Determinants of Sovereign Ratings: A Comparison of Case-Based Reasoning and Ordered Probit Approaches

The paper compares two alternative techniques for the modelling of the determinants of sovereign ratings, specifically, ordered probit and case-based reasoning. Despite the differences in approach the two alternative modelling approaches produce similar results in terms of which variables are significant and forecast accuracy. This suggests that either approach can be used, and that there is some robustness in the results. As regards significant variables, both models find that a proxy for technological development, specifically, mobile phone use, is the most important variable. Apart from the technology proxy, a range of conventional macroeconomic variables are found to be significant, in particular GDP and inflation. The models are then used to produce forecasts for 2002 and for a set of unrated countries. The forecast comparison indicates the critical role played by the technology proxy variable in the modelling.Sovereign Ratings, Ordered Response Models, Case-Based Reasoning

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Decision analysis techniques for adult learners: application to leadership

Most decision analysis techniques are not taught at higher education institutions. Leaders, project managers and procurement agents in industry have strong technical knowledge, and it is crucial for them to apply this knowledge at the right time to make critical decisions. There are uncertainties, problems, and risks involved in business processes. Decisions must be made by responsible parties to address these problems in order to sustain and grow the company business. This study investigates some of the most recognized decision analysis techniques applied by global leaders from 2006 to 2016. Several decision analysis tools are introduced such as heuristic decisions, multi-attribute rating, decision trees, Monte-Carlo simulations and influence diagrams. The theoretical development framework is presented. The approach for this research is Analyze, Design, Develop, Implement, and Evaluate (ADDIE), which included cognitive, behavioral, and constructive learning theories. Some of the top decision analysis skills needed for today’s leaders and managers from literature review over the past decade (2006 to 2016), were taught to organization leadership doctorate students. Research scheme, the method chosen for selecting the topic, group of contributors, and the method selected for collecting the data are offered. The learners were in their senior year of a leadership doctorate program and they did not need leadership training along with decision analysis technique training. Older learners had more interest in learning the fishbone and influence diagrams prior to the training. Students with intermediate math were more interested in learning about strategic planning techniques before training. The trainees with more computer skills were interested in learning the Zachman framework technique, which was surprising to the researcher since this tool does not require extensive computer skills. After the training, the researcher observed that learners with higher computer skills showed more interest in learning about group decision-making (consensus versus analytic hierarchy process). That students with intermediate math skills were more interested in top-down induction of decision trees, algorithm decision making (data mining and knowledge discovery), and strategic planning techniques. Spearman correlations with a moderate strength showed that older respondents tended to be more interested in the analytical hierarchy process, fishbone diagram, and risk analysis tool. After the training, students with stronger computer skills showed greater curiosity about learning more about the decision tree analysis, Zachman framework, and risk analysis. It made sense that students with weaker computer skills were less eager to learn about the Monte-Carlo simulation

Sukuk Rating Prediction using Voting Ensemble Strategy

Islamic finance development has grown into a focal point in many countries accros the globe. Sukuk, in particular, an Islamic investment product that has received growing attention from sovereigns, multinational and national organizations from both developed and emerging economies. Its uses has been aimed to finance investments in a varieties of economic activities and development projects. Despite the promising look of Sukuk, currently there is lack of studies had been to examine and predict the rating of the Sukuk. As a result, many practitioners adopted the conventional bond hence ignore the fact that these two instruments are different in nature. In order to fill the gap in the literature, it is the aim of this research to develop an ensemble model that can be used to predict Sukuk rating. The effectiveness of the proposed models were evaluated using dataset on Sukuk issuance for domestic from 2006 to 2016. The results indicate that the overall performance of the ensemble model is fall short behind the i duction decision tree (IDT) model. However, the class precision of the ensemble model improved, particularly in predicting the lowest rating of Sukuk

Simulation-Based Pricing of Convertible Bonds

We propose and empirically study a pricing model for convertible bonds based on Monte Carlo simulation. The method uses parametric representations of the early exercise decisions and consists of two stages. Pricing convertible bonds with the proposed Monte Carlo approach allows us to better capture both the dynamics of the underlying state variables and the rich set of real-world convertible bond specifications. Furthermore, using the simulation model proposed, we present an empirical pricing study of the US market, using 32 convertible bonds and 69 months of daily market prices. Our results do not confirm the evidence of previous studies that market prices of convertible bonds are on average lower than prices generated by a theoretical model. Similarly, our study is not supportive of a strong positive relationship between moneyness and mean pricing error, as argued in the literature.Convertible bonds, Pricing, American Options, Monte Carlo simulation

Does AI Research Aid Prediction? A Review and Evaluation

Despite the increasing application of Artificial Intelligence (AI) techniques to business over the past decade, there are mixed views regarding their contribution. Assessing the contribution of AI to business has been difficult, in part, due to lack of evaluation criteria. In this study, we identified general criteria for evaluating this body of fiterature. Within this framework, we examined applications of AI to business forecasting and prediction. For each of the seventy studies located through our search, we evaluated how effectively the proposed technique was compared with alternatives (effectiveness of validation) as well as how well the technique was implemented (effectiveness of implementation). We concluded that by using acceptable practice and providing validated comparisons, 31% (22) of the studies contributed to our knowledge about the applicability of the AI techniques to business. Of these twenty-two studies, twenty supported the potential of AI in forecasting. This small number of studies indicates a need for improved research in this area

Predicting Financial Distress Within Indian Enterprises: A Comparative Study on the Neuro-Fuzzy Models and the Traditional Models of Bankruptcy Prediction

The financial distresses is of major importance in the financial management system particularly in the case of this competitive environs. There are several traditional methods existing for predicting the financial distress within the country. Major factors influencing the financial distress is the stock market, credit risk and so on. Hence there is a need of models which could make dynamic predictions with the use of dynamic variables. There are several machine learning and artificial intelligence-based bankruptcy prediction models available. The neural network concepts and the computational intelligence-based methods are highly acceptable in the prediction arena. This research presents a comprehensive review of the existing prediction approaches and suggests future research directions and ideas. Some of the existing methods are support vector machines, artificial neural network, multi-layer perceptron, and the linear models such as principal component analysis. Neuro-fuzzy approaches, Deep belief neural networks, Convolution neural networks are also discussed

Investigating effort prediction of web-based applications using CBR on the ISBSG dataset

As web-based applications become more popular and more sophisticated, so does the requirement for early accurate estimates of the effort required to build such systems. Case-based reasoning (CBR) has been shown to be a reasonably effective estimation strategy, although it has not been widely explored in the context of web applications. This paper reports on a study carried out on a subset of the ISBSG dataset to examine the optimal number of analogies that should be used in making a prediction. The results show that it is not possible to select such a value with confidence, and that, in common with other findings in different domains, the effectiveness of CBR is hampered by other factors including the characteristics of the underlying dataset (such as the spread of data and presence of outliers) and the calculation employed to evaluate the distance function (in particular, the treatment of numeric and categorical data)