• 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.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.57-62
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• 2023

This research is a comparative analysis of the U.S. S&P 500 index using the volatility breakout strategy against the Buy and Hold approach. The volatility breakout strategy is a trading method that exploits price movements after periods of relative market stability or concentration. Specifically, it is observed that large price movements tend to occur more frequently after periods of low volatility. When a stock moves within a narrow price range for a while and then suddenly rises or falls, it is expected to continue moving in that direction. To capitalize on these movements, traders adopt the volatility breakout strategy. The 'k' value is used as a multiplier applied to a measure of recent market volatility. One method of measuring volatility is the Average True Range (ATR), which represents the difference between the highest and lowest prices of recent trading days. The 'k' value plays a crucial role for traders in setting their trade threshold. This study calculated the 'k' value at a general level and compared its returns with the Buy and Hold strategy, finding that algorithmic trading using the volatility breakout strategy achieved slightly higher returns. In the future, we plan to present simulation results for maximizing returns by determining the optimal 'k' value for automated trading of the S&P 500 index using artificial intelligence deep learning techniques.

• Journal of Industrial Convergence
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• v.19 no.5
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• pp.21-34
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• 2021

The purpose of this study is to find out what the investment efficiency of BW is from an investor's point of view and to suggest an efficient investment plan to investors. The research method is to investigate the coupon interest rate, maturity interest rate, issuance date, right exercise start and end date, maturity date, exercise price, etc. for BW issued from 2014 to July 2021. By connecting them, it was attempted to quantitatively understand the efficiency of investment in BW and the effect of new stock acquisitions. As a result of the study, the ratio of the number of days in excess of the exercise price was 41.3% of the available days for new stocks, so it was analyzed that the investment efficiency of bonds with warrants was not high. The return on the exercise start date was 24.8% on average and the return on the end date was 52.6% on average, showing a positive return on average, so it was derived in line with investor expectations. The number of stocks with negative returns on the exercise start date was 1.47 times higher than the number of stocks with positive returns, and the number of stocks with negative returns on the end date was 1.16 times higher than the number of positive stocks.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.3
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• pp.211-219
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• 2010

This paper designs the dynamic symbol automatic trading system in Korean option market. This system is based on Multichart program which is convenient and efficient system trading tool. But the Multichart has an important restriction which has only one constant symbol per chart. This restriction causes very useful strategies impossible. The proposed design uses global variables, signal chart selection and position order exchange. So an automatic trading system with dynamic symbol works on Multichart program. To verify the proposed system, BS(Buythensell)-SB(Sellthenbuy) strategies are tested which uses the change of open-interest of stock index futures within a day. These strategies buy both call and put option in ATM at start candle and liquidate all at 12 o'clock and then sell both call and put option in ATM at 12 o'clock and also liquidate all at 14:40. From 23 March 2009 to 31 May 2010, 301-trading days, is adopted for experiment. As a result, the average daily profit rate of this simple strategies riches 1.09%. This profit rate is up to eight times of commision price which is 0.15 % per option trade. If the method which raises the profitable rate of wining trade or lower commission than 0.15% is found, these strategies make fascinated lossless trading system which is based on the proposed dynamic symbol automatic trading system.

• Journal of Distribution Science
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• v.16 no.2
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• pp.83-88
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• 2018

Purpose - For distribution corporate, the method of recognizing sales revenue may be different depending on the type of distribution transaction. Until the change in accounting standards for revenue recognition was made in 2002, the distribution corporate recognized the full amount of sales of goods regardless of the type of transaction. However, in accordance with accounting standards for revenue recognition, which began to be applied in 2003, distribution corporate differ in sales revenue recognition by transaction type. The Purpose of this study is to analyze the impact of sales revenue on the corporate value after the change of the revenue recognition accounting standards. Research design, data, and methodology - We selected a comprehensive wholesale and retail corporate listed on Korea Exchange. The research model extends the Ohlson(1995) model and regresses whether sales revenue affecting the corporate value is discriminatory value relevance between the corporate affected by changes in accounting standards for revenue recognition and those not. Results - The results of the analysis are as follows. First, The average value of stock price, net asset per share, and earnings per share are all higher than those before the change of accounting standards for revenue recognition. However, the average value of sales per share is lower than that before the change of accounting standards for revenue recognition. Second, the relationship between corporate value and net asset per share, earnings per share and sales per share, the coefficient of net asset per share, earnings per share and sales per share are all statistically significant positive value. Therefore, in explaining corporate value, besides net asset per share and earnings per share, sales per share provides additional information. And the coefficient of interaction variable between accounting standard change and sales per share is a statistically significant positive value. This result indicating that after the change of the revenue recognition accounting standards the usefulness of sales revenue has increased. Conclusions - The change in accounting standards for revenue recognition led to a decrease in distribution corporate sales revenue but the higher the relevance of the corporate value of the sales revenue information. These results shows that the change of accounting standards that reflects the transaction type of retailers was a revision to increase the value relevance of sales revenue in valuation of corporate value.

• The Journal of Society for e-Business Studies
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• v.21 no.2
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• pp.81-96
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• 2016

This paper analyzes the impact of information breach on shareholder value by measuring the stock price reaction associated with the announcements of data breach. The breach firms in the sample lost, on average, 1.3% of their market value, amounting to 98.9 million won of loss within two-day of the event period after the announcement. We examine the abnormal returns in various categories (i.e., source, type, size, etc.) of information breach. Although the market does not react significantly to the announcements of outside breach, we find statistically significant market reactions to inside breach. We estimate abnormal returns over the following 60 days. The mean 60-day cumulative abnormal return and BHAR (buy-and-hold abnormal returns) are both significantly far from zero. We conclude that there is a coherent market reaction following the announcement. The difference between the market reactions to IT firms and Non-IT firms is statistically significant. But breach amount, firm size, and the year the breach occurred do not show to be significant variables.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2108-2114
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• 2011

I examine propping within chaebolgroups, using changes of bond rating events made by corporate credit evaluation institutions. Much studies related to the internal capital market and tunneling have enhanced our understanding of the important function of chaebolgroups in emerging market, but relatively little is known about propping within affiliated firms. In a common sense, propping implies capital reallocation within affiliated firms to save a financially troubled affiliate. In event study on announcement the changes of corporate bond rating, I found most positive numbers in chaebolgroup's CAR. Particularly when lower change than higher change, decrease ratio of CAR is higher positively in chaebolgroups, which relatively shows that there is more propping effects in chaebolgroups than non-chaebolgroups. In multi-regression analysis, after strengthen restriction of internal mutual investment, propping effects are decreased positively in chaebolgroups than non-chaebolgroups when credit rating adjust lower, which implies there was more propping in chaebolgroups.

• 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.

• Management & Information Systems Review
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• v.37 no.2
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• pp.259-281
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• 2018

Advocates of mandatory IFRS adoption claim that IFRS increase financial statement comparability, which in turn leads to greater cross-border investment(Securities and Exchange Commision, 2008). The notion is that improved financial statement comparability reduces the information acquisition costs of global investors and thereby increase their investment in foreign firms. The purpose of this study is to examine this assertion by examining whether the K-IFRS adoption rusults in improved comparability that leads to increased investment by foreign investment. We also examined whether the relation between comparability and foreign investment has strengthen after adoption of K-IFRS. To achieve the purpose of our study, we measure Korean firms comparability using stock price model, stock return model and cash flow from operation model by Barth et al.(2012). We use both foreign ownership in the end of year and average during the year for dependent variables were to reduce bias. We test our hypothesis using 1,817 firm-year observation of KOSPI firms during the period of our analysis, 2011-2015. Consistent with our hypothesis, we find K-IFRS adoption results in a greater increase in foreign investment in firms with high comparability firms. This result indicate that the adoption of K-IFRS intends to achieve the international accounting convergence as stated in the roadmap and to reduce the Korea Discount.

• 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.