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18 pages, 5148 KiB

Open AccessArticle

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

by Der-Jang Chi and Chien-Chou Chu

Sustainability 2021, 13(21), 11631; https://doi.org/10.3390/su132111631 - 21 Oct 2021

Cited by 5 | Viewed by 2291

“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 [...] Read more.

“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. Full article

(This article belongs to the Special Issue Financial Sustainability of Companies and Organizations under Globalization)

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20 pages, 2402 KiB

Open AccessArticle

Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry

by Chyan-Long Jan

Sustainability 2021, 13(17), 9879; https://doi.org/10.3390/su13179879 - 02 Sep 2021

Cited by 23 | Viewed by 6543

Abstract

Information asymmetry is everywhere in financial status, financial information, and financial reports due to agency problems and thus may seriously jeopardize the sustainability of corporate operations and the proper functioning of capital markets. In this era of big data and artificial intelligence, deep [...] Read more.

Information asymmetry is everywhere in financial status, financial information, and financial reports due to agency problems and thus may seriously jeopardize the sustainability of corporate operations and the proper functioning of capital markets. In this era of big data and artificial intelligence, deep learning is being applied to many different domains. This study examines both the financial data and non-financial data of TWSE/TEPx listed companies in 2001–2019 by sampling a total of 153 companies, consisting of 51 companies reporting financial statement fraud and 102 companies not reporting financial statement fraud. Two powerful deep learning algorithms (i.e., recurrent neural network (RNN) and long short-term memory (LSTM)) are used to construct financial statement fraud detection models. The empirical results suggest that the LSTM model outperforms the RNN model in all performance indicators. The LSTM model exhibits accuracy as high as 94.88%, the most frequently used performance indicator. Full article

(This article belongs to the Special Issue Financial Sustainability of Companies and Organizations under Globalization)

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17 pages, 462 KiB

Open AccessArticle

Hofstede’s Cultural Dimensions as a Moderator of the Relationship between Ambidextrous Learning and Corporate Sustainability in Born Global Firms

by Diana Escandon-Barbosa, Jairo Salas-Paramo and Josep Rialp-Criado

Sustainability 2021, 13(13), 7344; https://doi.org/10.3390/su13137344 - 30 Jun 2021

Cited by 5 | Viewed by 4212

Abstract

This research analyzes the moderation effects of Hofstede’s Cultural Dimensions (Power Distance, Uncertainty Avoidance and Indulgence) in the relationship between Ambidextrous learning and corporate sustainability in born global firms. The data were collected from exporting firms characterized by beginning international operations in the [...] Read more.

This research analyzes the moderation effects of Hofstede’s Cultural Dimensions (Power Distance, Uncertainty Avoidance and Indulgence) in the relationship between Ambidextrous learning and corporate sustainability in born global firms. The data were collected from exporting firms characterized by beginning international operations in the first three years and were thus classified as Born Global. A panel Dynamic Structural Equation Model (DSEM) was used to test the research hypothesis. One of the methodological contributions is the exploration of dynamic social behaviors that are difficult to study, specifically over time. Here, DSEM becomes in a data analysis technique that allows us to analyze this type of phenomena. The research results show that the relationship between Ambidextrous learning (AL) and Corporate Sustainability (CS) is positive in the short- and long-term. The cultural dimension’s Power Distance and Uncertainty Avoidance moderates the relation between (AL) and (CS) and this dimension can predict their inertia. However, while Uncertainty Avoidance has a moderating effect, it does not predict future behaviors. Published literature on the Born Global company. that includes the moderation of Hofstede’s dimensions (Power distance, Avoidance of uncertainty, and Indulgence) from a company perspective that study the relationship between Ambidextrous Learning and Corporate Sustainability is scarce. Full article

(This article belongs to the Special Issue Financial Sustainability of Companies and Organizations under Globalization)

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