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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.

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]

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]

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]

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]