단위가축영양 : 다양한 유기산제들의 급여가 이유자돈의 성장과 영양소 소화율에 미치는 효과

주지환 ( Ji Whan Joo ),최재용 ( Jae Yong Choi ),최순천 ( Soon Chon Choi ),조원탁 ( Won Tak Cho ),채병조 ( Byung Jo Chae ),( Yu Xin Yang ) 한국동물자원과학회 2009 한국축산학회지 Vol.51 No.1

재무비율의 극단치에 대한 통계적 분석

주지환 ( Jihwan Joo ) 한국지식경영학회 2021 지식경영연구 Vol.22 No.2

투자자들은 기업가치를 평가하기 위하여 재무비율을 활용하는데 특히 PER과 PBR은 적정 기업가치를 판단하는데 중요한 역할을 하는 대표적인 수치로 알려져 있다. 금융자료는 꼬리가 매우 두터운 형태의 분포를 따르는 경우가 많은데, PER과 PBR은 첨도가 매우 높으며 해당 재무비율의 극단치들은 기업의 다양한 이해관계자들의 의사결정 시 중요한 역할을 한다. 본 논문에서는 통계학의 극단치이론에서 주로 활용되는 GPD와 최근 새롭게 제안된 분포인 exGPD를 도입하고, 두 분포 간의 성능을 비교하기 위해 시뮬레이션을 수행하여 적합도를 살펴본 후 우측 꼬리에 속하는 90, 95, 99% 퍼센타일 값을 추정하여 실제 값과 비교한다. 다음으로 국내 증권시장에 상장된 정보기술군(IT) 기업들의 PER, PBR 자료에 근거하여 실증분석을 수행한다. 분석 결과 특히 PBR에서 exGPD가 GPD에 비해 자료의 우측 꼬리 영역을 보다 효과적으로 설명함을 확인하였다. 따라서, 재무비율에 기반한 기업가치평가 또는 위험관리 시 극단치의 특성을 효과적으로 반영할 수 있는 exGPD와 같은 분포를 활용한다면 꼬리 영역에 담긴 정보를 보다 정확하게 파악할 수 있다. 이는 기업 내부 위험관리자의 효과적인 지식경영을 돕고, 투자자를 비롯하여 다양한 외부 이해관계자들에게 유용한 지식을 제공할 수 있다. Investors mainly use PER and PBR among financial ratios for valuation and investment decision-making. I conduct an analysis of two basic financial ratios from a statistical perspective. Financial ratios contain key accounting numbers which reflect firm fundamentals and are useful for valuation or risk analysis such as enterprise credit evaluation and default prediction. The distribution of financial data tends to be extremely heavy-tailed, and PER and PBR show exceedingly high level of kurtosis and their extreme cases often contain significant information on financial risk. In this respect, Extreme Value Theory is required to fit its right tail more precisely. I introduce not only GPD but exGPD. GPD is conventionally preferred model in Extreme Value Theory and exGPD is log-transformed distribution of GPD. exGPD has recently proposed as an alternative of GPD(Lee and Kim, 2019). First, I conduct a simulation for comparing performances of the two distributions using the goodness of fit measures and the estimation of 90-99% percentiles. I also conduct an empirical analysis of Information Technology firms in Korea. Finally, exGPD shows better performance especially for PBR, suggesting that exGPD could be an alternative for GPD for the analysis of financial ratios.

위드코로나 시대 글로벌 음식관광 마케팅을 위한 지역음식관광 디지털 전환 사례 연구

오문향(Oh, Mun-Hyang),주지환(Joo, Ji-Hwan) 한국외식경영학회 2021 외식경영연구 Vol.24 No.7

본 연구는 위드코로나 시대를 맞아, 글로벌 음식관광시장 재개에 선제적으로 대응하고 있는 전라남도 사례를 분석하였다. 전라남도는 국내 음식관광 선호도 1위 지역으로서, 글로벌 음식관광 마케팅을 위한 지역음식관광 디지털 전환을 주도하고 있다. 본 사례연구를 통해 첫째, 왜 음식관광은 디지털로 전환하는지, 둘째, 음식관광 디지털 전환의 대상은 무엇인지, 셋째, 음식관광 디지털 전환의 프로세스는 어떠한지, 넷째, 디지털 전환을 통해 음식관광 마케팅은 어떻게 변화할 것인지, 다섯째, 음식관광 디지털 전환의 최종 결과는 무엇인지를 살펴보았다. 연구 결과에 따르면, 코로나19 확산 이후 관광행동 변화, 관광기업 정책적 지원 니즈 증가에 따라 디지털 전환을 추진하게 된 것으로 나타났다. 데이터는 음식관광상품을 다루는 매장 정보, 음식관광상품 자체인 메뉴 정보, 품질 정보 등을 수집하고 다국어 번역, 메뉴속성 태깅 등 가공을 실시하였다. 수집된 데이터는 실제 스마트관광서비스로 구현하여 관광객들이 실시간 예약‧주문‧결제할 수 있도록 제공하였다. 음식관광상품 디지털 전환의 결과로 서비스 프로세스 개선, 서비스제공자와 관광객간 관계 변화와 가치 창조, 음식관광시장 성장, 관광객 경험 변화, 지역관광객 유입 증가를 기대할 수 있다. The purpose of this study is to analyze the digital transformation case of local food tourism products, which is proactively responding to the resumption of the global food tourism market in the era of ‘Living with COVID-19’. Jeollanam-do is the No. 1 region in food tourism preference in Korea and is leading the digital transformation of local food tourism products for global food tourism marketing. According to the results of the study, digital transformation was promoted mainly because of external pressure(after the spread of COVID-19), such as changes in tourism behavior and policy support needs of tourism companies. The objects of digital transformation were restaurant information, menu information, and quality information. The data was processed with multilingual translation and menu attribute tagging. The collected data was implemented as an actual smart food tourism service so that tourists could make reservations, orders, and payments in real-time. As a result of the digital transformation of food tourism products, it can be expected to improve the service process, change the relationship between service providers and tourists, create values, grow the food tourism market, improve the tourist experience, and increase the influx of local tourists.

기계학습을 이용한 수출신용보증 사고예측

조재영(Cho, Jaeyoung),주지환(Joo, Jihwan),한인구(Han, Ingoo) 한국지능정보시스템학회 2021 지능정보연구 Vol.27 No.1

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altmans Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accur

원주지역 남은 음식물의 계절별 성분 함량 및 비육돈에 대한 건조 남은 음식물 급여 효과

채병조,주지환,심영호,권일경,김상헌 한국동물자원과학회 2003 한국축산학회지 Vol.45 No.3

본 연구는 남은 음식물(FW)의 성분변이를 조사하고, 그것이 급여시 비육돈의 성장과 육질에 미치는 영향을 조사하기 위하여 실시하였다. 사료화를 위해 기초자료로 활용하고져 FW를 1년간 (월 6회) 수집하였다. 8주간의 사양시험을 위해 삼원교잡종(LxYxD) 비육돈 (54.80±4.60㎏) 117두를 공시하였다 (3처리 3반복, 반복당 13두). 처리는 대조구 (옥수수-대두박 위주사료), 건조 방법에 따른 단순 건조 (simple dry : SD) 및 진공발효 (vacuum fermentation: VF)로서 구분하였다. FW의 건물기준으로 에너지, 조단백질, 조지방, 화분, 칼슘 및 인의 4계절 평균치는 각각 5,11kcal/㎏, 22.92%, 14.31%, 15.48%, 2.7% 및 1.05%였다. 에너지와 단백질 함량은 각각 겨울과 여름에 가장 높았다 (p<0.05). 건조한 FW의 유산균 함량은 SD와 VF 처리간에 차이가 없었다. 사양성적에서는 ADG에서 대조구가 FW이 첨가된 사료에 비해 유의적으로 높았으나 (p<0.05), FCR에서는 처리간에 차이가 없었다. SD와 FD와의 생산성 비교에서는 유의차가 인정되지 않았다. 지육율, 등지방두께, 그리고 기타육질(색깔, drip loss 및 TBARS)에서 처리간에 차이가 없었다. 전 시험기간의 증체 ㎏당 사료비를 살펴보면, FW가 포함된 사료급여군이 대조구에 비해 낮았다. 이상의 결과를 요약해 보면, 남은 음식물을 건조한 후 사료에 20% 이내를 첨가하여 펠렛사료로 급여할 경우 비육돈의 사료비를 절감할 수 있을 것으로 사료된다. 그러나 FW의 가공시 FD공정은 필요치 않을 것으로 사료된다. A study was conducted to evaluate seasonal variations in chemical composition of food waste (FW) and its feeding effects on growth performance and pork quality in finishing pigs. FW was collected for 1 year ( 6 times a month ) to establish a database for use of FW as a feed ingredient. For a feeding trial (8 weeks), a total of 117 pigs (L×Y×D; 54.80±4.60㎏) were used to evaluate the processing effects of FW. Treatments were: Control (a corn-soybean meal diet without FW), simple dried FW (SD) and vacuum fermented FW (VF). The gross energy, crude protein, crude fat, ash, calcium and phosphorus in FW (DM, average of 4 seasons) were 5,111㎉/㎏, 22.92%, 14.31%, 15.48%, 2.7% and 1.05%, respectively. Among seasons, the energy and crude protein contents were the highest (p<0.05) in winter and summer, respectively. In lactic acid bacterial counts, there was no difference between SD and VF. Pigs fed the control diet grew faster (p<0.05) than those fed diets containing food waster, but not feed conversion ratio. There were no differences in production traits between SD and VF. No differences were also found in dressing percentage, backfat thickness, and pork quality (color, drip loss and TBARS) among treatments. The feed cost (￦/㎏ body weight) was lower in pigs fed FW than those fed a control diet. In conclusion, a pelleted diet containing food waste less than 20% would reduce feed cost in finishing pigs. However, it seems that a vacuum fermentation of food waste is not necessary for diet processing.