• Korean Journal of Construction Engineering and Management
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• v.16 no.6
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• pp.144-155
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• 2015

This paper is to understand investment's efficiency and performance of commercial real estate assets diversified by use and district. To do so, this paper divides two different commercial real estate markets(office build market and retail real estate market) in Seoul city by district into "GBD(Gangnam Business District), YBD(Yeouido Business District), and CBD(Central Business District)" and "GBD(Gangnam Business District), SBD(Shinchon Business District), and CBD(Central Business District)" respectively, configures these districts each other to structure portfolios as its portion varies based on Markowitz's Mean-Variance principle, and looks at risk-return relationship of portfolios to find out efficiency, performance, and optimal investment chosen based upon Sharpe's Performance Index. As a result, the portfolio configured by "10 to 30% of office building asset at CBD" and "70 to 90% of retail real estate asset at CBD" is shown to be the most optimal, suggesting the highest quarterly Sharpe's performance index of 2.7118~2.7776 with quarterly rate of return of 1.826%~1.838% and quarterly standard deviation of 0.573~0.589. Furthermore, it is obvious that diversified portfolio configured by use(office-retail) shows better investment performance than that by district with same type of asset(office-office or retail-retail). Finally, results driven from this research will play an important role to stimulate real estate and construction markets through enlarging ideas as to diversified investment by use and district on real estate indirect investment products.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.169-178
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• 2020

In this paper, we provide an overview of financial development in Vietnam. Particularly, a new approach of this study is to measure financial development through improvements in depth, efficiency and access of the banking system and stock market. Further, the study examines the factors significantly affecting financial development in Vietnam. The data are collected in Vietnam, an emerging country with a limited financial development. We employ the Autoregressive Distributed Lag (ARDL) approach, which generates a high reliability and suits data characteristics of emerging countries like Vietnam. We observe that Vietnam's banking system plays a key role in supplying credits to the economy while the nascent stock market at a limited size shows its potential for a considerable growth in the future. We also find the influential determinants of financial development in Vietnam including real estate market (RE), economic growth (EG), consumer price index (CPI), and global financial crisis (GFC). These findings are essential for Vietnamese authorities in providing practical solutions in order to build a sustainable and synchronous financial development. They are also first empirical evidence relating to an overview of financial development in an emerging country, so they are not only valuable to Vietnam but also crucial to other emerging economies.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.4
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• pp.213-224
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• 1998

Since the late 1980s, an Increasing number of neural network models have been studied in the areas of financial prediction and analysis. The purpose of this study is to Investigate the possibility of building a neural network model that is able to construct a profitable trading strategy in the Korean Stock Market. This study classifies stocks into the future market winners and losers from the publicly available accounting information and builds portfolios based on this information. The performances of the winner portfolios and the loser portfolios are compared with each other and against the market index. The empirical result of this research is consistent with the traditional fundamental analysis where it is claimed that the financial statements contain firm values that may not be fully reflected In stock prices without delay. Despite the supporting empirical evidence. It is somewhat Inconclusive as to whether or not the abnormal return in excess of market return is the result of the extra knowledge obtained in the neural network models derived from the historical accounting data. This research attempts to open another avenue using neural network models for searching for evidence against market efficiency where statistics and intuition have played a major role.

• The Journal of Information Systems
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• v.29 no.3
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• pp.119-144
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• 2020

Purpose Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market. Design/methodology/approach In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities. Findings This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.

• Knowledge Management Research
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• v.23 no.1
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• pp.89-109
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• 2022

Due to COVID-19 and soaring participation of individual investors, large-scale transactions exceeding system capacity limits have been reported frequently in the capital market. The capital market IT systems, which the impact of system failure is very critical, have encountered unexpectedly tremendous transactions in 2020, resulting in a sharp increase in system failures. Despite the fact that many companies maintained large-scale system capacity planning policies, recent transaction influx suggests that a new approach to capacity planning is required. Therefore, this study developed capital market IT system capacity planning models using machine learning techniques and analyzed those performances. In addition, the performance of the best proposed model was improved by using sentiment index that can promptly reflect the behavior of investors. The model uses empirical data including the COVID-19 period, and has high performance and stability that can be used in practice. In practical significance, this study maximizes the cost-efficiency of a company, but also presents optimal parameters in consideration of the practical constraints involved in changing the system. Additionally, by proving that the sentiment index can be used as a major variable in system capacity planning, it shows that the sentiment index can be actively used for various other forecasting demands.

• Journal of Agricultural Extension & Community Development
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• v.28 no.3
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• pp.153-165
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• 2021

This study aims to analyze the difference in production elasticity based on the types of agricultural labor input regarding its qualitative difference in Jeju mandarin production. To estimate the production function of qualityadjusted labor, we have set up a Quality-Adjusted Index based on the market wage of the agricultural field. We have conducted a multiple regression analysis of the newly estimated labor inputs using the Ordinary Least Squares regression. Results show that the production efficiency of aggregate total labor hours (quality-unadjusted labor input) is overestimated compared to quality-adjusted labor with qualitative labor homogeneity. Moreover, by analyzing household labor and employment labor, we have observed that the marginal productivity of household labor exceeds that of employment labor. In conclusion, this study verifies that securing labor input homogeneity is crucial for analyzing agricultural labor hours' economic efficiency accurately.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.132-136
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• 2002

Optical collimating lenses are play a role as maintenance parallel light and as a kind of optical collimating lens, there is Ball lenses, GRIN-rod lenses, spherical lenses and aspherical lenses etc. but recently GRIN lens has monopolized a market. The performance of optical collimator depended on the coupling efficiency. In this paper, we were compared and analyzed to be measured values of coupling efficiency with respect to optical working distance using GRIN rod lenses and spherical lenses. In the case of GRIN lenses with a beam size of 420 ${\mu}{\textrm}{m}$, the minimum coupling efficiency was obtained to a measured value of 0.15 ㏈ and in the case of spherical lenses was obtained to a measured value of 0.12 ㏈ on the same condition. In results, we found that a performance of spherical lenses be better as compared with a that of GRIN lens.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.323-326
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• 2008

Data Envelopment Analysis (DEA) is a theoretically sound framework for performance analysis that offers many advantages over traditional methods such as performance ratios and regression analysis. Largely the result of multidisciplinary research during the last three decades in economics, engineering and management, DEA is best described as an effective new way of visualizing and analyzing performance data. Besides, overseas information technology companies have aggressively tried to enter the domestic market. In the age of globalization and high competition, it is imperative that the system integration (SI) companies need to introduce the performance evaluation models of SI projects, including Capability Maturity Model and Software Process Improvement and Capability Determination, to gain a competitive advantage. Therefore, it makes our research regarding evaluation of SI projects very opportune. The purpose of the study is not only to evaluate efficiency of each project by DEA but also to gain insight into various factors such as project complexity, team members' man-months structure, and process index(project management index) that link to the projects performance.

• Asia pacific journal of information systems
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• v.26 no.4
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• pp.542-560
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• 2016

This study determines whether or not firms can achieve high productivity growth through external technology acquisition. It also identifies the key factors affecting adopting firms' productivity growth by employing the Malmquist productivity index (MPI) methodology, which features computational ease, low data dependency, and decomposition of productivity growth into technical efficiency change and technical change. Results showed that the effects of productivity growth arising from technology transfer became stronger over time. Moreover, patent transfer guaranteed firms' productivity growth, but no evidence was found that factors such as age and size could increase productivity. Finally, cultural similarity could be another factor conditioning the effectiveness of technology transfer in the productivity of adopting firms.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.6
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• pp.811-816
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• 2000

Five experiments were conducted to evaluate blood urea nitrogen (BUN) as a potential index of feed efficiency (G/F) and lean growth in growing-finishing pigs. Exp. 1 was conducted to examine the relationship between feeding protocol and BUN values. Fasted-refed pigs exhibited BUN peaks 3 h post-prandially while those given ad libitum access to diet had inconsistent BUN patterns in 10 h blood sampling with an 1 h interval. In Exp. 2 and 3, it was revealed that the peak BUN values were negatively correlated (p<0.01) with G/F in both barrows and gilts at 20 kg body weight (BW) and 50 kg to 90 kg BW. In Exp. 4, it was found that BUN values between 55 kg and 70 kg BW, when lean gain is maximized, were best correlated with average daily lean gain (ADLG). In Exp. 5, 18 barrows and 21 gilts were used to examine the relationship between BUN values at 65 kg BW and ADLG from birth to market weight. The BUN values at 65 kg BW and ADLG were negatively correlated (p<0.01) in both genders. These experiments demonstrated that there was a correlation between peak BUN values, and G/F and ADLG under specific circumstances.