scholarly journals Using Deep Learning Algorithms for CPAs’ Going Concern Prediction

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 73
Author(s):  
Chyan-Long Jan
Keyword(s):  
Deep Learning ◽  
Prediction Models ◽  
Stock Exchange ◽  
Learning Algorithms ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Going Concern ◽  
Taiwan Stock Exchange ◽  
Going Concern Opinion ◽  
Economic Journal

Certified public accounts’ (CPAs) audit opinions of going concern are the important basis for evaluating whether enterprises can achieve normal operations and sustainable development. This study aims to construct going concern prediction models to help CPAs and auditors to make more effective/correct judgments on going concern opinion decisions by deep learning algorithms, and using the following methods: deep neural networks (DNN), recurrent neural network (RNN), and classification and regression tree (CART). The samples of this study are companies listed on the Taiwan Stock Exchange and the Taipei Exchange, a total of 352 companies, including 88 companies with going concern doubt and 264 normal companies (with no going concern doubt). The data from 2002 to 2019 are taken from the Taiwan Economic Journal (TEJ) Database. According to the empirical results, with the important variables selected by CART and modeling by RNN, the CART-RNN model has the highest going concern prediction accuracy (the accuracy of the test dataset is 95.28%, and the average accuracy is 93.92%).

scholarly journals Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction

2021 ◽  
Vol 13 (21) ◽  
pp. 11631
Author(s):  
Der-Jang Chi ◽  
Chien-Chou Chu
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Prediction Models ◽  
Short Term Memory ◽  
Corporate Sustainability ◽  
Learning Algorithms ◽  
Error Rates ◽  
Type I ◽  
Going Concern ◽  
Financial Variables

“Going concern” is a professional term in the domain of accounting and auditing. The issuance of appropriate audit opinions by certified public accountants (CPAs) and auditors is critical to companies as a going concern, as misjudgment and/or failure to identify the probability of bankruptcy can cause heavy losses to stakeholders and affect corporate sustainability. In the era of artificial intelligence (AI), deep learning algorithms are widely used by practitioners, and academic research is also gradually embarking on projects in various domains. However, the use of deep learning algorithms in the prediction of going concern remains limited. In contrast to those in the literature, this study uses long short-term memory (LSTM) and gated recurrent unit (GRU) for learning and training, in order to construct effective and highly accurate going-concern prediction models. The sample pool consists of the Taiwan Stock Exchange Corporation (TWSE) and the Taipei Exchange (TPEx) listed companies in 2004–2019, including 86 companies with going concern doubt and 172 companies without going concern doubt. In other words, 258 companies in total are sampled. There are 20 research variables, comprising 16 financial variables and 4 non-financial variables. The results are based on performance indicators such as accuracy, precision, recall/sensitivity, specificity, F1-scores, and Type I and Type II error rates, and both the LSTM and GRU models perform well. As far as accuracy is concerned, the LSTM model reports 96.15% accuracy while GRU shows 94.23% accuracy.

scholarly journals Cytokine signature and COVID-19 prediction models in the two waves of pandemics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Serena Cabaro ◽  
Vittoria D’Esposito ◽  
Tiziana Di Matola ◽  
Silvia Sale ◽  
Michele Cennamo ◽  
...  
Keyword(s):  
Prediction Models ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Diagnostic Tools ◽  
Linear Discriminant ◽  
Diagnosis And Prognosis ◽  
Machine Learning Approach ◽  
Set Up ◽  
Definition Of ◽  
Second Wave

AbstractIn Europe, multiple waves of infections with SARS-CoV-2 (COVID-19) have been observed. Here, we have investigated whether common patterns of cytokines could be detected in individuals with mild and severe forms of COVID-19 in two pandemic waves, and whether machine learning approach could be useful to identify the best predictors. An increasing trend of multiple cytokines was observed in patients with mild or severe/critical symptoms of COVID-19, compared with healthy volunteers. Linear Discriminant Analysis (LDA) clearly recognized the three groups based on cytokine patterns. Classification and Regression Tree (CART) further indicated that IL-6 discriminated controls and COVID-19 patients, whilst IL-8 defined disease severity. During the second wave of pandemics, a less intense cytokine storm was observed, as compared with the first. IL-6 was the most robust predictor of infection and discriminated moderate COVID-19 patients from healthy controls, regardless of epidemic peak curve. Thus, serum cytokine patterns provide biomarkers useful for COVID-19 diagnosis and prognosis. Further definition of individual cytokines may allow to envision novel therapeutic options and pave the way to set up innovative diagnostic tools.

scholarly journals Pengaruh Rasio Profitabilitas, Rasio Likuiditas, Pertumbuhan Perusahaan Terhadap Opini Audit Going Concern

2021 ◽  
Vol 13 (2) ◽  
pp. 283-299
Author(s):  
Kimberli Kimberli ◽  
Budi Kurniawan
Keyword(s):  
Stock Exchange ◽  
Secondary Data ◽  
Financial Statements ◽  
Audit Opinion ◽  
Going Concern ◽  
Analysis Technique ◽  
Going Concern Opinion ◽  
Company Growth ◽  
The Relationship ◽  
Profitability Ratios

Abstract The problems that will be discussed in this journal are regarding the relationship between Profitability Ratios, Liquidity Ratios and Company Growth on Audit Delay. The research method used in this study uses secondary data. The population in this study is all Real Estate companies and the Property sub-sector registered on the BEI which are listed on the Indonesia Stock Exchange in 2017, 2018, 2019 and 2020. The sampling method in this study is purposive sampling. The criteria for companies that are sampled are companies that publish audited financial statements for four consecutive years and use the rupiah currency, so that the total number of samples in this study is 165 data. The independent variables in this study are Profitability Ratios, Liquidity Ratios and Company Growth. The dependent variable in this study is audit delay. The data analysis technique used is the Logistics Regression Test with the use of Software Eviews 10. The results of the analysis show that profitability has no significant effect on going concern audit opinion. Meanwhile, company growth and liquidity have no effect on going concern audit opinion. Keywords: Going Concern Opinion, Profitability, Liquidity, and Company Growth

scholarly journals A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms

2021 ◽  
Vol 35 (3) ◽  
pp. 209-215
Author(s):  
Pratibha Verma ◽  
Vineet Kumar Awasthi ◽  
Sanat Kumar Sahu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Deep Learning ◽  
Regression Tree ◽  
Principal Component ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
R Programming ◽  
Hidden Layer

Data mining techniques are included with Ensemble learning and deep learning for the classification. The methods used for classification are, Single C5.0 Tree (C5.0), Classification and Regression Tree (CART), kernel-based Support Vector Machine (SVM) with linear kernel, ensemble (CART, SVM, C5.0), Neural Network-based Fit single-hidden-layer neural network (NN), Neural Networks with Principal Component Analysis (PCA-NN), deep learning-based H2OBinomialModel-Deeplearning (HBM-DNN) and Enhanced H2OBinomialModel-Deeplearning (EHBM-DNN). In this study, experiments were conducted on pre-processed datasets using R programming and 10-fold cross-validation technique. The findings show that the ensemble model (CART, SVM and C5.0) and EHBM-DNN are more accurate for classification, compared with other methods.

UKURAN KANTOR AKUNTAN PUBLIK, DISCRETIONARY ACCRUALS DAN OPINI AUDIT GOING CONCERN

2019 ◽  
Vol 15 (1) ◽  
pp. 11
Author(s):  
Ravaela Amba Masiku ◽  
Christine Novita Dewi
Keyword(s):  
Earnings Management ◽  
Financial Reporting ◽  
Stock Exchange ◽  
Discretionary Accruals ◽  
Audit Firm ◽  
Going Concern ◽  
Discretionary Accrual ◽  
Audit Firms ◽  
Going Concern Opinion ◽  
Positive Effect

The purpose of this study is to examine auditor’s concervatism in term of their reaction to client’s earnings management behavior and their limitations to issue the going concern opinions (GCO). The population of this study consists of 672 observations from 69 companies are listed on the Indonesia Stock Exchange (BEI) during 2012-2017. The author used the modified Jones model to measure discretionary accruals as a proxy of earnings management. The results of this study indicate that size of audit firm has a positive effect to discretionary accrual. Companies that have been audited by the Big4 tend to apply discretionary accrual in their financial reporting than companies audited by Non-Big4. Further, to strenghten the first hypothesis, we examine the effect of discretionary accruals and going concern opinion on companies that audited by audit firms Big4 lower than companies that audited by audit firms Non-Big4. We found that the result is consistent with the first hypothesis. Keywords : auditor reputation, discretionary accruals, going concern opinion, audit firm  ABSTRAK Tujuan dari penelitian ini adalah untuk menguji konservatisme auditor dalam hal reaksi auditor terhadap akrual diskresioner yang dilakukan oleh perusahaan dan keterbatasan auditor untuk menerbitkan opini Going Concern (GC). Populasi penelitian terdiri dari 672 pengamatan dari 69 perusahaan yang terdaftar di Bursa Efek Indonesia (BEI) selama tahun 2012-2017. Penulis menggunakan model modifikasi Jones untuk mengukur akrual diskresioner sebagai proksi manajemen laba. Hasil dari penelitian ini menjelaskan bahwa ukuran kantor akuntan publik berpengaruh positif terhadap akrual diskresioner, hal tersebut diperkuat dengan pengaruh akrual diskresioner dan opini audit going concern yang diaudit oleh kantor akuntan publik Big4 lebih rendah dari perusahaan yang tidak diaudit oleh kantor akuntan publik Non-Big4. Kata kunci : reputasi auditor, akrual diskresioner, opini audit going concern, kantor akuntan publik

scholarly journals KEMAMPUAN PERGANTIAN AUDITOR DAN PRIOR OPINION MEMODERASI PENGARUH POTENSI FINANCIAL DISTRESS TERHADAP OPINI GOING CONCERN

2017 ◽  
pp. 3259
Author(s):  
Putu Yudha Asteria Putri ◽  
Ida Bagus Putra Astika ◽  
Made Gede Wirakusuma
Keyword(s):  
Empirical Evidence ◽  
Financial Distress ◽  
Stock Exchange ◽  
Secondary Data ◽  
Manufacturing Companies ◽  
Potential Influence ◽  
Going Concern ◽  
Going Concern Opinion ◽  
Prior Opinion ◽  
Contingency Approach

This study aimed to get empirical evidence the auditor's ability to change and prior opinion in moderating influence on the potential financial distress Award going concern opinion. There’s a going concern opinion because the company indicated no longer able to carry out its life. The results of previous studies get inconsistent results in terms of the potential influence on the provision of financial distress going concern opinion. The existence of a contingency approach can be completed in this study, where the variables change of auditor and prior opinion allegedly moderating influence on the potential financial distress Award going concern opinion. This study uses secondary data. Manufacturing companies listed in Indonesia Stock Exchange period 2009-2015 the population in this study by the amount total of the samples are 77 samples were selected by purposive sampling.

The analysis of influencing factors on the going concern audit opinion – a study in manufacturing firms in Indonesia

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Thomas Averio
Keyword(s):  
Audit Quality ◽  
Stock Exchange ◽  
Secondary Data ◽  
Sampling Technique ◽  
Manufacturing Firms ◽  
Annual Reports ◽  
Content Type ◽  
Audit Opinion ◽  
Going Concern ◽  
Going Concern Opinion

PurposeIt is argued that the going concern opinion is issued if auditors have a doubt about financial condition of a company. Provision of the going concern audit opinion may worsen the company in terms of gaining public trust and may even indicate bankruptcy. This study aims to determine the factors that affect the auditor's going concern opinion.Design/methodology/approachThis research used secondary data obtained from annual reports and independent audit reports published by the Indonesia Stock Exchange. The population of this research included manufacturing firms registered in the Indonesia Stock Exchange from 2015 to 2019. The sample after the purposive sampling technique being applied consisted of 33 companies. The data were analyzed using logistic regression performed in the statistical analysis software, SPSS 24.0.FindingsThe results indicated that leverage positively affected the going concern audit opinion, then the audit quality, profitability and liquidity negatively affected the going concern audit opinion, whereas firm size and audit lag did not affect the going concern audit opinion.Originality/valueThis study is in contrast to several existing studies on the determinants of the auditor's going concern opinion and provides knowledge on developing more factors affecting the auditor's going concern opinion.

scholarly journals The Impact of Bankruptcy Prediction, Company’s Financial Condition, Previous Year Audit Opinion, Firm Size and Audit Tenure Towards Auditor’s Going Concern Opinion

2016 ◽  
Vol 1 (1) ◽  
pp. 51-58
Author(s):  
Lisa Cellica ◽  
Ratnawati Kurnia
Keyword(s):  
Firm Size ◽  
Stock Exchange ◽  
Secondary Data ◽  
Jel Classification ◽  
Bankruptcy Prediction ◽  
Audit Opinion ◽  
Going Concern ◽  
Financial Condition ◽  
Going Concern Opinion ◽  
The Impact

Objective – The auditor is responsible for obtaining sufficient audit evidence about the accuracy and proper use of the going concern assumption from the company’s management through its financial statements. These evidence are used for the purpose of deciding whether there are material uncertainties about the entity's ability to maintain the continuity of its business. Thus, the objective of this paper is to examine the impact of bankruptcy prediction, company’s financial condition, previous year audit opinion, firm size and audit tenure towards Auditor’s going concern opinion. Methodology/Technique – The object of this paper is the service companies listed on the Indonesia Stock Exchange for the period of 2011-2014. This paper uses secondary data and samples taken were determined based on the purposive sampling method. The regression logistic is used to analyse data. Findings – The results of this research show that bankruptcy prediction, company’s financial condition, previous year audit opinion, firm size, and audit tenure all simultaneously, have a significant impact towards Auditor’s going concern opinion, particularly Previous Year Audit Opinion. Novelty – This paper provides insights into the factors affecting auditors in providing a going concern opinion in the case of Indonesian companies. Type of Paper: Empirical Keywords: Bankruptcy Prediction; Company’s Financial Condition; Previous Year Audit Opinion, Firm Size; Audit Tenure; Auditor’s Going Concern Opinion. JEL Classification: D81, M42.

scholarly journals The Effect of Financial Distress, Firm Growth, and Previous Year’s Opinion on The Firm’s Going Concern Opinion

2021 ◽  
Vol 58 (1) ◽  
pp. 247-258
Author(s):  
Amiruddin, Grace T. Pontoh, Marina Lauren
Keyword(s):  
Financial Distress ◽  
Firm Growth ◽  
Stock Exchange ◽  
Secondary Data ◽  
Substantial Impact ◽  
Audit Opinion ◽  
Going Concern ◽  
Service Companies ◽  
Going Concern Opinion ◽  
The Impact

This research aims to examine and determine the impact of financial distress, firm growth, and opinion on previous year to firms‘going concern. The study was carried on service companies that are listed on Indonesia Stock Exchange during 2015-2017. A total of 210 samples were selected using the purposive sampling method. This research utilizes secondary data in the form of the firm’s financial statements and independent auditor’s reports. This research utilized logistic regression analysis to process the data. Results showed that financial distress and previous year’s opinion has significantly affect the firm’s going concern audit opinion while the firm growth has no substantial impact on the firm’s going concern audit opinion. Simultaneously, financial distress, firm growth, and previous year's opinion significantly affected the firm's going concern opinion.

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