Corporate tax, financial leverage, and portfolio risk

Choi, Paul Moon Sub; Chung, Chune Young; Kim, Dongnyoung

doi:10.1016/j.najef.2020.101264

Cited by 1 publication

(2 citation statements)

References 19 publications

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“…According to (11) and (12), the learning factors are improved in this paper to encourage the movement of particles in the whole search space and strengthen the convergence rate.…”

Section: Changes In V T+1mentioning

confidence: 99%

“…Due to the lack of software and hardware platforms for receiving tax returns and electronic payments, the electronically classified data of companies could not be obtained in previous years. For this reason, a shift to provide an intelligent software system could not detect tax evasion and design a suitable criterion for it [11]. By implementing various software and hardware infrastructures in the country's tax affairs organization, various intelligent structures can be designed and developed along with the above systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Tax Evasion Detection Using Improved Particle Swarm Optimization Algorithm

Mojahedi

Sangar

Màsdàrí

2022

Mathematical Problems in Engineering

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This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively.

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“…According to (11) and (12), the learning factors are improved in this paper to encourage the movement of particles in the whole search space and strengthen the convergence rate.…”

Section: Changes In V T+1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%