Financial Distress Prediction Using Hybrid Machine Learning Techniques

Chen, Suduan; Shen, Zong-De

doi:10.9734/ajeba/2020/v16i230231

Cited by 11 publications

(17 citation statements)

References 35 publications

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“…Moreover, the way the bagging procedure is implemented addresses the multicollinearity issue, which is a serious concern in traditional econometric methodologies (Garg and Tai 2013). As outlined in the previous section, random forests have been used in the literature in recent years to address solvency risk and financial distress (Fantazzini and Figini 2009;Behr and Weinblat 2017a;Ruxanda et al 2018;Chen and Shen 2020;Gregova et al 2020).…”

Section: Methodsmentioning

confidence: 99%

“…The indicators we used are based on the corporate finance and financial analysis literature (Brealey et al 2019;Berk and DeMarzo 2019), as well as in existing literature that addressed similar topics-Behr and Weinblat (2017a), Behr and Weinblat (2017b), Balasubramanian et al (2019), or Chen and Shen (2020), under the restrictions of data availability. The final sample distributions across countries are the following: Belgium (54 companies), Croatia (65), Czech Republic (46), Estonia (42),Finland (195), France (1271), Germany (12), Greece (19), Hungary (63), Ireland (2), Latvia (77), Lithuania (13), Netherlands (9), Poland (541), Portugal (298), Romania (644), Slovakia (43), Slovenia (37), Spain (366), and Sweden (799).…”

Section: Methodsmentioning

confidence: 99%

“…This approach is still employed in numerous studies-see, for example, (Lee et al 2010;Ong et al 2011;Jabeur and Fahmi 2018;Ruxanda et al 2018;Rahman et al 2021;Shetty and Vincent 2021), given its relative ease of use and superior econometric performance comparative to multidimensional discriminant analysis models. Chen and Shen (2020), in a study on financial distress, in a sample of 262 financially distressed and 786 non-financially distressed Taiwanese listed companies during 2012-2018, stated that the most rigorous and accurate methods for building models that measure financial distress were LASSO-CART (89.74%) and LASSO-RF (86.30%). Research on financial distress and solvency risk benefited enormously in the last two decades, from the advent and further development of artificial neural networks, machine learning, and artificial intelligence.…”

Section: Research Backgroundmentioning

confidence: 99%

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Solvency Risk and Corporate Performance: A Case Study on European Retailers

Horobeț

Curea

Popoviciu

et al. 2021

JRFM

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This paper proposes a new approach toward understanding the financial performance dynamics in the EU retail sector (pre-pandemic); we focus on the connection between indebtedness and solvency risk and other areas of corporate performance (e.g., liquidity, assets efficiency, and profitability). Its contribution resides in identifying the drivers behind solvency risk in a sector that went through significant transformations in recent decades, as well as the links between the various areas of performance of retailers, and their impacts on solvency risk, using the machine-learning random forest methodology. The results indicate a declining trend for solvency risk of EU food retailers after the global financial crisis and up until the beginning of the pandemic, which may reflect their maturity on the market, but also an adjustment to legal changes in the EU, meant to equalize the tax advantages of debt versus equity financing. Solvency risk accompanied by liquidity risk is a mark of the retail sector, and our results indicate that the most critical trade that EU retailers face is between solvency risk and liquidity, but is fading over time. The volatility of liquidity levels is an important predictor of solvency risk; hence, sustaining a stable and good level of liquidity supports lower risks of financial distress, and may mitigate the shock impacts for EU retailers. A higher solvency risk was accompanied by increased efficiency of asset use, but reduced profitability levels, which led to higher returns available to shareholders for high solvency risk retailers. Overall, retailers should focus on operational performance evidenced by financial indicator levels than on the volatility of these indicators as predictors of solvency risk.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

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Solvency Risk and Corporate Performance: A Case Study on European Retailers

Horobeț

Curea

Popoviciu

et al. 2021

JRFM

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show abstract

“…The global economy, including Taiwan's, was seriously hit. In Taiwan, many companies and factories closed, causing huge unemployment and heavy investment losses [1,2]. The 2008 global financial crisis shows that even powerful international enterprises may encounter financial distress and must be constantly alert to their financial conditions [3].…”

Section: Introductionmentioning

confidence: 99%

Financial Distress Prediction: A Hybrid Tracking Model Approach

Shen

Chen

2022

AJEBA

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The purpose of this study was to build a highly accurate corporate financial distress tracking and prediction model based on hybrid machine learning technology. The research data were from Taiwan Economic Journal, and the research subjects were enterprises with financial distress risk announced in September 2022. In consideration of enterprise features, this study excluded the finance and insurance industries. The research period was three years (2019, 2020, and 2021) before the distress announcement. This study matched enterprises with financial distress and enterprises without financial distress (normal enterprises) at a ratio of 1:1 for each year. The sample size for each year included 374 enterprises with financial distress and 374 enterprises without financial distress. This study applied several machine learning technologies. At first, important variables were screened by applying artificial neural networks (ANNs). Next, prediction models were built based on decision tree C5.0 and random forest (RF) and were compared. According to the empirical result, the ANN-RF model provided a higher accuracy.

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“…The application of data mining [11] and machine learning techniques [12][13][14], with a specific application in Knowledge Discovery from Databases (KDD) algorithms, makes it possible to find real relationships and dependencies between processes and discover knowledge to improve organizational transparency. This contributes to the sustainability of companies that can focus their investments on the technological and corporate governance processes (internal controls, compliance, systems outsourcing, quality management, automation, risk control, information security, etc.)…”

Section: Introductionmentioning

confidence: 99%

Data Mining to Assess Organizational Transparency across Technology Processes: An Approach from IT Governance and Knowledge Management

et al. 2021

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Information quality and organizational transparency are relevant issues for corporate governance and sustainability of companies, as they contribute to reducing information asymmetry, decreasing risks, and improving the conduct of decision-makers, ensuring an ethical standard of organizational control. This work uses the COBIT framework of IT governance, knowledge management, and machine learning techniques to evaluate organizational transparency considering the maturity levels of technology processes applied in 285 companies of southern Brazil. Data mining techniques have been methodologically applied to analyze the 37 processes in four different domains: Planning and organization, acquisition and implementation, delivery and support, and monitoring. Four learning techniques for knowledge discovery have been used to build a computational model that allowed us to evaluate the organizational transparency level. The results evidence the importance of IT performance monitoring and assessment, and internal control processes in enabling organizations to improve their levels of transparency. These processes depend directly on the establishment of IT strategic plans and quality management, as well as IT risk and project management, therefore an improvement in the maturity of these processes implies an increase in the levels of organizational transparency and their reputational, financial, and accountability impact.

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Financial Distress Prediction Using Hybrid Machine Learning Techniques

Cited by 11 publications

References 35 publications

Solvency Risk and Corporate Performance: A Case Study on European Retailers

Solvency Risk and Corporate Performance: A Case Study on European Retailers

Financial Distress Prediction: A Hybrid Tracking Model Approach

Data Mining to Assess Organizational Transparency across Technology Processes: An Approach from IT Governance and Knowledge Management

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