Predicting unlisted SMEs' default: Incorporating market information on accounting-based models for improved accuracy

Andrikopoulos, Panagiotis; Khorasgani, Amir

doi:10.1016/j.bar.2018.02.003

Cited by 29 publications

(19 citation statements)

References 62 publications

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“…Thus, according to the above notation, our data base can be described as follows ( Table 2). and [22] almost all factors that may influence the accuracy of the prediction of the models have been analyzed through empirical research. As a result of these studies financial ratios eventually became widely used in predicting corporate collapse.…”

Section: Chief Executive Officer and Chairman Are Different Persons Amentioning

confidence: 99%

CEO Duality and Firm Distress

Kolias¹,

Arnis²,

Kypriotelis³

2019

OJAcct

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This study examines the relationship between firm performance and corporate governance structure, mainly leadership structure. The leadership structure is strongly related to CEO duality. There are several aspects and dimensions of this relation, which may influence the corporate performance but this study focuses on the extreme situation where this relation reaches its ends, namely the corporation collapse. This paper has considered the factors that can cause corporate failure and its governance inability to attain their objectives. Data were collected from 385 bankrupt and 14.000 non-bankrupt unlisted Greek firms for a period of ten years in order for a model to be drawn, indicating the possibility of those firms incorporated under duality to bankrupt.

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Section: Chief Executive Officer and Chairman Are Different Persons Amentioning

confidence: 99%

CEO Duality and Firm Distress

Kolias¹,

Arnis²,

Kypriotelis³

2019

OJAcct

View full text Add to dashboard Cite

show abstract

“…In contrast, the unemployment level and the average duration of insolvency proceedings have positively correlated the probability of bankruptcy. Attempting to integrate accounting-based models, Andrikopoulos and Khorasga ( 2018 ) propose a ‘hybrid’ default prediction model, combining traditional financial ratios of unlisted SMEs with market information of listed SMEs. Ciampi et al ( 2018 ) highlight the need to design SME prediction models based on quantitative predictors other than financial ratios and macroeconomic variables.…”

Section: Results Of the Vos Analysis And The Systematic Literature Rementioning

confidence: 99%

Rethinking SME default prediction: a systematic literature review and future perspectives

et al. 2021

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Over the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007–2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results.

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“…Indeed, the LR models are popular in the financial distress prediction literature. For instance, Andrikopoulos and Khorasgani [17] applied LR to predict defaults by small enterprises by integrating accounting and market information. Unfortunately, the LR model failed to effectively handle the highly imbalanced dataset of defaulted and nondefaulted firms.…”

Section: Literature Reviewmentioning

confidence: 99%

Tax Default Prediction Using Feature Transformation-Based Machine Learning

et al. 2021

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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.INDEX TERMS Default prediction, corporate tax, machine learning, feature transformation.

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Predicting unlisted SMEs' default: Incorporating market information on accounting-based models for improved accuracy

Cited by 29 publications

References 62 publications

CEO Duality and Firm Distress

CEO Duality and Firm Distress

Rethinking SME default prediction: a systematic literature review and future perspectives

Tax Default Prediction Using Feature Transformation-Based Machine Learning

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