Financial distress prediction using a corrected feature selection measure and gradient boosted decision tree

Qian, Hongyi; Wang, Baohui; Yuan, Minghe; Gao, Songfeng; Song, You

doi:10.1016/j.eswa.2021.116202

Cited by 75 publications

(18 citation statements)

References 35 publications

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“…The Random forest and gradient boosting decision tree regression are used to import the random states to avoid the created bias of the linear ML regression algorithms. While the RFR may better handle the bias, under-fitting might be a problem 15 . On the other hand, the GBDTR as a hybrid method avoids the issues of the previous methods.…”

Section: Resultsmentioning

confidence: 99%

Machine learning-assisted analysis for agronomic dataset of 49 Balangu (Lallemantia iberica L.) ecotypes from different regions of Iran

Shafagh-Kolvanagh

Dehghanian

Mohammadi-Nassab

et al. 2022

Sci Rep

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The Balangu (Lallemantia iberica) species have a high gastronomical impact in the Middle East and Balkan region. It is widely used in the local food industry, such as confectionery, edible oil, and protein food. In this study, 49 ecotypes were collected from different regions of Iran. 37 agronomic traits were measured during the growing season and at harvest time. To find the correlation between the grain yield per unit area, grain yield per single plant (GYSP), oil percent (OP), and protein percent (PP) with other measured traits, which these were utilized as the labels of different machine learning (ML) procedures including Linear Regression (LR), Support Vector Regression (SVR), Random Forest Regression (RFR), and Gradient Boosting Decision Tree Regression (GBDTR). It was observed that there is a linear relationship between the measured agronomic traits and the considered labels. So, the LR, RFR, and GBDTR models showed the lowest mean absolute error, mean square error, and root mean square error than SVR models and good prediction ability of the test data. Although, the RFR and GBDTR have naturally lower bias than other methods in this study, but the GBDTR scheme is preferred because of the over-fitting shortcoming of the RFR technique. The GBDTR method showed better results rather than the other ML regression methods according to the RMSE 3.302, 0.040, 0.028, and 0.060 for GYUA, GYSP, OP, and PP, respectively.

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Section: Resultsmentioning

confidence: 99%

Machine learning-assisted analysis for agronomic dataset of 49 Balangu (Lallemantia iberica L.) ecotypes from different regions of Iran

Shafagh-Kolvanagh

Dehghanian

Mohammadi-Nassab

et al. 2022

Sci Rep

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“…It visually demonstrates the dependence between the response variable and a set of predictors marginalizing over the values of remaining input features connected in a machine learning model ( Qian et al, 2022 ). PDP can effectively reveal linear, monotonic, or complex types of interrelationships.…”

Section: Methodsmentioning

confidence: 99%

Role of proliferation COVID-19 media chatter in predicting Indian stock market: Integrated framework of nonlinear feature transformation and advanced AI

Ghosh

Alfaro

Gámez

et al. 2023

Expert Systems with Applications

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“…Hence, many alternative models have been developed to address these potential biases and to overcome the restrictive assumptions of MDA. These alternative models, for instance, include the conditional logit model [ 20 ], the probit model [ 21 ], the discrete hazard logit model [ 22 ], the market-based model [ 23 ], the machine learning model [ 24 ], or the deep learning-based model [ 25 ].…”

Section: Review Of Related Literaturementioning

confidence: 99%

Examining financial distress of the Vietnamese listed firms using accounting-based models

Tran

Nguyen

et al. 2023

PLoS ONE

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Financial distress is generally considered the most severe consequence for firms with poor financial performance. The emergence of the Covid-19 pandemic has adversely impacted the global business system and exacerbated the number of financially distressed firms in many countries. Only firms with strong financial fundamentals can survive extreme events such as the Covid-19 pandemic and the ongoing Russia-Ukraine conflict. Vietnam is no exception. However, studies examining financial distress using accounting-based indicators, particularly at the industry level, have largely been ignored in the Vietnamese context, particularly with the emergence of the Covid-19 pandemic. This study, therefore, comprehensively examines financial distress for 500 Vietnamese listed firms during the 2012–2021 period. Our study uses interest coverage and times-interest-earned ratios to proxy a firm’s financial distress. First, our findings confirm the validity of Altman’s Z”- score model in Vietnam only when the interest coverage ratio is used as a proxy for financial distress. Second, our empirical findings indicate that only four financial ratios, including EBIT/Total Assets, Net Income/Total Assets, Total Liabilities/Total Assets, and Total Equity/Total Liabilities, can be used in predicting financial distress in Vietnam. Third, our analysis at the industry level indicates that the "Construction & Real Estates" industry, a significant contributor to the national economy, exhibits the most significant risk exposure, particularly during the Covid-19 pandemic. Policy implications have emerged based on the findings from this study.

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Financial distress prediction using a corrected feature selection measure and gradient boosted decision tree

Cited by 75 publications

References 35 publications

Machine learning-assisted analysis for agronomic dataset of 49 Balangu (Lallemantia iberica L.) ecotypes from different regions of Iran

Machine learning-assisted analysis for agronomic dataset of 49 Balangu (Lallemantia iberica L.) ecotypes from different regions of Iran

Role of proliferation COVID-19 media chatter in predicting Indian stock market: Integrated framework of nonlinear feature transformation and advanced AI

Examining financial distress of the Vietnamese listed firms using accounting-based models

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