On logit and artificial neural networks in corporate distress modelling for Zimbabwe listed corporates

Muparuri, Louisa; Gumbo, Victor

doi:10.1016/j.samod.2022.100006

Cited by 10 publications

(12 citation statements)

References 29 publications

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“…Artificial neural networks (ANNs) are powerful artificial intelligence technologies that are widely-used as they are able to combine several nonlinear functions to express non-linear relationships between input data and a class label [18]. A previous study on corporate distress prediction examined the precision of Logit and ANN to establish a comparison between using statistical and artificial intelligence in modeling financial risk [4].…”

Section: B Prediction Accuracy Enhancementmentioning

confidence: 99%

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Improving Prediction Accuracy using Random Forest Algorithm

Elsayed,

Elaleem,

Marie

2024

IJACSA

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One of the latest studies in predicting bankruptcy is the performance of the financial prediction models. Although several models have been developed, they ofte n do not achieve high performance, especially when using an imbalanced data set. This highlights the need for more exact prediction models. This paper examines the application as well as the benefits of machine learning with the purpose of constructing pre diction models in the field of corporate financial performance. There is a lack of scientific research related to the effects of using random forest algorithms in attribute selection and prediction process for enhancing financial prediction. This paper tes ts various feature selection methods along with different prediction models to fill the gap. The study used a quantitative approach to develop and propose a business failure model. The approach involved analyzing and preprocessing a large dataset of bankrupt and non-bankrupt enterprises. The performance of the model was then evaluated using various metrics such as accuracy, precision, and recall. Findings from the present study show that random forest is recommended as the best model to predict corporate bankruptcy. Moreover, findings write down that the proper use of attribute selection methods helps to enhance the prediction precision of the proposed models. The use of random forest algorithm in feature selection and prediction can produce more exact and more reliable results in predicting bankruptcy. The study proves the potential of machine learning techniques to enhance financial performance.

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Section: B Prediction Accuracy Enhancementmentioning

confidence: 99%

“…Financial crisis prediction indicators included Profitability, Solvency, Growth ability, Cash flow and Capital structure [3]. Enhanced prediction accuracy is bound to increase the earnings to shareholders by improving financial risk management inside rising markets [4].…”

Section: Introductionmentioning

confidence: 99%

Improving Prediction Accuracy using Random Forest Algorithm

Elsayed,

Elaleem,

Marie

2024

IJACSA

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“…As a result, they found both methods achieve a good prediction model. The comparison of the accuracy between Logit and Artificial Neural Networks (ANN) in corporate distress prediction by Muparuri and Gumbo (2022) found that the Logit model outperformed the ANN by an overall accuracy of 92.21% compared to ANN with 85.8%.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the efficacy of financial distress prediction models in Malaysian public listed companies

Nayan,

Ilias,

Ishak

et al. 2024

Int. j. adv. appl. sci.

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This research critically examines the precision of financial distress prediction models, with a particular focus on their applicability to Malaysian publicly listed companies under Practice Note 17 (PN17) from 2017 to 2021. Financial distress, defined as the imminent risk of bankruptcy evidenced by an inability to satisfy creditor demands, presents a significant challenge in corporate finance management. The study underscores the necessity of an efficient prediction model to strategize preemptive measures against financial crises. Unlike prior research, which predominantly compared prediction models without assessing their accuracy, this study incorporates an accuracy analysis to discern the most effective model. Utilizing the Grover and Zmijerski models, it assesses whether companies listed under PN17 are experiencing financial distress. A noteworthy finding is the substantial correlation between the return on assets (ROA) and the prediction of financial distress in these companies. Furthermore, the Grover model demonstrates a remarkable 100% accuracy rate, indicating its exceptional efficiency in forecasting financial distress. This research not only contributes to the existing body of knowledge on financial distress prediction but also offers practical insights for companies and stakeholders in the Malaysian financial market.

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“…Financial distress causes companies to have difficulty paying their obligations and interest on their obligations due to a lack of liquidity (ElBannan, 2021). Companies can also be considered to be experiencing financial distress when their cash flow cannot cover their debts, forcing them to restructure or change their debt payment plan (Muparuri & Gumbo, 2022).…”

Section: Literature Review and Hypothesismentioning

confidence: 99%

“…This can be an early indication that the companies are experiencing financial distress. Muparuri and Gumbo (2022) stated that a company is said to be in financial distress if it receives losses for two consecutive years in its financial statements.…”

Section: Introductionmentioning

confidence: 99%

Are Indonesian construction companies financially distressed? A prediction using artificial neural networks

Kristanti

Safriza

Salim

2023

Investment Management and Financial Innovations

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Construction companies are very dependent on the projects carried out by a company. Therefore, measuring whether a company is distressed or non-distressed can be done by looking at the ratios derived from the components of the financial statements from both the balance sheet and the company’s profit and loss. This study offers a new method for measuring financial distress in companies with Artificial Neural Networks (ANN). The model provided comes from several financial ratios in 17 construction companies listed on the Indonesia Stock Exchange. The model is expected to produce the best model by showing the lowest prediction error rate. The results showed that the best ANN model has 25 inputs, 20 hidden layer neurons, and 1 best model output. The model obtained will be tested directly on the sample used; the results are that 6 construction companies in Indonesia have financial distress and 11 non-distress problems. This result proves that the best model obtained can predict the level of financial distress of companies with a small error rate to produce 6 companies identified as financially distressed. This result can be a warning for companies to increase revenue by adding new projects to get out of financial distress status. Traditional financial distress models such as Altman, Zmijewski, Springate, and Fulmer, which have become researchers’ guidelines for measuring financial distress, can be added to the ANN 25-20-1 model as a comparison to strengthen the research results.

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On logit and artificial neural networks in corporate distress modelling for Zimbabwe listed corporates

Cited by 10 publications

References 29 publications

Improving Prediction Accuracy using Random Forest Algorithm

Improving Prediction Accuracy using Random Forest Algorithm

Evaluating the efficacy of financial distress prediction models in Malaysian public listed companies

Are Indonesian construction companies financially distressed? A prediction using artificial neural networks

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