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The recommendation of the analyst report is not only limited to a small number of ratings, but also biased toward a buy opinion with the absence of sell opinion. As an alternative to this, this paper aims to extract analysts' textual opinions embedded in the report body through text analysis and examine the profitability of investment strategies. Analyst opinion about a firm is measured by calculating the frequency of positive and negative words in the report text through the Korean sentiment lexicon for finance (KOSELF). To verify the usefulness of textual opinions, the author constructs a calendar-time based portfolios by the analysts' textual opinion variable of each stock. When opinion level is used, investment strategy has no significant hedged portfolio return. However, hedged portfolio constructed by opinion change shows significant return of 0.117% per day (2.57% per month). In addition, the hedged return increases to 0.163% per day (3.59% per month) when the opening price is used instead of closing price. This study show that the analysts’ opinion extracted from text analysis contains more detailed spectrum than recommendation and investment strategies using them give significant returns.

Surprisingly little is known of the various methods of security analysis used by financial analysts with industry-specific knowledge. Financial analysts’ industry knowledge is a favored and appreciated attribute by fund managers and institutional investors. Understanding analysts’ use of industry-specific valuation models, which are the main value drivers within different industries, will enhance our understanding of important aspects of value creation in these industries. This paper contributes to the broader understanding of how financial analysts in various industries approach valuation, offering insights that can be beneficial to a wide range of stakeholders in the financial market.

This paper systematically reviews existing research to consolidate the current understanding of analysts’ use of valuation models and factors. It aims to demystify what can often be seen as a “black box”, shedding light on the valuation tools employed by financial analysts across diverse industries.

The use of industry-specific valuation models and factors by analysts is a subject of considerable interest to both academics and investors. The predominant model in several industries is P/E, with some exceptions. Notably, EV/EBITDA is favored in the telecom, energy and materials sectors, while the capital goods industry primarily relies on P/CF. In the REITs sector, P/AFFO is the most commonly employed model. In specific sectors like pharmaceuticals, energy and telecom, DCF is utilized. However, theoretical models like RIM and AEG find limited use among analysts.

This is the first paper systematically reviewing the research on analyst’s use of industry-specific stock valuation methods. It serves as a foundation for future research in this field and is likely to be of interest to academics, analysts, fund managers and investors.

The purpose of the study is to identify the soft skills and abilities that are crucial to success in the fields of operations management (OM) and supply chain management (SCM), using the O*NET database and the classification of a set of professional figures integrating values for task skills and abilities needed to operate successfully in these professions.

The study used the O*NET database to identify the soft skills and abilities required for success in OM and SCM industries. Correlation analysis was conducted to determine the tasks required for the job roles and their characteristics in terms of abilities and soft skills. ANOVA analysis was used to validate the findings. The study aims to help companies define specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the job position.

As a result of the work, a set of soft skills and abilities was defined that allow, through correlation analysis, to explain a large number of activities required to work in the operations and SCM (OSCM) environment.

The work is inherently affected by the database used for the professional figures mapped and the scores that are attributed within O*NET to the analyzed elements.

The information resulting from this study can help companies develop specific assessments and tests for the roles of OM and SCM to measure individual attitudes and correlate them with the requirements of the job position. The study aims to address the need to identify soft skills in the human sphere and determine which of them have the most significant impact on the OM and SCM professions.

The originality of this study lies in its approach to identify the set of soft skills and abilities that determine success in the OM and SCM industries. The study used the O*NET database to correlate the tasks required for specific job roles with their corresponding soft skills and abilities. Furthermore, the study used ANOVA analysis to validate the findings in other sectors mapped by the same database. The identified soft skills and abilities can help companies develop specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the requirements of the job position. In addressing the necessity for enhanced clarity in the domain of human factor, this study contributes to identifying key success factors. Subsequent research can further investigate their practical application within companies to formulate targeted growth strategies and make appropriate resource selections for vacant positions.

Using a sample of 9,898 firm-year observations from 1,821 unique Chinese listed firms over the period from 2004 to 2019, this study aims to investigate whether the market rewards meeting or beating analyst earnings expectations (MBE).

The authors use an event study methodology to capture market reactions to MBE.

The authors document a stock return premium for beating analyst forecasts by a wide margin. However, there is no stock return premium for firms that meet or just beat analyst forecasts, suggesting that the market is skeptical of earnings management by these firms. This market underreaction is more pronounced for firms with weak external monitoring. Further analysis shows that meeting or just beating analyst forecasts is indicative of superior future financial performance. The authors do not find firms using earnings management to meet or just beat analyst forecasts.

The authors provide evidence of market underreaction to meeting or just beating analyst forecasts, with the market's over-skepticism of earnings management being a plausible mechanism for this phenomenon.

The findings of this study are informative to researchers, market participants and regulators concerned about the impact of analysts and earnings management and interested in detecting and constraining managers' earnings management.

The authors provide new insights into how the market reacts to MBE by showing that the market appears to focus on using meeting or just beating analyst forecasts as an indicator of earnings management, while it does not detect managed MBE. Meeting or just beating analyst forecasts is commonly used as a proxy for earnings management in the literature. However, the findings suggest that it is a noisy proxy for earnings management.

With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

The purpose of this study is to test the determinant of financial report quality and its consequences to the company values.

This research is using a quantitative approach and testing a theory by formulating some hypotheses. The sample of this study is 85 go public companies listed in the Indonesia Stock Exchange, for a 5-year observation period from 2016 to 2020. Hence, it has a total of 425 observations. Data were analyzed using path analysis.

The results found that innate factors from financial reporting quality (FRQ) consists of dynamic factors (operation cycle and sales volatility) as well as static factors (firm’s size, FS). These factors help to achieve FRQ and are able to provide a positive response to the market. On the other hand, static factors (firm’s age, FA) and institution risk factors (leverage) are not able to produce FRQ. Thus, it cannot be considered as an economic decision maker for an investor.

Research implications include theoretical and practical implications. Theoretical implications prove that the valuation of clean surplus theory, which shows the market value of the company, is reflected in the components of the financial statements. This study also uses more than one quality of financial reporting. The practical implication of the research is that the research results are expected to provide information for the company’s management, to fulfill quality financial reporting and so that the market or investors will respond positively to these conditions. In addition, quality financial reporting information provides benefits for investors and capital market analysts (consisting of investors, brokers and market securities analysts) in determining investment decisions. The Financial Services Authority is also able to improve the implementation of corporate governance practices in Indonesia, through reform of the framework supervision of the financial services sector.

This research examines the determinants of FRQ and its consequences on firm’s value (FV). Innate factors proxies from FRQ include dynamic factors (operation cycle and sales volatility), static factors (FS and FA) and institution risk factors (leverage). A follow-up study on the value of the company because it shows the magnitude of the market response (financial statement users) on the quality of financial reporting, which is reflected in FV, the originality of this research is that the object of research is carried out in developing countries, specifically in Indonesia, because most of the previous research was carried out in developed countries.

The authors examine the role of analysts’ prior experience of forecasting for firms exposed to epidemics on analysts’ forecast accuracy during the COVID-19 pandemic.

The authors examine the impact of analysts’ prior epidemic experience on forecast accuracy by comparing the changes from the pre-COVID-19 period (calendar year 2019) to the post-COVID period extending up to March 2023 across HRE versus non-HRE analysts. The authors consider a full sample (194,980) and a sub-sample (136,836) approach to distinguish “Recent” forecasts from “All” forecasts (including revisions).

The study's findings reveal that forecast accuracy for HRE analysts is significantly higher than that for non-HRE analysts during COVID-19. Specifically, forecast errors significantly decrease by 0.6% and 0.15% for the “Recent” and “All” forecast samples, respectively. This finding suggests that analysts’ prior epidemic experience leads to an enhanced ability to assess the uncertainty around the epidemic, thereby translating to higher forecast accuracy.

The finding that the expertise developed through an experience of following high-risk firms in the past enhances analysts’ performance during the pandemic sheds light on a key differentiator that partially explains the systematic difference in performance across analysts. The authors also show that industry experience alone is not useful in improving forecast accuracy during a pandemic – prior experience of tracking firms during epidemics adds incremental accuracy to analysts’ forecasts during pandemics such as COVID-19.

The study findings should prompt macroeconomic policymakers at the national level, such as the central banks of countries, to include past epidemic experiences as a key determinant when forecasting the economic outlook and making policy-related decisions. Moreover, practitioners and advisory firms can improve the earning prediction models by placing more weight on pandemic-adjusted forecasts made by analysts with past epidemic experience.

The uncertainty induced by the COVID-19 pandemic increases uncertainty in global financial markets. Under such circumstances, the importance of analysts’ role as information intermediaries gains even more importance. This raises the question of what determines analysts’ forecast accuracy during the COVID-19 pandemic. Building upon prior literature on the role of analyst experience in shaping analysts’ forecasts, the authors examine whether experience in tracking firms exposed to prior epidemics allows analysts to forecast more accurately during COVID-19. The authors find that analysts who have experience in forecasting for firms with high exposure to epidemics (H1N1, Zika, Ebola, and SARS) exhibit higher accuracy than analysts who lack such experience. Further, this effect of experience on forecast accuracy is more pronounced while forecasting for firms with higher exposure to the risk of COVID-19 and for firms with a poor ex-ante informational environment.

The authors examine the effect of split environmental, social and governance (ESG) ratings on information asymmetry, corporate value and trading behavior. The authors test the risk-based hypothesis and the optimism-bias hypothesis on the relationship between diverging opinions and future stock prices. The authors results show that split ESG ratings is positively related to idiosyncratic volatility, an alternative measure for information asymmetry. Further, the negative effect of split ESG ratings on cumulative abnormal return under short-selling constraints is consistent with the optimism bias hypothesis. The authors find a negative relationship between split ESG ratings and the net purchase ratio (NPR) of pension funds. Considering that the NPR is a direct measure of net demand, ESG disagreement may hinder socially responsible investing (SRI) in a firm. This study directly demonstrates the negative effect of ESG disagreement on firm value and investment by Korea's National Pension Service (NPS). The results offer valuable insights into policymakers, as the wide divergence in ESG ratings requires urgent attention to expand SRI.

This paper aims to verify whether the integration of sustainability in executive compensation positively affects firms’ non-financial performance and whether corporate governance characteristics enhance the relationship between sustainability compensation and firms’ non-financial performance and to expand the domain of the impact of sustainability on non-financial performance.

This analysis is based on a sample of companies listed on the Milan Italian Stock Exchange from the Financial Times Milan Stock Exchange Index over the 2016–2020 period. Regression analysis was used by using data retrieved from the Refinitiv Eikon database and the sample firms’ remuneration reports.

The findings of this paper show that embedding sustainability in executive compensation positively affects firms’ non-financial performance. The results of this paper also reveal that specific corporate governance features can improve the impact of sustainability on non-financial performance.

This analysis is limited to Italian firms included in the Financial Times Milan Stock Exchange Index; however, the findings are highly significant.

The findings provide regulators with useful insights for considering the integration of sustainability goals into executive remuneration. Another implication is that policymakers should require – at least – listed firms to fulfil specific corporate governance structural requirements. Finally, the findings can provide investors and financial analysts with a greater awareness of the role played by executive remuneration in the long-term value-creation process.

This paper contributes to addressing the relationship among sustainability, remuneration and non-financial disclosure, drawing on the stakeholder–agency theoretical framework and focusing on Italian firms. This issue has received limited attention with controversial results in the literature.