• Asia-Pacific Journal of Business
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• v.13 no.1
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• pp.247-262
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• 2022

Purpose - The purpose of this study is to examine the impact of the Pandamic Declaration on 'untact firms' listed in KOSPI and KOSDAQ market in order to verify Investor Mania Hypothesis. Design/methodology/approach - This study collected financial data for 44 untact firms in KOSPI and KOSDAQ market. Then, we employed ESM(Event Study Methodology), EGARCH model and DID(Difference-In-Difference) for analysis. Findings - First, in contrast with the benchmarking index, KOSPI 200 which shows a negative (-) abnormal return trend, the untact firms have positive abnormal return trend consistently. Second, after the Pandemic Declaration, the variability of abnormal return for the untact firms is found to be significantly positive. Third, we find that the cumulative abnormal return and volatility of the untact firms significantly increase after the Pandemic Declaration. Research implications or Originality - Based on the Investor Mania Hypothesis, we confirm that the market potential of untact firms after the Pandemic Declaration is observed when compared with the KOSPI 200.

• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.6
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• pp.873-883
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• 2000

The purpose of this study were to investigate the relationships of situation variables, product variables. consumer variables and impulse buying behavior in apparel store. We collected data from 462 consumers of adult women living in Pusan and analysed by factor analysis, cluster analysis, analysis of variance, t-test and discriminant analysis. The results were as follows: First, The purchase situation influenced on the impulse buying behavior consisted of the Pre-purchase condition and the Point-of-purchase state. The in-store situation consisted of the Salesman/store atmosphere, the Low price and the Possibility of out of stock. And the consumption values of apparel are divided into four factors ; Emotional/aesthetical value, Epistemic value, Functional value and Social value. The clothing shopping orientation as consumer variable extracted six factors ; Recreational orientation, Economical orientation, Brand/store loyalty orientation, Careful orientation, Apathetic orientation and Positive orientation. Consumers were classified by the cloting shopping orientation into the Convenience shopper, the Recreational shopper, the Economical shopper and the Careful shopper. Second, In comparison with the unimpulse-buyin groups, the impulse-buying group is more effected by in-store situation than purchase situation, and were more effected by Emotional/aesthetical value, Social value and Epistemic value of the consumption value. In consumer types, the more was the Recreational shopper and the Convenience shopper, the more showed impulse buying behavior. And the important factor distinguished between the impulse buying group and the unimpulse buying group was the Salesman/store atmosphere of the in-store situation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4354-4358
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• 2011

Many Korean construction companies have fallen on hard times because the construction business continues to stagnate. Therefore it is necessary to measure the management efficiency for efficient operation and strengthening competitiveness of them in order to survive a difficult situation. This paper proposes a combined ANP and DEA model to analyze the efficiency of the listed construction firms. In order to determine the input and output variables of DEA, the ANP model is applied to evaluate the importance of input and output variables. The benchmarking companies and efficiency value for the construction firms with inefficiency are also provided to improve the their efficiency. The 57 listed construction companies consisted of 36 listed on KOSPI and 21 listed on KOSDAQ are analyzed in this study. The analysis results show that 11 companies whose values of CCR are 1, and 14 firms whose values of BCC efficiency are 1. In additions, the 19 firms have the scalability efficiency. Finally, we test the correlation between efficiency and the stock price.

• Journal of Korea Port Economic Association
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• v.33 no.4
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• pp.99-116
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• 2017

This paper analyzes the characteristics of change in economic indicators logistics business performance indicators in Japan over the past decade. We compare the differences in management performance of groups related to logistics business strategy. This is because we want to show that the logistics business strategy is reflected in the management performance. Research methods include correlation analysis, crossover analysis, and variance analysis. The main results are as follows. First, logistic companies' sales are highly correlated with economic indicators such as GDP, trade value, and stock price. Second, there is a correlation between the business sectors and the proportion of tangible assets. It is understood that different business strategies are appropriate for each industry and each period. Third, the effects of business strategy variables on business performance variables were significant. In particular, the interaction effect of three variables showed a difference in the effect on the yield. The results of this study provide a better understanding of how logistics companies achieve a high performance in the changing economic environment over the past decade.

• The Journal of Korean Society for Radiation Therapy
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• v.6 no.1
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• pp.21-30
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• 1994

To analyze the states of operation of radiotherapy facilities in the period from 1979 to 1992 and to get the base for efficient operation and maintenance of the radiotherapy facilities. Data on the records of annual number of patients operated by each facility; span of suspension of operation, the cost and span of repairing, and parts of oui-of-order in the period from 1979 to 1992 were analyzed. We made a comparative analysis of average annual number of patients, annual span of the suspension of operation, annual cost ratio of repair, span of repairing per break down, and total number of broken parts. We could get following annual number of patients(day), span of the suspension of span ($\%$)(day). annual cost ratio fo repair($\%$), span of repairing per break down(Min-Max, day), and number of broken parts from this analysis. 1. Cobalt unit (Picker C-9) : 10,389(43), 0.4(0.83) 0.07, 1hr-2, 3 2. Linac(Clinac 6/100) : 11,492(50), 4.0(9.57), 0.98, 1hr-30, 12 3. Linac(Clinac 18) : 9.115(44), 12.7(30.5), 3.54, 1hr-108. 41 4. Simulator(Picker Ther-X) : 2,017(9), 0.51(1.3), 0.24, 1hr-2, 7 5. RTP(Capentec Cap-plan) : 528(2), 0.4(0.93), - hrs, - The conclusion obtained from statistical analysis above are as follows. 1. The rate of operation of Cobalt unit($99.6\%$) was higher that of Linear Accelerators ($87.3\%$). The rates operation of Simulator and RTP computer were very close to that of Cobalt unit. 2. In order to raise up the working ratio of accelerator. it is desirable that we keep our engineer to learn a sufficient technical skill and the equipment agent to stock sufficient spare parts. 3. In order to maintain Linear Accelerator efficiently, it is desirable to have annually $2.3\%$ of the purchase price of equiment for repairing.

• 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.

• Asia-Pacific Journal of Business
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• v.12 no.1
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• pp.135-149
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• 2021

Purpose - I analyzes risk-to-performance evaluated in the market using data from sale and lease back. Specifically, I analyze from the perspective of financial institutions that purchase sale and lease back based on the cases of investment by ship investment companies and acquisition of ships. Design/methodology/approach - I use 49 sale and lease back data from 2017 to 2019 for empirical analysis. Findings - The main results of this paper are as follows. First, after sale and lease back of domestic ships, the average amount of sales by the leased shipping company is 25.1 billion won, the average amount of investment by the purchased financial institution is 14.6 billion won (60%) and the average length of the ship is nine years. In ship finance, sale and lease back is deemed to be appropriately used as a means of restructuring for a large amount of money. Second, the main risk factor for sale and lease back of domestic ships is credit risk and can be measured in VaR in practice. As a result of the empirical analysis, the average credit risk burden ratio is 9%. As a major risk factor, low creditworthiness of restructuring companies is the key. Third, as a result of measuring the profitability of financial institutions that purchase sale and lease back of domestic ships at a net current price, it has an average value of 300 million won, but the deviation by case is very large. Fourth, the risk adjusted performance of sale and lease back of domestic ships is 0.54 on average compared to the total risk capital, and 0.52 compared to the stock-risk capital, and as with profitability earlier, the deviation of each case is very large and misaligned. In order to boost the sale and lease back market for large and long-term assets, in order to overcome low profitability as a prerequisite for future participation of commercial purchased financial institutions, it is expected that purchase decisions based on expectations versus risk will be necessary. Research implications or Originality - The results of this paper are expected to broaden the understanding of sale and lease back and foster the ability to assess long-term risk and performance. Based on this, it is believed that rapid restructuring of companies through sale and lease back of large amounts of long-term assets will greatly increase the utility of the domestic financial market.

• Management & Information Systems Review
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• v.33 no.1
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• pp.45-63
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• 2014

A trading system is a computer trading program that automatically submits trades to an exchange. Mechanical a trading system to execute trade is spreading in the stock market. However, a trading system to trade a single asset might occur instability of the profit because payoff of this system is determined a asset movement. Therefore, it is necessary to develop a trading system that is trade two assets such as a pair trading that is to sell overvalued assets and buy the undervalued ones. The aim of this study is to propose a relative value based trading system designed to yield stable and profitable profits regardless of market conditions. In fact, we propose a procedure for building a trading system that is based on the rough set analysis of indicators derived from a price ratio between two assets. KOSPI 200 index futures and S&P 500 index futures are used as a data for evaluation of the proposed trading system. We intend to examine the usefulness of this model through an empirical study.

• Environmental and Resource Economics Review
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• v.27 no.1
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• pp.67-104
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• 2018

In this paper, we compare the spread effects of the carbon tax imposition method using the real business cycle model considering the productivity and energy price shocks. Scenario 1 sets the carbon tax rate that encourages the representative firm to maintain a constant $CO_2$ reduction ratio in accordance with its green house gas reduction targets for each period. Scenario 2 sets the method of imposing the steady state value of the carbon tax rate of Scenario 1 during the analysis period. The impulse response analysis shows that the responses of $CO_2$ emissions to external shocks are relatively sensitive in scenario 2. And simulation results show that the cost of $CO_2$ abatement is more volatile in scenario 1, and $CO_2$ emissions and $CO_2$ stock are more volatile in scenario 2. In particular, the percentage changes in volatility between the two scenarios of $CO_2$ emissions and $CO_2$ stock increase as the green house gas reduction target is harder. When the green house gas reduction target is 60% and over, the percentage changes(absolute value) between the two scenarios exceed the percentage change(absolute value) of the $CO_2$ reduction cost between them.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.655-660
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• 2022

This paper analyzes the problem of the economic order quantity (lot size) of a retailer in a two-stage supply chain consisting of a supplier, a retailer(distributor), and a customer. In this two-stage supply chain, the supplier permits the retailer to defer payment for a certain fixed period of time for the purchase cost to be paid by the retailer as a price differentiation strategy with his competitor. In addition, in the case of customer goods such as food and grain, it is common to see that end-customer demand is generally depend on the level of inventory displayed by the retailer. From this perspective, this paper analyzes the inventory problem of retailers under the assumption that the supplier may allow a certain period to suspend payments for the purchase of goods and the end customer demand is a function of the retailer's inventory level increasing with size. In this regard, we need to analyze how much the length of the grace period for product purchase costs affect the retailer's lot-sizing policy. Therefore, we formulate the retailer's annual net profit and analyze the effect of the length of credit period on the retailer's inventory policy numerically.