• Title/Summary/Keyword: Liquidity Index

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Rheological Models for Describing Fine-laden Debris Flows: Grain-size Effect (세립토 위주의 토석류에 관한 유변학적 모델: 입자크기 효과)

  • Jeong, Sueng-Won
    • Journal of the Korean Geotechnical Society
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    • v.27 no.6
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    • pp.49-61
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    • 2011
  • This paper presents the applicability of rheological models for describing fine-laden debris flows and analyzes the flow characteristics as a function of grain size. Two types of soil samples were used: (1) clayey soils - Mediterranean Sea clays and (2) silty soils - iron ore tailings from Newfoundland, Canada. Clayey soil samples show a typical shear thinning behavior but silty soil samples exhibit the transition from shear thinning to the Bingham fluid as shear rate is increased. It may be due to the fact that the determination of yield stress and plastic viscosity is strongly dependent upon interstructrual interaction and strength evolution between soil particles. So grain size effect produces different flow curves. For modeling debris flows that are mainly composed of fine-grained sediments (<0.075 mm), we need the yield stress and plastic viscosity to mimic the flow patterns like shape of deposition, thickness, length of debris flow, and so on. These values correlate with the liquidity index. Thus one can estimate the debris flow mobility if one can measure the physical properties.

Analysis of Consolidation and Shear Characteristics for the Kwangyang Bay Clay (실내시험을 통한 광양만 점토의 압밀 및 전단특성분석)

  • 이영휘;김용준;김대길
    • Journal of the Korean Geotechnical Society
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    • v.15 no.1
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    • pp.151-160
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    • 1999
  • A series of laboratory tests for the marine clay sampled under the sea of Kwangyang bay have been conducted. The main types of tests are the general index property tests, the oedometer tests and the triaxial compression tests in both undrained(CIU) and drained(CID) conditions. The clayey samples, classified as CL, CH with natural water content of 38.3~84.6% and liquidity index of 0.71~0.98, are in the normally consolidated state with O.C.R. of 1.0l~l.60. The undrained stress path from CIU tests can be normalized with isotropic consolidation pressure$(p_0)$ and equal shear strain contour is linear passing through the origin in the (q, p) plot. The undrained shear strain is found to be the only function of the stress ratio($\eta$) and linear with intercept in the ($\varepsilon/\eta,\eta$) plot. The built-up pore pressure normalized with pc is also linear with respect to $\eta$. and its slope is defined by ´C´ as a pore pressure parameter. Equations to predict the undrained stress path and the shear strain are proposed. It is proved that the proposed equations give better agreements to the measured values than the Cam-clay theories. The failure points of the stress path are located on the same C.S.L. in (q, p) plot during both CIU and CID tests, which justifies the concept of critical state theory.

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A Study on The Factors Affecting the Managerial Performance of Hospitals (병원경영의 수익성 결정요인에 관한 연구)

  • Chung Bhum-Suk
    • Management & Information Systems Review
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    • v.17
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    • pp.107-133
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    • 2005
  • The purpose of this study was to analyze a trend of profitability classified by characteristics of hospitals and to analyze related factors. The data for this study were derived from survey material conducted by the Korean Hospital Association on 33 hospitals in Korea between 1993 and 2002. Profitability was measured in the aspect of investment profit rate and operation profit rate with net profit to total assets, normal profit to total assets and operating margin to gross revenue as dependent variables. Independent variables were classified by general factors (ownership, number of beds, period of establishment, region), financial factors (total asset turnover, liabilities to total assets, current ratio, fixed ratio, inventories turnover, personnel costs per operation profit, material costs per operation profits), composition of manpower and facilities(personnel and area per beds), productivity index(the number of daily patients per medical doctor, the number of daily patients per nurse), the score of quality assurance activities. First, Concerning the specialists per beds or area per beds and profitability of hospitals there was not statistically significant. Second, Those hospitals having the most daily patients per nurse had significantly higher profitability than the others, but the number of daily patients per medical doctor had little effect on the profitability. Thirds, Those hospitals having a higher proportion total asset turnover tended to show significantly higher profitability compared to other hospitals, but the liabilities to total assets and liquidity ratio had a little difference to the profitability. Those hospitals having a higher proportion personnel costs per operation profit and material costs per operation profits tended to show significantly lower hospital profitability compared to other hospitals. Fourth, In regression analysis, hospital profitability had negative relationship with personnel costs per operation profit or material costs per operation profits. While it had positive relationship with total asset turnover, the number of daily patients per nurse. In conclusion, private hospitals had higher profitability than that of public hospitals. Though factors related to profitability of hospital were different according to ownership, it is important for securing appropriate profitability by operating appropriate number of nurse, raising total asset turnover, and reducing personnel costs, material costs per operation profits. This study can be used as a baseline data for planning of hospital management. But the study may be limited in that the results cannot be generalized due to its small sample size. However, this longitudinal observation of 33 hospitals over ten year period has significant merit alone.

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(A) Study on the Structure Change of Financial Industrial for strengthening Global Financial Control (글로벌 금융 규제 강화에 따른 금융산업의 구조변화에 대한 연구)

  • Ham, Hyung-Bum;Choi, Chang-Youl
    • International Commerce and Information Review
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    • v.16 no.2
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    • pp.47-67
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    • 2014
  • Since the global financial crisis, criticism against the integrity of financial institutions proposed new financial regulations such as Basel III. These systems are expected to have impacts multilaterally on management and structure of mid- and long-term financial industry. It is also believed that financial institutions will inevitably review business model to respond to these enhanced regulations. The ongoing global financial regulation pursues regulation scope extension, introduction of global regulatory capital system, introduction of global liquidity, etc. As for quantitative index, Basel Committee on Banking Supervision is promoting QIS which is discussed mainly on implementation time from the juridical point of view. This study aims to present domestic banking industry's structural changes depending on regulation enhancement of foreign countries after global financial crisis, and suggest strategy that improves competitiveness of products. Looking at the research result, global financial regulation requires compliance with the regulations through treaties but it shows negative time center around banks. Furthermore, it is also pointed out financially advanced countries' passive attitude on regulation enhancement is problem. Therefore, regulations differentiated between developing and developed countries, dualistic regulations on financial industry, participation of advanced nations, etc are the postulation to change the structure of financial industry.

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A Study on Commodity Asset Investment Model Based on Machine Learning Technique (기계학습을 활용한 상품자산 투자모델에 관한 연구)

  • Song, Jin Ho;Choi, Heung Sik;Kim, Sun Woong
    • 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.

Predicting hospital bankruptcy in Korea (병원도산 예측에 관한 연구)

  • Lee, Moo-Sik;Seo, Young-Joon
    • Journal of Preventive Medicine and Public Health
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    • v.31 no.3 s.62
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    • pp.490-502
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    • 1998
  • This study purports to find the predictor of hospital bankruptcy in Korea and to examine the predictive power of the discriminant function model of hospital bankruptcy. Data on 17 financial and 4 non-financial indicators of 31 bankrupt and 31 profitable hospitals of 1, 2, and 3 years before bankruptcy were obtained from the hospital performance databank of Korea Institute of Health Services Management. Significant variables were identified through mean comparison of each indicator between bankrupt and profitable hospitals, and the discriminant function model of hospital bankruptcy was developed. The major findings are as follows 1. As for profitability indicators, net worth to total assets, operating profit to total capital, operating profit ratio to gross revenues, normal profit to total assets, normal profit to gross revenues, net profit to total assets were significantly different in mean comparison test in 1, 2, and 3 years before hospital bankruptcy. With regard to liquidity indicators, current ratio and quick ratio were significant in 1 year before bankruptcy. For activity indicators, patients receivable turnover was significant in 2 and 3 years before bankruptcy and added value per adjusted inpatient days was significant in 3 years before bankruptcy. 2. The discriminant function in 1, 2, and 3 years before bankruptcy were; $Z=-0.0166{\times}quick$ ratio-$0.1356{\times}normal$ profit to total assets-$1.545{\times}total$ assets turnrounds in 1 year before bankruptcy, $Z=-0.0119{\times}quick$ ratio-$0.1433{\times}operating$ profit to total assets-$0.0227{\times}value$ added to total assets in 2 years before bankruptcy, and $Z=-0.3533{\times}net$ profit to total assets-$0.1336{\times}patients$ receivables turn-rounds-$0.04301{\times}added$ value per adjusted $patient+0.00119{\times}average$ daily inpatient census in 3 years before bankruptcy. 3. The discriminant function's discriminant power in 1, 2, and 3 years before bankruptcy was 77.42, 79.03, 82.25% respectively.

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Innovation Technology Development & Commercialization Promotion of R&D Performance to Domestic Renewable Energy (신재생에너지 기술혁신 개발과 R&D성과 사업화 촉진 방안)

  • Lee, Yong-Seok;Rho, Do-Hwan
    • Journal of Korea Technology Innovation Society
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    • v.12 no.4
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    • pp.788-818
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    • 2009
  • Renewable energy refers to solar energy, biomass energy, hydrogen energy, wind power, fuel cell, coal liquefaction and vaporization, marine energy, waste energy, and liquidity fuel made out of byproduct of geothermal heat, hydrogen and coal; it excludes energy based on coal, oil, nuclear energy and natural gas. Developed countries have recognized the importance of these energies and thus have set the mid to long term plans to develop and commercialize the technology and supported them with drastic political and financial measures. Considering the growing recognition to the field, it is necessary to analysis up-to-now achievement of the government's related projects, in the standards of type of renewable energy, management of sectional goals, and its commercialization. Korean government is chiefly following suit the USA and British policies of developing and distributing renewable energy. However, unlike Japan which is in the lead role in solar rays industry, it still lacks in state-directed support, participation of enterprises and social recognition. The research regarding renewable energy has mainly examinedthe state of supply of each technology and suitability of specific region for applying the technology. The evaluation shows that the research has been focused on supply and demand of renewable as well as general energy and solution for the enhancement of supply capacity in certain area. However, in-depth study for commercialization and the increase of capacity in industry followed by development of the technology is still inadequate. 'Cost-benefit model for each energy source' is used in analysis of technology development of renewable energy and quantitative and macro economical effects of its commercialization in order to foresee following expand in related industries and increase in added value. First, Investment on the renewable energy technology development is in direct proportion both to the product and growth, but product shows slightly higher index under the same amount of R&D investment than growth. It indicates that advance in technology greatly influences the final product, the energy growth. Moreover, while R&D investment on renewable energy product as well as the government funds included in the investment have proportionate influence on the renewable energy growth, private investment in the total amount invested has reciprocal influence. This statistic shows that research and development is mainly driven by government funds rather than private investment. Finally, while R&D investment on renewable energy growth affects proportionately, government funds and private investment shows no direct relations, which indicates that the effects of research and development on renewable energy do not affect government funds or private investment. All of the results signify that although it is important to have government policy in technology development and commercialization, private investment and active participation of enterprises are the key to the success in the industry.

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Rough Set Analysis for Stock Market Timing (러프집합분석을 이용한 매매시점 결정)

  • Huh, Jin-Nyung;Kim, Kyoung-Jae;Han, In-Goo
    • Journal of Intelligence and Information Systems
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    • v.16 no.3
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    • pp.77-97
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    • 2010
  • Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

A Study on Industries's Leading at the Stock Market in Korea - Gradual Diffusion of Information and Cross-Asset Return Predictability- (산업의 주식시장 선행성에 관한 실증분석 - 자산간 수익률 예측 가능성 -)

  • Kim Jong-Kwon
    • Proceedings of the Safety Management and Science Conference
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    • 2004.11a
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    • pp.355-380
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    • 2004
  • I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in

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