• Journal of Korean Society of Rural Planning
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• v.9 no.2 s.19
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• pp.13-17
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• 2003

A purpose of this study is to develop practical indexes for improving a village environment in Kyongbuk, Korea. From a questionnaire survey of the public employees (71 persons) who take charge of the environment in Kyongbuk province, the indexes were produced and the contents of questionnaire consisted of preferential and supplementary indexes to apply. The preferential indexes which apply to an agricultural village and a stock raising village are environmental sanitation facilities, a hygienic check of livestock and a purge of livestock shed's surroundings. In case of mountainous village, and natural-ecological village the preferential indexes are habitat reservation for wildlife, poaching prohibition (monitoring for it), and food supplement for livestock. To the traditional-cultural village, prevention of noise pollution, environmental sanitation facilities, and a monitoring or water quality change are the prior indexes in order. For the village near city and the industrial village the preferential indexes are patrol removing of garbage, pollution index planting and monitoring for noise pollution. For a fishing village and a village fronting waterside excess uses of agricultural chemicals, monitoring for a water quality change, and realignment of green house with vinyl and warehouse. In conclusion the research presents the practical and preferential index for residents to improve their environment in accordance with village settings and suggests guidelines for further research.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.651-668
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• 2010

Various estimators of two risk measures of a specific financial portfolio, Value-at-Risk and Expected Shortfall, are compared for each case of 1-day and 10-day horizons. We use the Korea Composite Stock Price Index data of 20-year period including the year 2008 of the global financial crisis. Indexes of five foreign stock markets are also used for the empirical comparison study. The estimator considering both the heavy tail of loss distribution and the conditional heteroscedasticity of time series is of main concern, while other standard and new estimators are considered too. We investigate which estimator is best for the Korean stock market and which one shows the best overall performance.

• The Journal of Information Systems
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• v.32 no.1
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• pp.179-199
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• 2023

Purpose This study builds a prediction model to find stocks that can reach intrinsic value among KOSPI and KOSDAQ-listed companies to improve the stability and profitability of the stock investment. And investment simulations are conducted to verify whether stock investment performance is improved by comparing the prediction model, random stock selection, and the market indexes. Design/methodology/approach Value investment theory and machine learning techniques are applied to build the model. Various experiments find conditions such as the algorithm with the best predictive performance, learning period, and intrinsic value-reaching period. This study selects stocks through the prediction model learned with inventive variables, does not limit the holding period after buying to reach the intrinsic value of the stocks, and targets all KOSPI and KOSDAQ companies. The stock and financial data are collected for 21 years (2001-2021). Findings As a result of the experiment, using the random forest technique, the prediction model's performance was the best with one year of learning period and within one year of the intrinsic value reaching period. As a result of the investment simulation, the cumulative return of the prediction model was up to 1.68 times higher than the random stock selection and 17 times higher than the KOSPI index. The usefulness of the prediction model was confirmed in that the number of intrinsic values reaching the predicted stock was up to 70% higher than the random selection.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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• pp.30-45
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• 2013

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.

• Journal of Technology Innovation
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• v.11 no.2
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• pp.151-173
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• 2003

This paper analyses the efficiency and productivity of R&D system across time (1991${\sim}$2000) and 16 OECD countries using multi-output and multi-input non-parametric frontier methods such as DEA (data envelopement analysis) and Malmquist productivity indexes. Malmquist productivity indexes are decomposed into two components measures, namely technical change and efficiency change. To calculate R&D efficiency and productivity, we used R&D stock and the number or researchers as R&D input proxies and the number of adjusted SCI papers and U.S. patent applications as R&D output proxies. Empirical result shows that Switzerland, Canada, U.S., Australia's R&D efficiencies are the highest and Korea's R&D productivity growth is the highest in the sample for the period. Technical efficiency growth was a more important source of productivity growth than technological innovation.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.281-291
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• 2020

This study used Gauss-Poisson jump diffusion process on standard assets to estimate the statutory reserves of Variable Annuity (VA) guarantees specified in Korean bylaw of insurance supervision and calculated guarantee fees and risks based on the model to see the effect of considering the jumps. Financial assets, except KOSPI 200, have fat-tailed return distributions, which is an indirect evidence of discontinuous jumps. In the case of a domestic stock index and foreign stock indexes(Korean Won), guarantee fees and risks decrease when jumps are considered in models of underlying assets. This is explained by decreases in standard deviations after the jump diffusion is considered. On the other hand, in the case of domestic bond indexes and a foreign bond index(Korean Won), guarantee fees and risks tend to increase when jumps are considered. Results from a foreign stock index(US Dollar) and a foreign bond index(US Dollar) were opposite to those from the same kinds of Korean Won indexes. We conclude that VA guarantee fees and risks may be under or over estimated when jumps are not considered in models of underlying assets.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.461-475
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• 2021

Hidden Markov model (HMM) is a statistical model in which the system consists of two elements, hidden states and observable results. HMM has been actively used in various fields, especially for time series data in the financial sector, since it has a variety of mathematical structures. Based on the HMM theory, this research is intended to apply the domestic KOSPI200 stock index as well as the prediction of global stock indexes such as NIKKEI225, HSI, S&P500 and FTSE100. In addition, we would like to compare and examine the differences in results between the HMM and support vector regression (SVR), which is frequently used to predict the stock price, due to recent developments in the artificial intelligence sector.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.561-569
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• 2008

In this paper, we propose the double robust estimators which are the solutions of the double robust estimating equations to analyze and treat the outliers in the stock market data in Korea including the IMF period. The feasibility study shows that the proposed estimators work quitely better than the least squares estimators and the conventional robust estimators.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1494-1500
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• 2008

Life cycle of the rolling stock is normally 20 to 40 years, though there is some difference in accordance with each vehicle. Maintenance cost is over the twice of purchasing price. and also it is true that precise statics is not managed properly except for some developed countries due to the difference of maintenance method, skills. After KORAIL introduced ERP system in 2007, maintenance cost is managed by type of cars, by unit. but, afterwards it should be controlled as an index and also more precisely. it is the best pending issues to make train maintenance efficiency, to utilize accumulated indexes. I want to attribute to train maintenance efficiency by analysing what is the problems in the present maintenance method.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.659-671
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• 2018

In this study, we consider the extension of the heterogeneous autoregressive (HAR) model for realized volatility by incorporating a neural network (NN) structure. Since HAR is a linear model, we expect that adding a neural network term would explain the delicate nonlinearity of the realized volatility. Three neural network-based HAR models, namely HAR-NN, $HAR({\infty})-NN$, and HAR-AR(22)-NN are considered with performance measured by evaluating out-of-sample forecasting errors. The results of the study show that HAR-NN provides a slightly wider interval than traditional HAR as well as shows more peaks and valleys on the turning points. It implies that the HAR-NN model can capture sharper changes due to higher volatility than the traditional HAR model. The HAR-NN model for prediction interval is therefore recommended to account for higher volatility in the stock market. An empirical analysis on the multinational realized volatility of stock indexes shows that the HAR-NN that adds daily, weekly, and monthly volatility averages to the neural network model exhibits the best performance.