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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.

Taking a communication perspective, the paper explores management's rhetoric in profit warnings, whose sole purpose is to disclose unexpected bad news.

Adopting a close-reading approach to text analysis, the authors analyse three profit warnings of the now-collapsed Carillion, contrasting the rhetoric with contemporaneous investor conference calls to discuss the profit warnings and board minutes recording boardroom discussions of the case company's precarious financial circumstances. The analysis applies an Aristotelian framework, focussing on logos (appealing to logic and reason), ethos (appealing to authority) and pathos (appealing to emotion) to examine how Carillion's board and management used language to persuade shareholders concerning the company's adverse circumstances.

As non-routine communications, the language in profit warnings displays and mimics characteristics of routine communications by appealing primarily to logos (logic and reason). The rhetorical profiles of investor conference calls and board meeting minutes differ from profit warnings, suggesting a different version of the story behind the scenes. The authors frame the three profit warnings as representing three stages of communication as follows: denial, defiance and desperation and, for our case company, ultimately, culminating in defeat.

The research is limited to the study of profit warnings in one case company.

The paper views profit warnings as a communication artefact and examines the rhetoric in these corporate documents to elucidate their key features. The paper provides novel insights into the role of profit warnings as a corporate communication vehicle/genre delivering bad news.

The term structure of KTB (Korea Treasury Bond) is empirically implemented and forecasted by the extended 2-factor CIR model. Pearson and Sun model. MLE is applied to estimate parameters. Using KTB prices forecasted by the model, strategies of trading and hedge between KTB, KTF (Korea Treasury Futures) are established. In this article we can see that Pearson and Sun model appropriately explains the term structure of KTB but does not fit forecasting KTB prices. However, the model well forecasts the direction of interest rate moving up or down. Through such a forecast‘ profit via trading KTB and KTF can be realized.

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.

The purpose of this paper is to provide an empirical evidence concerning the influence of Corporate governance and voluntary disclosures in annual reports: a post-International Financial Reporting Standards adoption evidence from an emerging capital market.

Data were collected from the annual reports of all 22 listed non-financial firms over a five-year period. Using content analysis, the audited annual reports of the firms were scored on the extent of overall and four specific types of voluntary disclosures made. The panel data obtained were analyzed using a generalized ordinary least squares regression model.

The findings of the study show that voluntary disclosures among the firms are low even after the adoption of IFRS. Corporate governance attributes of board size and board leadership structure are significant determinants of the extent of voluntary disclosures made by the firms. However, board independence and auditor type exhibit only a significant positive effect on voluntary financial and forward-looking information disclosures.

Firms’ voluntary information disclosure and governance variables were restricted to those in annual reports, which may partially reflect the reality of firms’ disclosure and governance practices.

The present study offers useful insights to regulators of the capital market to strengthen monitoring of firms to ensure strict adherence to corporate governance best practice guidelines as a means of improving information environment.

This study is one of the very few ones in Africa, especially in the context of Ghana Stock Exchange, to use post-IFRS data and examine a disaggregated voluntary disclosure by firms.

The purpose of this study was to uncover representative emergent areas and to examine the research area of marketing, tourism and big data (BD) to assess how these thematic areas have developed over a 27-year time period from 1996 to 2022. This study analyzed 1,152 studies to identify the principal thematic areas and emergent topics, principal theories used, predominant forms of analysis and the most productive authors in terms of research.

The articles for this research were all selected from the Web of Science database. A systematic and quantitative literature review was performed. This study used SciMAT software to extract indicators. Specifically, this study analyzed productivity and produced a science map.

The findings suggest that interest in this area has increased gradually. The outputs also reveal the innovative effort of industry in new technologies for developing models for tourism marketing. Ten research areas were identified: “destination marketing,” “mobility patterns,” “co-creation,” “gastronomy,” “sustainability,” “tourist behavior,” “market segmentation,” “artificial neural networks,” “pricing” and “tourist satisfaction.”

This work is unique in proposing an agenda for future research into tourism marketing research with new technologies such as BD and artificial intelligence techniques. In addition, the results presented here fill the current gap in the research since while there have been literature reviews covering tourism with BD or marketing, these areas have not been studied as a whole.

El objetivo de esta investigación fue descubrir nichos representativos de áreas emergentes y examinar el área de Marketing, Turismo y Big Data, evaluando cómo han evolucionado estas áreas temáticas durante un período de 27 años desde 1996–2022. Analizamos 1.152 investigaciones para identificar las principales áreas temáticas y temas emergentes, las principales teorías utilizadas, las formas de análisis predominantes y los autores más productivos en términos de investigación.

Todos los artículos para esta investigación fueron seleccionados de la base de datos Web of Science. Realizamos una revisión sistemática y cuantitativa de la literatura. Utilizamos el software SciMAT para extraer indicadores. Específicamente, analizamos la productividad y elaboramos un mapeo científico.

Los hallazgos sugieren que el interés en esta área ha aumentado gradualmente. Los resultados también revelan el esfuerzo innovador de la industria en nuevas tecnologías para desarrollar modelos de marketing turístico. Se identificaron diez áreas de investigación (“marketing de destinos”, “patrones de movilidad”, “co-creación”, “gastronomía”, “sostenibilidad”, “comportamiento turístico”, “segmentación de mercado”, “redes neuronales artificiales”, “precios”, y “satisfacción del turista”).

Este trabajo es único al proponer una agenda para futuras investigaciones en investigación de Marketing Turístico con nuevas tecnologías como Big Data y técnicas de Inteligencia Artificial. Además, los resultados presentados aquí llenan el vacío actual en la investigación ya que si bien se han realizado revisiones de literatura que cubren Turismo con Big Data o Marketing, estas áreas no se han estudiado como un conjunto.

这一特定研究领域的目标是发现具有代表性的新兴领域, 并考察市场营销、旅游和大数据研究领域, 以评估这些主题领域在1996年至2022年的27年间是如何发展的。我们分析了1152项研究, 以确定主要专题领域和新兴主题、使用的主要理论、主要的分析形式以及在研究方面最有成效的作者。

本研究的文章都是从Web of Science数据库中选出的。我们进行了系统化的定量文献审查, 并使用SciMAT软件来提取指标。具体来说, 我们分析了生产力并制作了一个科学研究地图。

研究结果表明, 人们对这一领域的兴趣已经逐渐增加。本文也揭示了工业界在开发旅游营销模式的新技术方面的创新努力。研究确定了十个研究领域：“目的地营销”、“流动模式”、“共同创造”、“美食”、“可持续性”、“游客行为”、“市场细分”、“人工神经网络”、“定价 “和游客满意度”。

这项研究的独特之处在于提出了未来利用大数据和人工智能技术等新技术进行旅游营销研究的议程。此外, 本文的结果填补了目前的研究空白, 因为虽然有文献综述涉及旅游与大数据或市场营销, 但这些领域还没有被作为一个整体来研究。

There exist long-term fluctuations in the process of capital accumulation. The economic long wave is an essential part of research into non-mainstream western economics. After the Second World War, the capitalist world experienced the fourth long wave of expansion and then entered into a downward phase of the long wave in the 1970s. Regarding to whether a new long wave of expansion took place in the 1980s, left-wing scholars hold different viewpoints. The purpose of this paper is to focus on this issue.

First, based on the review of the long wave history, this paper discusses three kinds of long wave theories with significant influence and puts forward the theoretical framework of analyzing the long wave of capitalist economy. Next, under the guidance of this theoretical framework and in combination with the actual development and evolution of the capitalist economy, the issue of whether the fifth long wave of the capitalist economy began to emerge in the 1980s is discussed deeply.

This paper argues that, from the early 1980s to 2007, the US-dominated developed countries experienced a new long wave of expansion driven by the information technology revolution, the adjustment of the neoliberalism system and the economic globalization. However, the financial-economic crisis of 2008–2009 led to a new phase of long wave downswing.

This paper does not agree with the single-factor analysis of the intrinsic formation mechanism of economic long wave and sticks to the multi-factor analysis centering on the fluctuation of accumulation rate. It is pointed out that the evolution of the long wave of capitalist economy depends on the combined influence of technology, institutions and market. The study of the long wave of the economy will help us to correctly understand the historical stage and characteristics of the current world capitalist economy in the long-term fluctuations, so that we can make an appropriate and positive response.

This study examines the role of behavioural factors, such as confidence, optimism, pessimism and rational expectation, in affecting investment decisions in the Pakistani stock market.

Using daily trading data of Karachi Stock Exchange-100 index from January 2012 to December 2015, different regression models, including descriptive statistics and stationarity tests, are performed.

Results indicate that stock market trading has suffered from pessimistic behaviour of investors. In the first model, the authors find a positive sign of confidence and negative sign of optimism with the trading volume. The second model shows a positive role of confidence and rational expectations in affecting the trading volume in daily, Monday and Friday samples. The results of the third model show a negative sign of both optimism and rational expectation with the trading volume. Furthermore, the next model shows a negative sign of confidence combined with pessimism while testing their relationship with the trading volume. Finally, results of the final model suggest that optimism negatively affects the trading volume, and on the other hand, pessimism has a positive impact on the trading volume.

The method and empirical testing of behavioural biases and their relationship with economic variable used in this study seem to be a promising way to better understand the role of psychology in deriving financial decisions for academics and policymakers.

This study uses secondary data for measuring behavioural biases and decomposes the effect between rational expectation and behavioural biases.

The purpose of this paper is to evaluate the influence of the informational environment on the relevance of accounting information in companies traded in stock exchanges of emerging markets.

For this purpose, the authors calculated indicators based on figures derived from the financial statements and variables that sought to capture the influence of the economic and institutional environment. The sample consisted of publicly traded companies from 20 countries classified as emerging by Standard & Poors. Macroeconomic information was obtained through the International Country Risk Guide database. The analysis period ranged from 2004 to 2013, excluding missing data, variables considered as outliers, besides the exclusion of data from companies that presented negative equity.

It was observed that the financial variables presented signs consistent with the literature, except for the price-to-book variable and the asset change variable. The inclusion of variables related to the accounting informational environment offered evidence that the more opaque the accounting environment in the country, the lesser the ability of the profits to portray the variations of stock returns. The variable that captured the adoption of international standards was consistent with expectations, i.e. the adoption of international standards would increase the quality of accounting information, showing a positive signal. Moreover, the variable aggressiveness of the earnings was statistically significant and negative, consistent with the literature.

The variables earnings smoothing and aversion to losses did not show the expected behaviour though, highlighting the possible limitations of these proxies used to capture the opacity of the earnings.

When institutional moderators were included, it was observed that the adoption of the IFRS standards positively affected the relationship, which is more relevant when the accounting figures were under its aegis. Recently, countless nations’ transition to international accounting standards has been justified by the need to use high-quality reporting standards. The research sought to contribute to strengthen this dimension, presenting evidence that the dummy variable included to capture the adoption of international standards had a positive effect on the relationship.

The current study aims to investigate the impacts of two behavioral biases, namely, loss aversion and overconfidence on the performance of US companies. First, the impact of loss aversion on the economic performance of companies was assessed. Second, the impact of overconfidence on market performance was discussed.

This study used around 6,777 quarterly observations on the population of US-insured industrial and services companies over the 2006-2016 period. Ordinary least squares (OLS) regression in two panel data models were used to test the hypotheses formulated for the study.

It was documented that the loss-aversion bias negatively affects the economic performance of companies and this is achieved for both sectors. In contrast, the findings suggest that overconfidence positively affects market performance of industrial firms but negatively affects market performance in service firms. Further robust evidence was found that overconfidence bias seems to be dominant, and hence, investors may tend to be more overconfident rather than more loss-averse.

This research can be extended by focusing on the following question: What is the impact of the contradictory (positive and negative) effects of an investor's loss aversion and overconfidence on the US company performance in case of realization of a stock market crisis or stock market crash?