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Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

The purpose of this study is to develop a model that accurately forecasts hotel room cancelations and further determines the key cancelation drivers.

Predictive modeling, specifically the machine learning methods, is used to forecast room cancelations and identify the main cancelation factors.

By using three different classification algorithms, this study demonstrates that hotel room cancelation can be accurately predicted using XGBoost, as well as the ensemble method involving Support Vector Machine, Random Forest and XGBoost.

This study attempted to forecast hotel room cancelations by applying a relatively new method, machine learning. By implementing predictive modeling, one of the most emerging and innovative research methods, this study ultimately provides prediction suggestions in various aspects and levels for hotel management operations.

本研究旨在开发一个能够准确预测酒店客房取消的模型, 并进一步确定主要的取消因素。

采用预测建模, 具体来说是机器学习方法, 来预测客房取消, 并识别主要的取消因素。

通过使用三种不同的分类算法, 本研究表明使用XGBoost以及涉及支持向量机、随机森林和XGBoost的集成方法可以准确预测酒店客房取消。

本研究尝试通过应用相对较新的方法, 即机器学习, 来预测酒店客房取消。通过实施预测建模, 这是目前新兴和创新的研究方法之一, 本研究最终为酒店管理运营在各个方面和层面提供了预测建议。

This paper aims to evaluate the performance of the multiple linear regression (MLR) using a fixed-effects model (FE) and artificial neural network (ANN) models to predict the level of customer deposits on a sample of Tunisian commercial banks.

Training and testing datasets are developed to evaluate the level of customer deposits of 15 Tunisian commercial banks over the 2002–2021 period. This study uses two predictive modeling techniques: the MLR using a FE model and ANN. In addition, it uses the mean absolute error (MAE), R-squared and mean square error (MSE) as performance metrics.

The results prove that both methods have a high ability in predicting customer deposits of 15 Tunisian banks. However, the ANN method has a slightly higher performance compared to the MLR method by considering the MAE, R-squared and MSE.

The findings of this paper will be very significant for banks to use additional management support to forecast the level of their customers' deposits. It will be also beneficial for investors to have knowledge about the capacity of banks to attract deposits.

This paper contributes to the existing literature on the application of machine learning in the banking industry. To the author's knowledge, this is the first study that predicts the level of customer deposits using banking specific and macroeconomic variables.

The study examines the IPO resilience grounded on the firm’s intrinsic factors.

We examine the association of IPO performance and post-listing firm’s performance with issuers' pre-listing financial and qualitative traits using panel data regression.

IPOs floated in the Indian market from July 2009 to March 31, 2022, evince the notable influence of issuers' pre-IPO fundamentals and legitimacy traits on IPO returns and post-listing earning power. Where the pandemic’s favorable impact is discerned on the post-listing year earning power of the issuer firms, the loss-making issuers appear to be adversely affected by the Covid disruption. Perhaps, the successful listing equipped the issuers with the financial flexibility to combat market challenges vis-à-vis failed issuers deprived of desired IPO proceeds.

High initial returns followed by a declining pattern substantiate the retail investors to be less informed vis-à-vis initial investors, valuers and underwriters, who exit post-listing after profit booking. Investing in the shares of the newly listed ventures post-listing in the secondary market can shield retail investors from the uncertainty losses of being uninformed. The IPO market needs stringent regulations ensuring the verification of the listing valuation, the firm’s credentials and the intent of utilizing IPO proceeds. Healthy development of the IPO market merits reconsidering the listing of ventures with weak fundamentals suspected to withstand the market challenges.

Given the tremendous rise in the new firm venturing into the primary market and the spike in IPOs countering the losses immediately post-opening, the study examines the loss-making and young firms IPOs separately, adding novelty to the study.

The purpose of this study is to explore how the personal traits of the informal entrepreneurs influence their formalization decisions.

This study adopted a qualitative approach using a multicase design in which 28 informal entrepreneurs situated in Kampala district, Uganda, were engaged. An interview guide, recorders and note books were used in data collection.

The results indicate that the traits of informal and semiformal entrepreneurs are distinct. Informal entrepreneurs have been noted to be more courageous and resilient, while their semiformal counterparts have greater passion for their businesses. It is thus observed that the formalization prospects are higher for the semiformal entrepreneurs than for their informal counterparts. Entrepreneurs that would be willing to formalize their businesses are discouraged by distance, technology and the cost of involving middlemen. Whereas the resilient entrepreneurs are noted to work through these challenges, the passive ones in both the informal and semiformal categories will not formalize their businesses by giving such excuses.

This study contributes to the extant literature on informal entrepreneurship by providing initial empirical evidence on how the personal traits of the entrepreneurs influence their formalization decisions specifically.

This study aims to investigate the effect of the dynamics and potential of the board of directors on investment efficiency and the comparability of financial information in companies listed on the Tehran Stock Exchange.

The number of observations for this study includes 1,218 observations from companies listed on the Tehran Stock Exchange during 2014–2020. The authors used econometric statistical methods such as multiple linear regression, the Chow and Hausman test and the Kendall correlation coefficient using Eviews software to conduct the research. To measure the board’s effectiveness, two variables are used, including board dynamics and potential.

The results showed a positive and significant relationship between dynamics, board potential and investment efficiency. Also, no significant relationship was observed between the board dynamics and the comparability of financial information. Finally, a positive and significant relationship exists between the board’s potential and the comparability of financial information.

The importance of this research is the use of board proxies, including the dynamics and potential of the board. In addition, other variables of board characteristics, such as size, independence, ownership and gender, and the relationship between these variables with investment efficiency and comparability of financial information, have been examined in this study.

The purpose of our study is to study the impact of chief executive officer (CEO) optimism and narcissism on the company's capital structure.

(1) The authors opt for regression, machine learning and text analysis to explore the impact of narcissism and optimism on the capital structure. (2) We analyze CEO interviews and employ three methods to evaluate narcissism: the dictionary proposed by Anglin, which enabled us to assess the following components: authority, superiority, vanity and exhibitionism; count of first-person singular and plural pronouns and count of CEO photos displayed. Following this approach, we were able to make a more thorough assessment of corporate narcissism. (3) Latent Dirichlet allocation (LDA) technique helped to find the differences in the corporate rhetoric of narcissistic and non-narcissistic CEOs and to find differences between the topics of interviews and letters provided by narcissistic and non-narcissistic CEOs.

Our research demonstrates that narcissism has a slight and nonlinear impact on capital structure. However, our findings suggest that there is an impact of pessimism and uncertainty under pandemic conditions when managers predicted doom and completely changed their strategies. We applied various approaches to estimate the gender distribution of CEOs and found that the median values of optimism and narcissism do not depend on sex. Using LDA, we examined the content and key topics of CEO interviews, defined as positive and negative. There are some differences in the topics: narcissistic CEOs are more likely to speak about long-term goals, projects and problems; they often talk about their brand and business processes.

First, we examine the COVID-19 pandemic period and evaluate how CEO optimism and pessimism affect their financial decisions under specific external conditions. The pandemic forced companies to shift the way they worked: either to switch to the remote work model or to interrupt operations; to lose or, on the contrary, attract clients. In addition, during this period, corporate management can have a different outlook on their company’s financial performance and goals. The LDA technique helped to find the differences in the corporate rhetoric of narcissistic and non-narcissistic CEOs. Second, we use three methods to evaluate narcissism. Third, the research is based on a set of advanced methods: machine learning techniques (random forest to reveal a nonlinear impact of CEO optimism and narcissism on capital structure).

This study aims to investigate the tendency for firms, exhibiting an entrepreneurial spirit in their growth strategies, to engage in misconduct within the context of China's rapidly developing economy. The authors also examine how this relationship is influenced by governance mechanisms, specifically management shareholding and executive functional diversity. Furthermore, the authors explore the mediating roles of organizational complexity and performance pressure in linking entrepreneurial growth to firm misconduct. This research provides a novel perspective for understanding the impact of entrepreneurial growth on corporate ethical risks, and offers practical insights for maintaining ethical standards in firms during their pursuit of growth.

This study focuses on publicly traded, mature companies that exhibit an entrepreneurial inclination in their growth strategies, demonstrating entrepreneurial vigor through activities such as product innovation and market expansion. This exploration incorporates both theoretical and empirical approaches, scrutinizing A-share listed companies in China from 2008 to 2019. To validate the robustness of this study's findings, the authors have applied diverse methodologies such as propensity score matching, classification regression, and alternative indicator analysis.

This study found that the entrepreneurial growth-oriented strategy is positively related to firm misconduct. It also uncovers that governance mechanisms like management shareholding and executive functional diversity moderate this relationship. Moreover, organizational complexity and performance pressure partially mediate the relationship between an entrepreneurial growth strategy and firm misconduct.

For instance, more detailed categorization of corporate misconduct, based on punishment severity, could be explored. Additional characteristics like age, education, gender, and team/board diversity could help further understand the relationship between entrepreneurial growth strategy and misconduct. By addressing these limitations and exploring further avenues for research, the authors can deepen the understanding of this relationship and provide valuable insights for firms seeking to mitigate potential risks.

First, for regulators, shareholders, creditors and investors, knowing and understanding the relationship between growth-oriented strategies and corporate violations is helpful for them to scientifically evaluate the potential risks that may exist in the company, and can also carry out differentiated supervision on the company based on different types of company-oriented strategies. Second, when designing the corporate governance mechanism, listed companies should fully consider the role of management shareholding. Finally, executives should treat cross-functional experience dialectically, especially in growth oriented strategic companies.

This research provides a novel perspective for understanding the impact of entrepreneurial growth on corporate ethical risks, and offers practical insights for maintaining ethical standards in firms during their pursuit of growth.

This study stands out by examining the influence of entrepreneurial growth strategy on firm misconduct, thus enhancing previous studies that primarily centered on entrepreneurial start-ups. The authors offer a nuanced comprehension of the potential risks intrinsic to corporate entrepreneurship and highlight the crucial role of efficient governance structures in curbing corporate misbehavior while fostering entrepreneurial growth.

This research paper aims to empirically explore how stock market investors’ perceptions are affected by extreme climatic events like El Nino and floods in Malaysia.

This study uses structural equation modelling (SEM) to analyse the empirical data gathered through a questionnaire survey involving 273 individual investors from Bursa Malaysia between January and June 2019.

Results reveal that companies’ efforts, especially for agriculture and plantation-based industries, to adapt to climate change risk at the production, business and stock market levels significantly impact investors’ behaviour and investment decisions. Moreover, stock market investors’ climate change knowledge shows a significant moderating effect on corporate climate change adaptation initiatives and investors’ decisions to invest in Malaysian agricultural and plantation industry stocks.

This research has significant implications for practice and policy, as it measures the stock market investors’ level of awareness about climate change events and explores the companies’ strategies to reduce climatic risks to their business model.

This study shows the way to adjust the climate change information in the stock market investment decision to improve market efficiency and sustainable stock exchanges initiative.

To the best of the authors’ knowledge, this paper is the pioneer one to provide a comprehensive link between climate change events and business performances at production level, business level and stock market levels by drawing inferences from empirical data on investors’ behaviours. This study also added value in investment theories and financial literature by observing the climate change as an important factor to determine the investors’ decisions in the stock market.