Predicting Audit Opinion in Consolidated Financial Statements with Artificial Neural Networks

Sánchez-Serrano, José Ramón; Alaminos, David; Garcìa-Lagos, Francisco; Callejón-Gil, Ángela M.

doi:10.3390/math8081288

Cited by 11 publications

(3 citation statements)

References 39 publications

(57 reference statements)

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“…Hence, when a neuron obtains a result, it is sent to all the neurons in the following layer [82] (see Figure 3). The most used supervised neural network is known as a multilayer perceptron (MLP) [75,83]. It consists of a three-layer network (input, hidden and output) that uses sigmoid functions as the transference function in the hidden layer.…”

Section: Methodsmentioning

confidence: 99%

Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing

2021

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We used a large sample of 188,652 properties, which represented 4.88% of the total housing stock in Catalonia from 1994 to 2013, to make a comparison between different real estate valuation methods based on artificial neural networks (ANNs), quantile regressions (QRs) and semi-log regressions (SLRs). A literature gap in regard to the comparison between ANN and QR modelling of hedonic prices in housing was identified, with this article being the first paper to include this comparison. Therefore, this study aimed to answer (1) whether QR valuation modelling of hedonic prices in the housing market is an alternative to ANNs, (2) whether it is confirmed that ANNs produce better results than SLRs when assessing housing in Catalonia, and (3) which of the three mass appraisal models should be used by Spanish banks to assess real estate. The results suggested that the ANNs and SLRs obtained similar and better performances than the QRs and that the SLRs performed better when the datasets were smaller. Therefore, (1) QRs were not found to be an alternative to ANNs, (2) it could not be confirmed whether ANNs performed better than SLRs when assessing properties in Catalonia and (3) whereas small and medium banks should use SLRs, large banks should use either SLRs or ANNs in real estate mass appraisal.

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Section: Methodsmentioning

confidence: 99%

Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing

2021

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“…Bao and ke et al [19] used ensemble learning to predict accounting fraud of listed companies in the United States, and the input data were original accounting figures rather than financial ratios, which was proved to have a good prediction effect. Sánchez-Serrano and José Ramón et al [20] taking a group of Spanish companies as research samples, compares the effects of several different neural networks in the prediction of audit opinions on the company's consolidated financial statements, and MLP obtains the best prediction effect, with an accuracy of more than 86%. Chyan-Long Jan [21] forecasts the CPA's going concern audit opinions of Listed Companies in Taiwan.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Audit Opinion Prediction of Listed Companies Based on Sparse Principal Component Analysis and Kernel Fuzzy Clustering Algorithm

Zeng

2022

Mathematical Problems in Engineering

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The prediction of audit opinions of listed companies plays a significant role in the security market risk prevention. By introducing machine learning methods, many innovations can be implemented to improve audit quality, lift audit efficiency, and cultivate the keen insight of auditors. However, in a realistic environment, category imbalance and critical feature selection exist in the prediction model of company audit opinions. This paper firstly combines batched sparse principal component analysis (BSPCA) with kernel fuzzy clustering algorithm (KFCM) and proposes a sparse-kernel fuzzy clustering undersampling method (S-KFCM) to deal with the imbalance of sample categories. This method adopts the kernel fuzzy clustering algorithm to down-sample the normal samples, and their features are extracted from abnormal sample sets based on the group sparse component method. The sparse normal sample set can maintain the original distribution space structure and highlight the classification boundary samples. Secondly, considering the company’s characteristic attributes and data sources, 448 original variables are grouped, and then BSPCA is used for feature screening. Finally, the support vector machine (SVM) is adopted to complete the classification prediction. According to the empirical results, the SKFCM-SVM model has the highest prediction accuracy.

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“…In the related literature on bankruptcy and financial failure prediction applying machine learning techniques (Kumar & Ravi, 2007), audit data has been used for prediction purposes, demonstrating a predictive power like that of financial ratios when assessing firms' financial distress (McKee, 2003;Muñoz-Izquierdo et al, 2019a, 2019b. The efficiency of machine learning techniques has also been shown in explaining audit opinions (Sánchez-Serrano et al, 2020). Gaganis et al (2007) find a high explanatory power of probabilistic neural networks to identify qualified audit opinions.…”

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confidence: 99%

Machine learning in corporate credit rating assessment using the expanded audit report

et al. 2022

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We investigate whether key audit matter (KAM) paragraphs disclosed in extended audit reports-paragraphs in which the auditor highlights significant risks and critical judgments of the company-contribute to assess corporate credit ratings. This assessment is a complicated and expensive process to grade the reliability of a company, and it is relevant for many stakeholders, such as issuers, investors, and creditors. Although credit rating evaluations have attracted the interest of many researchers, previous studies have mainly focused only on financial ratios. We are the first to use KAMs for credit rating modelling purposes. Applying four machine learning techniques to answer this real-world problem-C4.5 decision tree, two different rule induction classifiers (PART algorithm and Rough Set) and the logistic regression methodology-, our evidence suggests that by simply identifying the KAM topics disclosed in the report, any decision-maker can assess credit scores with 74% accuracy using the rules provided by the PART algorithm. These rules specifically indicate that KAMs on both external (such as going concern) and internal (such as company debt) aspects may contribute to explaining a company's credit rating. The rule induction classifiers have similar predictive power. Interestingly, if we combine audit data with accounting ratios, the predictive power of our model increases to 84%, outperforming the accuracy in the existing literature. KeywordsCorporate credit rating • Machine learning techniques • Accounting ratios • Expanded audit report • Key audit matters

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Predicting Audit Opinion in Consolidated Financial Statements with Artificial Neural Networks

Cited by 11 publications

References 39 publications

Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing

Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing

Research on Audit Opinion Prediction of Listed Companies Based on Sparse Principal Component Analysis and Kernel Fuzzy Clustering Algorithm

Machine learning in corporate credit rating assessment using the expanded audit report

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