Newton Method-based Subspace Support Vector Data Description

In this paper, we present an adaptation of Newton's method for the optimization of Subspace Support Vector Data Description (S-SVDD). The objective of S-SVDD is to map the original data to a subspace optimized for one-class classification, and the iterative optimization process of data mapping and description in S-SVDD relies on gradient descent. However, gradient descent only utilizes first-order information, which may lead to suboptimal results. To address this limitation, we leverage Newton's method to enhance data mapping and data description for an improved optimization of subspace learning-based one-class classification. By incorporating this auxiliary information, Newton's method offers a more efficient strategy for subspace learning in one-class classification as compared to gradient-based optimization. The paper discusses the limitations of gradient descent and the advantages of using Newton's method in subspace learning for one-class classification tasks. We provide both linear and nonlinear formulations of Newton's method-based optimization for S-SVDD. In our experiments, we explored both the minimization and maximization strategies of the objective. The results demonstrate that the proposed optimization strategy outperforms the gradient-based S-SVDD in most cases.Comment: 8 pages, 2 figures, 2 tables, 1 Algorithm. Accepted at IEEE Symposium Series on Computational Intelligence 202

A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

Privatization in oligopoly : the impact of the shadow cost of public funds

The aim of this paper is to investigate the welfare eect of privatization in oligopoly when the government takes into account the distortionary eect of rising funds by taxation (shadow cost of public funds). We analyze the impact of the change in ownership not only on the objective function of the rms, but also on the timing of competition by endogenizing the determination of simultaneous (Nash-Cournot) versus sequential (Stackelberg) games. We show that, absent effciency gains, privatization never increases welfare. Moreover, even when large effciency gains are realized, an ineffcient public rm may be preferred

Mixed duopoly, privatization and the shadow costs of public funds

The purpose of this article is to investigate how the introduction of the shadow cost of public funds in the utilitarian measure of the economywide welfare affects the behavior of a welfare maximizer public firm in a mixed duopoly. We prove that when firms play simultaneously, the mixed-Nash equilibrium can dominate any Cournot equilibria implemented after a privatization, with or without efficiency gains. This can be true both in terms of welfare and of public firm's profit. When we consider endogenous timing, we show that either mixed- Nash, private leadership or both Stackelberg equilibria can result as subgameperfect Nash equilibria (SPNE). As a consequence, the sustainability of sequential equilibria enlarges the subspace of parameters such that the market performance with an inefficient public firm is better than the one implemented after a full-efficient privatization. Absent efficiency gains, privatization always lowers welfare

ENTERPRISE CREDIT RISK ASSESSMENT ANALYZING THE DATA OF SHORT TERM ACTIVITY PERIOD

This research investigates the possibility to classify the companies into default and non-default groups analyzing the financial data of 1 year. The developed statistical model enables banks to predict the default of new companies that have no sufficient financial information for the credit risk assessment using other models. The classification and regression tree predicts the default of companies with the 96% probability. The complementary analysis the financial data of 2 years by probit model allows to increase the classification accuracy to 99%. DOI: https://doi.org/10.15544/ssaf.2012.2

An ICA-ensemble learning approaches for prediction of RNA-seq malaria vector gene expression data classification

Malaria parasites introduce outstanding life-phase variations as they grow across multiple atmospheres of the mosquito vector. There are transcriptomes of several thousand different parasites. (RNA-seq) Ribonucleic acid sequencing is a prevalent gene expression tool leading to better understanding of genetic interrogations. RNA-seq measures transcriptions of expressions of genes. Data from RNA-seq necessitate procedural enhancements in machine learning techniques. Researchers have suggested various approached learning for the study of biological data. This study works on ICA feature extraction algorithm to realize dormant components from a huge dimensional RNA-seq vector dataset, and estimates its classification performance, Ensemble classification algorithm is used in carrying out the experiment. This study is tested on RNA-Seq mosquito anopheles gambiae dataset. The results of the experiment obtained an output metrics with a 93.3% classification accuracy

Mixed duopoly, privatization and the shadow cost of public funds

The purpose of this paper is to investigate the effect of privatization in a mixed duopoly, where a private firm competes in quantities with a welfare-maximizing public firm. We consider two inefficiencies of the public sector: a possible cost inefficiency, and an allocative inefficiency due to the distortionary effect of taxation (shadow cost of public funds). Furthermore, we analyze the effect of privatization on the timing of competition by endogenizing the determination of simultaneous (Nash-Cournot) versus sequential (Stackelberg) games using the model developed by Hamilton and Slutsky (1990). The latter is especially relevant for the analysis of privatization, given that results and policy prescription emerged in the literature crucially rely on the type of competition assumed. We show that privatization has generally the effect of shifting from Stackelberg to Cournot equilibrium and that, absent efficiency gains privatization never increases welfare. Moreover, even when large efficiency gains are realized, an inefficient public firm may be preferred.mixed oligopoly, privatization, endogenous timing, distortionary taxes.

Corporate Credit Rating: A Survey

Corporate credit rating (CCR) plays a very important role in the process of contemporary economic and social development. How to use credit rating methods for enterprises has always been a problem worthy of discussion. Through reading and studying the relevant literature at home and abroad, this paper makes a systematic survey of CCR. This paper combs the context of the development of CCR methods from the three levels: statistical models, machine learning models and neural network models, summarizes the common databases of CCR, and deeply compares the advantages and disadvantages of the models. Finally, this paper summarizes the problems existing in the current research and prospects the future of CCR. Compared with the existing review of CCR, this paper expounds and analyzes the progress of neural network model in this field in recent years.Comment: 11 page

Mixed duopoly, privatization and the shadow cost of public funds

The purpose of this paper is to investigate the effect of privatization in a mixed duopoly, where a private firm complete in quantities with a welfare-maximizing public firm. We consider two inefficiencies of the public sector : a possible cost inefficiency and an allocative inefficiency due to the distortionary effect of taxation (shadow cost of public funds). Furthermore, we analyze the effect of privatization on the timing of competition by endogenezing the determiantion of simultaneous (Nash-Cournot) versus sequential (Stackelberg) games using the model developed by Hamilton and Slutsky (1990). The latter is especially relevant for the analysis of privatization, given that results and policy prescription emerged in the literature crucially rely on the type of competition assumed. We show that privatization has generally the effect of shifting from Stackelberg to Cournot equilibrium and that, absent efficiency gains privatization never increases welfare. Moreover, even when large efficiency gains are realized, an inefficient public firm may be preferred.mixed oligopoly, privatization, endogenous timing, distortionary taxes

Forecasting Financial Distress With Machine Learning – A Review

Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical & Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic