• The Journal of Asian Finance, Economics and Business
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• v.8 no.9
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• pp.287-300
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• 2021

The study is conducted to find out the impact of the country- and corporate-level governance and firm internationalization on stock liquidity of 120 listed firms in Japan, Hong Kong, Pakistan, and India. Panel data is used in the current study. The annual time span covered in the current study is 10 years. The current study explores results based on secondary data. The findings of the 'robust panel corrected standard error' estimator shows that the internationalization strategy of firms positively influences the stock liquidity. The internationalization strategy of multinational corporations proves to be an effective methodology for improving stock liquidity in the home market as well as abroad. The study also shows that a stronger relationship exists between stock liquidity and internationalization in those countries where the regulatory settings are effective, the judiciary system is efficient and shareholders' rights are protected. Corporate governance and stock liquidity are negatively associated. The study also finds a negative relationship between country-level governance mechanisms and stock liquidity. Whereas the 'robust panel corrected error' estimator shows a positive association between corporate governance mechanisms and firm internationalization. The study depicts that effective corporate governance motivates multinational companies to expand their business abroad.

• Asia-Pacific Journal of Business
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• v.10 no.2
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• pp.27-41
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• 2019

Following the methodology of Ben-Rephael, Kadan and Wohl (2015), this paper examines whether firm-level liquidity premium exists and whether the premium exhibits a long-term trend in the Korean stock market. The results show that over the whole sample period (1998-2018), a liquidity premium of 0.083% exists in the cross-section of stocks. Interestingly, the pricing of liquidity declines significantly over the sample period. Sub-period analysis indicates that liquidity is priced mainly in the first sub-period (1998-2004) with a significant monthly premium of 0.304%, while the pricing of liquidity becomes weaker or insignificant in the second (2005-2011) and the third (2012-2018) period. I also find that the significance of the liquidity premium in the first period is attributed to small stocks. To explore underlying reasons that might affect the decline in the liquidity premium, I decompose liquidity premium into the product of firm-level liquidity and the sensitivity of expected stock returns on liquidity. The results reveal that the long-term decline is explained by both an increase in firm-level liquidity and a decrease in the sensitivity of expected returns on liquidity.

• Journal of Korean Academy of Nursing Administration
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• v.19 no.1
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• pp.7-16
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• 2013

Purpose: The purpose of this study was to analysis the financial statements of university hospitals and to apply the results to build nursing management strategies. Methods: Data on the financial statements of university hospitals were collected each hospital's homepage or internet search from February to June, 2010. Financial statements of 11 hospitals were analyzed using the 4 categories of financial ratio analysis method: liquidity, performant, growth and turnover. Results: Overall results showed that the financial status the university hospitals were unstable, and many financial indicators did not meet financial standard ratios. Only 8 financial indicators of total 19 indicators satisfied financial standard ratios. Conclusion: The results of financial statements analysis suggest that nurse managers should develop the blue ocean strategy for diversification of nursing services to improve financial ratios of liquidity, performance, and growth. Using a unit-based just-in-time system for effective supply management would help to increase profits and to decrease costs of hospital by improving financial ratios of turnover.

• Information Systems Review
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• v.9 no.2
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• pp.129-147
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• 2007

At the time the local credit-card companies plunged into a liquidity crisis in 2002, S-Card was urged to take into account the estimated customer income (ECI) to enhance its customer credit evaluation function for the first time in Korean financial industry. Before this new attempt by S-Card, most credit-card companies including S-Card had performed a customer's credit evaluation based on the customer's behavioral factors such as the amount of purchase on credit, debt payment, and financial history that is provided from the Credit Bureau. However, this approach failed to measure customer's potential value which is one of the major factors in judging the customer's ability to pay, and hence, led to difficulties in risk management. The purpose of this case study is to present the better approach to sophisticated risk management for financial firms in Korea by reviewing S-Card's process of customer income estimation and its application to risk management.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.267-276
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• 2015

This study divided hospital management performance as surplus and deficit, Liquidity, Growth, Turnover Ratios, Productivity, Operating Expense, Patient Care Performance and evaluate the relationship between profitability. In addition to providing a useful basis for seeking profitability and effective management measures based on the findings of the hospital has its purpose. The study period was 2013 to identify the hospital's financial performance as evaluation criteria, were selected for a total of 147 hospitals surveyed. In conclusion, the more profitable medical and hospital financial performance, results showed a higher rate. In addition to the factors affecting the profitability of the Salaries, Administrative Expenses, Material Costs was a major factor. To to enhance the future profitability of hospital care it is also important to increase revenue, but Salaries, the cost reduction-effective strategy for reducing Administrative Expenses may be required.

• 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.