Bankruptcy prediction in the agribusiness sector: Lessons from quantitative and qualitative approaches

Boratyńska, Katarzyna; Grzegorzewska, Emilia

doi:10.1016/j.jbusres.2018.01.028

Cited by 35 publications

(21 citation statements)

References 26 publications

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“…Combining relational and economic data gives the most substantial performance increase (Tobback et al 2017). Managers are expected to carefully build bankruptcy prediction models and adjust them to the size, type, and risk of the activities of the company (Boratyńska and Grzegorzewska 2018).…”

Section: Earnings Management and Bankruptcy Predictionmentioning

confidence: 99%

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Ogachi

Ndege

Gaturu

et al. 2020

JRFM

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Predicting bankruptcy of companies has been a hot subject of focus for many economists. The rationale for developing and predicting the financial distress of a company is to develop a predictive model used to forecast the financial condition of a company by combining several econometric variables of interest to the researcher. The study sought to introduce deep learning models for corporate bankruptcy forecasting using textual disclosures. The study constructed a comprehensive study model for predicting bankruptcy based on listed companies in Kenya. The study population included all 64 listed companies in the Nairobi Securities Exchange for ten years. Logistic analysis was used in building a model for predicting the financial distress of a company. The findings revealed that asset turnover, total asset, and working capital ratio had positive coefficients. On the other hand, inventory turnover, debt-equity ratio, debtors turnover, debt ratio, and current ratio had negative coefficients. The study concluded that inventory turnover, asset turnover, debt-equity ratio, debtors turnover, total asset, debt ratio, current ratio, and working capital ratio were the most significant ratios for predicting bankruptcy.

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Section: Earnings Management and Bankruptcy Predictionmentioning

confidence: 99%

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Ogachi

Ndege

Gaturu

et al. 2020

JRFM

View full text Add to dashboard Cite

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“…However, past studies as those above mainly focus on a specific sector such as manufacture, bank, hotel, agribusiness, etc. [22][23][24]34]. Until now, to the best of our knowledge, only Doumpos et al [2] has explored corporate failure forecasting in the energy sector.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is well known that the character of corporates in different sectors is quite different [20]. Recently, researchers have paid more attention to the corporate failure forecasting in a specific sector, such as commercial banks distress prediction [21], bankruptcy forecasting in the agribusiness sector [22], manufacturing firms financial distress forecasting [23], hospitality firm failure prediction [11,24], etc. Up to date, to the best of our knowledge, only Doumpos et al [2] has explored corporate failure forecasting in the energy sector.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

Pan

Chen

et al. 2019

Energies

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Accurate forecasts of corporate failure in the Chinese energy sector are drivers for both operational excellence in the national energy systems and sustainable investment of the energy sector. This paper proposes a novel integrated model (NIM) for corporate failure forecasting in the Chinese energy sector by considering textual data and numerical data simultaneously. Given the feature of textual data and numerical data, convolutional neural network oriented deep learning (CNN-DL) and support vector machine (SVM) are employed as the base classifiers to forecast using textual data and numerical data, respectively. Subsequently, soft set (SS) theory is applied to integrate outputs of CNN-DL and SVM. Hence, NIM inherits advantages and avoids disadvantages of CNN-DL, SVM, and SS. It is able to improve the forecasting performance by taking full use of textual data and numerical data. For verification, NIM is applied to the real data of Chinese listed energy firms. Empirical results indicate that, compared with benchmarks, NIM demonstrates superior performance of corporate failure forecasting in the Chinese energy sector.

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“…(4) Qualitative methods have been rarely employed to construct ensemble predicting models. Meanwhile, some literatures have found out that qualitative approaches can play a valid role in predicting financial distress [29].…”

Section: Introductionmentioning

confidence: 99%

“…Both qualitative methods and quantitative methods are employed as classifiers to take full use of their advantages. We choose the expert system method (ES) as the qualitative classifier for its advantages [29]. For the same reason, convolutional neural network (CNN) is employed as the quantitative classifier [19].…”

Section: Introductionmentioning

confidence: 99%

A Novel Soft Ensemble Model for Financial Distress Prediction with Different Sample Sizes

Pan

2019

Mathematical Problems in Engineering

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This work presents a novel soft ensemble model (ANSEM) for financial distress prediction with different sample sizes. It integrates qualitative classifiers (expert system method, ES) and quantitative classifiers (convolutional neural network, CNN) based on the uni-int decision making method of soft set theory (UI). We introduce internet searches indices as new variables for financial distress prediction. By constructing a soft set representation of each classifier and then using the optimal decision on soft sets to identify the financial status of firms, ANSEM inherits advantages of ES, CNN, and UI. Empirical experiments with the real data set of Chinese listed firms demonstrate that the proposed ANSEM has superior predicting performance for financial distress on accuracy and stability with different sample sizes. Further discussions also show that internet searches indices can offer additional information to improve predicting performance.

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Bankruptcy prediction in the agribusiness sector: Lessons from quantitative and qualitative approaches

Cited by 35 publications

References 26 publications

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

A Novel Soft Ensemble Model for Financial Distress Prediction with Different Sample Sizes

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