• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.293-298
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• 2024

The purpose of an internal control system is to prevent the occurrence of errors and fraud in the process of producing accounting information, thereby providing investors with reliable information. For the effective operation of an internal control system, it is necessary to secure a sufficient number of personnel and experienced staff. This study focuses on the personnel directly involved in producing accounting information, examining whether companies that invest in their internal control staff experience a mitigation in the phenomenon of valuation errors. The analysis revealed that the size and experience months of the personnel responsible for internal control have a significant negative relationship with valuation errors. This result implies that by securing sufficient personnel for the smooth operation of the internal control system and placing experienced staff within the system, investors can effectively make judgments about the intrinsic value based on quality accounting information, thereby reducing valuation errors.

• Journal of Arbitration Studies
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• v.32 no.3
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• pp.3-27
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• 2022

Climate change is a man-made disaster that has become a major global concern today. With increasingly visible symptoms of climate change in recent years, it has become evident that climate action can no longer be dismissed as a mere matter of choice, but as a matter of survival for the human being. To address the impending climate change crisis in a collaborative and sustainable manner, the international community has been taking various measures including Kyoto protocol and the Paris Agreement. With respect to the private investor's project investment in line with international agreements on climate change, recently we have seen multiple legal judgments which clearly indicate the subject of judicial responsibility for investment in climate change related projects. However, in order to hold judicial responsibility occurring during the implementation of climate change related projects, a causal relationship between the responsible entities and clear responsibility must be demonstrated, and applicable institutional arrangements need to be arranged. It may be the right time for global community to consider shifting not only to human ethical obligations but also legal obligations. In this regard, concerned governments should consider legislating arbitration laws, regulations, and institutional arrangements in more specific and applicable manner.

• Journal of Arbitration Studies
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• v.25 no.4
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• pp.3-23
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• 2015

The Chevron saga including Chevron/TexPet v. Ecuador, PCA Case No. 34877(hereinafter referred to as "Chevron I") and Chevron/TexPet v. Ecuador, PCA Case No. 2009-23(hereinafter referred to as "Chevron II") started out of domestic litigations between TexPet and Ecuador in the early 1990s. In Chevron I, the Tribunal decided that Article 2(7) of the U.S.-Ecuador BIT on effective means of provision was breached because of undue delays in the seven legal proceedings TexPet had brought against Ecuador in respect to contractual obligations. In Chevron II, it was contended that through the actions and inactions of the judiciary and the executive, Ecuador breached her several obligations under the BIT. Ecuador objected to the jurisdiction of the Tribunal because TexPet's investment was terminated in 1992, and because Chevron is not a party to the 1995 Settlement Agreement and 1998 Final Release. In its Interim Award on Jurisdiction and Admissibility, the Tribunal applied a prima facie standard to the facts alleged by the Claimants but denied by the Respondent, and decided that questions in respect of the Respondent's jurisdictional objections should be joined to the merits under Article 21(4) of the UNCITRAL Arbitration Rules. In the merits phase of Chevron II, the Tribunal divided the merits of the Parties' dispute into two parts, entitled "Track 1" and "Track 2". In its Partial Award on Track 1, the Tribunal decided that Chevron is a "Releasee" under the 1995 Settlement Agreement. In a decision on "Track 1B", the Tribunal decided that the Lago Agrio complaint cannot be read as pleading "exclusively" or "only" diffuse claims, and that, to this extent, the Claimants' reliance on the 1995 Settlement Agreement as a complete bar to the Lago Agrio complaint must fail, as a matter of Ecuadorian law. The Tribunal maintained the position that the Parties' disputes on both merit and jurisdiction should be reserved for Track 2. It remains to be seen how the Tribunal addresses the Claimants' allegations of multiple denials of justice under international law against the judgments of the Respondent's Courts, together with the Respondent's jurisdictional objections in Track 2 of the arbitration.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.