• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.287-295
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• 2017

Since Google's AlphaGo defeated a world champion of Go players in 2016, there have been many interests in the deep learning. In the financial sector, a Robo-Advisor using deep learning gains a significant attention, which builds and manages portfolios of financial instruments for investors.In this paper, we have proposed the a deep learning algorithm geared toward identification and forecast of the KOSPI index direction,and we also have compared the accuracy of the prediction.In an application of forecasting the financial market index direction, we have shown that the Robo-Advisor using deep learning has a significant effect on finance industry. The Robo-Advisor collects a massive data such as earnings statements, news reports and regulatory filings, analyzes those and recommends investors how to view market trends and identify the best time to purchase financial assets. On the other hand, the Robo-Advisor allows businesses to learn more about their customers, develop better marketing strategies, increase sales and decrease costs.

• Journal of the East Asian Society of Dietary Life
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• v.20 no.5
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• pp.823-835
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• 2010

The research aimed to measure the efficiency of using multi stores in a foodservice company using by DEA (data envelopment analysis) which is a new management science technique. The study also attempted to identify relevant variables affecting DEA efficiency in order to suggest methods for improving efficiency. The data were collected from 148 contract foodservice operations, which were operated in similar fashion in October 2009. The DEA efficiency was calculated as an output-oriented BCC Model. Sales, and CSI (customer satisfaction index) were used as output variables whereas food cost, labor cost, and management expense were used as input variables to calculate the DEA efficiency. Operation process variables of the unit consisted of the were consist of ratio of regular employee, ratio of housekeeper, meal counts, meal price, food cost per meal, contract period, number of menu items, forecasting accuracy, order accuracy, inventory turnover, use of processed food, deviation of food cost, number of new menus, and number of events. According to the BCC score and profitability, units were classified into four groups: High efficiency-high profitability (HEHP), High efficiency-low profitability (HELP), Low efficiency-high profitability (LEHP), and Low efficiency-low profitability (LELP). The HEHP group contained 54 units, which mostly contracted management fee type and had a high meal price. The units were also very large and, served three meals. Twenty of the units were operated with high labor cost: most of these were factories and hospitals. The LEHP group contained 20 units, that were mainly office stores of large scale and medium price. Fifty-four LELP group had a low meal price. A high performance group must have high efficiency, profitability, and satisfaction. The BCC score was over 0.969, the meal price was over 4,116 won, the food cost was over 2,077 won, and meal counts per month were over 10,212 meals.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.191-196
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• 2015

There have been many studies on statistical forecasting on firm's performance and stock price by applying various financial indicators such as debt ratio and sales growth rate. Selecting predictors for constructing a prediction model among the various financial indicators is very important for precise prediction. Most of the previous studies applied variable selection algorithms for selecting predictors. However, the variable selection algorithm is considered to be at risk of eliminating certain amount of information from the indicators that were excluded from model construction. Therefore, we propose a firm's performance prediction model which principal component analysis is applied instead of the variable selection algorithm, in order to reduce dimensionality of input variables of the prediction model. In this study, we constructed the proposed prediction model by using financial data of American IT companies to empirically analyze prediction performance of the model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1234-1243
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• 2021

This study intends to provide a forecast of the diffusion of countries participating in a newly proposed G to G mechanism named as the "International Defense Industry Cooperation Program of Korea", modeled after the U.S. Foreign Military Sales(FMS). For this purpose, the study analyses 40 years of statistical data of U.S. FMS customers to find two parameters, coefficient of innovation and imitation, which explain the diffusion in FMS customers. Furthermore, the study forecasts the diffusion in international participation to the proposed mechanism taking account of the differences in the level of government competitiveness and the strength of defense industrial base of Korea and the U.S. This study also provides recommendations for accelerating the desired outcomes under the new program. While Korea is likely to have relative advantages over 'imitators' in the international market, it will need to gain competitiveness in high-level capabilities going beyond the realm of medium-high level systems, and present attractive alternatives for offsets.

• The Journal of Bigdata
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• v.5 no.2
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• pp.111-120
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• 2020

Despite the recent economic contraction caused by the Corona 19 incident, interest in the residential environment is growing as more people live at home due to the increase in telecommuting, thereby increasing demand for remodeling. In addition, the government's real estate policy is also expected to have a visible impact on the sales of the interior and furniture industries as it shifts from regulatory policy to the expansion of housing supply. Accurate demand forecasting is a problem directly related to inventory management, and a good demand forecast can reduce logistics and inventory costs due to overproduction by eliminating the need to have unnecessary inventory. However, it is a difficult problem to predict accurate demand because external factors such as constantly changing economic trends, market trends, and social issues must be taken into account. In this study, LSTM model and 1D-CNN model were compared and analyzed by artificial intelligence-based time series analysis method to produce reliable results for manufacturers producing furniture components.

• Korean Journal of Construction Engineering and Management
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• v.17 no.2
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• pp.21-30
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• 2016

The current PF project is entirely relying on construction company's credibility. As such, it has increased a negative and bad recognition in domestic real estate economy. In addition, PF experts has a perception that a project's safety of future cash flow profitability is more important than the construction company's credibility. So many PF experts make an effort in order to set aside safe project structure of PF and analyse systematically the risks of the project. In common feasibility study of the PF Project, financial specialists and real estate specialists are forecasting and evaluating the suitability of the project through reviewing the development profit from the project of sales. However, cash flow analysis and evaluation from the perspective of mid-sized construction companies are still in the primary level. Therefore, this study has analysed the current feasibility study and go/no go decision making procedures. Then the authors have a new cash flow analysis method from the perspective of mid-sized construction companies, by improving the feasibility study and go/no go decision making procedures.

• Journal of Distribution Research
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• v.14 no.1
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• pp.45-65
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• 2009

As a number of people using the internet for their shopping steadily rises, it is increasingly important for retailers to understand why consumers decide to buy products via online or offline. The main purpose of this study is to develop and test a model that enhance our understanding of how consumers respond future online and offline channels for their purchasing. Rather than merely adopting statistical models like most other studies in this field, the present study develops a model that combines double-cohort method with multinomial logit model. It is desirable if one can adopt an overall encompassing criterion in the study of consumer behaviors form diverse sales channels. This study uses the concept of cohort or aging to enable this comparison. It enables us to analyze how consumers respond to online and offline channels as people aged by measuring their shopping behavior for an online and offline retailers and their subsequent purchase intentions. Based on some empirical findings, this study concludes with policy implications and some necessary fields of future studies desirable.

• Korean Journal of Construction Engineering and Management
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• v.18 no.4
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• pp.3-16
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• 2017

Forecasting cash flow is very important but is difficult and complicated to analysis in high-rise development projects. And An expected value which was forecasted on the early stage is likely to fluctuate due to uncertainties around such complicated huge project to consider the probable uncertainty. There are not objectified method which are able to cope with uncertainty of project, and feasibility study based on selected financial analysis does not include liquidity of cash flow. Through such a stochastic method, developer can cope with cash flow fluctuation and set up a financial plan. Also this study is meaningful for laying the foundation for high-rise development project and feasibility study as well as the suitability and accuracy of feasibility study. Analysis showed that NPV and IRR include residential apartments shows surplus revenue as return of apartments offset deficit of hotel and office. Factors influencing the project feasibility for high-rise development project are sales account of $1^{st}$ year and annual vacancy rate of office.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.133-143
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• 2017

Data is the most important asset in the financial sector. On average, 71 percent of financial institutions generate competitive advantage over data analysis. In particular, in the card industry, the card transaction data is widely used in the development of merchant information, economic fluctuations, and information services by analyzing patterns of consumer behavior and preference trends of all customers. However, creation of new value through fusion of data is insufficient. This study introduces the analysis and forecasting of consumption trends of credit card companies which convergently analyzed the social data and the sales data of the company's own. BC Card developed an algorithm for linking card and social data with trend profiling, and developed a visualization system for analysis contents. In order to verify the performance, BC card analyzed the trends related to 'Six Pocket' and conducted th pilot marketing campaign. As a result, they increased marketing multiplier by 40~100%. This study has implications for creating a methodology and case for analyzing the convergence of structured and unstructured data analysis that have been done separately in the past. This will provide useful implications for future trends not only in card industry but also in other industries.

• The Journal of Natural Sciences
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• v.10 no.1
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• pp.199-208
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• 1998

This study forecasts the trend of ethnic theme through market survey, concentrating on Los Angeles market. First, the background of ethnic theme was examined, and the present situation of shops, department sores, and headquarter was also surveyed. After that, fashion trend suitable for market was suggested by analyzing the life style of consumers through zip code. The results of the study are as follows. The conspicuous trend of '97 F/W retail stores is ethnic. This reaction to complicated modern life, and symbolizes the desirable evaluation on the simpleness of basic life and nature. The model of ethnic design is identified in natural clothing, primitive arts, ethnic culture and African theme. In short, this ethnic fashion is expressed as simpleness, naturalism convenience and freedom. On the other hand, the standard of general department stores such as Broadway and Robinson May which are the headquarter of this trend is to satisfy various consumers with various styles. Ethnic goods from Broadway has not arrived at the top for its introducing step. To elevate sales of these goods, promotion through VMD and suggesting various ethnic goods should be done. Besides, when analyzing the consumers of Beverly center Broadway, the target of these goods are mostly professional young people in their 25-34 and 35-44. The life style of these people emphasizes sophisticated life in aspects such as job-oriented activities, and up-to-date fashion. Especially, image is very important. They want individuality different from others. These images are diversified from simpleness, naiveness to sexy character. Accordingly, suggesting fashion trend satisfying the demand of consumers through market survey will make fashion market create infinite possibilities.