abandonment. Thus, the effective management of FOREX fluctuations is crucial. Previous studies have focused on the need for contract safeguards, adequate insurance, careful planning and management, as well as foreign exchange futures hedging to address some of the risks triggered by FOREX fluctuations. An analysis of FOREX fluctuations in the international construction industry revealed that more often it was focused on project-specific issues. Currently, there is a relative lack of awareness on Organizational Capabilities (OC), the abilities that owned by the organization, which is essential in managing the impact of FOREX fluctuations. Where research has focused on OC, these are viewed in isolation. Therefore, this study attempts to close the gap by proposing a framework on managing the impact of FOREX fluctuations in the international construction industry, employing the OC perspective.

• 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.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.165-187
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• 2005

This paper tries to investigate the relationships among stock return volatility, time-varying risk premium and Korea Discount. Using Korean Composite Stock Price Index (KOSPI) return from January 4, 1980 to August 31, 2005, this study finds possible links between time-varying risk premium and Korea Discount. First of all, this study classifies Korean stock returns during the sample period by three regime-switching volatility period that is to say, low-volatile period medium-volatile period and highly-volatile period by estimating Markov-Switching ARCH model. During the highly volatile period of Korean stock return (09/01/1997-05/31/2001), the estimated time-varying unit risk premium from the jump-diffusion GARCH model was 0.3625, where as during the low volatile period (01/04/1980-l1/30/1985), the time-varying unit risk premium was estimated 0.0284 from the jump diffusion GARCH model, which was about thirteen times less than that. This study seems to find the evidence that highly volatile Korean stock market may induce large time-varying risk premium from the investors and this may lead to Korea discount.

• 재활복지
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• v.22 no.3
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• pp.225-257
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• 2018

The purpose of this study is to find the application point of hearing augmentation technology development through examining the risk context experience of people with hearing impairment and the use of assistive device used as an alternative technology. Data of 355 people with hearing impairment with official disability grading was analyzed. The results of this study are first, research participants had no experience of recognizing any sound or vibration in situations highest in the order of means of transportation, material, and nature. Especially the ratio of being unable to recognize the sound and vibration of means of transportation was high, which implies the high possibility of people with hearing impairment experiencing risk. Secondly, the risk context that people with hearing impairment will most likely to experience are highest in the order of traffic accident, pedestrian accident, and daily life at home. Thirdly, the recognition of 2G phone/smart phone, vibrating digital alarm clock, light bar, vibrating wrist watch as assistive device for risk context awareness and notification was high and the satisfaction level of 2G phone/smart phone was the highest. Fourthly, the research participants had high recognition of assistive device for communication in the order of hearing aid, smart phone, videophone, cochlear implant and 2G phone and it was found that the satisfaction level and communication improvement level was the highest using the smart phone. Lastly, for the development of hearing augmentation technology the research participants recognized the importance of portable/wear convenience, price, and motion accuracy and for notification delivery means they preferred the method of using sight(text and light). Based on the results of this study policy and practical plans for hearing augmentation technology development for people with hearing impairment in risk context are proposed.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.359-366
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• 2022

While the Forward Freight Agreement (FFA) has emerged as an effective hedging tool since early 1990, the basis risk and cash flow distortions have been addressed as obstacles to the active use of FFAs. This research analyses the basis risk of FFAs and provides a feasible suggestion to reduce it. Basis risk is divided into timing basis, route basis, size basis, and low liquidity basis. The timing basis is defined as the difference between the physical hire, fixed on the specific contract date and the FFA settlement price, calculated by averaging spot rates for a certain period. Timing basis is considered the worst in eroding the effectiveness of FFAs. This paper suggests a change of hire payment criterion from contract date to 15-day moving average, as a means of mitigating the basis risk, and analyzed the effectiveness through historical simulation. The result revealed that the change is effective in mitigating the timing basis. This study delivers a meaningful implication to shipping practice in that the change of hire payment criterion mitigates the basis risk and eventually activates the use of FFAs in the future.

• The Korean Journal of Financial Management
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• v.21 no.2
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• pp.125-151
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• 2004

This study tests the price discovery from US Treasury bond markets to Korean bond markets using the daily returns of Korean bond data (CD, 3-year T-note, 5-year T-note, 5-year corporate note) and US treasury bond markets (3-month T-bill, 5-year T-note 10-year T-bond) from July 1, 1998 to December 31, 2003. For further research, we divide full data into two sub-samples on the basis of the start-up of bond valuation system in Korean bond market July 1, 2000, employing uni-variate AR(1)-GARCH(1,1)-M model. The main results are as follows. First the volatility spillover effects from US Treasury bond markets (3-month T-bill, 5-year T-note, 10-year T-bond) to Korean Treasury and Corporate bond markets (CD, 3-year T-note, 5-year T-note, 5-year corporate note) are significantly found at 1% confidence level. Second, the price discovery function from US bond markets to Korean bond markets in the sub-data of the pre-bond valuation system exists much stronger and more persistent than those of the post-bond valuation system. In particular, the role of 10-year T-bond compared with 3-month T-bill and 5-year T-note is outstanding. We imply these findings result from the international capital market integration which is accelerated by the broad opening of Korean capital market after 1997 Korean currency crisis and the development of telecommunication skill. In addition, these results are meaningful for bond investors who are in charge of capital asset pricing valuation, risk management, and international portfolio management.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• KDI Journal of Economic Policy
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• v.36 no.4
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• pp.135-169
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• 2014

This study empirically analyzes changes in production patterns of farmers by agricultural disaster insurance. The aim of this project is to achieve stability of farm management by paying insurance in case of a natural disaster. However, it causes farmers to change production patterns in the direction of increasing production, and leads the crop price to drop. This can be explained by producers' risk reduction through the disaster insurance. The empirical analysis is based on IV approach with using two stage least squares method. The first stage estimates by difference-in-differences methodology indicate that the production of insurable crops increases more about 80,000ton on average than that of non-insurable crops. In addition, to solve the endogeneity problem caused by general supply and demand model, I use the first stage estimates and find that the price index of the crops drops about 2.3% according to the production increase by 10,000ton. The credibility of these results is also attained by various robustness checks. These findings suggest that it is necessary for government to analyze the whole economy which consists of producer and consumer welfare when it determines the policy. Besides, it implies that it is essential to develop a new market to cope with the unintended effect.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.243-257
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• 2017

In this paper, we compare several methods to approximate option prices: Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method using normal inverse gaussian (NIG) distribution, and an asymptotic method using nonlinear regression. We used two different types of approximation. The first (called the RNM method) approximates the risk neutral probability density function of the log return of the underlying asset and computes the option price. The second (called the OPTIM method) finds the approximate option pricing formula and then estimates parameters to compute the option price. For simulation experiments, we generated underlying asset data from the Heston model and NIG model, a well-known stochastic volatility model and a well-known Levy model, respectively. We also applied the above approximating methods to the KOSPI200 call option price as a real data application. We then found that the OPTIM method shows better performance on average than the RNM method. Among the OPTIM, A-type Gram-Charlier expansion and the asymptotic method that uses nonlinear regression showed relatively better performance; in addition, among RNM, the method of using NIG distribution was relatively better than others.

• Environmental and Resource Economics Review
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• v.29 no.2
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• pp.219-246
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• 2020

From the Phase II (2018~2020) of K-ETS, the offset credit from 'CDM projects that domestic companies and others have carried out in foreign countries' can be used in the K-ETS. As a result, stakeholders in the K-ETS market are actively developing overseas CDM projects, such as the 'high-efficiency cook stove project'. which can secure a large amount of credits while marginal cost is relatively low. This paper develops the investment decision-making model of offset project for the 'high-efficiency cook stove project' using the real option approach. Under the uncertainty of the emission allowance price, the optimal investment threshold (p^⁎) is derived and sensitivity analysis is conducted. As a result, in the standard scenario (PoA-S), the optimal investment threshold is 29,054won/ton, which is lower than the stock price (p^⁎_spot). However, allocation entities are not only economics in the CDM project, but also CDM risk factors such as non-renewable biomass ratio, cook stove replacement ratio, equity ratio with host country, investment period and submission limitation of emission allowance. In addition, offset project developers will be able to derive the optimal investment threshold for each business stage and use it for economic feasibility checks.