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• 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]

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]

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]

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]

• 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]

Vehicle localisation is an important and challenging task in achieving autonomous driving. This work presents a box particle filter framework for vehicle self-localisation in the presence of sensor and map uncertainties. The proposed feature-refined box particle filter incorporates line features extracted from a multi-layer Light Detection And Ranging (LiDAR) sensor and information from OpenStreetMap to estimate vehicle states. A particle weight balance strategy is incorporated to account for the OpenStreetMap positional inaccuracy, which is assessed by comparing it to a high definition road map. The performance of the proposed framework is evaluated on a LiDAR dataset and compared with box particle filter variants. Experimental results show that the proposed framework achieves respectively 10% and 53% localisation performance improvement with reduced box volumes of 25% and 41%, when compared with the state-of-the-art interval analysis based box regularisation particle filter and the box particle filter. • The FRBPF is proposed to deal with OpenStreetMap and sensor data uncertainties. • A contraction algorithm is developed to reduce the volume of box particles. • Theoretical proofs about the features-refined contractions are derived. • A weight balance strategy is designed to improve the performance of FRBPF. [ABSTRACT FROM AUTHOR]

This study points out the complexities inherent to stock assessment distortion and how accounting information is priced. We obtain evidence about how investors, who are considered to have bounded rationality, influence the dynamics of the formation of stock prices. This effect was investigated in a Brazilian scenario, an environment of low informational quality, but one that represents an important route for international investments. The results indicate that stock market values do not tend to reflect the accounting information for hard-to-value stocks. In this case, accounting information is priced gradually over time. There is evidence that under more complex conditions, stock prices tend to mainly reflect accounting information related to good news. The results also showed that the adoption of International Financial Reporting Standards (IFRS) in Brazil increased the relevance of accounting information by reducing the complexity involved in stock assessment. Additionally, greater comovement of stock prices was identified for hard-to-value-stocks. • Investors are susceptible to mistakes and judgment biases for hard-to-value stocks; • The Information Uncertainty Effect (IUE) generates anomalies in stock prices; • IFRS increases the relevance of accounting information, attenuating the IUE; • The good news is associated with an attention effect from the mass of investors; • Greater comovement of stock prices was identified for hard-to-value stocks. [ABSTRACT FROM AUTHOR]

The iterative and developing nature of designing a building involves the specification and handling of vague, imprecise, and incomplete information. A crucial factor for mitigating the impact of these uncertainties on the decision-making process is to effectively quantify and communicate them among the project stakeholders. The interactive visualization of 3D building models provides great support for evaluating building designs. However, the currently available visualization methods of the available authoring tools do not incorporate the potential uncertainties associated with the geometric and semantic information of building elements. Currently, building models appear precise and certain, even in the early design stages, which can lead to false assumptions and model evaluations, affecting the decisions made throughout the design stages. Hence, this paper presents a set of visualization approaches, including intrinsic, extrinsic, animation, and walkthroughs, that have been developed to present the uncertainties associated with the building elements' information. The efficiency of the approaches developed in this study was evaluated through an online survey and interviews. More specifically, the approaches were compared in terms of intuitiveness, applicability, and acceptance. The evaluation results positively indicated the participants' ability to understand the amount and impact of the uncertainties on the design by using the developed approaches. [ABSTRACT FROM AUTHOR]

We demonstrate that arbitrage risk, constructed using three measures — noise trader risk, trading cost and information uncertainty — can predict the return of stocks cross-sectionally in China. The findings are broadly consistent even when out-of-sample tests are conducted using the Fama-MacBeth cross-sectional regression approach. We also construct hypothetical portfolios using the information arising from arbitrage risk and find the existence of abnormal returns which is robust to the use of various portfolios constructed by re-sampling the observations through multiple approaches (e.g., by market capitalization and by book-to-market ratio). Lastly, we reconstruct our portfolios by considering the unique nature of the Chinese stock market (e.g., the dominance of individual investors). Our trading strategies again successfully obtain abnormal returns, suggesting that arbitrage risk can be useful to construct effective investment portfolios in China. • In- and out-of-sample tests are conducted on the cross-sectional stock return in China. • Arbitrage risk is decomposed into noise trader risk, trading cost and information uncertainty. • These risk components effectively forecast stock returns. • These risk components can be used to construct portfolios with abnormal returns. [ABSTRACT FROM AUTHOR]