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Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

SUMMARY: This study examines whether the presence of high-quality auditors (Big 4 or industry specialist auditors [ISAs]) in an industry facilitates accounting information transfer among industry peers and enhances investment decisions of firms not audited by high-quality auditors (non-Big 4-ISA client firms). Consistent with the prediction of informational influence theories, we find that non-Big 4-ISA client firms that belong to an industry with a greater presence of high-quality auditors are associated with lower investment inefficiency. The effect is more pronounced for firms with less precise private information. Path analysis shows that the association between high-quality auditor presence and learning firms' investment inefficiency is explained via a direct path of source credibility and an indirect path mediated by peer firms' accounting information quality. Finally, we find that firms in industries with larger reductions in Big 4 presence following the Sarbanes-Oxley Act of 2002 incur more investment inefficiency. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M40; M41; M42. [ABSTRACT FROM AUTHOR]

Robust Risk Quantification via Shock Propagation in Financial Networks Despite the significance of risk contagion in financial networks, uncertainties arise in interbank network structures because of limited information. To address this, proposed is a robust optimization approach to estimate worst-case default probabilities and capital requirements for a specific group of banks (e.g., systemically important financial institutions). By applying this tool, we analyze the impact of different incomplete network information structures and gain regulatory insights into gathering actionable network information. Given limited network information, we consider robust risk quantification under the Eisenberg–Noe model for financial networks. To be more specific, motivated by the fact that the structure of the interbank network is not completely known in practice, we propose a robust optimization approach to obtain worst-case default probabilities and associated capital requirements for a specific group of banks (e.g., systemically important financial institutions) under network information uncertainty. Using this tool, we analyze the effects of various incomplete network information structures on these worst-case quantities and provide regulatory insights into the collection of actionable network information. All claims are numerically illustrated using data from the European banking system. Funding: The work of D. Ahn was supported by the Hong Kong Research Grants Council, University Grants Committee [Early Career Scheme Grant 24210420]. N. Chen acknowledges funding support from the Hong Kong Research Grants Council, University Grants Committee [General Research Fund Grant 14207918 and General Research Fund Grant 14208620]. The work by K.-K. Kim was supported by the National Research Foundation of Korea [Grant NRF-2019R1A2C1003144]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2020.0722. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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 between corporate characteristics including the quality of financial information and the reactions of stock returns during COVID-19. The authors extend such studies by providing empirical evidence that managers respond to COVID-19 by increasing CSR disclosure.

We document strong evidence that CEO incentive compensation can predict the significance of stock price momentum through discretionary accrual and real activities manipulation. The profit of momentum strategy increases with CEO pay‐for‐performance incentive, but decreases with CEO risk‐taking incentive. It also evaluates the effects of information uncertainty on such relationship. The evidence is more significant for firms with older and longer tenured CEOs and firms with more informed traders. The relationship between the profit of momentum strategy and CEO pay‐for‐performance incentive is stronger among CEOs without the risk‐taking incentive. Our results are robust for different sub‐samples based on before and after Reg FD and Sarbanes–Oxley Act, even after controlling for the potential endogeneity. Further, our findings are consistent with the information diffusion explanation of momentum and the agency theory that incentivised CEOs tend to manipulate information by smoothing good news, concealing mildly bad news and accelerating the disclosure of extremely bad news. [ABSTRACT FROM AUTHOR]

We examine and compare the extent to which the reaction of investors to earnings announcements is influenced by a firm's governance profile and prevailing market conditions. We find that firms with better governance characteristics experience a larger initial reaction to both good and bad earnings announcements regardless of the prevailing sentiment and uncertainty conditions. However, the influence of governance is constrained to the announcement period. We demonstrate that changes in market uncertainty and/or investor sentiment are related to the post-earnings announcement drift. We also find that a major channel through which greater corporate governance influences the market response to unexpected earnings news is by lowering information uncertainty and so providing greater clarity of the implication of the news for firm value. Finally, we establish that two types of uncertainties (market and information) have very different influence on investor's response to information signals. JEL Classification: D81, G10, G14, G30, G32 [ABSTRACT FROM AUTHOR]

Firms engaging in innovative practices have patents to prevent competitive forces from eroding the resulting economic rents; however, there is limited evidence regarding the impact of innovation on risk. We shed new light on how firms' involvement in innovation activities impacts their volatility, particularly their idiosyncratic volatility. In this paper, we empirically examine the effect of innovation on idiosyncratic volatility. To do so, we empirically examine the impact of innovation, measured by patents weighted by citations and R&D expenditure, on the idiosyncratic volatility of firms. Using a large sample of 8256 US firms, we find that more innovation is associated with lower idiosyncratic volatility. We also find that information uncertainty is the channel through which innovation affects idiosyncratic risk. The results are robust for different measures of idiosyncratic volatility. These results have empirical implications for investors, managers, and firms engaging in innovation-related activities. [ABSTRACT FROM AUTHOR]

Introduction. Meaning-in-life orientations and self-reflection are important indicators of the regulation of personal meanings. In a modern rapidly changing society, individuals do not fully manage their lives due to the influence of various uncertainty factors. The COVID-19 pandemic that began in early 2020 and swept across the world is considered a global factor of uncertainty. The increased uncertainty of life associated with the pandemic is an additional stressor. This paper represents a first effort in obtaining empirical data on gender characteristics of teachers’ meaning-based regulation in the context of the coronavirus pandemic. This paper reports the results of an empirical study of differences in meaning-in-life orientations and self-reflection among teachers before and during the pandemic as different conditions of information uncertainty in their lives. Methods. The study was conducted among female teachers of secondary schools in the Rostov region in March 2017 (n = 53) and in October 2020 (n = 43), i.e. before and during the pandemic, which can be considered different conditions of information uncertainty in their lives. We used the following psychological assessment tools: (a) the Meaning-in-Life Orientations test by D. A. Leont'ev and (b) the Self-reflection Questionnaire by I. A. Stetsenko. Results. We observed an increase in all means of both tests during the pandemic, compared to the pre-pandemic values. Differences in all parameters are statistically significant (according to the Mann–Whitney U test). Discussion. Our findings indicate that the pandemic represents a surmountable stressor for the homogeneous acmeological category of female teachers surveyed in this study. Information uncertainty contributes to constructive restructuring of the processes of meaning-based regulation and psychological adaptation, which leads to an increase in the level of meaningfulness of life orientations and self-reflection among the respondents.

The possibility and expediency of estimation of risk factors based on fundamental positions of information and entropy are grounded. In accordance with the principle of addiction, the possibility of using the H-criterion as an indicator of business uncertainty is shown. The algorithm of risk estimation of these investments is offered.

Due to the constant growth of cities in terms of population and territory, the volume of passenger traffic is sharply increasing. A lot of attention is paid to the study of transport infrastructure in scientific works, but the current problems of the functioning of various types of transport in large cities remain not fully covered. First of all this concerns the great uncertainty in the current information about the movement of transport and their work in the event of emergency situations. The article proposes to use the mathematical apparatus of the theory of queuing systems for the analysis of passenger traffic on urban transport. As an example, a graph of states for fixed-route taxis is constructed and ratios are obtained for evaluating the main indicators of the quality of their work. In the conditions of frequent changes in road situations and uncertainty of the conditions for obtaining information about the current situation, it is proposed to carry out traffic management using intelligent decisionmaking support tools. In this regard, it is advisable to use an approach based on fuzzy situational management. For this purpose, the article proposes an algorithm for fuzzy situational management, which is the basis of intelligent IDSS. . For an optimal transition from the current situation to a situation that meets the new operating conditions, in the sense of the selected criterion, it is proposed to use the dynamic programming method. Ref. 12, pic. 4.

• The trend factor performs significantly better following high market volatility periods. • Results remain robust under different asset price models and after controlling for various macroeconomic state variables. • Stock market volatility serves as an important time-series determinant of trend factor performance. This paper investigates how stock market volatility predicts trend factor profits. We find that the trend factor performs significantly better following high market volatility periods. From 1931 to 2022, the average monthly risk adjusted return following high volatility periods is 2.47 %, significantly higher than that following low volatility periods. We hypothesize that fundamental signals are more likely to be imprecise when the stock market is more volatile, leading investors to rely more heavily on trend signals. Consequently, the trend factor could deliver higher profits. Collectively, our paper suggests that market volatility is an important time-series determinant of trend factor performance. [ABSTRACT FROM AUTHOR]

• There is a U-shaped relationship between the performance of carbon neutrality and firm value. • The U-shaped relationship is weaker in state-owned firms and firms operating in B2C industries. • The U-shaped relationship is stronger when information uncertainty is higher. Carbon neutrality is critical in addressing the climate crisis. Using the data of Chinese listed firms from 2018 to 2022, this paper shows a significant U-shaped association between the performance of carbon neutrality and firm value. Our key findings hold after a battery of robustness tests and endogeneity concerns. The U-shaped relationship is weaker in state-owned firms and those operating in B2C industries, and stronger when information uncertainty is higher. This paper has implications for the promotion of carbon-neutral practices in enterprises and the realization of sustainable development. [ABSTRACT FROM AUTHOR]

We propose a timing momentum strategy by incorporating moving‐average signals in the price momentum and show that the proposed strategy substantially outperforms the buy‐and‐hold strategy. The performance of the timing momentum is better than that of Barroso and Santa‐Clara's (2015) constant‐volatility momentum and is identical to that of Daniel and Moskowitz's (2016) dynamic momentum. One advantage of the timing momentum is that its weights on winner and loser portfolios are lower than the other two strategies, thus leading to lower transaction costs. Further, we show that the profitability of the timing momentum is enhanced when information uncertainty is high. More importantly, the timing momentum has time‐invariant profitability across various time‐series predictors and during periods of momentum crashes. [ABSTRACT FROM AUTHOR]

Abstract Background Individuals’ stress in responding to the current COVID-19 pandemic may be exacerbated by information uncertainty driven by inconsistent, unverified, and conflicting news from various sources. The current study aims to test if information uncertainty during the COVID-19 outbreak was related to acute stress disorder (ASD) over and above other psychosocial stressors. Methods An anonymous online survey was conducted with 7800 college students throughout China from January 31 through February 11, 2020. Existing scales were modified to measure ASD and six potential stressors including information uncertainty during the COVID-19 outbreak. Hierarchical regression analysis was conducted to assess the unique association of information uncertainty with ASD. To minimize the effect of large sample size and also to get a sense of whether the effects of information uncertainty were similar to people at the center of the epidemic, we repeated the hierarchical regression among 10% of the students who were randomly selected from the entire sample (“10% random sample”; n = 780) and 226 students from Hubei Province where the outbreak started. Results Information uncertainty was highly prevalent among the respondents (64%). It was significantly associated with ASD beyond other key variables and potential stressors across three samples. In the hierarchical regression among the entire sample, demographic variables accounted for 9.4% of the variance in ASD. The other five stressors added 5.1% of the variance. The information uncertainty (β = .159; p

One of the main technical types of military intelligence of Ukraine, which is constantly carried out (conducted) in peacetime, a special period, in the interests of national security and defense of the state and the fulfillment of tasks in operations (military operations) is electronic intelligence. Electronic intelligence extracts intelligence information by opening up the operation of electronic equipment and systems used to control troops (forces) and weapons, and also collects, processes, analyzes, and brings the intelligence obtained to specific consumers on time. For the continuous and timely provision of consumers with certain information, information (information and analytical) bodies, a set of measures is undertaken for intelligence and information activities, intelligence and information work. In the conditions of dynamism, versatility of the processes of processing, analysis, storage of large volumes of information, the key task remains to reduce the uncertainty of information caused by factors of evolutionary development, factors of the use of complex information systems in various conditions of a complex electronic and operational environment.

This article explores instinctive frames of human decision-making in environmental and resource economics. Higher-moment asset pricing combines rational, mathematically informed economic reasoning with psychological and biological insights. Leptokurtic blindness and skewness preference combine in particularly challenging ways for carbon mitigation. At their worst, human heuristics may generate perverse decisions. Information uncertainty and the innate preference for bonds-and-bullets portfolios may impair responses to catastrophic climate change.

Rittel and Webber boldly challenged the conventional assumption that ‘scientific’ approaches to social policy and planning provide the most reliable guidance for practitioners and researchers who are addressing complex, and contested, social problems. This provocative claim, that scientific-technical approaches would not ‘work’ for complex social issues, has engaged policy analysts, academic researchers and planning practitioners since the 1970s. Grappling with the implications of complexity and uncertainty in policy debates, the first generation of ‘wicked problem’ scholars generally agreed that wicked issues require correspondingly complex and iterative approaches. This tended to quarantine complex ‘wicked’ problems as a special category that required special collaborative processes. Most often they recommended the inclusion of multiple stakeholders in exploring the relevant issues, interests, value differences and policy responses. More than four decades later, however, there are strong arguments for developing a second-generation approach which would ‘mainstream’ the analysis of wicked problems in public policy. While continuing to recognize the centrality of complexity and uncertainty, and the need for creative thinking, a broader approach would make better use of recent public policy literatures on such topics as problem framing, policy design, policy capacity and the contexts of policy implementation.

The possibility and expediency of estimation of risk factors based on fundamental positions of information and entropy are grounded. In accordance with the principle of addiction, the possibility of using the H-criterion as an indicator of business uncertainty is shown. The algorithm of risk estimation of these investments is offered. [ABSTRACT FROM AUTHOR]

Background: Individuals' stress in responding to the current COVID-19 pandemic may be exacerbated by information uncertainty driven by inconsistent, unverified, and conflicting news from various sources. The current study aims to test if information uncertainty during the COVID-19 outbreak was related to acute stress disorder (ASD) over and above other psychosocial stressors.Methods: An anonymous online survey was conducted with 7800 college students throughout China from January 31 through February 11, 2020. Existing scales were modified to measure ASD and six potential stressors including information uncertainty during the COVID-19 outbreak. Hierarchical regression analysis was conducted to assess the unique association of information uncertainty with ASD. To minimize the effect of large sample size and also to get a sense of whether the effects of information uncertainty were similar to people at the center of the epidemic, we repeated the hierarchical regression among 10% of the students who were randomly selected from the entire sample ("10% random sample"; n = 780) and 226 students from Hubei Province where the outbreak started.Results: Information uncertainty was highly prevalent among the respondents (64%). It was significantly associated with ASD beyond other key variables and potential stressors across three samples. In the hierarchical regression among the entire sample, demographic variables accounted for 9.4% of the variance in ASD. The other five stressors added 5.1% of the variance. The information uncertainty (β = .159; p < .001) explained an additional 2.1% of the variance. Likewise, the information uncertainty explained an additional 2.1 and 3.4% of the variance in ASD beyond all other variables among the 10% random sample (β = .165; p < .001) and the Hubei sample (β = .196; p < .01), respectively.Conclusion: Information uncertainty is a unique correlate of psychological stress during the COVID-19 outbreak. Reducing information uncertainty is essential not only for halting virus transmission but also for mitigating negative impacts of the pandemic on people's psychosocial wellbeing. Transparent, timely, and accurate communication can reduce public confusion, fear, and stress. Capacity building in governments, communities, and media outlets to prevent, reduce and manage information uncertainty should be a critical part of the response to an emerging global health crisis such as the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Many companies adopt concurrent engineering in their product-development projects in order to reduce time to market. Concurrent engineering requires careful management, since the uncertainty of transferred information leads to rework and renders a development process complex. Most of the existing studies on concurrent engineering have analyzed overlapping between two activities. The present study expanded the research scope by modeling the developmental process of the concurrent execution of multiple activities as the result of management decisions, the overlapping ratio, and the information transfer frequency. When multiple activities are overlapped, it is more difficult to model a development process, since the uncertainty of cascaded information has impacts on many activities. The quantitative model developed in the present study provides insights into the uncertainty of information and the product-development process. Based on the generalized process model for multiple-activity overlapping, the optimal decision to minimize project duration was considered. As a solution to the dilemma of the high computational burden of considering all feasible decision sets, this paper proposes a myopic heuristic. The project management heuristic proposed in this paper is intended to assist managers in the decision-making process, as well as reducing the intermediate deadlines in project main plan. [ABSTRACT FROM AUTHOR]

We investigate how firms use information technology (IT) implementation to mitigate an anomaly in capital markets: investors underreacting to new public information. The theory of information uncertainty (IU) suggests that the anomaly is amplified with IU; that is, with ambiguity in information about firm value. We theorize that a firm's IT in general—and enterprise systems (ES) in particular—can mitigate IU, thus reducing the IU-induced underreaction anomaly. Based on a difference-in-differences analysis of a sample of 572 ES implementations, our main finding is that ES implementation does reduce IU-induced underreaction anomaly. This is achieved through a reduction in the firm's fundamentals volatility and an improvement in information quality. We also find that firms with greater IT capability are better positioned to realize the anomaly-reducing benefits of ES implementation and that ES's anomaly-reducing effect is most pronounced when high levels of both functional and operational ES modules are implemented. We obtain remarkably consistent results when using alternate empirical design, samples, and measures of news. Such IT impacts are economically highly consequential because they improve capital market efficiency. This paper was accepted by Anandhi Bharadwaj, information systems. [ABSTRACT FROM AUTHOR]

Prior literature observes that information uncertainty exacerbates investor underreaction behavior. In this paper, I investigate whether, as professional investment intermediaries, sellside analysts suffer more behavioral biases in cases of greater information uncertainty. I show that greater information uncertainty predicts more positive (negative) forecast errors and subsequent forecast revisions following good (bad) news, which corroborates previous findings on the post-analyst-revision drift. The opposite effects of information uncertainty on forecast errors and subsequent forecast revisions following good versus bad news support the analyst underreaction hypothesis and are inconsistent with analyst forecast rationality or optimism suggested in prior literature. [ABSTRACT FROM AUTHOR]

This paper provides the inter-temporal capital asset pricing model with incomplete information and short sales constraints. We derive the general equilibrium market equation and the security market line of the "classical" capital asset pricing model in continuous time in the presence of incomplete information and short sales. [ABSTRACT FROM AUTHOR]

The potential use of auxiliary summary information to improve the efficiency of estimation has attracted significant interest. Most existing methods assume that the data distribution is the same for the sample data and for the population that generates the auxiliary information. However, recent works have relaxed this assumption by allowing heterogeneity between the two covariate distributions. We consider an empirical likelihood approach that guarantees that using auxiliary information will increase the efficiency of estimation when the variability associated with this information is sufficiently small. We also investigate the effects of this variability on the efficiency. Furthermore, we implement the proposed approach using a Newton{Raphson-type algorithm. Lastly, we discuss our simulation results, which demonstrate the efficiency gains and confirm the large sample approximations. [ABSTRACT FROM AUTHOR]

Rittel and Webber boldly challenged the conventional assumption that 'scientific' approaches to social policy and planning provide the most reliable guidance for practitioners and researchers who are addressing complex, and contested, social problems. This provocative claim, that scientific-technical approaches would not 'work' for complex social issues, has engaged policy analysts, academic researchers and planning practitioners since the 1970s. Grappling with the implications of complexity and uncertainty in policy debates, the first generation of 'wicked problem' scholars generally agreed that wicked issues require correspondingly complex and iterative approaches. This tended to quarantine complex 'wicked' problems as a special category that required special collaborative processes. Most often they recommended the inclusion of multiple stakeholders in exploring the relevant issues, interests, value differences and policy responses. More than four decades later, however, there are strong arguments for developing a second-generation approach which would 'mainstream' the analysis of wicked problems in public policy. While continuing to recognize the centrality of complexity and uncertainty, and the need for creative thinking, a broader approach would make better use of recent public policy literatures on such topics as problem framing, policy design, policy capacity and the contexts of policy implementation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract 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. Highlights • We modelled the base classifiers' output by using a granular prototype. • We quantified the distance between base classifiers' output and a granular prototype. • We proposed a novel framework to combine multiple classifiers in an ensemble system. • The proposed method is highly competitive to some state-of-the-art ensemble methods. [ABSTRACT FROM AUTHOR]

This paper empirically investigates how firm-level information uncertainty impacts momentum profits in the Chinese Class A share market. We employ seven different factors to gauge the degree of firm-level information uncertainty—firm size, firm age, analysts’ coverage, return volatility, dispersion in analysts’ earnings forecast, trading volume, and the quality/strength of corporate governance (free float ratio). We find evidence showing that information uncertainty has an amplifying effect over the momentum profits, and the amplifying effect is more pronounced over the time periods following DOWN market state over the sample period from January 1996 to December 2013. The robustness of the empirical evidence is warranted by a risk-adjustment test based on the FF3F model and Wang and Xu’s (Financ Anal J 60(6):65-77, 2004) FF3F model, a sub-period analysis, and a different definition of market states. The empirical findings can provide an important reference point for international and domestic investors when adjusting investment strategies and portfolio positions in relatively volatile financial markets such as the Chinese stock market. [ABSTRACT FROM AUTHOR]

This study examines the effect of the Jumpstart Our Business Startups Act (JOBS Act) on information uncertainty in IPO firms. The JOBS Act creates a new category of issuer, the Emerging Growth Company (EGC), and exempts EGCs from several disclosures required for non-EGCs. Our findings are consistent with proprietary cost concerns motivating EGCs to eliminate some of the previously mandatory disclosures, which increases information uncertainty in the IPO market, attracts investors who rely more on private information, and leads EGCs to provide additional post-IPO disclosures to mitigate the increased information uncertainty. Our results also are consistent with agency explanations, whereby EGCs exploit the JOBS Act provisions to avoid compensation-related disclosures, which results in larger IPO underpricing for such firms. Overall, we provide evidence on how reduced mandatory disclosure affects the IPO market. [ABSTRACT FROM AUTHOR]

This study investigates whether and when differences in the credit rating agencies' methodologies result in differences in rating properties. In particular, this study focuses on differences in information processing constraints between a rating agency that utilizes qualitative analysis and direct access to borrowers' management in its rating process (Standard & Poor's) compared to one that does not (Egan Jones Ratings Company) and how these differences affect rating quality. We find that as information uncertainty about borrowers increases, Egan Jones's rating accuracy, informativeness, and timeliness decrease relative to Standard & Poor's. Our findings suggest that Egan Jones's more restricted rating methodology can lead to limitations in information processing and, thus, reductions in Egan Jones's rating quality advantage for borrowers with greater information uncertainty. [ABSTRACT FROM AUTHOR]

The physical-chemical monitoring of vast oceanic areas aims at assessing the status and evolution of the environmental resource for its exploration, protection and/or better understanding. However, the interpretation of monitoring data is affected by ocean seasonality and heterogeneity, and by the quality of sampling and characterization tools used to study the environment. All these factors contribute to the uncertainty of collected information that should be expressed in determined parameter values or trends. A trend of a studied parameter quantified by values difference is significant if the observed absolute value of the difference is larger than their expanded uncertainty. The correlation of studied parameters, useful for their interpretation, is equality affected by the mentioned sources of uncertainty. This work describes the metrologically sound evaluation of trends and correlations of physicochemical parameters of vast oceanic areas where all uncertainty sources affecting the information are considered by simulating their complex impact by the Monte Carlo Method. The described methodology was successfully used to study the impact of summer upwelling in an 800 km2 coastal area offshore two large cities in Portugal. Nutrients, conductivity, salinity and temperature trends and correlations are distinguished from system heterogeneity, sampling and sample analysis uncertainty for a 99% confidence level. [Display omitted] • Simulation of the impact of the heterogeneity, and sampling and analysis uncertainty on oceanic area characterisation. • Mean value of nutrients and fundamental oceanographic parameters from a vast oceanic area reported with uncertainty. • Evaluation of the variation of mean composition values between October 2019 and May 2018 for a 99% confidence level. • Correlations between nutrients and fundamental oceanic parameters evaluated taking information uncertainty into account. • Upwelling trends and correlations confirmed from modelled spatial variation of a vast oceanic area with uncertainty. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer among Canadian women and the second cause of death from cancer. Fine needle aspirate (FNA) is a technology used to investigate early breast tumors to detect cancer. In this paper, we demonstrate the application of a new ordered weighted averaging operator (OWA) to the problem of breast tumor classification. The OWA operator employs the Laplace distribution to calculate the weight vector to aggregate the uncertain information about the breast tumors. The aggregated information is used along with the tumor label, i.e., benign or malignant, to train a nearest neighbor, support vector machine, and logistic regression classifiers. The result of this study based on the nearest neighbor classifier achieves 99.71% accuracy that outperforms other studies that utilize other OWA operators using the same dataset. [ABSTRACT FROM AUTHOR]

The article considers main principles of building up an information system of support of making decisions on the process of exploring and development of petroleum and natural gas deposits. It offers a system that is based on an inter-disciplinary analysis of data and operative assessment of new information that is received in the course of projects realisation from the point of view of its impact on the range of changes of uncertainties of the gas-and-petroleum producing projects and existing levels of risk. Specific feature of the proposed system lies in application of methods of stochastic modelling of uncertainties of parameters of a project and project-connected risks and their quality assessment. Probabilistic modelling and dynamic analysis of uncertainties allow optimisation of managerial decisions with consideration of interconnection of possible risks and technical and economic parameters of projects. The article offers algorithms of identification and analysis of risks, development and making the decisions based on the dynamic analysis of data, probabilistic modelling of uncertainties and risks connected with them. It considers main tasks of scientific studies on improvement of the proposed dynamic system of support of decision making in geological survey and gas-and-petroleum producing branches of production and also methods of their realisation.Рассматриваются основные принципы построения информационной системы поддержки принятия решений (СППР) процесса разведки и разработки месторождений нефти и газа. Предлагается система, которая базируется на междисциплинарном анализе данных и оперативной оценке новой информации, поступающей в ходе реализации проектов с точки зрения ее влияния на диапазон изменения неопределенностей нефтегазодобывающих проектов и существующих уровней риска. Особенность предлагаемой системы состоит в применении методов стохастического моделирования неопределенностей параметров проекта и связанных с ними рисков и их количественном оценивании. Вероятностное моделирование и динамический анализ неопределенностей позволяют оптимизировать управленческие решения с учетом взаимосвязи возможных рисков и технико-экономических параметров проектов. Предложены алгоритмы идентификации и анализа рисков, разработки и принятия решений, базирующихся на динамическом анализе данных, вероятностном моделировании неопределенностей и связанных с ними рисков. Рассмотрены основные задачи научных исследований по усовершенствованию предлагаемой динамической системы поддержки принятия решений в геологоразведочной и нефтегазодобывающей отраслях производства, а также методы их реализации.

Reliability-based multiobjective optimization (RBMO) is a method that integrates multiobjective optimization with a reliability analysis. The method is useful for a large or complicated design problem such as aerospace structure design. Reliability analysis generally requires the probabilistic distribution parameters of random variables such as the mean and standard deviation. However, for an actual design problem, the probabilistic parameters are sometimes estimated with insufficient accuracy because of a limited number of experiments. In that case, the uncertainty in the distribution parameter is not negligible. This study proposes the evaluation method to estimate the effect of the information uncertainty at first, where the uncertainty is evaluated by using the confidence interval. Some numerical examples illustrates the effectiveness of the proposed method in comparison with a conventional method, Gibbs sampling. Then, the effect of the parameter uncertainty on the RBMO is illustrated through numerical examples. The RBMO problem is formulated by using the satisficing trade-off method (STOM), where the multiobjective optimization problem is transformed into the equivalent single-objective optimization method. For the reliability-based design optimization, a modified SLSV (single-loop-single-vector) method is adopted for the computational efficiency. The effects of the parameter uncertainty on the selected Pareto solutions according to the aspiration level are investigated by using the confidence intervals of the Pareto solutions.

Using a sample of Taiwan stock market, this paper investigates the role of information uncertainty in the profitability of technical analysis by applying a moving average (MA) strategy to portfolios grouped according to whether firms issue stock options. Results indicate that, even though considering transaction costs, the MA strategy significantly outperforms the buy-and-hold strategy on the portfolio without option issuance, but not on the portfolio with option issuance. The results support the hypothesis that stocks that do not issue options exhibit greater information uncertainty, and thus greater price continuation, which in turn implies a superior performance of the MA strategy. [ABSTRACT FROM AUTHOR]

We investigate the role played by the Securities and Exchange Commission (SEC) in monitoring fair value disclosures in regulatory filings. Specifically, we assess whether SEC action via the issuance of fair value comment letters to registrants is followed by reductions in uncertainty about the firms' fair value estimates. We hypothesize that registrants that receive a comment letter focusing on their fair value disclosure policies experience reductions in investor uncertainty regarding their fair value estimates in the post-letter period, compared to the pre-letter period. Supporting this prediction, we find that for the periods after the fair value comment letters, the associations between Level 2 and 3 fair value assets and our measures of uncertainty are significantly reduced. These findings are robust to a series of tests designed to ensure that we do not simply capture general changes in market uncertainty levels for firms investing in these types of assets. Our study contributes to the further understanding of market participants' perception of fair value disclosures by investigating the role of SEC enforcement. This finding is important given recent criticisms of fair value reporting emanating from the highest levels of government and industry. [ABSTRACT FROM AUTHOR]

• COVID-19 pandemic had an adverse impact on the IPO market. • IPOs during the pandemic experience greater information uncertainty compared to those before the pandemic. • Increased uncertainty is mainly driven by the IPOs from the high-technology and the healthcare sectors. • IPO underpricing and post-IPO return volatility is positively associated with pandemic intensity and the associated government responses. Anecdotal evidence seems to suggest that the initial public offering (IPO) market performed remarkably well through the COVID-19 pandemic. To further understand this peculiar observation, we carry out a comprehensive analysis of IPOs during the pandemic vis-a-vis IPOs before the pandemic. Our findings imply that IPOs during the pandemic experience greater information uncertainty compared to those before the pandemic, and this greater uncertainty is mainly driven by the IPOs from the high-technology and the healthcare sectors. Furthermore, we find that an average IPO firm experiences larger underpricing and more post-IPO return volatility as the pandemic and the associated government responses increase in severity before the offering. Overall, our study indicates that the COVID-19 pandemic had an adverse impact on the IPO market. [ABSTRACT FROM AUTHOR]