• Health Policy and Management
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• v.6 no.1
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• pp.191-210
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• 1996

This study purported to acquire information necessary to improve the operational efficiency of general hospitals. It tried to determine major indices which represent managerial performance of general hospitals and to identify the managerial characteristics of general hospital which affect the major financial indices. 201 hospitals which were subject to standardization audit by the Korean Hospital Association were investigated and 80 hospitals were finally chosen for this study. Their financial and managerial data during the period between January 1991 and December 1991 were collected. Considering financial indices in this study were the ration of net income to total asset, income growth rate, and quick ration. The results of study are summarized as followings. First. The ration of net income to total assets and quick ration were highly related to managerial characteristics of general hospitals. Therefore, the standardization of three financial indices should be needed to systematically check the operational efficiency of general hospitals. Second, the sample hospitals can be classified as four groups on the basis of their financial indices' level. 4 of those hospitals(5.0%) showed high level of performance in terms of three financial indices and 27 of them(33.7%) showed that they are highly related to only two financial indices. 34 hospitals(42.5%) showed they have high level of relationship with only one indices and 15 hospitals(18.8%) showed very weak performance level with three indices. In addition, there is no hospitals to show mid-range level of managerial performance in relation to all three financial indices. Third, there is no significant relationship between three financial indices and the managerial characteristics of hospitals such as the number of beds, type of operation, location of hospitals, and etc. However, in the case of hospitals which have high level of managerial performance, they have more specialists and medical support personnel in comparison to low performance hospitals. They also have high level of bed occupancy rate and average length of stay(ALOS). In conclusion, the study showed the standardization of 3 financial indices are necessary to systematically evaluate the managerial performance of general hospitals and provide more accurate operational information for each hospital. To do so, it is necessary to focus on management side of hospital such as the effective human resource management and quality enhancement of medical treatment.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.363-370
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• 2021

This study aims to revisit the issues and misconceptions about financial inclusion (FI) indices. For indices construction, this study uses two approaches: one approach following the methodology of Sarma (2008) which is based on UNDP methodology, while the other is the Dynamic Factor Model (DFM)-based index of Stock and Watson (2002) and Rehman et al. (2021). The data of 18 economies of Asia from 1997 till 2017 is used for indices construction and analysis. The authors constructed macro and micro-level financial inclusion indices based on the different types of financial inclusion indicators. Second, the authors have critically evaluated two different approaches, and the results show that Sarma (2008)-based index show financial inclusion's level, while DFM-based index reveal fluctuation in the current year's financial inclusion level due to the prior variations. For measuring the level of financial inclusion, the Sarma (2008) index is effective, while for forecasting the level of financial inclusion, the DFM approach is more appropriate. Furthermore, the micro and macro aspects of financial inclusion should be reflected in separate indices for better understanding and in-depth insights.

• International Journal of Quality Innovation
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• v.7 no.3
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• pp.1-14
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• 2006

Economic value added (EVA) is introduced on two levels: as index for evaluation of corporation and as index for evaluation of business unit. In the latter case, application of one and the same cost of capital to all business units of a business corporation may be possible, but it is a fundamental policy for EVA to apply different cost of capital to business units with different risks. Estimate of cost of capital of business units is a problem to be resolved. The author, focusing on the question of the estimate of cost of capital of business units, has conducted a demonstrative study on risk structure of cost of capital estimates by using financial indices of Japanese manufacturers (37 automotive industries, 141 electrical and electronic machinery industries, 63 food processing industries, 98 chemical industries, 125 general machinery industries) for a period of 5 years from 1995 to 1999. The author presumes that $\beta$ is explained by a regression formula ${\beta}=B_0+{\Sigma}B_iY_i+{\alpha}$ ($Y_i$: financial indices) and selects 40 explanatory variables from financial statements as risk components. Using their financial indices, the author concludes through a series of statistical analyses that there is a good likelihood of estimating cost of capital for Japanese industries and is convinced that it will lead to more reliable and practical results by assigning averages and variances to 40 primary financial indices for a period of 3 to 5 years selected in this demonstrative study.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.5
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• pp.37-50
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• 2020

This study aims to investigate meaningful relationship between technology appraisal indices and SMEs' financial performances for their continuous growth. The empirical data for this study were based on the technology appraisal results of Korea Technology Finance Corporation(KOTEC) and the financial data of the following 2 years 0f 3,688 SMEs. The meaningful differences between SMEs with superb financial performances and the others, by using t-test analysis, statistically were verified in 25 indices(75.8%) out of total 33 indices. All of five independent variables, namely CEO's capability, technology manpower, R&D intensiveness, market competitiveness and investment feasibility, were verified to have a positive effect on business feasibility respectively and business feasibility also has a positive influence on financial performance, such as sales growth, labor productivity and financial stability.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Korean Journal of Mathematics
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• v.22 no.3
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• pp.529-552
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• 2014

As we know, some indices and data are strong influence to the price movement of some assets now, but not to another assets and in future. Thus we define some asset models for several time intervals; intraday, weekly, monthly, and yearly asset models. We define these asset models by using Brownian motion with volatility and Poisson process, and several deterministic functions(index function, twitter data function and big-jump simple function etc). In our asset models, these deterministic functions are the positive or negative levels of auxiliary indices, of analyzed data, and for imminent and extreme state(for example, financial shock or the highest popularity in the market). These functions determined by indices, twitter data and shocking news are a kind of one of speciality of our asset models. For reasonableness of our asset models, we introduce several real data, figurers and tables, and simulations. Perhaps from our asset models, for short-term or long-term investment, we can classify and reference many kinds of usual auxiliary indices, information and data.

• IE interfaces
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• v.25 no.3
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• pp.319-326
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• 2012

To effectively predict financial crisis, this paper presents an early warning system based on artificial intelligence technologies. Both Genetic Algorithms and Neural Networks are utilized for the proposed system. First, a genetic algorithm has been developed for the effective selection of economic indices, which are used for monitoring financial crisis. Then, an optimum weight of the selected indices has been determined by a neural network method. To validate the effectiveness of the proposed system, a series of experiments has been conducted by using the Korean economic indices from 2005 to 2008.

• Journal of Distribution Science
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• v.15 no.12
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• pp.95-102
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• 2017

Purpose - This study aims to develop diagnostic indices for managerial performance of agro-food distribution corporations. In particular, weights of diagnostic indices were estimated using the AHP method. Management diagnosis on agro-food distribution corporations is expected to increase their competitiveness in the domestic market as well as in international markets. Research design, data, and methodology - It develops weights or importance of the diagnostic indices based upon the survey of 21 experts in food distribution management. The survey was carried out using e-mail. Management diagnostic indices were developed based upon four BSC(Balanced Scorecard) perspectives of finance, learning/growth/leadership, customer, and internal process/technology. Results - Diagnostic indices on financial perspective consist on profitability, productivity, growth, stability and activity. Learning and leadership perspective indices consist of management will, CEO leadership, level of learning, innovation, and level of management information system. Customer perspective indices are branding, customer and channel management and internal process/technology indices consist of fourteen sub-indices representing technologies, efficiency, and dynamics. It was estimated that the weight of financial perspective index was 0.3, internal process/technology perspective index 0.248, customer category index 0.247, and learning, growth and leadership perspective index 0.205. This study also estimates weights of sub-indices for managerial diagnosis by four different perspectives. Estimated weight of profitability (0.085) is the greatest among financial perspective indices, followed by stability (0.072), growth (0.053), productivity (0.051), and activity (0.038). While estimated weights of leadership, capability, and information indices are 0.100, 0.061, and 0.044 respectively, weights of marketing, customer management, and quality and service indices are 0.104, 0.093, and 0.051, respectively. Among internal process/technology perspective, estimated weights of efficiency, technology, and innovation indices are 0.106, 0.088, and 0.054, respectively. Conclusions - The diagnostic indices for managerial performance of agro-food distribution corporations would be utilized by agro-food distribution corporations themselves, extension service institutions, and consultants. It is also expected that central and local governments use diagnostic indices developed in this study for the purpose of evaluating the effects of governmental support programs for agro-food distribution corporations. Futhermore researchers and consultants would modify diagnostic indices developed in this study, reflecting characteristics and situation of types of agro-food distribution corporations.

• Communications for Statistical Applications and Methods
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• v.21 no.4
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• pp.335-347
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• 2014

This paper employs dynamic conditional correlation (DCC) model to examine time-varying comovement in the Korean stock market with a focus on the financial industry. Analyzing the daily returns of KOSPI 200 eight sector indices from January 2008 to December 2013, we find that stock market correlations significantly increased during the GFC period. The Financial Sector had the highest correlation between the Constructions-Machinery Sector; however, the Consumer Discretionary and Consumer Staples sectors indicated a relatively lower correlation between the Financial Sector. In terms of model fitting, the DCC with t distribution model concludes as the best among the four alternatives based on BIC, and the estimated shape parameter of t distribution is less than 10, implicating a strong tail dependence between the sectors. We report little asymmetric effect in correlation dynamics between sectors; however, we find strong asymmetric effect in volatility dynamics for each sector return.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.79-95
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• 2020

The globalization of financial markets has broadened investment opportunities. International investors' investment portfolios consist of financial instruments from various countries; consequently, the risks associated with economic dependence among countries should be carefully considered. Step-down equity-linked securities (ELS) are a structured financial product that have recently become popular among Korean investors. Payoffs are based on two or three stock indices from different regions; therefore, dependence between the indices should be reflected in the risk analysis. In this study, we consider a regime-switching copula model to describe the joint behavior of two stock indices- the Eurostoxx50 and the Hang Seng China Enterprises Index (HSCEI). These indices are commonly used as underlying assets of step-down ELS. Using historical data, we analyze the risk associated with step-down ELS through the probabilities of early redemption. A regime-switching copula model can accommodate complicated dependence. Thus, it should be considered in the risk analysis of step-down ELS.