• Economic Analysis
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• v.25 no.1
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• pp.1-33
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• 2019

This paper examines the economic factors that are related to the dynamics of the variance risk premium, and specially, which economic factors are related to the forecasting power of the variance premium regarding future index returns. Eleven general economic variables, eight interest rate variables, and eleven sentiment-associated variables are used to figure out the relevant economic variables that affect the variance risk premium. According to our empirical results, the won-dollar exchange rates, foreign reserves, the historical/implied volatility, and interest rate variables all have significant coefficients. The highest adjusted R-squared is more than 65 percent, indicating their significant explanatory power of the variance risk premium. Next, to verify the economic variables associated with the predictability of the variance risk premium, we conduct forecasting regressions to predict future stock returns and volatilities for one to six months. Our empirical analysis shows that only the won-dollar exchange rate, among the many variables associated with the dynamics of the variance risk premium, has a significant forecasting ability regarding future index returns. These results are consistent with results found in previous studies, including Londono (2012) and Bollerslev et al. (2014), which show that the variance risk premium is related to global risk factors.

• Information Systems Review
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• v.25 no.4
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• pp.183-204
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• 2023

Due to the COVID-19 pandemic, lots of occasions need to be held in online environment. This is the reason why "Metaverse" gets lots of attention in 2021. A number of companies made announcements on Metaverse, and this situation also boomed stock market. This paper investigates the relationship between Metaverse initiatives and business value of the firm (i.e., stock prices). We examine this relationship by using event study method with Lexis-Nexis News data from 2019 to 2021. The results indicate that Metaverse initiatives significantly impact positive influence on firm's value. In the technological perspective, technical factors affect more positive market returns, including Metaverse enablers (e.g., NFT, VR devices, digital twin) and common infrastructure (e.g., semiconductor, AI, cloud), and especially virtual environment was emphasized. Additionally, in the strategical perspective, radical innovation (e.g., pivoting, acquisition) impact more positive market return rather than incremental innovation (e.g., partnership, investment). Also, firms from non-service industries can achieve benefits from Metaverse initiatives rather than service industry in some degree.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.255-266
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• 2022

The quality control of coarse aggregate among aggregates, which are the main ingredients of concrete, is currently carried out by SPC(Statistical Process Control) method through sampling. We construct a smart factory for manufacturing innovation by changing the quality control of coarse aggregates to inspect the coarse aggregates based on this image by acquired images through the camera instead of the current sieve analysis. First, obtained images were preprocessed, and HED(Hollistically-nested Edge Detection) which is the filter learned by deep learning segment each object. After analyzing each aggregate by image processing the segmentation result, fineness modulus and the aggregate shape rate are determined by analyzing result. The quality of aggregate obtained through the video was examined by calculate fineness modulus and aggregate shape rate and the accuracy of the algorithm was more than 90% accurate compared to that of aggregates through the sieve analysis. Furthermore, the aggregate shape rate could not be examined by conventional methods, but the content of this paper also allowed the measurement of the aggregate shape rate. For the aggregate shape rate, it was verified with the length of models, which showed a difference of ±4.5%. In the case of measuring the length of the aggregate, the algorithm result and actual length of the aggregate showed a ±6% difference. Analyzing the actual three-dimensional data in a two-dimensional video made a difference from the actual data, which requires further research.

• Information Systems Review
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• v.22 no.4
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• pp.21-39
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• 2020

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data

• The Korean Journal of Financial Management
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• v.10 no.1
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• pp.249-280
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• 1993

Recent financial theory views insurance policies as financial instruments that are traded in markets and whose prices reflect the forces of supply and demand. This article analyzes individual's insurance purchasing behavior along with capital market investment activities, which will provide a more realistic look at the tradeoff between insurance and investment in the individual's budget constraint. It is shown that the financial economic concept of insurance cost should reflect the opportunity cost of insurance premium. The author demonstrates the importance of riskless and risky financial assets in reaching an equilibrium insurance premium. In addition, the paper also investigates how the investment income could affect the four established theorems on traditional insurance literature. At the present time in Korea, the price deregulation is being debated as the most important current issue in insurance industry. In view of the results of this paper, insurance companies should recognize investment income in pricing their coverage if insurance prices are deregulated. Otherwise. price competition may force insurance companies to restrict coverage or to leave the market.

• International Area Studies Review
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• v.22 no.3
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• pp.121-148
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• 2018

This paper examines the role of accounting conservatism on investment expenditure for non-financial Korean listed firms around the 2007-2008 global financial crisis using a differences-in-differences design. Specifically, this paper examines the association between an ex ante classification of firms by their level of accounting conservatism prior to the credit crisis and the ex post magnitude of the decline in investment. Consistent with prior literature, this study found that firms experienced a decline in their investment when hit by the financial crisis (Campello et al. 2010). And also this study found that firms with more conservative financial reporting experienced a smaller decline in investment activity following the financial crisis than did firms with less conservative financial reporting. Together, the results suggest that negative shocks to the supply of external finance hampers firm-level investment and that conservative financial reporting can lessen the sensitivity of firms' investment to such negative shocks. Next, this study shows that the magnitude of our findings is greater for firms more likely to suffer from underinvestment (as opposed to overinvestment). Firms that are financially constrained or have greater demand for external finance are more likely to experience underinvestment. Consistent with the predictions, this study finds stronger benefits of conservatism for firms that face relatively greater costs in raising external capital (i.e., financially constrained firms) or that have a relatively greater need to do so (i.e., firms that lack internal financial resources). This study also finds that the role for conservatism is greater in firms with a higher level of information asymmetry, consistent with the notion that conservatism mitigates financing frictions arising from information problems.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.2
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• pp.286-305
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• 2013

The purpose of this study was to investigate the recognition and satisfaction with the menu quality of food services in an immigration detention center. The survey was conducted from January 22, 2010 to April 22, 2010 by questionnaires. A survey with 265 respondents was conducted and data analyzed by the SAS Program. In analyzing leftovers, the most common was kimchi (37.61%), followed by breads (21.52%), and beans/bean curd (17.99%). The common cause for leftover were undesirable taste (31.84%), sickness or a lack of desire for eating (19.85%). In terms of cooking methods, stir-frying, broiling, and frying were highly preferred to steaming, boiling, and salting. In the analysis of preferences in the taste and satisfaction of food service, there were significant differences in hot, sour, bitter, and light tastes (p<0.05, p<0.01, p<0.001). Satisfaction was low with hot and light tastes, whereas sour and the bitter tastes showed a high degree of satisfaction. In the opinions for quality improvement, most immigrants wanted a tastier food supply (58.69%), a diverse food supply (40.54%), and clean utensils (36.68%). In the analysis of the gap between importance and performance, food taste, variety, and sanitation were recognized as poorly performed, causing major dissatisfaction with the food. The overall satisfaction score was 'average' (3 points out of 5 points) with 3.26 points. The satisfaction score showed insignificant difference depending on religions and duration of stay in Korea, but showed significant differences depending on nationality (p<0.001).

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1737-1740
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• 2006

본 연구에서는 1차원 수치모형을 이용하여 댐 여수로에서 발생하는 흐름을 해석하였다. 본 연구에서 사용한 1차원 수치모형은 매우 복잡한 지형에 적용이 가능하며, 상류가 사류가 동시에 발생하거나 도수가 발생하는 흐름도 해석할 수 있는 모형이다. 이 모형은 해석해를 가지는 도수의 경우에 적용하여 검증된 바 있으며, 자연하천의 보에서 발생하는 불연속 흐름에 대해서도 적용된 바 있다. 또한 매우 불규칙한 자연하천에서 발생하는 흐름도 효과적으로 모의할 수 있다. 본 연구에서는 댐 여수로 흐름 해석을 평가하기 위해 단순화된 하도에서 발생하는 불연속 흐름에 대해 이론적인 해와 비교하였다. 또한 댐 수리모형 결과에 수치모형을 적용하여 빈도별로 발생하는 흐름을 해석하였다. 해석결과 여수로에서 발생하는 매우 작은 수심을 정확하게 모의하였으며, 여수로 직하류부에서 발생하는 도수의 발생위치, 발생 전후의 수위도 잘 모의하는 것으로 나타났다. 또한 여수로 및 도수 전후에 발생하는 유속도 정확하게 모의하는 것으로 나타났다. 또한 본 연구에서는 1차원 수치모형을 여수로 뿐만아니라 댐 상류 하천, 저수부, 여수로 접근수로, 여수로, 도수, 하류하천등으로 구성되는 전 구간의 흐름을 동시에 모의하기 적용하였다. 적용 결과 댐 저수지 상류 하천과 저수지, 저수지와 접근수로, 접근 수로와 여수로, 여수로와 도수, 도수 발생 후와 하류하천 등에서 복잡하게 형성되는 흐름을 동시에 잘 모의하는 것으로 나타났다. 본 연구에서 사용한 1차원 수치모형을 이용하면 댐 여수로나 저수지, 연계되어 있는 상하류 하천에서의 흐름을 동시에 해석할 수 있기 때문에 향후 수리모형실험과 연계하여 댐 설계에 효율적으로 사용될 수 있을 것으로 전망된다.지는 것으로 평가되었다. 그러나 본 연구결과를 통하여 투수성 포장과 지하수에 관련된 매개변수의 집적과 분석결과는 현장기술 적용 시 매개변수의 유용한 선택과 도시유역의 물 순환 건전화 대안기술 적용에 효과적인 방법론을 제시할 수 있을 것으로 사료된다.첨두홍수량을 저류하기 위해서 상대적으로 넓은 저류면적이 필요한 것으로 나타난다. 대등한 수위감소값의 홍수저감효과를 발휘하기 위해서 본 연구에서는 On-Line 저류지 면적은 Off-Line 저류지에 비 두배 이상이 필요한 것으로 보여졌다.들에 관한 정보는 종종 현장관측에서 조차 무시되는 경우가 많다. 이에 본 연구에서는 수질모형의 매개변수 중 특히 수리특성에 관련된 매개변수들이 수질에 미치는 영향을 파악하는 것을 목적으로 하고 있다. 이를 위해 적용된 수질모형은 QualKo를 사용하였으며, 대상 하천은 낙동강 본류 경남구간 시점 부근인 회천 합류 전부터 낙동강 본류 경남구간 종점 부근인 밀양강 합류 전까지의 경남 오염총량관리 기본계획 시 구축된 모형 매개변수를 바탕으로 분석을 수행하였다. 일차오차분석을 이용하여 수리매개변수와 수질매개변수의 수질항목별 상대적 기여도를 파악해 본 결과, 수리매개변수는 DO, BOD, 유기질소, 유기인 모든 항목에 일정 정도의 상대적 기여도를 가지고 있는 것을 알 수 있었다. 이로부터 수질 모형의 적용 시 수리 매개변수 또한 수질 매개변수의 추정 시와 같이 보다 세심한 주의를 기울여 추정할 필요가 있을 것으로 판단된다.변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1686-1690
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• 2006

현재 우리나라에서는 하천의 개수사업 및 교량, 제방 등과 같은 수공구조물 설계시 계획 홍수위를 산정하는데 있어 실무에서는 1차원 모형인 HEC-RAS 모형이 널리 이용되고 있다. 그러나 HEC-RAS 모형과 같은 1차원 모형의 경우 모형의 한계로 인해 하폭의 확대, 축소, 만곡부 및 섬 등과 같은 장애물 존재시 하천횡단면에 따른 수위 및 유속변화를 표현하는데 많은 어려움이 있으며, 이런 한계로 인해 1차원 모형만을 이용하여 하도 및 하천공간계획을 수립하기란 사실상 불가능하다. 또한 최근 들어 하천기능에 대한 인식이 과거 홍수소통, 용수이용 등 이.치수중심에서 인간과 다양한 생명체가 공존하는 서식처로서의 기능을 부여하는 생태하천의 개념으로 변화하는 추세이며, 이와 같이 변화된 패러다임하에서 생태하천복원사업 등과 같은 하천관련 사업추진 시 합리적인 하도 및 하천의 공간계획을 수립하기 위해서는 하도내 2차원 흐름특성 등과 같은 기초자료가 절실히 요구된다. 따라서, 본 연구에서는 합리적인 하도 및 하천의 공간계획을 수립하기 위한 기초자료를 제공함은 물론, 보 철거에 따른 하도내 흐름개선 효과를 분석하기 위해 연구대상 하천인 두계천에 대하여 2차원 수치모의를 통한 하도내 흐름특성 변화를 조사하였다. 본 연구에서 선정한 2차원 모형으로는 유한요소법에 기반을 둔 RMA-2모형의 범용프로그램인 SMS 모형을 선정하였고, 1차원 수치해석을 통하여 선정된 하류단의 경계조건을 적용하여 2차원 수리특성 분석을 실시하였다. 분석결과 철거대상 취수보 15개에 대하여 보철거 후 수위에 대한 개선 효과는 금암보를 제외한 대부분의 보 철거 구간에서는 그리 크지 않은 것으로 나타난 반면, 보 철거에 따른 유속의 개선효과는 대부분 큰 것으로 나타났다.는 경우보다 낮게 나타나고 있다. On-Line 저류지의 경우 Off-Line 의 경우에 비해 수위, 유량이 저류지의 상류단에서 크게 나타났다. On-Line 저류지의 경우 Off-Line 의 경우에 비해 같은 값의 첨두홍수량을 저류하기 위해서 상대적으로 넓은 저류면적이 필요한 것으로 나타난다. 대등한 수위감소값의 홍수저감효과를 발휘하기 위해서 본 연구에서는 On-Line 저류지 면적은 Off-Line 저류지에 비 두배 이상이 필요한 것으로 보여졌다.들에 관한 정보는 종종 현장관측에서 조차 무시되는 경우가 많다. 이에 본 연구에서는 수질모형의 매개변수 중 특히 수리특성에 관련된 매개변수들이 수질에 미치는 영향을 파악하는 것을 목적으로 하고 있다. 이를 위해 적용된 수질모형은 QualKo를 사용하였으며, 대상 하천은 낙동강 본류 경남구간 시점 부근인 회천 합류 전부터 낙동강 본류 경남구간 종점 부근인 밀양강 합류 전까지의 경남 오염총량관리 기본계획 시 구축된 모형 매개변수를 바탕으로 분석을 수행하였다. 일차오차분석을 이용하여 수리매개변수와 수질매개변수의 수질항목별 상대적 기여도를 파악해 본 결과, 수리매개변수는 DO, BOD, 유기질소, 유기인 모든 항목에 일정 정도의 상대적 기여도를 가지고 있는 것을 알 수 있었다. 이로부터 수질 모형의 적용 시 수리 매개변수 또한 수질 매개변수의 추정 시와 같이 보다 세심한 주의를 기울여 추정할 필요가 있을 것으로 판단된다.변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간은 24개월이하의 중단기가 적합함을 발견하였다. 이상의 행태적 측