• The Journal of Economics, Marketing and Management
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• v.4 no.4
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• pp.1-16
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• 2016

In today's knowledge-based economy, measurement and disclosure (M&D) of intellectual capital (IC) are crucial for enhancing business performance and competitiveness. In the global world, M&D of IC are useful means to keep investors well-informed and reduce information asymmetry. At present, very few leading corporations in India have disclosed IC information on a 'voluntary' basis. Traditional accounting practices, therefore, will need to assimilate innovations that seek to meaningfully represent the 'true-value' of the intangible assets of the company. This is an exploratory study of IC M&D by 8 Indian companies over 5-year period, using 'content' analysis and market-value-added (MVA) as research methodologies. The annual reports of companies were collected from their respective websites. As part of present study, various statistical techniques have been used to analyze the data. The findings show that the sample companies, on an average, reported a positive value of IC, along with wide-disparity, low-level of ICD. Unfortunately, the omission of IC information may adversely influence the quality of decisions made by shareholders, or lead to material misstatements. Finally, we recommend to "the international accounting bodies, to take the lead by establishing a harmonized ICD standard, and provide guidance to the big listed-companies for proper measurement and disclosure of IC, both for internal and external users."

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.895-907
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• 2020

Information transparency ensures that market players all have the opportunity to access the same information to come up with their assessment of the banks' financial situation, performance and risks to reach effective investment decisions. This research is conducted to investigate the levels of impact of determinants on information transparency by examining the case studies of Vietnamese commercial banks. This study combines both qualitative and quantitative research methods, based on interviews of 32 specialists in banking, accounting and auditing fields, which were conducted to explore determinants influencing information transparency and to develop measurement scales. Then, a survey of 160 managers of commercial banks, audit firms, and accounting managers of firms who frequently had transactions with banks was carried out to investigate the statistical significance of these determinants. The results show that, out of seven determinants that have significant impacts on the banks' information transparency, commitment from banks' senior management regarding transparency in information disclosure has the highest impact, followed by state governance, auditing, information infrastructure, credit rating agencies, personnel and bank performance. Accordingly, we provide some recommendations for improving information transparency in the Vietnamese banking industry context as a case study and in emerging countries context in general.

• East Asian Journal of Business Economics (EAJBE)
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• v.5 no.1
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• pp.10-26
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• 2017

This paper explains the concept of Economic Value Added (EVA) that is gaining popularity in India. We also examine whether EVA is a superior performance measure, both for corporate disclosure and for internal governance. Of late, companies in India have started focusing on shareholders wealth creation by adopting value-based models for measuring shareholder value that helps to align managerial decision-making with the firm preferences. In recent years, the EVA framework is gradually replacing the 'traditional' measures of financial performance on account of its robustness and its immunity from 'creative' accounting. Even though some leading Indian companies have already joined the band wagon of their American counterparts in adapting the EVA-based corporate performance systems, many other are hesitating as there is no strong evidence that the EVA system works in India. Till now, EVA disclosures are "not mandatory for the Indian companies." Also, we examine the value-creation strategies of selected Indian companies by analyzing whether EVA better represents the market-value of these companies in comparison to conventional performance measures. The study indicates that "there is no strong evidence to support Stern Stewart's claim that EVA is superior to the traditional performance measures in its association with MVA." As part of this study, we have also extensively surveyed the EVA disclosures in the Annual Reports made by the same sample group of 500 corporations from India.

• Management & Information Systems Review
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• v.19
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• pp.49-81
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• 2006

The private college education plays a crucial role both in training and supplying manpower needed for national economic growth and in increasing employability and personal labor earnings of individual workers. In oder for private college education to effectively respond to the rapid changes in industrial and occupational structures, it is necessary to secure appropriate level of investment funds and manage them efficiently. For this, it is required to discuss the structure, magnitude and management mechanism of the current private college education finance, changes in future demand for private college education and resultant changes in budget estimates, and new financial resources and allocation schemes. This study attempted to analyze current status and problems of private college education finance in Korea and, based on this analysis, to suggest future policy directions to improve private college education finance system. In order to make the private college education system in Korea competent and competitive enough to survive in international market, it is prerequisite to provide enough budget for the private college education and to manage the private college education finance in more efficient ways. First, for securing the adequacy and stability of investment budget for the private college education, it is recommended to 1) increase the government budget and put emphasis on the private college education; 2) diversify financial resources and induce financial contribution from private sector such as school juridical persons and enterprises. Second, for higher efficiency of financial management, it is recommended 1) make valid allocation standards and mechanism; 2) introduce competition system; 3) develop and utilize evaluation mechanism for the private college education finance to check adequacy, efficiency, accountability, and effectiveness; 4) apply consumer-oriented financial management scheme. In addition to the above policy measures, it is necessary to 1) make scientific forecasts of industrial and occupational structures periodically and apply these analyses to medium & long-term the private college education planning; and 2) redesign budget accounting system and develop the private college education performance indicators for the evaluation of accountability of the private college education institutions and administration institutes.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.943-952
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• 2021

The purpose of this study is to evaluate the effect of COVID-19 on corporate governance attributes and firm performance association. This research used a sample of 188 non-financial firms from the Malaysian market for the years 2019-2020. We found that the COVID-19 has affected all firm characteristics including firm performance, governance structure, dividend, liquidity, and leverage level, yet, the difference between prior and post COVID-19 pandemic is not significant. Also, the investigation revealed that board size exerts a significant positive impact on firm performance. After splitting the sample based on year, however, we found that board size does not matter in the uncertain time of the current crisis, while board diversity appeared to be significantly enhancing firm performance in the crisis time compared to the prior year where it has an inverse association with firm performance in both indicators. Board meetings and audit committee meetings seemed to have a significant negative influence on firm performance pre and post-COVID-19. This study contributes to the limited literature by providing the first empirical evidence on the impact of Coronavirus on the firm performance and corporate governance association.

• Management & Information Systems Review
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• v.31 no.1
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• pp.45-63
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• 2012

The purpose of this study is to investigated the association between the percentage of common stock held by a company's CEO and measure firm performance in down and up markets. We found that managerial ownership is associated positively with firm performance. We also found that although firms with high insider ownership generally outperform other firms, this relationship is diminished in down markets and is increased in up market. These results suggest that investment strategies based on the assumption that high insider ownership is associated positively with financial performance may be faulty in declining market.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.