• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.271-274
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• 1998

In this paper we examine long-term memory of the financial time-series by employing the R/S analysis, the Hurst exponent estimation, and the modified R/S analysis. The null hypothesis of white-noise is tested using the NYSE daily indexes from January 1966 to July 1998, and the results show that long-range dependence exists before the apparent structural break of the Black Monday in 1987.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.1113-1119
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• 2021

This study aims to determine the impact of the news coverage of the COVID-19 pandemic on the composite stocks' movement (IHSG) in Indonesia. This study used secondary data of daily time series with an observation range of March 2020-June 2020. This study used three main variables, namely, COVID-19 news, the daily price of a composite stock market index (IHSG), and interest rate. This study clarifies pandemic news into two forms to facilitate quantitative analysis, namely, good news and bad news. Both pandemic news conditions, which have been clarified, are then processed into the index and reprocessed along with two other variables using vector autoregressive (VAR). The results showed that the good news have a dominant effect on developing the composite stock price index (IHSG) in Indonesia during the COVID-19 pandemic. Although the good news dominates the composite stock price index (IHSG) movement in Indonesia, the bad news must also be anticipated. By implementing a series of macroeconomic policies that follow the conditions of the composite stock price index (IHSG) movements on the stock exchange floor, the bad news response can decrease the potential for a decline in investor confidence, so that the financial system's macroeconomic stability is maintained.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.95-104
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• 2020

In financial economics studies, the autoregressive model has been a workhorse for a long time. However, the model has a fixed value on every parameter and requires the stationarity assumptions. Time-varying coefficient autoregressive model that we use in this paper offers some desirable benefits over the traditional model such as the parameters are allowed to be varied over-time and can be applies to non-stationary financial data. This paper provides the Monte Carlo simulation studies which show that the model can capture the dynamic movement of parameters very well, even though, there are some sudden changes or jumps. For the daily data from January 1, 2015 to February 12, 2020, our paper provides the empirical studies that Thailand, Taiwan and Tokyo Stock market Index can be explained very well by the time-varying coefficient autoregressive model with lag order one while South Korea's stock index can be explained by the model with lag order three. We show that the model can unveil the non-linear shape of the estimated mean. We employ GJR-GARCH in the condition variance equation and found the evidences that the negative shocks have more impact on market's volatility than the positive shock in the case of South Korea and Tokyo.

• New & Renewable Energy
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• v.17 no.2
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• pp.9-23
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• 2021

In Korea, various community investment renewable project models are being implemented to increase community acceptance of renewable energy. An important factor for enhancing local acceptance is that renewable energy projects have a positive effect on revitalizing the local economy such as income increase or job creation for residents and local companies. To maximize the local economic effect of large-scale community investment renewable energy projects, this study developed an evaluation index for local economy activation, whose indicators are the local return on investment, local companies' participation, local job creation, regional cooperation, transparency, and governance. Analysis of existing evaluation indicators and current renewable projects, financial analysis, and expert interviews were used in this research. The pilot evaluation determined that, the local economic effect was high in the following order: a fund investment wind project (Gangwon), benefit-sharing wind project (Jeju), and general wind project. In particular, residents' investment amount, the number of participating residents, and the amount and transparency of the regional cooperation fund were key factors to expand the effect of local economy activation. This evaluation index could be used in public bidding for renewable energy projects such as offshore wind zoning areas of local government.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.196-209
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• 2018

This study is founded on banks' profitability factors. Unlike the previous study in terms of diversification of the banks' funding structure, this research performs multiple regression analysis during the entire period and examines the comparative analysis of before and after the financial crisis. the study establishes hypotheses by using the wholesale funding ratio as a key focus variable with 8 explanatory variables and the operating profit on assets as a profitability index. The Loan-deposit rate gap, the Number of stores and the Non-performing loan ratio prove to be a significant profitability factor for all periods of time. Korean banks are also more profitable when their the Loan-deposit rate gap get bigger and the Number of stores grows. The wholesale funding ratio is analyzed to have no statistically significant effect on the profitability of banks. Rather than being influenced by macroeconomic indicators, it is indicated that the situation of individual banks and other financial environments have been affected. And banks increase profitability as banks increase their loan after the financial crisis. The empirical analysis shows that profitability factors have periodical distinctions, and in this aspect, this research has implications. The study needs to be expanded to cover the entire domestic banking sector, in consideration of the profitability of the banking industry in the future.

• Health Policy and Management
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• v.9 no.1
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• pp.156-177
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• 1999

The current method of rate adjustment for inflation is based on the evaluation of the financial performance of hospitals. The method has the disadvantage such as too complicated, expensive process as well as low reliability. This study, therefore, develops the 'Korean Medical Insurance Economic Index(MIEI)' as a new model for the rate adjustment with the use of the macro economic indices. In addition, we calculate the 1992∼1998 rate adjustment with the MIEI, and examines the validity of the MIEI by comparing with the conventional method. Medical costs are classified into nine categories : physician salaries, nurse·pharmacist·medical technician salaries, assistants & others salaries, material cost(by imports), material cost(by domestics), depreciation & rent paid(by imports), depreciation & rent paid(by domestics), power utilities, other administrative costs. Then the category weight which is the ratio of category in the total cost is calculated. Macro economic indices are selected for each cost category in order to reflect the concept of the each cost category and inflation during the year of 1992∼1998. Finally MIEI which integrate all category according to the category weight and selected macro indices is calculated. The mean of hospital MIEI which weighting by amount paid by insurers was cacluated. The result from the application of empirical data to the MIEI model is very similar to that of the current method. Furthermore, this method is very simple and also easy to get social consensus. This MIEI model can be replaced the current method based on the analysis of the financial performance for the adjustment of medical fees.

• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.375-392
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• 2021

Purpose: Regional gaps and conflicts between regions due to Korea's economic development and industrialization have become important issues, and the issue of balanced regional development at regional level has been discussed as the size of the region has increased recently. Although evaluation of regional balance was attempted through various regional balanced development indexes, it is inappropriate as a standard for determining regional balance in Seoul. Therefore, this study aims to develop objective evaluation methodologies and evaluation indicators for balanced development of administrative districts in Seoul, not existing city and national units. Methods: We looked at existing regional balanced development indexes, and suggested a new regional balanced index reflecting regional development, backwardness, and spatial characteristics in Seoul using factor analysis. Results: As a result of factor analysis, the regional balanced development index for administrative districts and administrative dongs consists of two factors (regional revitalization, financial power) and three factors (commercial density, social security demand, regional retardness), respectively. Then the regional balanced development index scores for 116 administrative districts and 423 administrative dongs are calculated by multiplying each factor by a weight obtained through experts' survey. Conclusion: The proposed regional balanced development index can be used as an objective and quantitative basis for regional balanced development within a city. Further research may include continuously adding new indicators that reflect the direction and scale of development.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Health Policy and Management
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• v.7 no.1
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• pp.73-99
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• 1997

This study was carried out by using questionnaires with 126 insurance societies from Sept. 30, 1995 to Oct. 18, 1995. The primary data collected bythe survey have been significantly supplemented by secondary data obtained from sources such as health insurance statistical year books and internal data in the Ministry of Health and Wolfare. Major findings were summarized as follows: Two financial coordinating programs have significantly improved financial status of regional health insurance societies: the catastrophic program for high cost medical care that was initiated in 1991 and the program for hospitalization cost of the aged in 1995. Another finding is that there existed ambiguity and inconsistency of equity index that had been used by stabilization programs and its side effects could not be ignored. Regression analyses were made to identify factors that affect financial transfers. Inde pendent variables in the regression include utilization frequency, dependancy ration, insurance contribution per insured and medical expense per insured. All these variables were statistically significant in the equations of applying distribution rate (distribution/contribution) and transfer rate (transfer/contribution) as dependent variables. Policy suggestions for the catastrophic program for high cost medical care are modifying the definition of catastrophic case and setting the maximum amount of subsidies for each society based on distribution rates. To solve the problems of the financial coordinating program for the aged, we could consider reimbursing more than 50% of the copayment incurred by the aged 65 or more and determining the maximum amount of outpatient copayment at 10,000 Won per day or per visit for the elderly. More fundamental improvement could be made by amending the Welfare Benefit Act to establish and expand medical and welfare facilities for the elderly.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.729-742
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• 2019

Natural disasters such as floods has been increased in many parts of the world, also Korea is no exception. The biggest part of natural damage in South Korea was caused by the flooding during the rainy season in every summer. The existing flood vulnerability analysis cannot explain the reality because of the repeated changes in topography. Therefore, it is necessary to calculate a new flood vulnerability index in accordance with the changed terrain and socio-economic environment. The priority of the investment for the flood prevention and mitigation has to be determined using the new flood vulnerability index. Total 25 urban districts in Seoul were selected as the study area. Flood vulnerability factors were developed using Pressure-State-Response (PSR) structures. The Pressure Index (PI) includes nine factors such as population density and number of vehicles, and so on. Four factors such as damage of public facilities, etc. for the Status Index (SI) were selected. Finally, seven factors for Response Index (RI) were selected such as the number of evacuation facilities and financial independence, etc. The weights of factors were calculated using AHP method and Fuzzy AHP to implement the uncertainties in the decision making process. As a result, PI and RI were changed, but the ranks in PI and RI were not be changed significantly. However, SI were changed significanlty in terms of the weight method. Flood vulnerability index using Fuzzy AHP shows less vulnerability index in Southern part of Han river. This would be the reason that cost of flood mitigation, number of government workers and Financial self-reliance are high.