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There is substantial evidence of short-term stock price continuation, which the prior literature often attributes to investor behavioral biases such as underreaction to new information. This paper investigates the role of information uncertainty in price continuation anomalies and cross-sectional variations in stock returns. If short-term price continuation is due to investor behavioral biases, we should observe greater price drift when there is greater information uncertainty. As a result, greater information uncertainty should produce relatively higher expected returns following good news and relatively lower expected returns following bad news. My evidence supports this hypothesis. [ABSTRACT FROM AUTHOR]

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]

As organizations are increasingly challenged to find new sources of profit improvement, cost reduction becomes a top priority on the business agenda. Expectations for cost reductions are ongoing and influence both new and existing products and services. The costs for new product and service introductions are managed differently than ongoing cost reductions. Purchasing plays a central role, with different goals, in cost control for new products and services versus ongoing cost savings. This research uses a case study methodology to understand the conflict purchasing faces in managing both new product costs and ongoing cost reductions. Due to goal incongruence between new product development and ongoing savings initiatives, purchasing may act in its own best interest, rather than in the best interest of the organization or team. This is both a contracting and an information uncertainty problem, creating an opening for passive opportunism by purchasing. Thus, agency theory and information processing theory (IPT) are combined to examine how information uncertainty can be reduced and contractual goal alignment improved in these situations. The outcome of this research is to expose potential goal misalignment between new product development cost processes and ongoing cost savings, and suggest theoretically grounded methods for reducing the potential conflict. [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]

The uncertainty of alarm information in power grids can lead to incorrect diagnosis of faults. The authors propose a method for diagnosing power grid faults that considers the uncertainty of alarm information using intuitionistic fuzzy logic. To consider the influence of the uncertainty of alarm information in fault diagnosis, an intuitionistic fuzzy set is used to represent the certainty and the uncertainty of alarm information. Using topological analysis and the logical relationships among electrical devices, protective actions, and circuit breaker trips, the authors establish a diagnostic model for power systems based on intuitionistic fuzzy Petri nets. The certainty and the uncertainty of electrical device fault events are calculated using the reasoning method for intuitionistic fuzzy Petri nets. The results for some test cases show that this method can effectively diagnose complex faults in power systems when multiple components operate incorrectly in the complicated data environment and the information is not trusted. [ABSTRACT FROM AUTHOR]

Climate change poses significant new risks and challenges for businesses and their supply chains. Additionally, in many sectors, Scope 3 indirect greenhouse gas emissions resulting from the sourcing and distribution of goods and services are larger than firms' own carbon footprints. Here we study how firms engage their key stakeholders in their supply chains in obtaining, processing, and transferring relevant climate change‐related information designed to overcome information asymmetry and drive sustainable development. Grounded in organisational information‐processing theory (OIPT), we draw on data from the Carbon Disclosure Project's Climate Change Supply Chain initiative for a qualitative content analysis of a large sample of global firms. Consistent with OIPT, we find that although firms primarily engage their supply chain partners in a variety of ways to reduce information uncertainty around indirect emissions data, effectively interpreting and managing broader sustainability information equivocality becomes a growing priority. Our findings further suggest that firms engage suppliers, customers, and other supply chain partners through basic, transactional, and collaborative types of engagement. We contribute to literatures on interorganisational information processing and sustainable supply chain management by providing a more detailed understanding of how firms engage supply chain partners in the context of climate change. [ABSTRACT FROM AUTHOR]

Atmospheric state analysis is a difficult scientific problem but essential for atmospheric sciences. Data assimilation can generate accurate analyses by integrating information on the atmospheric state using probability density functions (PDFs), where the Gaussian approximation is typically used and PDFs are described by error covariance matrices (ECMs). However, ECMs have been estimated empirically, and dependency of the ECMs on meteorological conditions (flow) is only partially represented. These limit atmospheric state analysis accuracy. This is especially problematic for water substance‐sensitive microwave radiances (WS‐MWRs) because of their strong flow dependence. We objectively estimated ECMs of all data including flow‐dependence of the ECMs of WS‐MWRs. Since the ECM of each data is a component of one ECM representing one joint PDF as a whole, it is theoretically better to objectively estimate ECMs of all data, not just a particular data. For WS‐MWRs, we categorized flow into four using water substance amount and estimating an ECM for each category. Numerical experiments using the new ECMs on an operational global numerical weather prediction system show the followings. The new error standard deviations are generally smaller than those of empirical. Standard deviations and interchannel correlations of observation errors of WS‐MWRs increase with water substance amount. The effects of WS‐MWRs on analysis were approximately doubled. The analysis fields differ systematically such as increase of low‐level clouds over cold oceans. The forecast accuracy improved with 95% statistical significance up to 9%. Both the flow dependence of correlation and variance of WS‐MWRs contributed to the improvement of forecast accuracy. Plain Language Summary: Atmospheric state analysis is a difficult scientific problem but essential for atmospheric sciences. Data assimilation can generate accurate analysis by integrating information on the atmospheric state using probability density functions typically described by error covariance matrices (ECMs). However, the ECMs include empirical tunings, and their dependency on meteorological conditions (flow) is only partially represented. These limit atmospheric state analysis accuracy. These shortcomings are especially problematic for water substance‐sensitive microwave radiances (WS‐MWRs). We objectively estimate ECMs of all data including flow‐dependence of those of WS‐MWRs. For WS‐MWRs, we categorized flow into four categories by water substance amount and estimate ECMs for each category. Numerical experiments using the estimated objective ECMs (TEST) and conventional empirical ECMs (CNTL) on a global numerical weather prediction system show the followings. The error standard deviations (SDs) of TEST are smaller than those of CNTL for almost all data. For WS‐MWRs, SDs and correlations between different observation wavelengths increase with water substance amount. The effects of WS‐MWRs on analysis were approximately doubled. The analysis fields differ systematically, such as an increase in tropospheric low clouds over cold oceans. The forecast root‐mean‐square errors of TEST are smaller than those of CNTL with 95% statistical significance up to 9%. Key Points: We objectively estimated flow‐dependent error covariance matrices (ECMs) of water substance‐sensitive microwave radiances (WS‐MWRs)The new ECMs doubled effects of WS‐MWRs on analysis, increased low‐level clouds on cold oceans, and improved forecast accuracy up to 9%Both the flow dependence of correlation and variance contributed to the improvement of forecast accuracy [ABSTRACT FROM AUTHOR]

We study investor communication and stock comovement using a novel data set from an active online stock forum in China. We find substantial comovement among the returns of a stock and its "related stocks," which are frequently discussed in the subforum dedicated to the given stock. Comovement is greater when the discussion of related stocks is more intensive. Further, the effect of communication on comovement is stronger for stocks associated with higher information uncertainty. Codiscussed stocks are more actively traded and experience more correlated trading. A trading strategy that exploits communication‐driven comovement generates abnormal returns. Our findings highlight the impact of investor communication on asset comovement. [ABSTRACT FROM AUTHOR]

The tools provided by option-pricing theory are used to examine the wealth effects of interfirm cash tender offers. The analysis provides evidence consistent with the "synergy" theory of corporate takeovers and has implications concerning the economic effects of regulations of cash tender offers. The analysis further suggests that the market prices information uncertainty in a manner not captured by the standard Capital Asset Pricing Model. The study introduces a technique for unbundling the prices of a primary asset and a contingent claim when only the prices of the combination are observed. [ABSTRACT FROM AUTHOR]

As the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment. [ABSTRACT FROM AUTHOR]

Securities issued under Rule 144A do not have to file a public registration statement with the Securities and Exchange Commission, but can be sold only to qualified financial institutions. This paper examines industrial and utility bonds issued under Rule 144A. Rule 144A issues are found to have higher yields than publicly issued bonds after adjusting for risk. Yield premiums are higher if the issuer does not file periodic financial statements with the SEC. The yield premiums of Rule 144A issues may be due to lower liquidity, information uncertainty, and weaker legal protection for investors. Bonds issued under Rule 144A may have registration rights, which require the issuer to exchange the bonds for public bonds within a stated period, or pay higher yields. While high-yield bonds usually have registration rights, we find that the majority of investment-grade bonds do not. Registration rights have a greater impact on yields for high-yield than for investment-grade bonds. Underwriter fees for Rule 144A issues are not significantly different from underwriter fees for publicly issued bonds. [ABSTRACT FROM AUTHOR]

Background: Motor neuron disease (MND) (also known as amyotrophic lateral sclerosis) is a life‐limiting neurodegenerative condition. In up to 20% of people with MND, a pathogenic variant associated with autosomal dominant inheritance can be identified. Children of people carrying a pathogenic variant have a 50% chance of inheriting this and a higher, although harder to predict, chance of developing the disease compared to the general adult population. This paper explores the experience of living with the genetic risk of MND. Methods: We undertook a UK‐based interview study with 35 individuals, including: 7 people living with genetically‐mediated forms of MND; 24 asymptomatic relatives, the majority of whom had an increased risk of developing the disease; and 4 unrelated partners. Results: We explore how individuals make sense of genetic risk, unpacking the interplay between genetic knowledge, personal perception, experiences of the disease in the family, age and life stage and the implications that living with risk has for different aspects of their lives. We balance an emphasis on the emotional and psychological impact described by participants, with a recognition that the salience of risk fluctuates over time. Furthermore, we highlight the diverse strategies and approaches people employ to live well in the face of uncertainty and the complex ways they engage with the possibility of developing symptoms in the future. Finally, we outline the need for open‐ended, tailored support and information provision. Conclusions: Drawing on wider literature on genetic risk, we foreground how knowledge of MND risk can disrupt individuals' taken‐for‐granted assumptions on life and perceptions of the future, but also its contextuality, whereby its relevance becomes more prominent at critical junctures. This research has been used in the development of a public‐facing resource on the healthtalk.org website. Patient or Public Contribution: People with experience of living with genetic risk were involved throughout the design and conduct of the study and advised on aspects including the topic guide, sampling and recruitment and the developing analysis. Two patient and public involvement contributors joined a formal advisory panel. [ABSTRACT FROM AUTHOR]

We examine the informativeness of analyst forecast revisions that are directionally inconsistent with prior stock price movements (sign-inconsistent revisions). Sign-inconsistent revisions represent approximately one-half of the forecast revisions from 1995 through 2010. Our tests indicate that sign-inconsistent revisions are less informative than are sign-consistent revisions. Sign-inconsistent revisions are less likely to be closer to actual earnings realizations and they generate smaller stock price reactions. We also find evidence that sign-inconsistent revisions are associated with analysts' economic incentives to generate trading volume and their behavioural limitations related to information uncertainty. These results suggest that sign-inconsistent revisions do not necessarily benefit investors. [ABSTRACT FROM AUTHOR]

In determining its environmental disclosure strategy, a firm's management faces a tension between responding to the information needs of financial markets and maintaining its legitimacy within the community. In this paper, relying on information economics and legitimacy theory, we explore how firms resolve this tension. Results show that a firm's environmental disclosure enhances the quality of analysts' information context, which ultimately allows them to make better forecasts. Moreover, financial analysts seem to be able to decipher environmental information, discounting discourses that are inconsistent with a firm's underlying environmental performance. We find also that a firm's environmental disclosure serves another purpose, as it influences how its other stakeholders (beyond financial ones) perceive its legitimacy. Such enhanced legitimacy reduces the information uncertainty faced by financial analysts. Our results suggest also that both economic-based environmental disclosure and sustainable development and environmental disclosure are useful to analysts in making their forecasts and enhance a firm's legitimacy. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment. [ABSTRACT FROM AUTHOR]

Software architecture is the framework for all the technical decisions and as such has the tremendous impact on product cost and quality. Choosing a software architecture for any system remains a complex task, as existing software architecture evaluation methods fail to quantitatively analyze them. Software architecture evaluation framework addresses the competing objectives of cost minimization and quality maximization between different architectural options. Due to the uncertainty in the judgment for quality attributes, fuzzy based quantitative evaluation framework for accessing quality attributes is developed to facilitate the selection of the underlying architectures. Fuzzy TOPSIS is one of the sensitive methods of multi-criteria decision making (MCDM) that defines the positive ideal solution and negative ideal solution to maximize the benefit criteria and minimize the cost criteria. The various decision makers' quality requirements need to be considered collectively to describe the quality requirements of the envisioned system which forms the basis for the comparison and selection criteria. In this paper, a multi criteria decision making approach for architecture selection under partial or incomplete information (uncertainty) is presented and validated using a suitable case study. [ABSTRACT FROM AUTHOR]

This paper establishes a robust link between momentum and credit rating. Momentum profitability is large and significant among low-grade firms, but it is nonexistent among high-grade firms. The momentum payoffs documented in the literature are generated by low-grade firms that account for less than 4% of the overall market capitalization of rated firms. The momentum payoff differential across credit rating groups is unexplained by firm size, firm age, analyst forecast dispersion, leverage, return volatility, and cash flow volatility. [ABSTRACT FROM AUTHOR]

To fully utilize the voltage regulation capacity and interaction characteristics of the Transmission and Distribution (T&D) system, a novel Modified Partially Observed Markov Decision Process (MPOMDP)‐based Reinforcement Learning (RL) scheme for Autonomous Voltage Control is proposed, which incorporates Demand Response (DR) and cooperation with the Transmission Network. The proposed scheme consists of two vital components: an MPOMDP block and a Modified Asynchronous Advantage Actor‐Critic‐based RL block. The MPOMDP block innovatively exploits the confidence interval of the observed state to make a better perception of the precise system state by introducing two new probability vectors. Then the MPOMDP block is fed into the underlying architecture of the RL block for asynchronously capturing features and optimal decision‐making, where the solving framework additionally brings in a public data buffer to realize boundary information sharing. Case studies are conducted on a modified T&D system considering N‐1 contingencies, with a training dataset from a district in Suzhou, China. Simulation results demonstrate that the proposed scheme can achieve significant voltage optimization while ensuring fast convergence speed. [ABSTRACT FROM AUTHOR]

Diffusion weighted magnetic resonance imaging (DW-MRI) are now widely used to assess brain integrity in clinical populations. The growing interest in mapping brain connectivity has made it vital to consider what scanning parameters affect the accuracy, stability, and signal-to-noise of diffusion measures. Trade-offs between scan parameters can only be optimized if their effects on various commonly-derived measures are better understood. To explore angular versus spatial resolution trade-offs in standard tensor-derived measures, and in measures that use the full angular information in diffusion signal, we scanned eight subjects twice, 2 weeks apart, using three protocols that took the same amount of time (7 min). Scans with 3.0, 2.7, 2.5 mm isotropic voxels were collected using 48, 41, and 37 diffusion-sensitized gradients to equalize scan times. A specially designed DTI phantom was also scanned with the same protocols, and different b-values. We assessed how several diffusion measures including fractional anisotropy (FA), mean diffusivity (MD), and the full 3D orientation distribution function (ODF) depended on the spatial/angular resolution and the SNR. We also created maps of stability over time in the FA, MD, ODF, skeleton FA of 14 TBSS-derived ROIs, and an information uncertainty index derived from the tensor distribution function, which models the signal using a continuous mixture of tensors. In scans of the same duration, higher angular resolution and larger voxels boosted SNR and improved stability over time. The increased partial voluming in large voxels also led to bias in estimating FA, but this was partially addressed by using 'beyond-tensor' models of diffusion. Hum Brain Mapp 34:2688-2706, 2013. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

This study investigates whether financial analysts incorporate accounting conservatism into their earnings forecasts and whether it is more difficult for them to forecast earnings for less conservative firms, and then examines the impact of the findings on the return predictability of the value-to-price ( V/P) ratio. After controlling for the other factors affecting forecast accuracy, such as earnings predictability and information uncertainty, I find that analysts incorporate accounting conservatism into their earnings forecasts and that forecasting earnings is more difficult for less conservative firms. Consequently, the return predictability of the V/P ratio is stronger for more conservative firms, and previously reported return predictability of the V/P ratio is an average across firms with differing levels of conservatism. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one on macroeconomics and information uncertainty; one on literature densities and combination methods; and one on financial measurement uncertainty, data analysis, and indicators.

Reducing poverty in rural areas of developing countries requires sustained and sustainable increases in agricultural water productivity. However, aside from traditional measures of precipitation, little is known about water available to farmers or how productively they use it. We present a crop-water productivity index (a ratio of the value of annual crop production to a dimensionless potential water availability index) for large water basins using readily available low-resolution data. The index is transferable, permits direct inter-basin comparisons, and is simple to calculate. We calculate the index for each municipality in the São Francisco river basin in Brazil. No clear patterns linking water availability and value of agricultural output are evident, even though clusters of municípios with high- and low-crop-water productivity emerge, and the former may be useful in guiding policies aimed at increasing water productivity. Finally, analyses of the effects of information uncertainty on the crop-water productivity index suggest that the returns to agricultural investments in certain places in the São Francisco river basin are more risky than others. Improvements in data quality and quantity can help refine estimates of the index and reduce their uncertainty. [ABSTRACT FROM AUTHOR]

The 2D‐Cascade‐Transeform‐Logistic‐Sine Map (2D‐CTLSM) with good randomness and large parameter chaos range is obtained by improving the Logistic and Sine map in a cascade manner. By combining the chaotic map with DNA coding, an efficient encryption algorithm based on a chaotic map with chunking dislocation and alternate row diffusion is designed. The shortcomings of zigzag itself are overcome by performing different zigzag transformations in chunks while combining DNA encoding to accomplish the diffusion operation. Due to the excellent properties of DNA encoding, the diffusion operation is performed by the DNA computation method with alternate row diffusion so that each value can complete the diffusion operation twice. The simulation experimental results and security analysis show that the algorithm can effectively resist various typical attacks, such as the information entropy is close to the expected value of 8, the correlation coefficient is close to 0, and the NPCR and UACI values can meet the critical value range, and so on. This is a safe and reliable image encryption algorithm. [ABSTRACT FROM AUTHOR]

What is the role of credit scores in credit markets? We argue that it is, in part, the market's assessment of a person's unobservable type, which here we take to be patience. We postulate a model of persistent hidden types where observable actions shape the public assessment of a person's type via Bayesian updating. We show how dynamic reputation can incentivize repayment. Importantly, we show how an economy with credit scores implements the same equilibrium allocation. We estimate the model using both credit market data and the evolution of individuals' credit scores. We conduct counterfactuals to assess how more or less information used in scoring individuals affects outcomes and welfare. If tracking of individual credit actions is outlawed, poor young adults of low type benefit from subsidization by high types despite facing higher interest rates arising from lower dynamic incentives to repay. [ABSTRACT FROM AUTHOR]

In this paper, a lithium‐ion battery State of Health (SOH) estimation algorithm is proposed based on the fusion of multidomain features and the application of a CatBoost model. The aim is to address the issue of low prediction accuracy in SOH caused by the utilization of single‐feature extraction techniques. The algorithm encompasses the extraction of various features from the original charge–discharge data, including time‐domain, frequency‐domain, entropy, and time‐series features. Following the evaluation of feature importance, a feature selection process is conducted to eliminate redundant features that provide a limited contribution to the predictive results. Subsequently, a multiple‐set discriminative correlation analysis is employed to integrate high‐dimensional features. To attain accurate predictions, the CatBoost model is further optimized through the utilization of a sparrow search algorithm. Experimental results demonstrate that the proposed algorithm achieves accurate SOH estimations within individual batteries, as evidenced by mean square error values consistently below 4e−4 and goodness‐of‐fit values exceeding or equal to 0.98. Additionally, the algorithm exhibits reliable prediction capabilities across different batteries operating under the same charge/discharge strategy. Comparative analysis indicates that the adoption of the multidomain feature fusion approach yields improved prediction accuracy in contrast to the utilization of a single feature extraction method. [ABSTRACT FROM AUTHOR]

The corporate culture within firms is a significant concern for regulators, shareholders and other stakeholders. Drawing on a large sample of US firms, we use the political preferences of the top management team (TMT) to proxy for a firm's culture and examine whether it influences the decision to implement an effective internal control system (ICS) and whether the ICS plays a mediating role between the culture created by the TMT and financial reporting quality. We find that a Republican‐leaning TMT with a more conservative ideology is associated with a more effective internal control system. In addition, the TMT's political preferences affect financial reporting quality, both directly and indirectly, via the internal control system. A range of robustness tests reinforces our main findings. [ABSTRACT FROM AUTHOR]

We examine non‐GAAP earnings reporting following a going‐concern audit opinion (GCO). Using a propensity score‐matched sample, matching first‐time going‐concern issuing companies with firms in financial distress that did not receive a going‐concern report, we find that the likelihood and frequency of non‐GAAP earnings reporting are lower following GCOs. In additional analyses, we find the negative association between the announcement of GCOs and the likelihood and frequency of non‐GAAP earnings reporting stronger when GCOs are issued by industry‐specialist auditors and when GCOs are unexpected, but do not find litigation risk or managers' ability to affect the association. These results are consistent with a decrease in investor demand for non‐GAAP earnings disclosures following GCOs. [ABSTRACT FROM AUTHOR]

Using a sample of up to 2,503 initial public offerings (IPOs) in 32 countries from 2011–2017, we predict and find that higher levels of country‐level accounting enforcement are associated with lower levels of IPO underpricing. IPOs in countries with a relatively low accounting enforcement score (second quintile) exhibit a mean underpricing of 19%, whereas the mean underpricing amounts to just 9% in countries with a relatively high score (fourth quintile). The results remain qualitatively the same when we employ a multi‐level model or a difference‐in‐difference design. In countries that substantially strengthened their accounting enforcement in the 2003–2009 period, the level of IPO underpricing decreased significantly. We show that accounting enforcement matters for the cost of going public. [ABSTRACT FROM AUTHOR]

This paper investigates how the aftermath of the 2008 crisis affected firm productivity in the UK, focusing particularly on the cohort effect of firms established after 2008 – a previously overlooked aspect of the crisis – as well as the interaction with access to credit, which we test using firm‐specific and time‐varying credit scores. For identification, a matched sample is used based on credit score, firm age, size and ownership status, combining propensity score matching with difference‐in‐differences. While we find evidence that smaller firm size and changes in credit conditions affect productivity, about half of the difference in productivity remains unexplained. When we extend the matching analysis to examine across sectors and cohorts, we find that the low productivity performance during the years 2011–2016 is driven primarily by newer firms in the services sector, rather than in manufacturing. Within services, the underlying productivity puzzle is driven by a cessation of output growth in high‐productivity financial services, while abundant labour supply has led to a 'levelling down' of performance of newer firms in other services, converging towards the relatively low‐productivity manufacturing. [ABSTRACT FROM AUTHOR]

The battlefield situation changes rapidly because underwater targets' are concealment and the sea environment is uncertain. So, a great number of situation information greatly increase, which need to be dealt with in the course of scouting underwater targets. Situation assessment in sea battlefield with a lot of uncertain information is studied, and a new situation assessment method of scouting underwater targets with fixed‐wing patrol aircraft is proposed based on the cloud Bayesian network, which overcomes the deficiency of the single cloud model in reasoning ability and the defect of Bayesian network in knowledge representation. Moreover, in the method, the cloud model knowledge deal with the input data of Bayesian network reasoning, and the advantages in knowledge representation of cloud theory and reasoning of Bayesian network are applied; also, the fuzziness and stochasticity of cloud theory in knowledge expression, the reasoning ability of Bayesian network, are combined. Then, the situation assessment model of scouting underwater targets with fixed‐wing patrol aircraft is established. Hence, the directed acyclic graph of Bayesian network structure is constructed and the assessment index is determined. Next, the cloud model is used to deal with Bayesian network, and the discrete Bayesian network is obtained. Moreover, after CPT of each node and the transformation between certainty degree and probability are accomplished; the final situation level is obtained through a probability synthesis formula. Therefore, the target type and the operational intention of the other side are deduced to form the battlefield situation. Finally, simulations are carried out, and the rationality and validity of the proposed method are testified by simulation results. By this method, the battlefield situation can be gained. And this method has a wider application range, especially for large sample data processing, and it has better practicability. [ABSTRACT FROM AUTHOR]

This paper analyzes a BigTech lender's pricing strategies in the business‐to‐customer unsecured loan market using a proprietary data set of consumer loans in China. We find that the credit rating constructed by the BigTech lender is informative of the customers' default risk. Moreover, the interest rate decreases and the credit limit increases with the credit rating. Interestingly, the BigTech lender charges different interest rates to its customers based on the customer channel, although it does not provide information about the customers' default risk. Following the passage of the China Banking Regulatory Commission Act, which reduced credit market competition, the BigTech lender increased the current rate and decreased the credit limit. We rationalize these empirical findings in a simple model of credit contract design. [ABSTRACT FROM AUTHOR]

The Western Iberian Coast (WIC) is characterized by a dynamic coastal upwelling system with interactions between oceanographic features and continental river run‐off. The present study investigates the spatio‐temporal variability in the concentrations of Chlorophyll a (Chl a) and turbidity, and their relationships with physical and other biogeochemical properties along the entire coast of Portugal, as well as the response of phytoplankton to coastal upwelling pulses. In situ data were obtained during four oceanographic campaigns conducted in spring (April–May 2019), autumn (October 2018 and 2019), and early spring (February–March 2020). Upwelling conditions were evaluated through an upwelling index and auxiliary data such as sea surface temperature and height. In the WIC, the in situ surface temperature and salinity have a clear spatial pattern, both increasing from North to South. In autumn 2018 the highest temperatures coincided with the weakening of spring–summer upwelling conditions. Overall, high Chl a was associated with lower temperatures and higher dissolved oxygen concentrations (e.g., spring 2019). During spring 2019, an increase in Chl a, especially in the northern area, was associated with previous upwelling conditions. In early spring 2020, ongoing upwelling conditions in the northern area were associated with high turbidity and nutrients close to the shoreline with no immediate biological responses. An increase in Chl a was visible in satellite imagery a few days later. These results highlight the importance of understanding the complexity of coastal processes in the WIC. [ABSTRACT FROM AUTHOR]

This study investigates the role of auditor choice (Big 4/Non‐Big 4) in debt financing for private and public firms. We find private firms have less access to debt than public firms, and Big 4 auditors support debt raising in both private and public firms. Consistent with private firms facing greater information asymmetry, Big 4 auditors are more important for debt raising in private firms than in public firms. The benefit of appointing Big 4 auditors for private firms' debt raising is greater in the opaque information environment of the global financial crisis. It is also greater when firms are smaller, younger, or have poorer financial reporting quality. We also find evidence consistent with Big 4 auditors mitigating agency conflicts and enhancing debt raising when ownership concentration is higher in private firms. [ABSTRACT FROM AUTHOR]

This paper investigates whether managerial ability is associated with non‐GAAP earnings quality. I find that the quality of non‐GAAP earnings is greater for high‐ability managers than low‐ability managers. I also find that investors consider non‐GAAP earnings released by high‐ability management to be informative. Additional tests show that the positive association between managerial ability and the quality of non‐GAAP earnings is stronger when return volatility or managerial stock ownership is greater. The results are robust to alternative measures of managerial ability and non‐GAAP earnings quality and to controlling for endogeneity bias. Overall, this paper provides evidence that managers of high ability use non‐GAAP reporting as a signalling tool to reduce information asymmetry. [ABSTRACT FROM AUTHOR]

Earnings communication conferences in China have become the main platform for direct communication between listed firms and individual investors. This study investigates whether hosting an earnings communication conference and its tone affect post‐earnings‐announcement drift (PEAD). We find that hosting an earnings communication conference increases PEAD. One possible explanation for our results is that investors overreact to the stock prices of firms that hold earnings communication conferences. We also conclude that the conference tone is negatively correlated with PEAD. In addition, the market reacts more strongly to the managers' tone than it does to the investor's tone. [ABSTRACT FROM AUTHOR]

This paper proposes a procedure to measure firms' longitudinal accounting comparability and investigates whether it affects bond risk premiums. The results provide robust evidence that bonds of firms with more longitudinally comparable accounting information have lower credit spreads. This effect is stronger when the firms' financial performance is poor and for bonds with speculative credit ratings. Results also reveal that firms with less longitudinally comparable accounting information are more informationally asymmetric and do have a higher expected default probability. Finally, the effects of the longitudinal and the cross‐sectional comparability in reducing bond credit spreads are incremental to each other. [ABSTRACT FROM AUTHOR]

Most studies of corporate social responsibility (CSR) performance in a supply chain context have been conducted from the buyer's perspective. Few have paid attention to how suppliers leverage this kind of performance to expand exchange relationships with major customers. From a resource dependence and social exchange perspective, this article specifically examines whether two supplier CSR performance dimensions—environmental and product performance—can serve as a mechanism to expand a supplier's relationships with a smaller number of major buyers, where its increasing exchange dependence often measures this factor. Moreover, these dependent relationships may further develop and change under different conditions of uncertainty. Using large‐scale longitudinal data to test the proposed model, we find empirical evidence that a supplier's environmental and product performance relate positively to greater customer dependence and improved financial performance across diverse sets of industries. However, the findings also reveal that both demand‐driven and supply‐side uncertainty can weaken the effect. Specifically, the positive effect of environmental performance tends to weaken in the face of supply‐side uncertainty, whereas the positive effect of product performance tends to weaken amid demand‐driven uncertainty. Accordingly, we note important nuances and contingencies for suppliers to consider when considering how investments in these CSR performance dimensions affect exchange dependence. [ABSTRACT FROM AUTHOR]

Existing chaotic system exhibits unpredictability and nonrepeatability in a deterministic nonlinear architecture, presented as a combination of definiteness and stochasticity. However, traditional two‐dimensional chaotic systems cannot provide sufficient information in the dynamic motion and usually feature low sensitivity to initial system input, which makes them computationally prohibitive in accurate time series prediction and weak periodic component detection. Here, a natural exponential and three‐dimensional chaotic system with higher sensitivity to initial system input conditions showing astonishing extensibility in time series prediction and image processing is proposed. The chaotic performance evaluated theoretically and experimentally by Poincare mapping, bifurcation diagram, phase space reconstruction, Lyapunov exponent, and correlation dimension provides a new perspective of nonlinear physical modeling and validation. The complexity, robustness, and consistency are studied by recursive and entropy analysis and comparison. The method improves the efficiency of time series prediction, nonlinear dynamics‐related problem solving and expands the potential scope of multi‐dimensional chaotic systems. [ABSTRACT FROM AUTHOR]

In this study, a distributed fuzzy filter is proposed for a non‐linear state estimation problem on the possibilistic framework. Firstly, instead of Gaussian distribution on the probability framework, the process and observation noises are modelled as fuzzy random variables with trapezoidal possibility distributions. Secondly, a novel square root fuzzy cubature information filtering (SRFCIF) algorithm is proposed to deal with non‐linear state estimation with fuzzy noise; a fuzzy variable fusion (FVF) algorithm is used for fuzzy random variables fusion. Consequently, a distributed square root fuzzy cubature information filter (DSRFCIF) is proposed by embedding SRFCF and FVF into the consensus frame. Finally, consistency analysis and simulation demonstration are executed for the proposed filter. [ABSTRACT FROM AUTHOR]

Channel and spatial attention mechanisms have proven to provide an evident performance boost of deep convolution neural networks. Most existing methods focus on one or run them parallel (series), neglecting the collaboration between the two attentions. In order to better establish the feature interaction between the two types of attentions, a plug‐and‐play attention module is proposed, which is termed as 'CAT'—activating the Collaboration between spatial and channel Attentions based on learned Traits. Specifically, traits are represented as trainable coefficients (i.e. colla‐factors) to adaptively combine contributions of different attention modules to fit different image hierarchies and tasks better. Moreover, the global entropy pooling is proposed apart from global average pooling and global maximum pooling (GMP) operators, which is an effective component in suppressing noise signals by measuring the information disorder of feature maps. A three‐way pooling operation is introduced into attention modules and the adaptive mechanism is applied to fuse their outcomes. Extensive experiments on MS COCO, Pascal‐VOC, Cifar‐100, and ImageNet show that our CAT outperforms the existing state‐of‐the‐art attention mechanisms in object detection, instance segmentation, and image classification. The model and code will be released soon. [ABSTRACT FROM AUTHOR]

We investigate whether climate change disclosures in initial public offering (IPO) prospectuses affect the information environment in the IPO market. We find that climate change disclosures are associated with lower IPO underpricing. Further analyses reveal that reputable underwriters and the Securities Exchange Commission's Commission Guidance Regarding Disclosure Related to Climate Change enhance the information role of climate change disclosures in the IPO market. We demonstrate that firms with more extensive climate change disclosures provide stronger hedging benefits against climate change risks in the post‐IPO period. Overall, our results support the crucial role of climate change disclosures in improving the information environment of the IPO market. [ABSTRACT FROM AUTHOR]

This study investigates the effect of market competition on the reputational concerns of credit rating agencies (CRAs) in the Chinese bond market. We find credit ratings increase when the market share distance between an incumbent CRA and its closest peer competitor decreases. This spatial competition effect only exists at the provincial level. We also find the market competition deteriorates the ability of ratings to predict future bond defaults, while the correlation between credit ratings and market‐implied credit spreads is unaffected. Our findings suggest that because of the market inefficiency in emerging economies, CRAs privilege current profits over reputational concerns. [ABSTRACT FROM AUTHOR]

We examine how the profitability of long–short arbitrage strategies based on anomalies is affected after adjustment for two shorting costs: implicit cost due to unavailability of stocks in the short‐leg to sell short and loan fees actually paid to stock lenders. The combined shorting cost amounts to almost 40 percent of long–short gross returns over the sample period from January 2006 to December 2017. After adjustment for these shorting costs, long–short arbitrage profits are thus reduced by almost 40 percent. Even after adjustment for risk, the proportion of shorting costs is also substantial. If other trade‐related transaction costs are considered, long–short arbitrage profits would be reduced further. Our results provide explicit evidence that casts doubt on the profitability of long‐short arbitrage strategies based on anomalies. [ABSTRACT FROM AUTHOR]

Fault prediction is an important part of the safe operation and maintenance of pure electric vehicles, and it is crucial to predict faults efficiently and accurately. To address the problems that the pure electric vehicle fault prediction model has less than ideal prediction effect and classification bias to most classes due to unbalanced data set categories and high dimensionality of features, this paper firstly obtains a low dimensional balanced dataset based on hybrid sampling and joint feature selection. Then the classification ability of the model is improved through ensemble learning. Finally, a new hybrid model STLRF‐Stack is proposed to realize efficient fault prediction of pure electric vehicles. This paper is validated on a real operating dataset of pure electric vehicles. The experimental results show that the prediction effect of the hybrid model proposed in this paper is better than that of the single model and related literature. In addition, this model can successfully predict the fault 5 min before the actual fault of electric vehicles occurs, which provides the drivers with the opportunity of accident prevention and early treatment. Therefore, the model has specific feasibility and practical application value. [ABSTRACT FROM AUTHOR]

In this research, we developed a hybrid recommender system based on description/dialetheic logic, which provides an innovative architecture for the recommendation based on ambiguous reasoning. In addition, our approach allows enriching the knowledge during the reasoning using the linked data paradigm. Thus, the architecture allows the integration of linked data, in order to be exploited by the recommender. On the other hand, the reasoning mechanisms of dialetheic logic allow handling situations of contradiction or inconsistency, to determine the information to offer to users. According to the reviewed literature, our proposal is the first that implements a dialetheic engine in a Recommender System. In general, there are a lot of works about the utilization of linked open data (LOD) to improve the Recommender Systems, for example, to enrich the information to recommend or to solve the cold start problem. However, there are no proposals to deal with the problems of ambiguity, incoherence or incompleteness of the information in these environments extended with LOD. In this way, this paper proposes a hybrid reasoning engine that uses the linked data paradigm for the knowledge extraction, and dialetheic logic for the management of inconsistency/ambiguity information, in order to obtain recommendations. Particularly, the information extracted with Linked Data is processed with the Dialetheic Logic reasoner to solve ambiguous cases. Thus, each recommendation is enriched with related content extracted from the linked data sources, which has been disambiguated. The results are very promising since our hybrid reasoning mechanism allows obtaining more precise recommendations, considering the different classical states of ambiguity in dialetheic logic (contingent statements about the future, failure of a presupposition, vagueness, counterfactual reasoning), according to various metrics of quality used to evaluate the recommendations achieved. [ABSTRACT FROM AUTHOR]

The literature of multiple attribute decision making (MADM) is fruitful since there are various and successful applications of different fuzzy set extensions such as intuitionistic, Pythagorean and q‐Rung orthopair fuzzy sets (IFS, PFS and q‐ROFS). Besides their powerful aspects, some definitional limitations are known. In order to eradicate these boundaries regarding the definitions of membership and non‐membership degrees, linear Diophantine fuzzy set (LDFS) concept has been recently emerged. By considering two parameters, LDFS extends the representation area of the previous fuzzy set definitions and provides more extensive human judgement coverage field. In this study, the first distance and entropy measures in the literature have been developed for LDFSs. Their axiomatic definitions are given, and the proofs are shown. Also, thanks to our extensive literature review, we became aware that there is no MADM extension dedicatedly proposed for LDFS. So, the first MADM method extension for LDFS environment has also been developed in this study. A very well‐known MADM approach, TOPSIS, has been extended into LDFS environment for the first time in the literature. The applicability is shown in a healthcare management decision problem and the validity is checked and approved by comparing the alternative rankings LDF‐TOPSIS and the aggregation operators that were obtained from the literature produced. [ABSTRACT FROM AUTHOR]

Building resilient cities has become a hot topic in urban planning and risk management. Adopting the perspective of the social‐ecological system (SES), this paper incorporated economy–ecology–society–infrastructure into an integrated research framework and established a system for assessing the urban resilience development level. The entropy weight method was combined with a cloud model to systematically calculate and classify the resilience of 31 provinces (cities) in China. The evaluation results show that (1) the overall level of urban resilience in China was relatively low; the regional analysis showed that (2) the urban resilience of the eastern region was higher than the national average and that of the central and western regions, and from the perspective of the four subsystems, (3) the comprehensive development of each subsystem was the key to improving urban resilience. Finally, five improvement strategies were proposed to provide ideas for enhancing urban SES ability. [ABSTRACT FROM AUTHOR]

We analyze the impact of trading dynamics, including fragmentation of markets, undisplayed (dark), and algorithmic trading, on liquidity formation in initial public offerings (IPOs). We find that these various trading dynamics evolve throughout the IPO secondary market and are dependent on the IPO's initial offering‐day underpricing. Higher levels of fragmentation in displayed (lit) markets and algorithmic trading improve market quality in IPOs, while higher levels of undisplayed (dark) trading harm it. Overall, we find that, with the exception of the impact of dark trading, the concerns regarding the impact of fragmented markets and algorithmic trading on IPO liquidity are mostly unwarranted. [ABSTRACT FROM AUTHOR]

This study examines the impact of financial statement comparability on audit pricing. Using the comparability measures of De Franco, Kothari and Verdi, we find that auditors charge lower audit fees for clients with greater financial statement comparability. This negative relation is more pronounced when auditors are industry specialists or clients in industries with higher operational heterogeneity. We also find that clients with greater financial statement comparability have lower audit risk, and their auditors exert less audit effort to provide assurance. Our study contributes to the current literature by documenting that homogeneity in financial statement reporting decreases audit risk and increases audit efficiency, reducing audit pricing. [ABSTRACT FROM AUTHOR]

We provide a systematic literature review of the determinants and consequences of real earnings management (REM) in an international context. We provide a theoretical framework for REM, the development of REM measures, and review the determinants of REM, categorising these into financial reporting, auditing, governance and controls, capital market incentives, and regulatory determinants. We then review the empirical literature on the consequences of REM. We provide some suggestions for future research on measurement issues related to REM, and on filling gaps in the empirical research investigating its determinants and consequences. [ABSTRACT FROM AUTHOR]