• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.206-211
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• 2009

This paper presents a methodology to 1-day-forecast stock index using the automatic feature extraction method based on the neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by automatically removing the worst input features one by one. CPP$_{n,m}$(Current Price Position of the day n: a percentage of the difference between the price of the day n and the moving average from the day n-1 to the day n-m) and the 2 wavelet transformed coefficients from the recent 32 days of CPP$_{n,m}$ are selected as minimized features using bounded sum of weighted fuzzy membership functions (BSWFMs). For the data sets, from 1989 to 1998, the proposed method shows that the forecast rate is 60.93%.

• The Korean Journal of Applied Statistics
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• v.28 no.4
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• pp.703-719
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• 2015

We propose an extension of the Lee and Jho (2015) mean reverting the two factor mortality model by incorporating a period-specific cohort effect. We found that the consideration of cohort effect improves the mortality fit of Korea male data above age 65. Parameters are estimated by the weighted least squares method and Metropolis algorithm. We also emphasize that the cohort effect is necessary to choose the base survival index to calculate longevity bond issue price. A key contribution of the article is the proposal and development of a method to calculate the longevity bond price to hedge the longevity risk exposed to Korea National Pension Services.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.3
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• pp.393-402
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• 2014

ICT environment to the survey released by the Bureau of Statistics 2012 Household Finance. Korean Welfare survey 24.9% of all households in financial assets, real estate is about three times more than 69.9%, respectively. The problem is that the information is slow and income deciles(deciles 1-4), a relatively high proportion of households with low(78.8 to 69%) of the real estate assets of the expansion of the world economy with low growth and low uncertainty, work from home due to the information changes in the structure of the economy, such as increases in real estate prices remain exposed to the risk of a phenomenon such as Pour House Pour Talent and low-income people is bound to be more serious symptoms. This low correlation is by constructing a composite asset portfolio, the weighted average risk of the individual assets while increasing overall revenue decrease that risk is based on the principle of portfolio by type and different areas in the ICT environment in a portfolio of real estate price index low correlation to financial assets by including the effect of dispersion stable complex asset portfolio and empirical Growth was divided.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Water Engineering Research
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• v.6 no.1
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• pp.31-38
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• 2005

Groundwater recharge is defined in an addition of water to groundwater reservoir. Recently, many people have been moving to the Edwards aquifer and urban and agricultural industry have been expending. Hydrologists and water planning managers concern about insufficient groundwater amounts and irrigation water price variability. In this paper, I focus on estimates of local recharge volumes and quantify preferential flow through GIS technique. Chloride Mass Balance (CMB) and hydrochemical components have been widely applied to recharge rate and evaluate flow paths. The CMB method is based on relationship between wet-dry chloride deposition data and Rainfall data. These data are manipulated using ArcGIS. Especially, hydrochemical concentration distribution is good index for groundwater residence times or flow paths such as $[Mg^{2+}]/[Ca^{2+}],[Cl]$ and log$([Ca^{2+}]+[Mg^{2+}])/[Na^+]$. Well information such as hydrological-hydrochemical data are imported into ArcGIS and manipulated by interpolation techniques. For each potentiometric surface and water quality, point data are converted to spatial data through each Kriging and Inverse Distance Weighted (IDW) techniques.

• Management Science and Financial Engineering
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• v.11 no.3
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• pp.79-93
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• 2005

Despite the fact that the sourcing process in B2B e-Marketplaces is one of the most important tasks, the evaluation and selection process of suppliers have been ad-hoc based and mainly dependent on the experience of sourcing managers' subjective knowledge. To remedy the problem, we developed a decision support System (called Wise - I) that helps sourcing managers evaluate suppliers in a more systematic way. The system reflects company's strategy and know-how by adopting company enforced weighted scores for different factors and employing a more scientific method of considering factors other than price and on-time delivery rate, utilizing the AHP method. This paper reports the effectiveness of the system as well as the detailed description of the system. To investigate the effectiveness of the system, we collected information through interview and questionnaire survey. The information was also augmented through the firm key index system, which monitors average delivery lead time and on-time delivery rate. The result indicates that the system leads to the efficiency of purchasing section and the transparency of buying process, therefore reduces delivery time and cost.

• Proceedings of the Korean Institute of Interior Design Conference
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• 2002.04a
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• pp.39-43
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• 2002

IMF situation in Korean society in 1997 gave huge shock to all areas, such as politics, economy, and society which were covered by bubble economy. It has passed 4 years since IMF but it is continued to increase of consumer price index and depression of the society. Receiving an order in interior design become more and more tense competition domestically as well as internationally, due to the open of WTO. In order to achieve economic design of low cost-high design which pursues the power of competition in cost and quality, this study investigated specific methods for economic design and suggested the directions for economic design in interior design field. To suggest the directions of future interior design for economic design with the analysis of domestic and international cases for economic design. the following results are showed: According to the analysis of the cases, economic design is accomplished with the use of the materials. Interior designers should consider how they can use materials for economic design. Especially, use of cheap materials gives the reduction of the cost and the effects of differentiation of the spaces more. Therefore, experimentalism and knowledge of materials are needed and they should be more weighted as the method for achieving economic design,

• Asia-Pacific Journal of Business
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• v.15 no.2
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• pp.71-88
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• 2024

Purpose - This study provides an overview of existing research and practices related to market risk premiums(MRP), and empirically estimates the MRP in Korea, particularly using the related option prices. We also seek to improve the current MRP practices and explore alternative solutions. Design/methodology/approach - We present the option price-based MRP estimation method, as proposed by Martin (2017), and implement it within the context of the Korean stock market. We then juxtapose these results with those derived from other methods, and compare the characteristics with those of the United States. Findings - We found that the lower limit of the MRP in the Korean stock market shows a much lower value compared to the US. There seems to be the possibility of a market crash, exchange rate volatility, or a lack of option trading data. We investigated the predictive power of the estimated values and discovered that the weighted average of the results of various methodologies using the Principal Component Analysis (PCA) is superior to the individual method's results. Research implications or Originality - It is required to explore various methods of estimating MRP that are suitable for the Korean stock market. In order to improve the estimation methodology based on option prices, it is necessary to develop the methods using the higher-order(third order or above) moments, or consider additional risk factors such as the possibility of a crash.

• Journal of Korea Port Economic Association
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• v.36 no.2
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• pp.109-124
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• 2020

Using panel data on freight rates and ship prices in the dry freighter market from January 2015 to December 2019, this study investigates the characteristics of shipping industry fluctuations. The analysis aims at two aspects of academic contribution. First, this study analyzes the relationship between shipping indicators and ship price based on separate dry-bulk ships, while the previous research considered the overall shipping index and weighted average ship prices. Second, the VAR model for the causality test is extended to a heterogeneous mixed panel model capable of limiting coefficients. There is a peak estimated by removing the cross-correlation problem, which is mainly raised in panel data analysis, using bootstrap estimation and solving the problem of information loss due to differences in non-stationary data. An empirical investigation of the causal relationship between economic fluctuations and ship price shows that the effect on the ship price from the freight is significant at the 1% level. This implies that there is a one-way relationship with demand in the shipping industry rather than a bilateral relationship.

• Asia-Pacific Journal of Business
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• v.15 no.2
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• pp.257-270
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• 2024

Purpose - The purpose of this study is to analyze the changes in the business environment, business conditions, and detailed indicators related to management of Korea's port industry companies as global supply chains have been reorganized since the shock of COVID-19 in 2020 and uncertainties in the global economy have expanded. Design/methodology/approach - This study use the business survey index of the port industry to analyze. It is a weighted BSI with the weight of workers applied. Findings - Since 2020 the BSI in the port industry including business conditions, sales unit price, profitability, and financial conditions fluctuated, but it has been generally above normal level(100). During the analysis period, the BSI for export sales was found to be better than that of domestic sales. Employment BSI was generally stable, and in the case of facility BSI, the port industry was shown to be very active in facility investment. Research implications or Originality - First, it is necessary to make manpower training systems to cultivate experts in the port industry to cope with uncertainties caused by the rapidly changing global economy and the global supply chain environment. Second, it is necessary to support for investment in technology and facilities for automation and smartization. Finally, it is necessary to establish a continuous monitoring system for the business conditions.