A LASSO-based model for financial distress of the Vietnamese listed firms: Does the covid-19 pandemic matter?

Vu, Nam T.; Nguyen, Ngoc Hong; Tran, Thao; Le, Binh Thanh; Vo, Duc Hong

doi:10.1080/23322039.2023.2210361

Cited by 4 publications

(5 citation statements)

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“…ROA and ROE are used as proxies for firm performance [ 51 , 52 ]. For explanatory variables, we use CVaR and ICR to proxy for market risk [ 16 , 53 ] and financial distress risk [ 13 , 24 ]. We use HighICR as a dummy variable to interact with market risk.…”

Section: Data and Research Methodologymentioning

confidence: 99%

“…Among such financial ratios, the ICR, an indicator of the firm’s capacity to make its interest payments relying on the internal cash flow, appears to be particularly attractive. The reason is that ICR can be used to evaluate the probability of default for firms without credit ratings [ 13 , 48 ], which is suitable for emerging markets with immature credit ratings. For instance, the prediction model measured by ICR can signal financial distress one year ahead for most firms in the ASEAN region [ 24 ].…”

Section: Literature Review and Research Hypothesesmentioning

confidence: 99%

“…Specifically, financial ratios are often used to detect the financial health of firms. Significant financial ratios include profitability and the leverage ratio [9][10][11][12][13], the liquidity ratio [25][26][27][28], cash-flow ratios [29,30], and the ICR [31][32][33][34].…”

Section: Theoretical Background In Market Risk and Financial Distressmentioning

confidence: 99%

“…Thus, diagnosing financial distress by examining firm performance is critical, especially during extreme events in which the market risk is intensified. However, previous studies focus only on either building models to predict bankruptcy and financial distress [9][10][11][12][13] or measuring market risk during extreme events [14][15][16][17][18]. No studies to date have combined these two separate effects, market risk and financial distress, on firm performance.…”

Section: Introductionmentioning

confidence: 99%

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Market risk, financial distress and firm performance in Vietnam

2023

PLoS ONE

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In 2021, when the Covid-19 pandemic had a severe impact on the economy, a significant number of enterprises in Vietnam temporarily suspended doing business. Previous studies have focused on either model for predicting bankruptcy and financial distress or measuring market risk during extreme events. The effects of market risk and financial distress on a firm’s performance have largely been ignored in the literature, particularly in Vietnam. This study examines the effects of market risk, measured using the conditional value-at-risk technique and financial distress proxied by the interest coverage ratio (ICR) on firm performance for 500 nonfinancial listed firms in Vietnam from 2012 to 2021. We also estimate the optimal ICR for Vietnam’s listed firms. Two estimation techniques are used: dynamic panel models (two-step difference–and system–generalized method of moments) and panel threshold regression. We find that increased market risk reduces firm performance. However, a higher ICR (lower financial distress) also improves a firm’s performance. With increased market risk, the financial performance of firms with a high ICR deteriorates significantly.

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Section: Data and Research Methodologymentioning

confidence: 99%

Section: Literature Review and Research Hypothesesmentioning

confidence: 99%

Section: Theoretical Background In Market Risk and Financial Distressmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Market risk, financial distress and firm performance in Vietnam

2023

PLoS ONE

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“…In the initial year, LASSO identified seven significant features, followed by nine features in the subsequent year, with only four of them overlapping consistently between both years. In [78], the same four features were selected from a set of 12 financial features using the LASSO method, backward and forward stepwise LR techniques. However, the study analyzed only 492 Vietnamese-listed enterprises and the results were compared with the Altman z-score.…”

mentioning

confidence: 99%

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Kuizinienė,

Krilavičius

2024

Electronics

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Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises.

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Accounting‐based variables as an early warning indicator of financial distress in crisis and non‐crisis periods

Powell

Dinh

et al. 2023

Int. J Fin Econ

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Financial integration in the Association of Southeast Asian Nations (ASEAN) region is a key focus of the ASEAN Economic Community. Whereas many studies focus on modelling corporate default, this paper identifies early warning indicators of financial distress before a default, using multiple discriminant analysis (MDA) models with a sample of listed and delisted companies in the ASEAN region. The analysis examines 720 companies in 10 different industries across six ASEAN countries from 1997 to 2016. The study constructs individual models for each country as well as an overall model for the entire region, using both in‐sample and out‐of‐sample approaches. This overall model could be useful for an integrated banking system. To ensure robustness, the study also separately examines the predictive performance of the MDA models across different economic crises: the Asian financial crisis (AFC) from 1997 to 2000, the global financial crisis (GFC) from 2007 to 2009 and their pre‐ and post‐crisis periods. We find that profitability ratios are the best indicators of financial distress in the ASEAN region, followed by liquidity and leverage ratios. In addition, our findings reveal common indicators that can be used to predict financial distress across ASEAN countries. The single model performs reasonably well in predicting financial distress 1 year ahead. In addition, the model is extended to incorporate a market‐based indicator into the MDA models, the distance to default. However, the inclusion of this indicator does not significantly improve the accuracy of the models in predicting financial distress at listed firms in the ASEAN region.

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A LASSO-based model for financial distress of the Vietnamese listed firms: Does the covid-19 pandemic matter?

Cited by 4 publications

References 49 publications

Market risk, financial distress and firm performance in Vietnam

Market risk, financial distress and firm performance in Vietnam

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Accounting‐based variables as an early warning indicator of financial distress in crisis and non‐crisis periods

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