Search

Purpose – Stakeholders’ uncertainty about firms’ value drives their urge to get information, as well as managerial disclosure choices. In this study, the authors examine whether and how an important source of uncertainty – the recent COVID-19 pandemic’s effect on corporate social responsibility (CSR) disclosure – is beyond managerial and stakeholders’ control. Design/methodology/approach – The authors develop a novel construct for daily CSR disclosure by employing computer-aided text analysis (CATA) on the press releases issued by 125 New Zealand Stock Exchange (NZX) listed from 28 February 2020 to 31 December 2020. To capture COVID-19 intensity, the authors use the growth rate of the population-adjusted cumulative sum of confirmed cases in New Zealand on a specific day. To examine the association between the COVID-19 outbreak and companies’ CSR disclosure, the authors employed ordinary least squares (OLS) regression by clustering standard error at the firm level. Findings – The authors find a one standard deviation increase in the COVID-19 outbreak leads to a 28% increase in such disclosures. These results remained robust to a series of sensitivity tests and continue to hold after accounting for potential endogeneity concerns. In the channel analysis, the study demonstrates that the positive relationship between COVID-19 and CSR disclosure is more pronounced in the presence of a well- structured board (i.e. a large, more independent board and with a higher proportion of women on it). In further analysis, the authors find the documented relationship varies over the pandemic’s life cycle and is moderated by government stringency response, peer CSR pressure and media coverage. Originality/value – This paper is the first study that contributes to the scant literature examining the impact of the COVID-19 outbreak on CSR disclosure. Prior research either investigates the relationship of the CSR- stock return during the COVID-19 market crisis or examines the relationship

Purpose – Stakeholders’ uncertainty about firms’ value drives their urge to get information, as well as managerial disclosure choices. In this study, the authors examine whether and how an important source of uncertainty – the recent COVID-19 pandemic’s effect on corporate social responsibility (CSR) disclosure – is beyond managerial and stakeholders’ control. Design/methodology/approach – The authors develop a novel construct for daily CSR disclosure by employing computer-aided text analysis (CATA) on the press releases issued by 125 New Zealand Stock Exchange (NZX) listed from 28 February 2020 to 31 December 2020. To capture COVID-19 intensity, the authors use the growth rate of the population-adjusted cumulative sum of confirmed cases in New Zealand on a specific day. To examine the association between the COVID-19 outbreak and companies’ CSR disclosure, the authors employed ordinary least squares (OLS) regression by clustering standard error at the firm level. Findings – The authors find a one standard deviation increase in the COVID-19 outbreak leads to a 28% increase in such disclosures. These results remained robust to a series of sensitivity tests and continue to hold after accounting for potential endogeneity concerns. In the channel analysis, the study demonstrates that the positive relationship between COVID-19 and CSR disclosure is more pronounced in the presence of a well- structured board (i.e. a large, more independent board and with a higher proportion of women on it). In further analysis, the authors find the documented relationship varies over the pandemic’s life cycle and is moderated by government stringency response, peer CSR pressure and media coverage. Originality/value – This paper is the first study that contributes to the scant literature examining the impact of the COVID-19 outbreak on CSR disclosure. Prior research either investigates the relationship of the CSR- stock return during the COVID-19 market crisis or examines the relationship

This study examines the post-earnings announcement drift (PEAD) anomaly on the Swedish stock market. By constructing a corporate governance index based on share structure, board independence and board gender diversity, we test how the quality of firms’ corporate governance affects the drift – a link which is previously unexplored. We find no evidence of PEAD for firms with good corporate governance, while firms with bad corporate governance do experience a drift. Furthermore, a PEAD trading strategy based on bad governance firms yields significantly larger abnormal returns compared to the corresponding trading strategy for good governance firms. Our results are robust to controlling for the risk factors of the Fama-French 3-factor model. The findings support that investors tend to underreact to extreme earnings surprises reported by bad governance firms due to a higher degree of information uncertainty, while the stock price reactions are more complete for good governance firms.

This study examines the post-earnings announcement drift (PEAD) anomaly on the Swedish stock market. By constructing a corporate governance index based on share structure, board independence and board gender diversity, we test how the quality of firms’ corporate governance affects the drift – a link which is previously unexplored. We find no evidence of PEAD for firms with good corporate governance, while firms with bad corporate governance do experience a drift. Furthermore, a PEAD trading strategy based on bad governance firms yields significantly larger abnormal returns compared to the corresponding trading strategy for good governance firms. Our results are robust to controlling for the risk factors of the Fama-French 3-factor model. The findings support that investors tend to underreact to extreme earnings surprises reported by bad governance firms due to a higher degree of information uncertainty, while the stock price reactions are more complete for good governance firms.

Although there is substantial literature linking news to the asset return volatility of a single asset, little attention has been paid to how news influences the relationships between firms. This paper addresses this issue by examining how firm-specific scheduled and unscheduled news arrivals influence the systemic risk of individual firms based on a sample of 47 US financial institutions. Whereas negative surprises from scheduled news announcements and a higher rate of unscheduled news both increase the systemic risk of a firm, positive news surprises decrease this systemic risk. In addition, negative scheduled news and a higher rate of unscheduled news across the sector increases the total connectedness or systemic risk across the sector as a whole. These effects are magnified when the market is already in distress. The results indicate that regulators should consider more than volatility and pay attention to the news flow when monitoring systemic risk.

type:Article, identifier:Journal of Advanced Mechanical Design, Systems, and Manufacturing. 2016, 10 (6), JAMDSM0083

Every collaboration stands on a foundation of mutual trust. This is a pre-requisite for any information sharing as well as the basis to successfully carry out collaborative tasks. This article presents the use case of Open Digital Lab 4you, a digitized laboratory environment, and identifies relevant trust factors based on a literature review and action-based research. In this paper stakeholders’ needs and requirements are discussed and these are linked to several, critical aspects of trust when sharing resources among public institutions., QC20200310

Collaborations are based on mutual trust to strengthen confidence in the sharing of various resources such as information. Particularly in logistics, collaborations benefit emerged rich-data environments to successfully manage demand fluctuation and visibility of in-store logistics; as well as the sharing of physical assets. Shared information is gathered from various sources and manipulated by specific partner to match or maximize individual payoff. Such information may become vulnerable to information sharing behavior of the partner to henceforth affect trust. This paper investigates the influence of the information sharing behavior on trust. It focuses on a dimension of information accuracy to answer a research question: how do information sharing behaviors of partner affect trust in logistics collaboration? A framework of information behavior is established, and subsequently a trust model specified. Afterwards, simulation experiments are conducted to observe resulting impacts. Results unveil that both, the positively and negatively manipulated information influence trust in similar magnitudes. It is further argued that partner’s deceitful behavior underlying information sharing can be reduced although it might be difficult to eliminate., QC 20171101

Розглянуто проблемні аспекти інформаційної спроможності ринку економічної інформації. Визначено ефекти, які впливають на рівень інформаційної спроможності ринкової динаміки. Показано вплив факторів інформаційної спроможності на товарному ринку, ринку праці та ринку капіталів., Рассмотрены проблемные аспекты информационной способности рынка экономической информации. Определены эффекты, которые влияют на уровень информационной способности рыночной динамики. Показано влияние факторов информационной способности на товарном рынке, рынке труда и рынке капиталов., Considered problematic aspects of information capacity market economic information. Determined effects that affect the level of information capacity market dynamics. The influence factors of information capacities in the commodity market, labor market and capital market.

Розглянуто проблемні аспекти інформаційної спроможності ринку економічної інформації. Визначено ефекти, які впливають на рівень інформаційної спроможності ринкової динаміки. Показано вплив факторів інформаційної спроможності на товарному ринку, ринку праці та ринку капіталів., Рассмотрены проблемные аспекты информационной способности рынка экономической информации. Определены эффекты, которые влияют на уровень информационной способности рыночной динамики. Показано влияние факторов информационной способности на товарном рынке, рынке труда и рынке капиталов., Considered problematic aspects of information capacity market economic information. Determined effects that affect the level of information capacity market dynamics. The influence factors of information capacities in the commodity market, labor market and capital market.

Розглянуто проблемні аспекти інформаційної спроможності ринку економічної інформації. Визначено ефекти, які впливають на рівень інформаційної спроможності ринкової динаміки. Показано вплив факторів інформаційної спроможності на товарному ринку, ринку праці та ринку капіталів., Рассмотрены проблемные аспекты информационной способности рынка экономической информации. Определены эффекты, которые влияют на уровень информационной способности рыночной динамики. Показано влияние факторов информационной способности на товарном рынке, рынке труда и рынке капиталов., Considered problematic aspects of information capacity market economic information. Determined effects that affect the level of information capacity market dynamics. The influence factors of information capacities in the commodity market, labor market and capital market.

Although firm-initiated clawbacks reduce accounting manipulation, they also induce managers to engage in suboptimal activities (e.g., reduce research and development (R&D) expenses) to achieve earnings targets. To assess the effectiveness of clawback provisions, we examine their impact from debtholders' point of view. We find that banks use more financial covenants and performance pricing provisions in the loan contracts and decrease interest rates after firms initiate clawbacks. Moreover, we also find that loan maturity increases and loan collateral decreases subsequent to clawback adoption. Taken together, our findings indicate that firm-initiated clawback provisions enhance financial reporting quality, thereby reducing the information uncertainty that financing providers face. © 2013 Elsevier B.V.

Although firm-initiated clawbacks reduce accounting manipulation, they also induce managers to engage in suboptimal activities (e.g., reduce research and development (R&D) expenses) to achieve earnings targets. To assess the effectiveness of clawback provisions, we examine their impact from debtholders' point of view. We find that banks use more financial covenants and performance pricing provisions in the loan contracts and decrease interest rates after firms initiate clawbacks. Moreover, we also find that loan maturity increases and loan collateral decreases subsequent to clawback adoption. Taken together, our findings indicate that firm-initiated clawback provisions enhance financial reporting quality, thereby reducing the information uncertainty that financing providers face. © 2013 Elsevier B.V.

Although firm-initiated clawbacks reduce accounting manipulation, they also induce managers to engage in suboptimal activities (e.g., reduce research and development (R&D) expenses) to achieve earnings targets. To assess the effectiveness of clawback provisions, we examine their impact from debtholders' point of view. We find that banks use more financial covenants and performance pricing provisions in the loan contracts and decrease interest rates after firms initiate clawbacks. Moreover, we also find that loan maturity increases and loan collateral decreases subsequent to clawback adoption. Taken together, our findings indicate that firm-initiated clawback provisions enhance financial reporting quality, thereby reducing the information uncertainty that financing providers face. © 2013 Elsevier B.V.

This paper analyzes the historical relationship between the political coloration of the government and stock market performance in France between 1871 and 2008. The Left-wing/Right-wing dichotomy, which is ubiquitous in French political discourse, is utilized in order to build a comparative analytical framework. During the 150 months characterized by the appointment of a new government regardless the coloration, we find that the monthly stock return is, on average, three times higher than for other months. The market appreciates in value with all new governments. However, in the long run, the real return of French stocks averages 4.40% per year under Left-wing versus 0.11% under Right-wing governments. This difference, although statistically robust, is not the result of added compensation for higher risk investments, nor is it driven by short special periods. The existence of a more favorable macroeconomic context during the rule of Left-wing governments only explains one third of this difference. A large part of the difference is concentrated during the three months prior to a coloration change. Assuming that the market anticipates coloration changes three months in advance, we move the boundaries: the difference in stock returns becomes insignificant., info:eu-repo/semantics/published

This study examines whether disclosures of insider equity purchases on Securities and Exchange Commission (SEC) Form 4 resolve uncertainty regarding the valuation implications of reported earnings. Defining information uncertainty as ambiguity about firm value arising from low earnings precision, I predict and find that insider purchase filings trigger more positive market reactions in firms with greater information uncertainty (lower quality accruals). After controlling for future earnings changes, further find that market reactions to purchase filings are predictably associated with prior earnings changes. The strength of this effect is increasing in the magnitude of insider purchases, as well as the level of information uncertainty. Overall, these findings suggest that, in addition to signaling future earnings information, Form 4 purchase filings help investors learn about the valuation implications of past earnings signals.

This paper analyzes the historical relationship between the political coloration of the government and stock market performance in France between 1871 and 2008. The Left-wing/Right-wing dichotomy, which is ubiquitous in French political discourse, is utilized in order to build a comparative analytical framework. During the 150 months characterized by the appointment of a new government regardless the coloration, we find that the monthly stock return is, on average, three times higher than for other months. The market appreciates in value with all new governments. However, in the long run, the real return of French stocks averages 4.40% per year under Left-wing versus 0.11% under Right-wing governments. This difference, although statistically robust, is not the result of added compensation for higher risk investments, nor is it driven by short special periods. The existence of a more favorable macroeconomic context during the rule of Left-wing governments only explains one third of this difference. A large part of the difference is concentrated during the three months prior to a coloration change. Assuming that the market anticipates coloration changes three months in advance, we move the boundaries: the difference in stock returns becomes insignificant., info:eu-repo/semantics/published

This dissertation examines the effect of income smoothing on information uncertainty, stock returns, and cost of equity. Following existing literature, I construct two income smoothing measures - capturing income smoothing through both total accruals and discretionary accruals. I show that income smoothing tends to reduce firms' information uncertainty, as measured by stock return volatility, analyst forecast dispersion, and analyst forecast error. Further, I provide evidence that market prices income smoothing and rewards income smoothing firms with a premium. Controlling for unexpected earnings shocks and other firm characteristics, income smoothing firms have significantly higher abnormal returns around earnings announcement. Finally, I show that income smoothing, particularly through discretionary accruals, reduces firms' implied cost of equity.

Information uncertainty is inherent in many problems and is often subtle and complicated to understand. Although visualization is a powerful means for exploring and understanding information, information uncertainty visualization is ad hoc and not widespread. This paper identifies two main barriers to the uptake of information uncertainty visualization: first, the difficulty of modeling and propagating the uncertainty information and, second, the difficulty of mapping uncertainty to visual elements. To overcome these barriers, we extend the spreadsheet paradigm to encapsulate uncertainty details within cells. This creates an inherent awareness of the uncertainty associated with each variable. The spreadsheet can hide the uncertainty details, enabling the user to think simply in terms of variables. Furthermore, the system can aid with automated propagation of uncertainty information, since it is intrinsically aware of the uncertainty. The system also enables mapping the encapsulated uncertainty to visual elements via the formula language and a visualization sheet. Support for such low-level visual mapping provides flexibility to explore new techniques for information uncertainty visualization.

In this paper, we introduced a new approach of combining multiple classifiers in a heterogeneous ensemble system. Instead of using numerical membership values when combining, we constructed interval membership values for each class prediction from the meta-data of observation by using the concept of information granule. In the proposed method, the uncertainty (diversity) of the predictions produced by the base classifiers is quantified by the interval-based information granules. The decision model is then generated by considering both bound and length of the intervals. Extensive experimentation using the UCI datasets has demonstrated the superior performance of our algorithm over other algorithms including six fixed combining methods, one trainable combining method, AdaBoost, bagging, and random subspace.

In this study, a novel framework to combine multiple classifiers in an ensemble system is introduced. Here we exploit the concept of information granule to construct granular prototypes for each class on the outputs of an ensemble of base classifiers. In the proposed method, uncertainty in the outputs of the base classifiers on training observations is captured by an interval-based representation. To predict the class label for a new observation, we first determine the distances between the output of the base classifiers for this observation and the class prototypes, then the predicted class label is obtained by choosing the label associated with the shortest distance. In the experimental study, we combine several learning algorithms to build the ensemble system and conduct experiments on the UCI, colon cancer, and selected CLEF2009 datasets. The experimental results demonstrate that the proposed framework outperforms several benchmarked algorithms including two trainable combining methods, i.e., Decision Template and Two Stages Ensemble System, AdaBoost, Random Forest, L2-loss Linear Support Vector Machine, and Decision Tree.

The article considers the issue of outsourcing development in the field of information technologies. The work is devoted to the construction of a model that allows optimizing the organizational structure of the BigData information network and carrying out a comparative analysis of the effectiveness of its implementation in the format of IT outsourcing. In the process of research, the theoretical bases of outsourcing are analyzed and factors influencing the choice of the system implementation variant are generalized. In the critical review, a hypothetical assumption is made that the degree of IT outsourcing implementation effectiveness is inversely related to the degree of change in the characteristics of uncertainty, the frequency of queries and specificity. The study made it possible to characterize a set of indicators for evaluating the efficiency of outsourcing the BigData information system. The development model can be used to optimize the server complex of the network in order to reduce the number of cluster computing capacities involved during variable load. The criteria of the expediency of outsourcing application in practical activity of the companies are determined. © 2005 – ongoing JATIT & LLS.

In this paper, we introduced a new approach of combining multiple classifiers in a heterogeneous ensemble system. Instead of using numerical membership values when combining, we constructed interval membership values for each class prediction from the meta-data of observation by using the concept of information granule. In the proposed method, the uncertainty (diversity) of the predictions produced by the base classifiers is quantified by the interval-based information granules. The decision model is then generated by considering both bound and length of the intervals. Extensive experimentation using the UCI datasets has demonstrated the superior performance of our algorithm over other algorithms including six fixed combining methods, one trainable combining method, AdaBoost, bagging, and random subspace.

In this study, a novel framework to combine multiple classifiers in an ensemble system is introduced. Here we exploit the concept of information granule to construct granular prototypes for each class on the outputs of an ensemble of base classifiers. In the proposed method, uncertainty in the outputs of the base classifiers on training observations is captured by an interval-based representation. To predict the class label for a new observation, we first determine the distances between the output of the base classifiers for this observation and the class prototypes, then the predicted class label is obtained by choosing the label associated with the shortest distance. In the experimental study, we combine several learning algorithms to build the ensemble system and conduct experiments on the UCI, colon cancer, and selected CLEF2009 datasets. The experimental results demonstrate that the proposed framework outperforms several benchmarked algorithms including two trainable combining methods, i.e., Decision Template and Two Stages Ensemble System, AdaBoost, Random Forest, L2-loss Linear Support Vector Machine, and Decision Tree.