• Journal of Fashion Business
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• v.14 no.4
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• pp.10-22
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• 2010

To survive competitive global market, textile and fashion business incorporated Supply Chain Management strategy to make product and information flows fast and correct. Especially textile and fashion industry involves many complicated channels from up stream, middle stream, to down stream for delivering their production. Evaluating SCM performance is very critical to make better business profit model. Representative Scor model and BSC method are well fitted into textile and fashion business because of distributional complexity, non-financial factors to be considered, and innovative product characteristics. But there was little study to compare these two methods for textile and fashion business. Therefore, the purpose of this study was to investigate the Scor model and BSC method based upon review of literatures. The results of this study were as follows: Scor model had some strengths which were availability to apply for various industries due to standardized process, operation process emphasized, various customizable factors to compose for the company, and premise on SCM strategic execution. BSC method had some strengths which were the balance including financial and non-financial factors, qualitative analysis, and considering the goal and vision to convey organically from top to bottom of organization. The main differences between them were different scope to deal with performance estimating index from qualitative to quantitative analysis, the scope of human resources to manage, and possibility of performance comparison among companies.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.3
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• pp.149-156
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• 2020

The study aims to investigate a close relation between macro and non-macro variables on stock performance of tourism companies in Korea. The sample used in this study includes monthly data from January 2001 to December 2018. The stock price index of the tourism companies as a dependent variable are obtained from Sejoong, HanaTour, and RedcapTour as three leading Korean tourism companies that have been listed on the Korea Stock Exchange. This study assesses the tourism stock performance using the quantile regression approach. This study also investigates whether global crisis events as the Iraq War and the global financial crisis as non-macro variables have a significant effect on the stock performance of tourism companies in Korea. The results show that the oil prices, exchange rate and industrial production have negative coefficients on stock prices of tourism companies, while the effects of tourist expenditure and consumer price index are positive and significant. We estimate the result of quantile regression that non-macro determinants have statistically a significant and negative effect on tourism stock performance because the global crisis could threaten traveler's safety and economy. Overall, empirical results suggest that the effects of macro and non-macro variables are statistically asymmetric and highly related to tourism stock performance.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.129-139
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• 2023

Forecasting volatility is essential to avoiding the risk caused by the uncertainties of an financial asset. Complicated financial volatility features such as ambiguity between non-stationarity and stationarity, asymmetry, long-memory, sudden fairly large values like outliers bring great challenges to volatility forecasts. In order to address such complicated features implicity, we consider machine leaning models such as LSTM (1997) and GRU (2014), which are known to be suitable for existing time series forecasting. However, there are the problems of vanishing gradients, of enormous amount of computation, and of a huge memory. To solve these problems, a causal temporal convolutional network (TCN) model, an advanced form of 1D CNN, is also applied. It is confirmed that the overall forecasting power of TCN model is higher than that of the RNN models in forecasting VIX, VXD, and VXN, the daily volatility indices of S&P 500, DJIA, Nasdaq, respectively.

• 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.

• 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.

• Journal of Korean Academy of Nursing
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• v.45 no.3
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• pp.429-438
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• 2015

Purpose: This study was conducted to develop key performance indicators (KPIs) for home care nursing (HCN) based on a balanced scorecard, and to construct a performance prediction model of strategic objectives using the Bayesian Belief Network (BBN). Methods: This methodological study included four steps: establishment of KPIs, performance prediction modeling, development of a performance prediction model using BBN, and simulation of a suggested nursing management strategy. An HCN expert group and a staff group participated. The content validity index was analyzed using STATA 13.0, and BBN was analyzed using HUGIN 8.0. Results: We generated a list of KPIs composed of 4 perspectives, 10 strategic objectives, and 31 KPIs. In the validity test of the performance prediction model, the factor with the greatest variance for increasing profit was maximum cost reduction of HCN services. The factor with the smallest variance for increasing profit was a minimum image improvement for HCN. During sensitivity analysis, the probability of the expert group did not affect the sensitivity. Furthermore, simulation of a 10% image improvement predicted the most effective way to increase profit. Conclusion: KPIs of HCN can estimate financial and non-financial performance. The performance prediction model for HCN will be useful to improve performance.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.1-17
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• 2023

This study aimed to examine the characteristics of ESG activities among major domestic companies in the carbon-emitting industry compared to industries with lower emissions, as ESG has emerged as a significant agenda across various industries. Departing from the traditional focus on the "why" of ESG, which primarily centers around financial performance, this research sought to uncover the "how" of effective ESG management in domestic companies. The analysis involved studying the sustainability reports of 124 companies using the Global Reporting Initiative (GRI) indicators and comparing high-emitting and non-high-emitting industries. The findings revealed industry-specific patterns in companies' ESG activities, providing valuable insights for future ESG evaluations and assessments. Furthermore, the advancement of rating analysis methods holds implications for ESG rating agencies and financial authorities in terms of policy-making.

• Journal of Korea Technology Innovation Society
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• v.9 no.4
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• pp.812-841
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• 2006

Previous studies have shown that the ex-ante financial ratios, mainly used by financial institutions for loan evaluation purpose, are related to the ex-post finn's performance of venture firm's. The main objective of this study is to examine whether non-financial variables such as 'technology', 'marketability', and 'other business indexes' have extra explanatory power in forecasting the ex-post firm's performance of small and medium size venture firm's in Korea. The implications and results of this study are expected to be useful in loan evaluation, investment decision and internal management decisions of venture firms. Among small and medium sized manufacturing firms funded in the year of 1999 through 2005, 416 firms are selected for our analysis. The relationship between evaluation indexes and firm's success/failure is investigated using binary logistic regression analysis and factor analysis with an aid of SPSS program. The summarized results are as follows. First, current evaluation model, used for loan evaluation purpose for small and medium size manufacturing firms show the same discriminatory power as previous prediction model. Second, among the tested additional variables, significant indices are 'technological capability of CEO', 'managerial capability of CEO', and 'business feasibility'. Third, while previous studies on evaluation structure had 3 factors, this study showed that valuation's structure has 6 factors.

• Knowledge Management Research
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• v.22 no.4
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• pp.87-101
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• 2021

ESG Investment is emerging as a trend and common sense in the financial market. ESG Investment is an investment method that simultaneously pursue social sustainability and investment returns from a long-term perspective by reflecting non-financial factors such as environment, society and governance in addition to corporate financial performance in investment decisions. This study checked how the characteristics of ESG investment have been changed after Covid-19. Afterwards, it was confirmed that Covid-19 actually acted as a negative factor in the securities market by applying VAR model. At the same time, it was demonstrated that ESG indices of the US and Korea outperformed their benchmark in terms of return and risk during the pandemic regime. The result of this study hints that the importance of ESG investment will be unchanged after Covid-19. At the same time, it suggests that managers should avoid passive ESG management and engage in strategic ESG management based on knowledge management.

• Management Science and Financial Engineering
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• v.21 no.2
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• pp.13-23
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• 2015

This study addresses the question as to whether the option prices have useful predictive information on the direction of stock markets by investigating a forecasting power of volatility curvatures and skewness premiums implicit in S&P 500 index option prices traded in Chicago Board Options Exchange. We begin by estimating implied volatility functions and risk neutral price densities every minute based on non-parametric method and then calculate volatility curvature and skewness premium using them. The rationale is that high volatility curvature or high skewness premium often leads to strong bullish sentiment among market participants. We found that the rate of return on the signal following trading strategy was significantly higher than that on the intraday buy-and-hold strategy, which indicates that the S&P500 index option prices have a strong forecasting power on the direction of stock index market. Another major finding is that the information contents of S&P 500 index option prices disappear within one minute, and so one minute-delayed signal following trading strategy would not lead to any excess return compared to a simple buy-and-hold strategy.