• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.189-207
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• 2020

The government has achieved quantitative growth in university start-up education while supporting start-up education. However, it failed to systematize start-up education from an academic, policy, and practical perspective and to reveal the relationship between education and achievements in supporting start-ups. Therefore, there is a lack of interest and effort to promote effective education. In Europe, in-depth research has already been done over many years to establish an EntreComp system. Competences create values for others and attempt to apply them to education, viewing them as the people's lifelong competitiveness. On the other hand, it is urgent to improve the education system as domestic university start-up education is mainly focused on cultural level start-up skills and easy-to-access education from a business administration perspective. Based on this, the entrepreneurship competence-based start-up education system was designed. Next, eight EntreComp frames were drawn for university students through the Focus Group Interview (FGI) and Delphi survey methods, as well as domestic and international prior studies on EntreComp. In 2018, 919 start-up education programs of 42 start-up leading universities were conducted to derive the status of education by EntreComp. Prior studies of 25 entrepreneurship competences, including data from Bacigalupo et al.(2016), which studied EntreComp in the EU, were investigated and reflected the frequency of research and the importance of education and start-up perspectives. Based on the purpose of the university start-up education presented in this study, the entrepreneurship competence frame consisting of a total of eight, including spotting opportunities, value creation, self improvement, mobilising resources, technology application, strategic management, relationship, and learning through experience, was derived through expert verification. It also investigated the current status of education by competence, the degree of reflection of competence education, and the relationship with the results of support for start-ups that reflect the number of students enrolled in each university. Through this, it was suggested that future start-up education at universities could be improved from the EntreComp perspective. It has a differentiation in research in that it conducted a thorough survey using the data on start-up courses operated by leading startup universities for a certain period. However, it is difficult to generalize because the number of samples of leading startup universities is limited. Nevertheless, this study proposes the educational goal of advancing university start-up education from the perspective of entrepreneurial competence, cultivating future required competences, and fostering entrepreneurial talents that create value for others. In addition, it is meaningful in that it presents a clear direction for subsequent research by preparing a framework for research from a more essential perspective on the entrepreneurship competence frame.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• The Korean Journal of Financial Management
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• v.8 no.1
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• pp.179-197
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• 1991

선도환의 가격을 결정하는 접근방법에는 2차자산(derivative assets)이라는 선도계약의 기본특성에 기초한 재정거래(arbitrage)에 의한 방법이 가장 많이 이용되고 있다. 재정거래방식에는 선도환과 현물외환가격간의 상호관련성에 의하여 선도환가격을 이자율평가설(covered interest rate parity : CIRP), 즉 현물가격과 양국간의 이자율차이의 합으로 표시하고 있다. 특히 현물가격과 이자율은 모두 현재시점에서 의사결정자에게 알려져 있기때문에 선도환가격은 확실성하에서 결정되어 미래에 대한 예측이나 투자자의 위험회피도와는 관계없이 결정된다는 것이 특징이다. 이자율평가설에 관한 많은 실증연구는 거래 비용을 고려한 경우 현실적으로 적절하다고 보고 있다(Frenkel and Levich ; 1975, 1977). 다른 방법으로는 선도환의 미래예측기능에만 촛점을 맞추어 가격결정을 하는 투기, 예측접근방법(speculative efficiency approach : 이하에서는 SEA라 함)이 있다. 이 방법 중에서 가장 단순한 형태로 표시된 가설, 즉 '선도환가격은 미래기대현물가격과 같다'는 가설은 대부분의 실증분석에서 기각되고 있다. 이에 따라 SEA에서는 선도환가격이 미래에 대한 기대치뿐만 아니라 위험프리미엄까지 함께 포함하고 있다는 새로운 가설을 설정하고 이에 대한 실증분석을 진행한다. 이 가설은 이론적 모형에서 출발한 것이 아니기 때문에, 특히 기대치와 위험프레미엄 모두가 측정 불가능하다는 점으로 인하여 실증분석상 많은 어려움을 겪게 된다. 이러한 어려움을 피하기 위하여 많은 연구에서는 이자율평가설을 이용하여 선도환가격에 포함된 위험프레미엄에 대해 추론 내지 그 행태를 설명하려고 한다. 이자율평가설을 이용하여 분석모형을 설정하고 실증분석을 하는 것은 몇가지 근본적인 문제점을 내포하고 있다. 먼저, 앞서 지적한 바와 같이 이자율평가설을 가정한다는 것은 SEA에서 주된 관심이 되는 미래예측이나 위험프레미엄과는 관계없이 선도가격이 결정 된다는 것을 의미한다. 따라서 이자율평가설을 가정하여 설정된 분석모형은 선도환시장의 효율성이나 균형가격결정에 대한 시사점을 제공할 수 없다는 것을 의미한다. 즉, 가정한 시장효율성을 실증분석을 통하여 다시 검증하려는 것과 같다. 이러한 개념적 차원에서의 문제점 이외에도 실증분석에서의 추정상의 문제점 또한 존재한다. 대부분의 연구들이 현물자산의 균형가격결정모형에 이자율평가설을 추가로 결합하기 때문에 이러한 방법으로 설정한 분석모형은 그 기초가 되는 현물가격모형과는 달리 자의적 조작이 가능한 형태로 나타나며 이를 이용한 모수의 추정은 불필요한 편기(bias)를 가지게 된다. 본 연구에서는 이러한 실증분석상의 편기에 관한 문제점이 명확하고 구체적으로 나타나는 Mark(1985)의 실증연구를 재분석하고 실증자료를 통하여 위험회피도의 추정치에 편기가 발생하는 근본원인이 이자율평가설을 부적절하게 사용하는데 있다는 것을 확인 하고자 한다. 실증분석결과는 본문의 <표 1>에 제시되어 있으며 그 내용을 간략하게 요약하면 다음과 같다. (A) 실증분석모형 : 본 연구에서는 다기간 자산가격결정모형중에서 대표적인 Lucas (1978)모형을 직접 사용한다. $$1={\beta}\;E_t[\frac{U'(C_{t+1})\;P_t\;s_{t+1}}{U'(C_t)\;P_{t+1}\;s_t}]$$ (2) $U'(c_t)$와 $P_t$는 t시점에서의 소비에 대한 한계효용과 소비재의 가격을, $s_t$와 $f_t$는 외환의 현물과 선도가격을, $E_t$와 ${\beta}$는 조건부 기대치와 시간할인계수를 나타낸다. Mark는 위의 식 (2)를 이자율평가설과 결합한 다음의 모형 (4)를 사용한다. $$0=E_t[\frac{U'(C_{t+1})\;P_t\;(s_{t+1}-f_t)}{U'(C_t)\;P_{t+1}\;s_t}]$$ (4) (B) 실증분석의 결과 위험회피계수 ${\gamma}$의 추정치 : Mark의 경우에는 ${\gamma}$의 추정치의 값이 0에서 50.38까지 매우 큰 폭의 변화를 보이고 있다. 특히 비내구성제품의 소비량과 선도프레미엄을 사용한 경우 ${\gamma}$의 추정치의 값은 17.51로 비정상적으로 높게 나타난다. 반면에 본 연구에서는 추정치가 1.3으로 주식시장자료를 사용한 다른 연구결과와 비슷한 수준이다. ${\gamma}$추정치의 정확도 : Mark에서는 추정치의 표준오차가 최소 15.65에서 최대 42.43으로 매우 높은 반면 본 연구에서는 0.3에서 0.5수준으로 상대적으로 매우 정확한 추정 결과를 보여주고 있다. 모형의 정확도 : 모형 (4)에 대한 적합도 검증은 시용된 도구변수(instrumental variables)의 종류에 따라 크게 차이가 난다. 시차변수(lagged variables)를 사용하지 않고 현재소비와 선도프레미엄만을 사용할 경우 모형 (4)는 2.8% 또는 2.3% 유의수준에서 기각되는 반면 모형 (2)는 5% 유의수준에서 기각되지 않는다. 위와같은 실증분석의 결과는 앞서 논의한 바와 같이 이자율평가설을 사용하여 균형자산가격 결정모형을 변형시킴으로써 불필요한 편기를 발생시킨다는 것을 명확하게 보여주는 것이다.

• Journal of Nutrition and Health
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• v.9 no.1
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• pp.25-42
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• 1976

In order to determine nutrition education needs and related problems, a study was conducted of children's dietary habits, focusing on parental influence and degree of agreement between parent and child on foods liked, accepted, or disliked, in addition to a general survey of food atiitudes. This study was conducted throughout a two-month period, June to July of 1974. One thousand children of both sexes, from the fifth grade, junior and senior high schools of Seoul city, and their 2,000 matched parents, were surveyed, Teachers distributed questionnaires in the classroom and assisted the children in answering. Questionnaires also were distributed to the parents through their children, after the teachers explained the procedure of study. As to the influence of parents' food preferences, the following conclusiolns can be reached, in light of the results of chi-square tests conducted: 1. Agreement between mother and child on food preference was much higher than that between father and child, regardless of sex or birth order of the child. This observed difference in degree of agreement was greatest for children in the middle birth order, and greater for girls than for boys. 2. Various food attitudes: a. Food preferences: Beef, milk, and mandoo (boiled or steamed, filled dumplings) were extremely well liked by all subjects, regardless of age or sex. Cucumber, lettuce, and spinach also were lied. Most disliked foods were fatty layers of pork and liver. Cooked rice in the too wet or too dry state and pork were low preference items. b. Socioeconomic background and dietary practice: Higher educational background of the wife and higher income level of the family were associated with greater knowledge of nutrition, and interest in family nutrition and in introducing new foods to the family. But use of food as prize or punishment was found, regardless of the mother's educational and economic status. c. Change of food habit: Over 70 percent of subject had changed ad improved their dietary habits, mostly by reason of husbands' and wives' mutual influence after marriage. This study emphasized the great importance of nutrition eudation for mothers, and their prominent role and responsility in guiding the family to better nutrition, whatever the mother's educational background.

• Journal of the Korean Society of Food Culture
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• v.6 no.4
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• pp.421-432
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• 1991

This study was conducted to investigate the intake of a kind of Gom-Tang (Beef soup made with internal organs and bone), which is the Korean traditional food, and factors affecting the eating behavior of customers. Two hundred male customers of a H Korean Restaurant specialized in Gom-Tang, which is a well-known restaurant in Seoul, were surveyed from June 26 to 29, 1990. The results were shown as follows. (1) ${\ulcorner}$Frequency of intake${\lrcorner}$ and ${\ulcorner}$preference${\lrcorner}$ were very high. Those who took a kind of Gom-Tang ${\ulcorner}$more than once a week${\lrcorner}$ were 66.5% of the subjects. Those who evaluated ${\ulcorner}$good${\lrcorner}$ for the taste and flavor were 86.5% and 59.0% of the subjects, respectively. (2) The reasons why they chose a kind of Gom-Tang from among many Korean traditional foods were ${\ulcorner}$preference${\lrcorner}$ and ${\ulcorner}$phygiological condition${\lrcorner}$ in ${\ulcorner}$high frequency of intake${\lrcorner}$ group. ${\ulcorner}$Phygiological condition${\lrcorner}$ was more critical factor than ${\ulcorner}$preference${\lrcorner}$ in ${\ulcorner}$low frequency of intake${\lrcorner}$ group. (3) The effect of the intake of a kind of Gom-Tang on health was evaluated as ${\ulcorner}$healthy${\lrcorner}$ (80.5%). ${\ulcorner}$No effect${\lrcorner}$ and ${\ulcorner}$harmful${\lrcorner}$ were 30.5% and 6.5%, respectively. (4) ${\ulcorner}$High frequency of intake${\lrcorner}$ group, mainly more than 50 years of age, had a high ${\ulcorner}$preference${\lrcorner}$ and ${\ulcorner}$food knowledge${\lrcorner}$ as well as positive ${\ulcorner}$eating behavior${\lrcorner}$ and ${\ulcorner}$healthy state${\lrcorner}$, ${\ulcorner}$Middle frequency of intake${\lrcorner}$ group, mainly the forties, had a high ${\ulcorner}$preference${\lrcorner}$, but had less positive ${\ulcorner}$eating behavior${\lrcorner}$ than ${\ulcorner}$high frequency of intake${\lrcorner}$ group. ${\ulcorner}$Low frequency of intake${\lrcorner}$ group, mainly the twenties and thirties, had a medial ${\ulcorner}$preference${\lrcorner}$. They took a kind of Gom-Tang for reasons of ${\ulcorner}$on the recommendation of friends${\lrcorner}$ better than ${\ulcorner}$preference${\lrcorner}$. Foregoing results showed that ${\ulcorner}$a kind of Gom-Tang${\lrcorner}$ was a typical food recognized as ${\ulcorner}$healthy${\lrcorner}$ as well as ${\ulcorner}$delicious${\lrcorner}$. This may suggest that ${\ulcorner}$a kind of Gom-Tang${\lrcorner}$ is a candidate for the effective food on nutritional education.

• Asia Marketing Journal
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• v.14 no.1
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• pp.83-98
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• 2012

In a market where new and used cars are competing with each other, we would run the risk of obtaining biased estimates of cross elasticity between them if we focus on only new cars or on only used cars. Unfortunately, most of previous studies on the automobile industry have focused on only new car models without taking into account the effect of used cars' pricing policy on new cars' market shares and vice versa, resulting in inadequate prediction of reactive pricing in response to competitors' rebate or price discount. However, there are some exceptions. Purohit (1992) and Sullivan (1990) looked into both new and used car markets at the same time to examine the effect of new car model launching on the used car prices. But their studies have some limitations in that they employed the average used car prices reported in NADA Used Car Guide instead of actual transaction prices. Some of the conflicting results may be due to this problem in the data. Park (1998) recognized this problem and used the actual prices in his study. His work is notable in that he investigated the qualitative effect of new car model launching on the pricing policy of the used car in terms of reinforcement of brand equity. The current work also used the actual price like Park (1998) but the quantitative aspect of competitive price promotion between new and used cars of the same model was explored. In this study, I develop a model that assumes that the cross elasticity between new and used cars of the same model is higher than those amongst new cars and used cars of the different model. Specifically, I apply the nested logit model that assumes the car model choice at the first stage and the choice between new and used cars at the second stage. This proposed model is compared to the IIA (Independence of Irrelevant Alternatives) model that assumes that there is no decision hierarchy but that new and used cars of the different model are all substitutable at the first stage. The data for this study are drawn from Power Information Network (PIN), an affiliate of J.D. Power and Associates. PIN collects sales transaction data from a sample of dealerships in the major metropolitan areas in the U.S. These are retail transactions, i.e., sales or leases to final consumers, excluding fleet sales and including both new car and used car sales. Each observation in the PIN database contains the transaction date, the manufacturer, model year, make, model, trim and other car information, the transaction price, consumer rebates, the interest rate, term, amount financed (when the vehicle is financed or leased), etc. I used data for the compact cars sold during the period January 2009- June 2009. The new and used cars of the top nine selling models are included in the study: Mazda 3, Honda Civic, Chevrolet Cobalt, Toyota Corolla, Hyundai Elantra, Ford Focus, Volkswagen Jetta, Nissan Sentra, and Kia Spectra. These models in the study accounted for 87% of category unit sales. Empirical application of the nested logit model showed that the proposed model outperformed the IIA (Independence of Irrelevant Alternatives) model in both calibration and holdout samples. The other comparison model that assumes choice between new and used cars at the first stage and car model choice at the second stage turned out to be mis-specfied since the dissimilarity parameter (i.e., inclusive or categroy value parameter) was estimated to be greater than 1. Post hoc analysis based on estimated parameters was conducted employing the modified Lanczo's iterative method. This method is intuitively appealing. For example, suppose a new car offers a certain amount of rebate and gains market share at first. In response to this rebate, a used car of the same model keeps decreasing price until it regains the lost market share to maintain the status quo. The new car settle down to a lowered market share due to the used car's reaction. The method enables us to find the amount of price discount to main the status quo and equilibrium market shares of the new and used cars. In the first simulation, I used Jetta as a focal brand to see how its new and used cars set prices, rebates or APR interactively assuming that reactive cars respond to price promotion to maintain the status quo. The simulation results showed that the IIA model underestimates cross elasticities, resulting in suggesting less aggressive used car price discount in response to new cars' rebate than the proposed nested logit model. In the second simulation, I used Elantra to reconfirm the result for Jetta and came to the same conclusion. In the third simulation, I had Corolla offer $1,000 rebate to see what could be the best response for Elantra's new and used cars. Interestingly, Elantra's used car could maintain the status quo by offering lower price discount ($160) than the new car ($205). In the future research, we might want to explore the plausibility of the alternative nested logit model. For example, the NUB model that assumes choice between new and used cars at the first stage and brand choice at the second stage could be a possibility even though it was rejected in the current study because of mis-specification (A dissimilarity parameter turned out to be higher than 1). The NUB model may have been rejected due to true mis-specification or data structure transmitted from a typical car dealership. In a typical car dealership, both new and used cars of the same model are displayed. Because of this fact, the BNU model that assumes brand choice at the first stage and choice between new and used cars at the second stage may have been favored in the current study since customers first choose a dealership (brand) then choose between new and used cars given this market environment. However, suppose there are dealerships that carry both new and used cars of various models, then the NUB model might fit the data as well as the BNU model. Which model is a better description of the data is an empirical question. In addition, it would be interesting to test a probabilistic mixture model of the BNU and NUB on a new data set.