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Tax Default Prediction Using Feature Transformation-Based Machine Learning
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Mohammed Mohi Uddin,
Md. Shahriare Satu,
Md. Imran Khan ,
Petr Hajek
IEEE Access, Volume: 9, Pages: 19864 - 19881
Swansea University Author: Abedin Abedin
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DOI (Published version): 10.1109/access.2020.3048018
Abstract
This study proposes to address the economic significance of unpaid taxes by using an automatic system for predicting a tax default. Too little attention has been paid to tax default prediction in the past. Moreover, existing approaches tend to apply conventional statistical methods rather than advan...
| Published in: | IEEE Access |
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| ISSN: | 2169-3536 |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64271 |
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| Abstract: |
This study proposes to address the economic significance of unpaid taxes by using an automatic system for predicting a tax default. Too little attention has been paid to tax default prediction in the past. Moreover, existing approaches tend to apply conventional statistical methods rather than advanced data analytic approaches, including state-of-the-art machine learning methods. Therefore, existing studies cannot effectively detect tax default information in real-world financial data because they fail to take into account the appropriate data transformations and nonlinear relationships between early-warning financial indicators and tax default behavior. To overcome these problems, this study applies diverse feature transformation techniques and state-of-the-art machine learning approaches. The proposed prediction system is validated by using a dataset showing tax defaults and non-defaults at Finnish limited liability firms. Our findings provide evidence for a major role of feature transformation, such as logarithmic and square-root transformation, in improving the performance of tax default prediction. We also show that extreme gradient boosting and the systematically developed forest of multiple decision trees outperform other machine learning methods in terms of accuracy and other classification performance measures. We show that the equity ratio, liquidity ratio, and debt-to-sales ratio are the most important indicators of tax defaults for 1-year-ahead predictions. Therefore, this study highlights the essential role of well-designed tax default prediction systems, which require a combination of feature transformation and machine learning methods. The effective implementation of an automatic tax default prediction system has important implications for tax administration and can assist administrators in achieving feasible government expenditure allocations and revenue expansions. |
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| Keywords: |
Default prediction, corporate tax, machine learning, feature transformation. |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
This work has been supported by the Key Projects of National Natural Science Foundation of China (71731003), the General Projects of National Natural Science Foundation of China (72071026, 71873103, 71971051, and 71971034), the Youth Projects of National Natural Science Foundation of China (71901055, and 71903019), the Major Projects of National Social Science Foundation of China (18ZDA095), the scientific research project of the Czech Sciences Foundation Grant No. 19-15498S. This paper has also been supported by the Bank of Dalian and Postal Savings Bank of China. We thank the organizations mentioned above. |
| Start Page: |
19864 |
| End Page: |
19881 |