Critical factors for insolvency prediction: towards a theoretical model for the construction industry

Alaka, Hafiz; Oyedele, Lukumon O.; Oyedele, Azeez A.; Akinadé, Olúgbénga O.; Bilal, Muhammad; Ajayi, Saheed O.

doi:10.1080/15623599.2016.1166546

Cited by 28 publications

(24 citation statements)

References 68 publications

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“…It is not surprising to see working capital/ total assets as the chief contributor since it has to do with liquidity of the firm. Construction firms are known to always need high liquidity if they are to keep their projects running [29,48,68,69]. Poor liquidity is what led to the recent failure of Carilion construction firm in the United Kingdom.…”

Section: The Models and The Resultsmentioning

confidence: 99%

A framework for big data analytics approach to failure prediction of construction firms

Alaka

Oyedele

Owolabi

et al. 2018

ACI

Self Cite

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This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural network, Amazon Elastic Compute Cloud, Apache Spark and the R software. The BDA CB-FPM was developed in eight seconds while the same process without BDA was aborted after nine hours without success. This shows the issue of not wanting to use large dataset to develop CB-FPM due to tedious duration is resolvable by applying BDA technique. The BDA CB-FPM largely outperformed an ordinary CB-FPM developed with a dataset of 200 construction firms, proving that use of larger sample size with the aid of BDA, leads to better performing CB-FPMs. The high financial and social cost associated with misclassifications (i.e. model error) thus makes adoption of BDA CB-FPMs very important for, among others, financiers, clients and policy makers.

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Section: The Models and The Resultsmentioning

confidence: 99%

A framework for big data analytics approach to failure prediction of construction firms

Alaka

Oyedele

Owolabi

et al. 2018

ACI

Self Cite

View full text Add to dashboard Cite

show abstract

“…A lot of researches and authors specify that it is suitable to monitor both financial and non-financial indicators for the measurement of the enterprise's overall performance (Dobrovic, Lambovska, Gallo, & Timkova, 2018;Alaka et al, 2017;Zizlavsky, 2016). In connection with the assessment of the enterprise's performance, Hornungová (2017) emphasizes the importance of knowledge, and writes that it is sometimes considered as a fifth production factor, and it is, thus, necessary to consider it while evaluating the enterprise's results.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As key quantitative factors of financial stability are usually stated cash flow, liquidity, profitability, leverage (Kuběnka, 2015;Alaka et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Obvious and Hidden Features of Corporate Default in Bankruptcy Models

Kuběnka

Myšková

2019

Journal of Business Economics and Management

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The aim of this article is to prove the key role of the structure of the research sample used for accuracy determining on the accuracy of bankruptcy models. The creators of these models report the accuracy usually in the range of 60 to 90%. The authors of this article claim that these values are inaccurate and misleading. The real I. type error should be detected on a sample where obvious features of financial default were eliminated. The research tested more than 1200 of thriving businesses and also 270 businesses in future bankruptcy. The research has determined real current accuracy of selected three bankruptcy models on the standard sample of Czech businesses amounting 67.77%, 62.27% and 74.36%. This confirmed hypothesis no. 1, which says that actual accuracy of bankruptcy model is lower than original accuracy indicated by model makers. An accuracy of 58.70%, 61.59% and 65.94% was measured on a sample where businesses with obvious features of financial distress were eliminated. Due to the modification of the test sample, the order of accuracy has changed. This confirmed hypothesis no. 2. The Index of Karas and Reznakova reached the highest overall accuracy of 80.31% including incorrect prediction of bankruptcy also.

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“…Papers such as those by Yang et al (2011), Huang et al (2012), and Zhou et al (2014) address the issue of small sample size using the support vector machine (SVM) technique, confirming the special ability of this technique to perform well in terms of prediction using a small dataset. However, SVM models are quite complicated to understand because the coefficients that are assigned to the variables are difficult to interpret (Tseng, & Hu, 2010;Jeong et al, 2012;Alaka et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Economic and financial determinants of bankrupcy: evidence from Ecuador's private banks and the impact of dollarization on financial fragility

Uquillas

Flores

2020

RBGN

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Critical factors for insolvency prediction: towards a theoretical model for the construction industry

Cited by 28 publications

References 68 publications

A framework for big data analytics approach to failure prediction of construction firms

A framework for big data analytics approach to failure prediction of construction firms

Obvious and Hidden Features of Corporate Default in Bankruptcy Models

Economic and financial determinants of bankrupcy: evidence from Ecuador's private banks and the impact of dollarization on financial fragility

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