scholarly journals CROSS ENTROPY UNTUK OPTIMASI LAGRANGE MULTIPLIERS PADA SUPPORT VECTOR MACHINES SEBAGAI MODEL PREDIKSI FINANCIAL DISTRESS

Heuristic ◽  
2016 ◽  
Vol 13 (02) ◽  
Author(s):  
Herlina .
Keyword(s):  
Support Vector Machines ◽  
Prediction Model ◽  
Lagrange Multipliers ◽  
Financial Distress ◽  
Computing Time ◽  
Cross Entropy ◽  
Computational Time ◽  
Support Vector ◽  
Manufacturing Companies ◽  
Vector Machines

The competence in predicting financial distress becomes an important research due tothe advantage in preventing companies financial failure. Besides, financial distressprediction model will give benefit to the investors and creditors. This research developa financial distress prediction model for listed manufacturing companies in Indonesiausing Support Vector Machines (SVM). Mathematically, SVM is formulated in the formof quadratic programming, which requires high computational time in finding theoptimal solution. In this research, Cross Entropy (CE) is used to optimize one of theSVM’s parameter that is Lagrange multipliers to find the optimal solution or nearoptimal solution of dual Lagrange SVM. The accuracy of the prediction model andcomputation time will be compared between standard SVM and CE-SVM. Finally, notethat the CE-SVM can solve classification problems in computing time 9.7 times shorterthan the standard SVM with good accuracy results. Keywords: cross entropy, lagrange multipliers, support vector machines, financialdistress

Prediction Model of Non-Iterative Least Squares Support Vector Machines Based on Quadratic Renyi-Entropy

2011 ◽  
Vol 474-476 ◽  
pp. 967-972
Author(s):  
Guan Hua Zhao
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Financial Distress ◽  
Computing Time ◽  
Forecast Accuracy ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Support Vector ◽  
Svm Model ◽  
Vector Machines

By comparing and analysing the model of non-iterative least squares support vector machines (LS-SVM) based on quadratic Renyi-entropy, traditional LS-SVM model and standard support vector machines (SVM) model, this paper concludes whether the number of training samples or computing time,non-iterative LS-SVM model based on quadratic Renyi-entropy are significantly better than the model of traditional LS-SVM and standard SVM model and it also proves the effectiveness of applying the concept of quadratic Renyi-entropy on financial distress prediction. At the same time, by the comparison of different point of 3 years of ST which is from 1to 2, the author concludes the forecast accuracy of 1 year ago before ST, the further distance away from the piont of ST, the lower the prediction accuracy is.

scholarly journals Classification of the Priority of Auditing XBRL Instance Documents with Fuzzy Support Vector Machines Algorithm

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Guang-Yih Sheu
Keyword(s):  
Support Vector Machines ◽  
Financial Distress ◽  
Fuzzy Variable ◽  
Financial Ratios ◽  
Misclassification Rate ◽  
Benford’S Law ◽  
Support Vector ◽  
Vector Machines ◽  
Benford's Law ◽  
Fuzzy Support Vector Machines

Concluding the conformity of XBRL (eXtensible Business Reporting Language) instance documents law to the Benford's law yields apparently different results before and after a company's financial distress. These results bring an idea of finding fraudulent documents from the inspection of financial ratios since the unacceptable conformity implies a large likelihood of a fraudulent document. Fuzzy support vector machines models are developed to implement such an idea. The dependent variable is a fuzzy variable quantifying the conformity of an XBRL instance document to the Benford's law; whereas, independent variables are financial ratios. Nevertheless, insufficient data are available to define any membership function for describing the fuzziness in independent and dependent variables, but the interval factor method is introduced to express that fuzziness. Using the resulting fuzzy support vector machines model, it is suggested that the price-to-book ratio versus equity ratio may be used to classify the priority of auditing XBRL instance documents. The misclassification rate is less than 30 \%. In conclusion, a new and promising application of fuzzy support vector machines algorithm has been found in this study.

CREDIT SCORING MODELS WITH AUC MAXIMIZATION BASED ON WEIGHTED SVM

2009 ◽  
Vol 08 (04) ◽  
pp. 677-696 ◽  
Author(s):  
LIGANG ZHOU ◽  
KIN KEUNG LAI ◽  
JEROME YEN
Keyword(s):  
Support Vector Machines ◽  
Quantitative Methods ◽  
Credit Scoring ◽  
Feature Weighting ◽  
Computational Time ◽  
Support Vector ◽  
Operating Characteristics ◽  
Auc Maximization ◽  
Vector Machines ◽  
Rbf Kernel

Credit scoring models are very important tools for financial institutions to make credit granting decisions. In the last few decades, many quantitative methods have been used for the development of credit scoring models with focus on maximizing classification accuracy. This paper proposes the credit scoring models with the area under receiver operating characteristics curve (AUC) maximization based on the new emerged support vector machines (SVM) techniques. Three main SVM models with different features weighted strategies are discussed. The weighted SVM credit scoring models are tested using 10-fold cross validation with two real world data sets and the experimental results are compared with other six traditional methods including linear regression, logistic regression, k nearest neighbor, decision tree, and neural network. Results demonstrate that weighted 2-norm SVM with radial basis function (RBF) kernel function and t-test feature weighting strategy has the overall better performance with very narrow margin than other SVM models. However, it also consumes more computational time. In considering the balance of performance and time, least squares support vector machines (LSSVM) with RBF kernel maybe a better choice for large scale credit scoring applications.

scholarly journals MODEL PREDIKSI FINANCIAL DISTRESS PADA PERUSAHAAN MANUFAKTUR SEKTOR INDUSTRI BARANG DAN KONSUMSI

2019 ◽  
Vol 3 (2) ◽  
pp. 77
Author(s):  
Herlina Herlina ◽  
Ahmad Ridho’i ◽  
Anggie Erma Yunita ◽  
Mega Puja Azhari ◽  
Ade Reynaldi Saputra
Keyword(s):  
Logistic Regression ◽  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Financial Distress ◽  
Particle Swarm ◽  
Support Vector ◽  
Multiple Discriminant Analysis ◽  
Swarm Optimization ◽  
Vector Machines

Kesulitan keuangan (financial distress) adalah sebuah tahapan yang akan dilalui oleh sebuah perusahaan sebelum mengalami kebangkrutan. Dengan alasan tersebut maka kemampuan untuk memprediksi kesulitan keuangan dapat menjadi informasi yang bermanfaat bagi perusahaan maupun investor. Penelitian mengenai financial distress sudah dimulai dari penelitian Altman pada tahun 1968 menggunakan metode Multiple Discriminant Analysis (MDA). Dimulai dari penelitian Altman, muncul penelitian-penelitian lainnya menggunakan pengembangan metode statistik, seperti Logistic Regression. Dari metode statistik kemudian berkembang dengan munculnya penelitian-penelitian menggunakan metode-metode kecerdasan buatan, serta algoritma evolusi untuk berusaha mendapatkan model prediksi financial distress yang akurat. Tujuan dari penelitian ini adalah untuk membandingkan tingkat akurasi dari model prediksi financial distress perusahaan manufaktur terbuka pada sektor industri barang konsumsi yang terdaftar pada Bursa Efek Indonesia menggunakan metode kecerdasan buatan serta algoritma evolusi. Metode yang digunakan untuk metode kecerdasan buatan adalah metode Support Vector Machines dan untuk model algoritma evolusi menggunakan metode Particle Swarm Optimization-Support Vector Machines. Tingkat akurasi dari masing-masing metode akan diukur dari prosentase misklasifikasi terkecil yang dihasilkan. Dari pengujian model menggunakan metode Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 11,11% dengan menggunakan Kernel Linear dan untuk metode Particle Swarm Optimization-Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 5,56% dengan menggunakan Kernel RBF, ? = 2.

Sign in / Sign up

Export Citation Format

Share Document