• International Area Studies Review
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• 제14권3호
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• pp.62-84
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• 2010

Accurate forecasting of volatility is of considerable interest in financial volatility research, particularly in regard to portfolio allocation, option pricing and risk management because volatility is equal to market risk. So, we attempted to delineate a model with good ability to forecast and identified stylized features of volatility, with a focus on volatility persistence or long memory in the Australian futures market. In this context, we assessed the long-memory property in the volatility of index futures contracts using three conditional volatility models, namely the GARCH, IGARCH and FIGARCH models. We found that the FIGARCH model better captures the long-memory property than do the GARCH and IGARCH models. Additionally, we found that the FIGARCH model provides superior performance in one-day-ahead volatility forecasts. As discussed in this paper, the FIGARCH model should prove a useful technique in forecasting the long-memory volatility in the Australian index futures market.

• Journal of Convergence for Information Technology
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• 제11권2호
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• pp.82-89
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• 2021

The objective of this study is to propose a new Glide Path that dynamically adjusts the risky asset inclusion ratio of the Target Date Fund by simultaneously considering the market's forecast volatility as well as the time of investor retirement, and to compare the investment performance with the traditional Target Date Fund. Forecasts of market volatility utilize historical volatility, time series model GARCH volatility, and the volatility index VKOSPI. The investment performance of the new dynamic Glide Path, which considers stock market volatility has been shown to be excellent during the analysis period from 2003 to 2020. In all three volatility prediction models, Sharpe Ratio, an investment performance indicator, is improved with higher returns and lower risks than traditional static Glide Path, which considers only retirement date. The empirical results of this study present the potential for the utilization of the suggested Glide Path in the Target Date Fund management industry as well as retirees.

• Journal of the Korean Solar Energy Society
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• 제36권3호
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• pp.75-85
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• 2016

This research aims to predict and analyze green building certification market of Korean Peninsula after unification. First, it analyzes prospected unification time period, then it forecasts number of new residential and non-residential buildings to be constructed based on estimated number of residences in short at the time in North Korea. There exists a good chance that North Korea's new building market forms similar to that of South Korea, as unification would thoroughly proceed which would result levels of economic culture social politics in quasi-equal state. Thus, assuming the ratio of residential and non-residential building against population is similar in both Korea's, the number against North Korea's house supplied population can be estimated. Based on the expected numbers in North Korea, number of proceeded Building Energy Conservation Plan, Building Energy Rating Certification, and Green Standard for Energy and Environmental Design (G-SEED) are predicted. The research shows certification market related to green building in united Korean Peninsula to be \660 billion over 10 years. Not only certifications to newly built buildings but also including existing buildings, this market is to grow to a considerable extent. As this would largely influence eco-constructive materials, energy plant/equipment, and other relevant markets as well, it would require to make thorough preparations. In sum, to stabilize green building market even before the unification, the research proposes the necessities of appropriate systems in consideration of North Korea, through in-depth discussions and establishment of technology and policy directions in green building sector, such as building energy management and emission reduction technology.

• The Transactions of the Korea Information Processing Society
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• 제13권5호
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• pp.209-216
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• 2024

Real estate market prices are determined by various factors, including macroeconomic variables, as well as the influence of a variety of unstructured text data such as news articles and social media. News articles are a crucial factor in predicting real estate transaction prices as they reflect the economic sentiment of the public. This study utilizes sentiment analysis on news articles to generate a News Sentiment Index score, which is then seamlessly integrated into a real estate price prediction model. To calculate the sentiment index, the content of the articles is first summarized. Then, using AI, the summaries are categorized into positive, negative, and neutral sentiments, and a total score is calculated. This score is then applied to the real estate price prediction model. The models used for real estate price prediction include the Multi-head attention LSTM model and the Vector Auto Regression model. The LSTM prediction model, without applying the News Sentiment Index (NSI), showed Root Mean Square Error (RMSE) values of 0.60, 0.872, and 1.117 for the 1-month, 2-month, and 3-month forecasts, respectively. With the NSI applied, the RMSE values were reduced to 0.40, 0.724, and 1.03 for the same forecast periods. Similarly, the VAR prediction model without the NSI showed RMSE values of 1.6484, 0.6254, and 0.9220 for the 1-month, 2-month, and 3-month forecasts, respectively, while applying the NSI led to RMSE values of 1.1315, 0.3413, and 1.6227 for these periods. These results demonstrate the effectiveness of the proposed model in predicting apartment transaction price index and its ability to forecast real estate market price fluctuations that reflect socio-economic trends.

• The Journal of Natural Sciences
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• 제10권1호
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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.

• Management & Information Systems Review
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• 제32권1호
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• pp.63-85
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• 2013

This study tries to investigate the fundamental implications inherent in inventory asset information(specifically, unexpected inventory investment) by analyzing how the relationship between unexpected inventory investment and future operating performance. And we study how is the response of the stock market participants to the fundamental implications inherent in inventory asset information. Prior papers often assume the efficient market and they view the significant relation between stock prices and financial indicators as evidence of the contribution of such indicators to future earnings. Leading indicators are attracting the market's attention for equity valuation. We study whether one leading indicator (unexpected Inventories) forecasts future earnings, and whether market participants fully reflect the predictive ability when they sets share prices(Mishkin test, 1983). Our empirical results of the study are summarized as follows. Current unexpected inventory investment is negatively associated with future operating performance. Also, our evidence is that the stock market participants overprice the contribution of unexpected inventory investment when predicting future earnings. Furthermore, a hedge strategy that uses the overpricing gives significant future abnormal returns. The overall results help the users of financial reports, researchers of accounting, and the accounting principle setting body.

• International Commerce and Information Review
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• 제16권5호
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• pp.27-47
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• 2014

Gartner mentioned IoT (Internet of Things) as one of the ten growth engines in the world in 2014. In addition, IoT has been frequently invited as a theme by participants at CES held in the early this year. Gartner predicts that the number of devices connected will reach to 26 billion and a total of economic value added will grow to 1.9 trillion dollars until 2020. The definition of IoT has been arranged in a different way by each institution. Therefore, this study arranged the concept of IoT which has been variously defined and used together with M2M and IoE. Further, forecasts presented by research institutions which stated domestic and foreign market outlook were collected and arranged. As IoT is on the uptrend and is selected as one of the ten growth engines in the world, it was found out how policies on IoT in the US, China, Japan and Korea were established and promoted as well as how recent strategies on IoT of major large companies have been carried out. Lastly, the following countermeasures of our government and companies were discussed.

• STI Policy Review
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• 제1권1호
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• pp.1-21
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• 2010

To achieve the "low carbon green growth" vision, the first step is securing core technologies. Therefore, S&T policy direction for green technology development is urgently needed. As of 2008, investment in green technology (GT) development hovered around 10% of the government's total R&D budget. Thus, the Korean government developed a plan to increase that percentage to 15%, by 2013. To develop reasonable investment strategies for green technology development, targeted strategies that reflect technology and market changes by green technology area are needed. However, the overall planning and coordination of national GT development is currently split among, approximately, 10 government ministries. To establish an efficient green technology development system, the so-called "Green Technology R&D Council" should be launched in collaboration with the Presidential Committee on Green Growth and the National Science and Technology Council. Furthermore, to build a solid foundation for commercializing the outcomes of GT development projects and promote GT transfer, the government should undertake two initiatives. First, the government should reinforce GT R&D performance management, by establishing a GT R&D performance management and evaluation system. Second, the government should implement the "customized packaged support for promoting green technology business rights and commercialization" and present "e-marketplace for market-oriented green technologies". Creating a pan-ministerial policy for GT development policy would necessitate restructuring the HR(Human Resources) development system, which is currently separated by technology area. Based upon mid/long-term HR supply and demand forecasts, the government should design differentiated HR development projects, continuously evaluate those projects, and reflect the evaluation results in future policy development. Finally, to create new GT-related industries, the "Green TCS (Testing, Certification, and Standards) System" needs to be implemented. For objective evaluation and diffusion of R&D results by green technology area, a common standardization plan for testing, analysis, and measurement, like the "Green TCS", should be developed and integrated.

• Journal of Korea Port Economic Association
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• 제27권4호
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• pp.1-11
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• 2011

In much the same way as the US Lehman crisis of 2008-2009 severely impacted the European economy through financial market dislocation, a European banking crisis would materially impact the US economy through a generalized increase in global risk aversion. A deepening of the European crisis could very well derail the US economic recovery and have a harmful impact on the Asian economies. This kind of vicious circle could be a bad news to the shipping companies. The purpose of the study is to predict the Baltic Dry Index representing the shipping business during the period of 2012 using the ARIMA-type models. This include the ARIMA and Intervention-ARIMA models. This article introduces the four ARIMA models and six Intervention-ARIMA models. The monthly data cover the period January 2000 through October 2011. The out-of-sample forecasting performance is also calculated. Forecasting performance is measured by three summary statistics: root mean squared percent error, mean absolute percent error and mean percent error. The root mean squared percent errors, however, are somewhat higher than normally expected. This reveals that it is very difficult to predict the BDI The ARIMA-type models show that the shipping market will be bearish in 2012. These pessimistic ex-ante forecasts are supported by the Hodrick-Prescott filtering technique.

• Journal of the Korean Solar Energy Society
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• 제39권6호
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• pp.113-125
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• 2019

With the opening of the small power brokerage business market in December 2018, the small power trading market has started in Korea. Operators must submit the day-ahead estimates of power output and receive incentives based on its accuracy. Therefore, the accuracy of power generation forecasts is directly affects profits of the operators. The forecasting process for power generation can be divided into two procedure. The first is to forecast solar irradiation and the second is to transform forecasted solar irradiation into power generation. There are two methods for transformation. One is to simulate with physical model, and another is to use regression model. In this study, we found the best-fit regression model by analyzing hourly data of PV output and solar irradiation data during three years for 242 PV plants in Korea. The best model was not a linear model, but a sigmoidal model and specifically a Gompertz model. The combined linear regression and Gompertz curve was proposed because a the curve has non-zero y-intercept. As the result, R² and RMSE between observed data and the curve was significantly reduced.