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

Software Defect Prediction Using AWEIG+ADACOST Bayesian Algorithm for Handling High Dimensional Data and Class Imbalance Problem

The most important part in software engineering is a software defect prediction. Software defect prediction is defined as a software prediction process from errors, failures, and system errors. Machine learning methods are used by researchers to predict software defects including estimation, association, classification, clustering, and datasets analysis. Datasets of NASA Metrics Data Program (NASA MDP) is one of the metric software that researchers use to predict software defects. NASA MDP datasets contain unbalanced classes and high dimensional data, so they will affect the classification evaluation results to be low. In this research, data with unbalanced classes will be solved by the AdaCost method and high dimensional data will be handled with the Average Weight Information Gain (AWEIG) method, while the classification method that will be used is the Naïve Bayes algorithm. The proposed method is named AWEIG + AdaCost Bayesian. In this experiment, the AWEIG + AdaCost Bayesian algorithm is compared to the Naïve Bayesian algorithm. The results showed the mean of Area Under the Curve (AUC) algorithm AWEIG + AdaCost Bayesian yields better than just a Naïve Bayes algorithm with respectively mean of AUC values are 0.752 and 0.696

Corporate Credit Risk Assessment of BIST Companies

Assessing credit risk allows financial institutions to plan future loans freely, to achieve targeted risk management and gain maximum profitability. In this study, the constructed risk assessment models are on a sample data which consists of financial ratios of enterprises listed in the Bourse Istanbul (BIST). 356 enterprises are classified into three levels as the investment, speculative and below investment groups by ten parameters. The applied methods are discriminant analysis, k nearest neighbor (k-NN), support vector machines (SVM), decision trees (DT) and a new hybrid model, namely Artificial Neural Networks with Adaptive Neuro-Fuzzy Inference Systems (ANFIS). This study will provide a comparison of models to build better mechanisms for preventing risk to minimize the loss arising from defaults. The results indicated that the decision tree models achieve a superior accuracy for the prediction of failure. The model we proposed as an innovation has an adequate performance among the applied model

A Review of Algorithms for Credit Risk Analysis

The interest collected by the main borrowers is collected to pay back the principal borrowed from the depositary bank. In financial risk management, credit risk assessment is becoming a significant sector. For the credit risk assessment of client data sets, many credit risk analysis methods are used. The assessment of the credit risk datasets leads to the choice to cancel the customer\u27s loan or to dismiss the customer\u27s request is a challenging task involving a profound assessment of the information set or client information. In this paper, we survey diverse automatic credit risk analysis methods used for credit risk assessment. Data mining approach, as the most often used approach for credit risk analysis was described with the focus to various algorithms, such as neural networks. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</p

A Credit Rating Model in a Fuzzy Inference System Environment

One of the most important functions of an export credit agency (ECA) is to act as an intermediary between national governments and exporters. These organizations provide financing to reduce the political and commercial risks in international trade. The agents assess the buyers based on financial and non-financial indicators to determine whether it is advisable to grant them credit. Because many of these indicators are qualitative and inherently linguistically ambiguous, the agents must make decisions in uncertain environments. Therefore, to make the most accurate decision possible, they often utilize fuzzy inference systems. The purpose of this research was to design a credit rating model in an uncertain environment using the fuzzy inference system (FIS). In this research, we used suitable variables of agency ratings from previous studies and then screened them via the Delphi method. Finally, we created a credit rating model using these variables and FIS including related IF-THEN rules which can be applied in a practical setting

Random Forest Prediction of IPO Underpricing

The prediction of initial returns on initial public offerings (IPOs) is a complex matter. The independent variables identified in the literature mix strong and weak predictors, their explanatory power is limited, and samples include a sizable number of outliers. In this context, we suggest that random forests are a potentially powerful tool. In this paper, we benchmark this algorithm against a set of eight classic machine learning algorithms. The results of this comparison show that random forests outperform the alternatives in terms of mean and median predictive accuracy. The technique also provided the second smallest error variance among the stochastic algorithms. The experimental work also supports the potential of random forests for two practical applications: IPO pricing and IPO trading.The authors acknowledge financial support granted by the Spanish Ministry of Science under grant ENE2014-56126-C2-2-R

Average Weight Information Gain Untuk Menangani Data Berdimensi Tinggi Menggunakan Algoritma C4.5

Abstract. In the recent decades, a large data are stored by companies and organizations. In terms of use, big data will be useless if not processed into information according to the usability. The method used to process data into information is called data mining. The problem in data mining especially classification is data with a number of attributes that many and each attribute are irrelevant. This study proposes attribute weighting method using weight information gain method, then the attribute weights calculates the average value. Having calculated the average value of the attribute selection, the selected attributes are those with a value weights above average value. Attributes are selected then performed using an algorithm C4.5 classification, this method is named Average Weight Information Gain  C4.5 (AWEIG-C4.5). The results show that AWEIG-C4.5 method is better than C4.5 method with the accuracy of the average value of each is 0.906 and 0.898. Keywords: data mining, high dimensional data, weight information gain, C4.5 algorithmAbstrak. Dalam beberapa dekade terakhir, data yang besar disimpan oleh perusahaan dan organisasi. Dari segi penggunaan, data besar tersebut akan menjadi tidak berguna jika tidak diolah menjadi informasi yang sesuai dengan kegunaan. Metode yang digunakan untuk mengolah data menjadi informasi adalah data mining. Masalah dalam data mining khususnya klasifikasi adalah data dengan jumlah atribut yang banyak atau dalam bahasa komputer disebut data berdimensi tinggi. Pada penelitian ini diusulkan metode pembobotan atribut menggunakan metode weight information gain, kemudian bobot atribut tersebut dihitung nilai rata-rata. Setelah dihitung nilai rata-rata dilakukan pemilihan atribut, atribut yang dipilih adalah atribut dengan nilai bobot di atas nilai rata-rata. Atribut yang terpilih kemudian dilakukan klasifikasi menggunakan algoritma C4.5, metode ini diberi nama Average Weight Information Gain C4.5 (AWEIG-C4.5). Hasil penelitian menunjukkan metode AWEIG-C4.5 lebih baik daripada metode C4.5 dengan nilai rata-rata akurasi masing-masing adalah 0,906 dan 0,898. Dari uji paired t-Test terdapat perbedaan signifikan antara metode AWEIG C4.5 dengan metode C4.5.Kata Kunci: data mining, data berdimensi tinggi, weight information gain, algoritma C4.

Assessing and predicting small industrial enterprises’ credit ratings:A fuzzy decision-making approach

Corporate credit-rating assessment plays a crucial role in helping financial institutions make their lending decisions and in reducing the financial constraints of small enterprises. This paper presents a new approach for small industrial enterprises’ credit-rating assessment using fuzzy decision-making methods, and tests it using real bank loan data from 1,820 small industrial enterprises in China. The procedure of the proposed rating approach includes (1) using triangular fuzzy numbers to quantify the qualitative evaluation indicators; (2) adopting a correlation analysis, univariate analysis and stepping backwards feature selection method to select the input features; (3) employing the best-worst method (BWM) combined with the entropy weight method (EWM), the fuzzy c-means algorithm and the technique for order of preference by similarity to ideal solution (TOPSIS) to classify small enterprises into rating classes; and (4) applying the lattice degree of nearness to predict a new loan applicant’s rating. We also conduct a 10-fold cross-validation to evaluate the predictive performance of our proposed approach. The predictive results demonstrate that our proposed data-processing and feature selection approaches have better accuracy than the alternative approaches in predicting default, offering bankers a new valuable rating system to assist their decision making

PENGARUH PROFITABILITAS, LIKUIDITAS, SIZE DAN LEVERAGE PERUSAHAAN TERHADAP YIELD OBLIGASI DENGAN PERINGKAT OBLIGASI SEBAGAI VARIABEL INTERVENING

This Study aims to get empirical evidence on the influence of profitability (ROA), liquidity (QR), size (LnSize), and leverage (DER) on the bond yield (YTM) with bond rating as an intervening variable. The population in this study is all companies listed in Indonesian Stock Exchange (BEI) during 2008-2012 which has bonds in period 2009-2013.These bonds were assessed by rating agents of PT. Pefindo. Applying path analysis test, path one was used first one, used to test the influence of profitability, liquidity, size and leverage on bond rating. Second is used to test the influence of profitability, liquidity, size, leverage and bond rating on bond yield. Sobel test is used to test the mediating/intervening effect. The first test one showes that profitability and size had significant positive influence on bond rating while liquidity had significant negative influence, leverage had no influence on bond rating. The test second showes that profitability, liquidity, size, leverage and bond rating do not influence bond yield. Moreover sobel test showes that bond rating could not mediate the influence of profitability, liquidity, size and leverage on bond yield.Keywords:    bond rating, bond yield, leverage, liquidity, profitability, size