• The Journal of Fisheries Business Administration
• /
• v.28 no.2
• /
• pp.1-18
• /
• 1997

The purpose of this study is to investigate empirically the characteristics of financial structure by using 76 fisheries corporations in Korea, and to suggest implications of the empirical results for government's financial policy for fisheries corporations. For the empirical test, we choose the following factors as the explanatory variables of cross-sectional regression analysis:firm-size(SIZE), collateral value of assets(TFATA), business risk(BRISK), growth(GROWTH), effective tax(ET), profitability(PROFIT). Two different debt ratios are used as dependent variables. One is defined as the ratio of total debt to total assets and the other is as that of long-term debt to total asset in terms of book value. The sample consists of 76 fisheries firms and sample period is 14 years from 1982 till 1995. From the results of cross-sectional regression analysis, the adjusted R$^2$values were high, 16∼79% and the overall F values indicated to be statistically significant. The results of cross sectional regression analysis show that the characteristics of financial structure fur fisheries corporations are as follows ; (1) Firm-size and collateral value of assets are the major factors of financial structure for fisheries corporations. That is, the larger firm-size the higher is debt ratio. This means that financial institutions conventionally lend more collateral loans with fixed assets like land, building rather than management capacities or credits. (2) To be consistent with a pecking-order theory, the higher is profitability the lower is debt ratio in fisheries corporations. (3) Corporations with high effective tax rate have lower financial leverage. Although the empirical results are inconsistent with traditional static trade-off theory, we think it would be attributed to government's various tax shelterings for fisheries which are likely to reduce tax shield effect of interests.

• KIPS Transactions on Computer and Communication Systems
• /
• v.8 no.7
• /
• pp.177-180
• /
• 2019

A typical vulnerability scoring system is Common Vulnerability Scoring System(CVSS). However, since CVSS does not differentiate among the individual vulnerability impact of the asset and give higher priority for the more important assets, it is impossible to respond effectively and quickly to high-risk vulnerabilities on large systems. We propose a Layered weight based Vulnerability impact assessment Scoring System which can hierarchically group the importance of assets and weight the number of layers and the number of assets to effectively manage the impact of vulnerabilities on a per asset basis.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.11
• /
• pp.1113-1122
• /
• 2020

The purpose of this study is to examine how the Indian banks have adjusted their risk-based capital ratios during 2009-2018 to meet the regulatory requirements. Banks can, in principle, increase their risk-based regulatory capital ratio, either by increasing their levels of regulatory capital or by shrinking their risk-weighted assets by adjusting asset growth or risk in the portfolio. We investigate banks' capital behavior by decomposing the change in the capital ratio into the contribution of its components and analyzing their variance across regulatory regimes and banks' ownerships. We further investigate how each component of the capital ratio is adjusted by the banks by breaking down them into balance sheet items. We find that the banks' capital behavior significantly differed between public and private sector banks and between the two regulatory regimes. During Basel II, banks, in general, followed a strategy of aggressive asset growth with increased risk-taking. The decline in the CRAR because of such an expansionary strategy was adjusted by augmenting additional capital. However, during Basel III, due to higher capital requirements, both in terms of quantity and quality, banks followed a strategy of cutting back their asset growth and reducing the risk in their portfolio to maintain their CRAR.

• The Journal of Asian Finance, Economics and Business
• /
• v.6 no.4
• /
• pp.27-35
• /
• 2019

The paper examines the level of disclosure on Islamic banks' performance in the United Arab Emirates (UAE). The data was collected through content analysis of annual reports and financial statements of all fully-fledged Islamic banks working in the UAE over the period 2009 to 2013. Return on Assets is used as a proxy for the performance of Islamic banks while disclosure index is used as a proxy for Islamic banks' disclosure. Also, predetermined variables are used in the study like Size, Deposits, Non-Performing Investments and Capital to Risk Weighted Assets Ratio. Two-Stage Least-Square regression method is used to check the interdependence relationships between disclosure and performance of Islamic banks in the UAE. The results show a significant relationship between performance and disclosure in the UAE Islamic banks. Our regression results show that Islamic banks with higher levels of disclosure lead to higher operating performance. Furthermore, the performance has a great impact on the level of disclosure which means Islamic banks with high performance measures will disclose more information for investors and other institutions in order to reduce the cost of equity and increase their values in the market. This study is considered as a battery for further studies in the relationship between disclosure and financial performance of Islamic banks at a global level.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.9
• /
• pp.105-115
• /
• 2020

The study empirically examines the effects of loan portfolio diversification on bank risk and return in the nascent banking market of Vietnam. Loan portfolio diversification is captured through the Hirschman-Herfindahl index and the Shannon Entropy with sectoral exposures. We access each bank's financial reports to collect the required data, especially the breakdown of sectoral loan portfolios, thus constituting a unique dataset. To compute bank return, we use the traditional accounting indicators, including return-on-assets, return-on-equity, and net-interest margin. For bank risk, we utilize the loan-loss provisions and non-performing loans relative to gross customer loans. Using a sample of 30 commercial banks over the period from 2008 to 2019 and the system generalized method of moments estimator for the dynamic panel, we indicate the downsides of portfolio diversification. Concretely, we observe that all diversification measures exhibit significantly negative signs in all regressions across different bank return proxies. At the same time, the estimates display the significant and positive impact of diversification on the non-performing loan ratio. Hence, sectoral loan portfolio diversification significantly hampers bank performance in both aspects of lower return and higher credit risk. The results are robust across a rich set of bank performance and portfolio diversification measures.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.5
• /
• pp.81-90
• /
• 2020

This study investigates the nexus between the level of Corporate Social Responsibility Disclosures (CSRD) and Risk of Bankruptcy of companies that are listing in the Stock Exchanges of Vietnam. To investigate that relationship, this study collected secondary data from annual audited financial statements from 2014 to 2018 of listing companies. Applying two different regression models with two dependent variables and six independent and control variables, we find out that Vietnamese firms with higher level of CSRD performance can rapidly reduce their risk of bankruptcy. This phenomenon happens in the current year and in the coming years in all firms in the research sample. This result may be that the disclosures of social responsibility information can bring financial and non-financial benefits to the firms. In addition, the results also point out that there is a difference in risk of bankruptcy between the group of companies, which discloses and the one which does not disclose corporate social responsibility on their annual reports. This might be from the effects of various factors such as business size, financial leverage, market to book ratio, return on assets, cash flow from operations, etc. Our research results can be applied to other firms in Vietnam and in other similar jurisdictions.

• Journal of Digital Convergence
• /
• v.17 no.10
• /
• pp.141-147
• /
• 2019

This paper analyzes the price movements and the possibility of delisting by research and development intensity of firm which made technological innovation disclosure in the Korean stock market. The sample consists of firms listed on the KRX which made technological information disclosure between January 2002 and December 2014. The results are summarized as follows. The higher R&D intensity is observed for the delisted firms group. The logit regression result shows that the research and development intensity is a significant predictor of the possibility of delisting. This shows that exposure to the risk of delisting may increase as the proportion and uncertainty of intangible assets in the assets of individual firms increases. This empirical result is expected to serve as a good guide line for the stakeholders.

• IE interfaces
• /
• v.16 no.4
• /
• pp.421-431
• /
• 2003

Due to the increasing complexity of the information systems environment, modern information systems are facing more difficult and various security risks than ever, there by calling for a higher level of security safeguard. In this paper, an information technology security risk management model, which modified by adopting the concept of business processes, is applied to client/server distributed systems. The results demonstrate a high level of risk-detecting performance of the model, by detecting various kinds of security risks. In addition, a practical and efficient security control safeguard to cope with the identified security risks are suggested. Namely, using the proposed model, the risks on the assets in both of the I/O stage(on client side) and the request/processing stage(on server side), which can cause serious problems on business processes, are identified and the levels of the risks are analyzed. The analysis results show that maintenance of management and access control to application systems are critical in the I/O stage, while managerial security activities including training are critical in the request/processing stage.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.6
• /
• pp.23-32
• /
• 2022

This paper examines the effects of regulatory capital on a bank's profitability and risk. We employ annual data from Vietnamese commercial banks from 2005 to 2020 and use the dynamic GMM regression method to address the potential endogeneity issue, more suitable for panel data with relatively low time dimensions. Our panel regressions indicate that higher regulatory capital would significantly improve the bank's profitability and lower the bank risks. In particular, a one percent increase in the regulatory capital would significantly increase the bank's return on assets by 1.9%. We further explore the heterogeneous impacts of regulatory capital on the Vietnamese bank's performance across bank characteristics. We find that smaller, non-state-owned and non-listed banks would benefit from stringent regulatory capital requirements. The improvements in bank performance are mainly driven by reductions in the risk premium of the banks, resulting in lower funding costs and higher profitability. These findings are essential since Vietnam, as an emerging market, has only implemented the Basel II reform recently on a stable and fast-growing background rather than as a reaction to the global financial crisis. Thus, our empirical results support stringent regulatory capital in emerging countries to ensure a stable banking sector and boost economic growth.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.135-149
• /
• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.