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This emprical study surveyed 450 members of the New York Society of Security Analysts to determine the effect of management forecasted financial statements on their confidence in analyzing the financial condition of the film. A multivariate analysis of variance model was designed and hypothesis testing was conducted. The results demonstrated that the use of management forecasted financial statements increased the level of self‐confidence that analysts reported concerning their financial analysis. This may indicate that the current historical‐based model does not meet the needs of investors or creditors as well as would a new format including both historical and forecasted information.

This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations of these models in handling temporal information in financial time series.

Employing a robust methodological framework, the study systematically compares a range of Transformer models, including first-generation and advanced iterations like Informer, Autoformer, and PatchTST, against benchmark models (HAR, NBEATSx, NHITS, and TimesNet). The evaluation encompasses 80 different stocks, four error metrics, four statistical tests, and three robustness tests designed to reflect diverse market conditions and data availability scenarios.

The research uncovers that while first-generation Transformer models, like TFT, underperform in financial forecasting, second-generation models like Informer, Autoformer, and PatchTST demonstrate remarkable efficacy, especially in scenarios characterized by limited historical data and market volatility. The study also highlights the nuanced performance of these models across different forecasting horizons and error metrics, showcasing their potential as robust tools in financial forecasting, which contradicts the findings of Zeng et al. (2023)

This paper contributes to the financial forecasting literature by providing a comprehensive analysis of the applicability of Transformer-based models in this domain. It offers new insights into the capabilities of these models, especially their adaptability to different market conditions and forecasting requirements, challenging the existing skepticism created by Zeng et al. (2023) about their utility in financial forecasting.

Presents a case study demonstrating financial statement ratio analysis (FSRA). This analysis matches company to industry data and builds sales forecasting models. FSRA imputes forecast standards of sales and costs, and applies them to a budgeted financial statement variance analysis for the EE (electronic and electrical) industry. Develops the concept of industry base standards, integrating them into the more traditional statistical and accounting concepts of quality control standards. Provides an implementation example, and reviews possible improvements to the current methodology and approach. Uses a similar methodology to forecast the stock market value with some exceptions. Models sales and costs of an individual company and an industry based largely on aggregate industry databases. For this purpose, uses a multivariate linear trend regression analysis for the sales forecasting model. Defines and tests related hypotheses and evaluates their significance and confidence levels. For an illustration uses the EE industry and the APM company. Also demonstrates a microcomputer‐based FSRA software that speeds, facilitates, and helps to accomplish the stated objectives. The FSRA software uses industry financial statement databases, computes financial ratios and builds forecasting models.

This study examines whether a regulation on mandatory disclosure of financial forecasts since June 1991 and further sanction imposition since March 1998 contribute to lower IPO firms’ initial and aftermarket returns, and shorten honeymoon periods. The study is based on 423 IPO firms after the regulation required them to disclose their forecasts and 53 IPO firms prior to the regulation. The findings report that initial and aftermarket returns are lower, and honeymoon periods are shorter in the post‐regulation period than those in the pre‐regulation. The findings also report that initial and aftermarket returns are relatively smaller, and the honeymoon periods are shorter after the March 1998 regulatory sanction was imposed after controlling other variables. These results document that the financial forecasts disclosure regulation evidently contributes to mitigating information asymmetry.

The purpose of this paper is to test whether financial analysts’ rationality in making stocks’ earnings forecasts is homogenous or not across different information regimes in stocks’ past returns.

By treating stocks’ past returns as the information variable in this study, the authors employ a threshold regression model to capture and test threshold effects of stocks’ past returns on financial analysts’ rationality in making earnings forecasts in different information regimes.

The results show that three significant structural breaks and four respective information regimes are identified in stocks’ past returns in the threshold regression model. Across the four different information regimes, financial analysts react to stocks’ past returns quite differently when making one-quarter ahead earnings forecasts. Furthermore, the authors find that financial analysts are only rational in a certain information regime of stocks’ past returns depending on a certain return-window such as one-quarter, two-quarter or four-quarter time period.

This study is different from those in the existing literature by arguing that there could exist heterogeneity in financial analysts’ rationality in making earnings forecasts when using stocks’ past returns information. The finding that financial analysts react to stocks’ past returns differently in the different information regimes of past returns adds value to the research on financial analysts’ rationality.

The purpose of this paper is to detect quantitatively the existence of anchoring bias among financial analysts on the Tunisian stock market. Both non-parametric and parametric methods are used.

Two studies have been conducted over the period 2010–2014. A first analysis is non-parametric, based on observations of the sign taking by the surprise of result announcement according to the evolution of earning per share (EPS). A second analysis uses simple and multiple linear regression methods to quantify the anchor bias.

Non-parametric results show that in the majority of cases, the earning per share variations are followed by unexpected earnings surprises of the same direction, which verify the hypothesis of an anchoring bias of financial analysts to the past benefits. Parametric results confirm these first findings by testing different psychological anchors’ variables. Financial analysts are found to remain anchored to the previous benefits and carry out insufficient adjustments following the announcement of the results by the companies. There is also a tendency for an over/under-reaction in changes in forecasts. Analysts’ behavior is asymmetrical depending on the sign of the forecast changes: an over-reaction for positive prediction changes and a negative reaction for negative prediction changes.

The evidence provided in this paper largely validates the assumptions derived from the behavioral theory particularly the lessons learned by Kaestner (2005) and Amir and Ganzach (1998). The authors conclude that financial analysts on the Tunisian stock market suffer from anchoring, optimism, over and under-reaction biases when announcing the earnings.

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 paper is to investigate the association between corporate governance mechanisms and quality of forward-looking information in the Chinese stock market which presents a mandatory disclosure environment for forward-looking information.

Using sales forecasts to proxy forward-looking information and using precision and accuracy to measure the quality of information disclosure, the authors investigate the impact of corporate governance attributes on the precision and accuracy of sales forecasts made by listed Chinese firms in their 2010 annual reports, using logistics and ordinary least squares regressions.

The authors find good corporate governance has a positive and significant impact on the precision choice of sales forecasts disclosure. Firms with good corporate governance are more likely to disclose more precise sales forecasts than providing qualitative discussions on firms’ sales trend. In addition, good corporate governed firms are found more likely to provide precise non-financial information. The authors also find that good corporate governance is positively associated with making more conservative sales forecasts disclosure. However, the authors find no significant relationship between good corporate governance and smaller forecast error.

The study makes significant contributions to corporate disclosure literature. The authors investigate the determinants of the quality of forward-looking information in a mandatory disclosure regime while most forward-looking information disclosure literature have been conducted in a voluntary-based disclosure environment. The authors examine whether in a mandatory disclosure regime, corporate governance mechanisms can play a positive role in precision choices and accuracy of forward-looking information. Further, the study is the first to examine corporate governance and the quality of non-financial forward-looking information (sales target and production goal). The research findings therefore extend forward-looking information disclosure research from financial information to non-financial information.

The empirical findings will provide regulators with evidence on the quality of forward-looking information in a mandatory disclosure regime and the influence of corporate governance on forward-looking disclosure. The properties of forward-looking information disclosure in China should be of interest to policy makers, investors and financial analysts in other international jurisdictions.

The study investigates forward-looking information in a mandatory disclosure regime while most extant forward-looking information studies have been conducted in a voluntary disclosure environment. The study is the first to examine the quality of non-financial forward-looking information such as operational goals and plans, and to investigate the association between the quality of non-financial forward-looking information and corporate governance mechanisms. The research findings extend forward-looking information disclosure research from quantitative financial information to quantitative non-financial information.

The purpose of this paper is to investigate the relationship between voluntary disclosure of intangibles and financial analysts’ earnings forecasts properties.

Disclosures about intangible assets were hand-collected through content analysis of annual reports of a sample of US non-financial firms, while analysts’ earnings forecasts properties were collected from Bloomberg Professional database. The authors relied on correlation and multivariate regression analyses to test the research hypotheses.

The results show that increased intangible disclosures affect analysts’ earnings forecasts accuracy, dispersion, and favourable consensus recommendations. However, this effect varies according to the nature of intangible assets.

The results may be of interest to different market participants such as corporate managers, financial analysts, and standards setting bodies that recently published guidelines on voluntary disclosure of intangibles.

This study develops a new comprehensive index to measure the content of narrative disclosures about a large number of intangibles, such as human, structural, and relational assets. The findings contribute to the current debate on the value-relevance of narrative disclosures on intangibles to investors and financial analysts.

This paper aims to make a comparison, different from existing literature solely focusing on voluntary earnings forecasts and ex post earnings surprise, between the effects of mandatory earnings surprise warnings and voluntary information disclosure issued by management teams on financial analysts in terms of the number of followings and the accuracy of earnings forecasts.

This paper uses panel data analysis with fixed effects on data collected from Chinese public firms between 2006 and 2010. It uses an exogenous regulation enforcement to minimise the endogeneity problem.

This paper finds that financial analysts are less likely to follow firms which mandatorily issue earnings surprise warnings ex ante than those voluntarily issue earnings forecasts. Moreover, ex post, they issue less accurate and more dispersed forecasts on former firms. The results support Brown et al.’s (2009) finding in the USA and suggest that the earnings surprise warnings affect information asymmetries.

This paper justifies the mandatory earnings surprise warnings policy issued by Chinese Securities Regulatory Commission in 2006.

Mandatory earnings surprise is a unique practical regulation for publicly listed firms in China. This paper, for the first time, provides empirical evaluation on the effectiveness of a mandatory information disclosure policy in China. Consistent with existing literature on information disclosure by public firms in other countries, this paper finds that, in China, voluntary information disclosure captures more private information than mandatory information disclosure on corporate earnings ability.