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Accepted by: Aris Syntetos Exploring the effects of information uncertainty presents an extensive challenge to decision makers. This study presents a set of behavioural experiments that examine the impact of incomplete information on newsvendor decisions. Findings show that orders deviate from normative benchmarks when decision makers have incomplete information and this tendency is stronger when the demand distribution is not known. Comparison of decisions under incomplete information against behavioural benchmarks with full and no information reveal that the availability of price and cost information brings decisions significantly closer to normative levels when the underlying demand distribution is unknown. On the opposite spectrum, when demand information is available, not knowing price or cost does not lead to worse decisions. Analysing newsvendor profits under various information conditions, we find participants capture at most 84% of earnings they could have generated if they ordered the normative quantity in high-profit margin settings; the corresponding percentage is 51% in low-profit margin settings. Our results suggest decreasing uncertainty on the demand distribution has a consistently positive impact on profits, while uncertainty about cost or price does not have a significant effect. Implications of our findings on the differential impact of incomplete information are discussed via the backdrop of the prevalence of newsvendor framework across a wide range of operational decisions. [ABSTRACT FROM AUTHOR]

We use clickstream data to show that investors' demand for information about macroeconomic factors affecting the path of future interest rates is a measure of their uncertainty about this path. In particular, an increase in information demand ahead of influential economic announcements affecting investors' beliefs about future interest rates predicts a stronger reaction of U.S. Treasury note yields to these announcements, as it should if information demand positively covaries with uncertainty. This relationship does not vanish after using standard measures of uncertainty as predictors, suggesting that clickstream data contain unique information about investors' uncertainty. [ABSTRACT FROM AUTHOR]

This qualitative study aimed to map what information is used in the forest planning process at large forest-owning companies, how it is used, its level of uncertainty and currently employed strategies to handle forest information uncertainty. An additional aim was to assess the status of the paradigm of the forest planning hierarchy in forestry. We used data from semi-structured interviews with representatives of six large forest-owning companies in Sweden, representing 30 per cent of the productive forest land in the country. Our results show that the forest planning process is a hierarchical system of decisions where the information used in the different planning stages is of varying quality and that the traditional hierarchical planning paradigm still plays a vital role in forestry. The most central source of information in the whole forest-planning process is the forest stand database (forest inventory). This includes uncertain information from various sources, including subjective field measurements and aerial image interpretation. However, the use of remote sensing estimates to feed the databases is increasing, which will probably improve the overall quality. Another important finding is that forest companies tend not to use decision support systems or optimization models to solve planning problems outside the scope of strategic planning; thus, most planning is done manually, e.g. in a geographic information system (GIS) environment. Apart from the hierarchical division of the planning process itself, we identified six main strategies that the companies use to control information uncertainty, namely locking the future by making a decision, utilizing a surplus of available harvests, updating information before a decision is made, replanning when the plan is found to be infeasible, planning by looking back and ignoring the uncertainty, either intentionally or unintentionally. The results from this study increase our understanding of contemporary forest-planning practices and will be helpful in the development of decision support systems and methods for information collection. [ABSTRACT FROM AUTHOR]

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood‐based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t‐year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancer study. [ABSTRACT FROM AUTHOR]

The Jaffee and Russell [1976] model of credit rationing under imperfect information and uncertainty suggests that a single-contract equilibrium will tend to occur at a point of rationing and that a multiple-contract equilibrium will likely be unstable. This paper respecifies and extends the Jaffee-Russell model to incorporate default expectations on the demand side and to consider the price of credit more appropriately to be the net (after expected default) yield rather than the contract rate. Results show rationing is not necessary in the single-contract equilibrium case, nor is an unstable equilibrium possible in the multiple-contract case. [ABSTRACT FROM AUTHOR]

This article discusses the informational aspects of market structure. Opportunities that vary with the quality of their product, and select courses of action that vary systematically with product quality. Product guarantees can tell consumers about products because the expected cost of a guarantee is negatively correlated with the durability of the product. Analyzes contingent contracts in a labor market. Observation of the credit markets that create conflict between information and individual's intertemporal consumption pattern exists.

Background and Hypothesis Active inference has become an influential concept in psychopathology. We apply active inference to investigate conceptual disorganization in first-episode schizophrenia. We conceptualize speech production as a decision-making process affected by the latent "conceptual organization"—as a special case of uncertainty about the causes of sensory information. Uncertainty is both minimized via speech production—in which function words index conceptual organization in terms of analytic thinking—and tracked by a domain-general salience network. We hypothesize that analytic thinking depends on conceptual organization. Therefore, conceptual disorganization in schizophrenia would be both indexed by low conceptual organization and reflected in the effective connectivity within the salience network. Study Design With 1-minute speech samples from a picture description task and resting state fMRI from 30 patients and 30 healthy subjects, we employed dynamic causal and probabilistic graphical models to investigate if the effective connectivity of the salience network underwrites conceptual organization. Study Results Low analytic thinking scores index low conceptual organization which affects diagnostic status. The influence of the anterior insula on the anterior cingulate cortex and the self-inhibition within the anterior cingulate cortex are elevated given low conceptual organization (ie, conceptual disorganization). Conclusions Conceptual organization, a construct that explains formal thought disorder, can be modeled in an active inference framework and studied in relation to putative neural substrates of disrupted language in schizophrenia. This provides a critical advance to move away from rating-scale scores to deeper constructs in the pursuit of the pathophysiology of formal thought disorder. [ABSTRACT FROM AUTHOR]

Study Question: What are couples' decisional conflicts around family-building approaches before and after seeking a specialty consultation for infertility?Summary Answer: Decisional conflict is high among couples before an initial specialty consultation for infertility; on average, women resolved decisional conflict more quickly than men.What Is Known Already: Couples have multiple options for addressing infertility, and decisional conflict may arise due to lack of information, uncertainty about options and potential risks or challenges to personal values.Study Design, Size, Duration: We conducted a total of 385 interviews and 405 surveys for this longitudinal, mixed-methods cohort study of 34 opposite-sex couples who sought a new reproductive specialty consultation (n = 68), who enrolled before the initial consultation and were followed over 12 months.Participants/materials, Setting, Methods: The in-depth, semi-structured interviews included questions about information gathering, deliberation and decision-making, and self-administered surveys included the Decisional Conflict Scale (DCS), at six time points over 12 months. A DCS total score of 25 is associated with implementing a decision, and higher scores indicate more decisional conflict. A systematic content analysis of interview transcripts identified major themes. Paired t tests identified differences in DCS between women and men within couples. Linear mixed models predicted changes in DCS over time, adjusting for sociodemographic and fertility-related factors.Main Results and the Role Of Chance: The major qualitative themes were communication with partners, feeling supported and/or pressured in decision (s), changing decisions over time and ability to execute a desired decision. Average DCS scores were highest before the initial consultation. Within couples, men had significantly higher decisional conflict than women pre-consultation (48.9 versus 40.2, P = 0.037) and at 2 months (28.9 versus 22.1, P = 0.015), but differences at other time points were not significant. In adjusted models, predicted DCS scores declined over time, with women, on average, reaching the DCS threshold for implementing a decision at 2 months while for men it was not until 4 months.Limitations, Reasons For Caution: This is a convenience sample from a single center, and generalizability may be limited.Wider Implications Of the Findings: Understanding how couples discuss and make decisions regarding family-building could improve the delivery of patient-centered infertility care. Our findings are the first to prospectively explore decisional conflict at multiple time points in both men and women; the observed gender differences underlie the importance of supporting both partners in clinical decision-making for infertility.Study Funding/competing Interest(s): This work was supported by the National Institute of Child Health and Human Development under Grant [R21HD071332], the Research and Education Program Fund, of the Advancing a Healthier Wisconsin endowment at Medical College of Wisconsin, the National Research Service Award under Grant [T32 HP10030] and the use of REDCap for data collection from the National Center for Advancing Translational Sciences, National Institutes of Health under Grant through [8UL1TR000055]. The authors have no competing interests. [ABSTRACT FROM AUTHOR]

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

In this paper, we infer how the estimates of firm value by "optimists" and "pessimists" evolve in response to information shocks. Specifically, we examine returns and disagreement measures for portfolios of short-sale-constrained stocks that have experienced large gains or large losses. Our analysis suggests the presence of two groups, one of which overreacts to new information and remains biased over about 5 years, and a second group, which underreacts and whose expectations are unbiased after about 1 year. Our results have implications for the belief dynamics that underlie the momentum and long-term reversal effect. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Using satellite imagery of retail firms' parking lots to measure time-varying local firm-specific performance, we document that analysts incorporate local information into their forecasts. Analysts rely more on local signals when less firm-wide information is available. This incorporation of noisy local firm information has firm-level implications. Examining across industries, we find causal evidence that geographic concentration of analysts increases consensus forecast errors and decreases firm liquidity. These effects are stronger for harder-to-value stocks. The market values geographic firm information, as the abnormal return around forecast revisions is higher for analysts who cover a firm from a unique location. [ABSTRACT FROM AUTHOR]

We construct macroeconomic attention indexes (MAI), which are new measures of attention to different macroeconomic risks, including unemployment and monetary policy. Individual MAI tend to increase around related announcements and following changes in related fundamentals. Further, bad news raises attention more than good news. For unemployment and FOMC, attention predicts announcement risk premiums and implied volatility changes with large economic magnitudes. Our findings support theories of endogenous attention and announcement risk premiums, while demonstrating future research directions, including that announcements can raise new concerns. Macroeconomic announcements are important not only for contents and timing but also for attention. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Motivation The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to human disease research. However, there are still significant challenges in representing single-omics data and integrating multi-omics information. Results This article presents HyperTMO, a Trusted Multi-Omics integration framework based on Hypergraph convolutional network for patient classification. HyperTMO constructs hypergraph structures to represent the association between samples in single-omics data, then evidence extraction is performed by hypergraph convolutional network, and multi-omics information is integrated at an evidence level. Last, we experimentally demonstrate that HyperTMO outperforms other state-of-the-art methods in breast cancer subtype classification and Alzheimer's disease classification tasks using multi-omics data from TCGA (BRCA) and ROSMAP datasets. Importantly, HyperTMO is the first attempt to integrate hypergraph structure, evidence theory, and multi-omics integration for patient classification. Its accurate and robust properties bring great potential for applications in clinical diagnosis. Availability and implementation HyperTMO and datasets are publicly available at https://github.com/ippousyuga/HyperTMO [ABSTRACT FROM AUTHOR]

We estimate investor disagreement from synthetic long and short stock trades in the equity options market. We show that high disagreement predicts low stock returns after positive earnings surprises and high stock returns after negative earnings surprises. The negative effect is stronger for high-beta stocks and stocks that are more difficult to sell short. In the cross-section of all stocks and the subset of the 500 largest companies, high disagreement robustly predicts low monthly and weekly stock returns. [ABSTRACT FROM AUTHOR]

Using institutional investor demand as a proxy for revisions in sophisticated investors' expectations, we test whether financial strength information is gradually impounded over time. Consistent with the gradual incorporation of information, financial strength predicts both future returns and future institutional investor demand. Further consistent with the gradual incorporation of information, more sophisticated transient (high-turnover) institutions respond to financial strength signals prior to less sophisticated, nontransient institutions. A number of additional tests suggest that financial strength forecasts stock returns, at least in part, because it forecasts institutional demand, and institutional demand drives prices. [ABSTRACT FROM PUBLISHER]

We show that a new measure of buy-side competition explains momentum profits. The momentum quintile spread is 1.11% when competition is low and negligible when competition is high. Better alphas are attained with superior Sharpe and Sortino ratios, with no negative skewness, and in more investible strategies featuring value-weighted portfolios and large capitalization stocks. Stock characteristics traditionally related to momentum do not explain our results. Tests based on long-term reversals, the trading patterns of funds, their style peers, distant funds, and retail investors suggest that slow information diffusion explains the large momentum spreads and momentum reversals in low competition markets. [ABSTRACT FROM AUTHOR]

Big data is revolutionizing the finance industry and has the potential to significantly shape future research in finance. This special issue contains papers following the 2019 NBER-RFS Conference on Big Data. In this introduction to the special issue, we define the "big data" phenomenon as a combination of three features: large size, high dimension, and complex structure. Using the papers in the special issue, we discuss how new research builds on these features to push the frontier on fundamental questions across areas in finance—including corporate finance, market microstructure, and asset pricing. Finally, we offer some thoughts for future research directions. [ABSTRACT FROM AUTHOR]

We propose a methodology to use functional factors in empirical asset pricing models. We establish conditions under which it is possible to recover linear beta pricing. The proposed estimation approach allows us to use high-dimensional functional curves, such as the term structure of interest rates or the implied volatility smile, as factors. This framework enables the estimation of functional factor loadings as well as risk premium parameters of factor models. We derive estimation algorithms and establish the asymptotic consistency and normality of the parameter estimates. In an empirical application, we show that the implied variance smile of the S&P500 is a potential pricing factor for momentum-sorted portfolios. In particular, a positive risk premium is earned by the convexity of the implied variance curve. [ABSTRACT FROM AUTHOR]

We analyze communications between the SEC and firms prior to IPOs using LDA analysis and KL divergence. The SEC's concerns closely map onto the regulator's stated mandate: companies increase prospectus disclosures on precise topics of SEC concern. Revenue recognition is the dominant topic of SEC concern, and it is not independently discovered by investors. Increased SEC concern about it is associated with greater secondary sales, lower post-IPO liquidity, lower post-IPO returns, and a higher probability of withdrawal. The regulator's role during the capital raising process results in increased transparency but contributes to delays in the going public process. [ABSTRACT FROM AUTHOR]

This section presents a reply by Dwight M. Jaffee and Thomas Russell to a commentary about their article Imperfect Information, Uncertainty, and Credit Rationing which appeared in the November 1984 issue of the Quarterly Journal of Economics. In responding to the comments by Kerry D. Vandell and James D. Hess, it is useful to make one general point before turning to our specific responses to each of the papers. The general comment concerns a continuing feature or strategy of the credit rationing literature, which Jaffee and Russell share, but which both Vandell and Hess seem to have misunderstood. Turning to specifics, Vandell respecifies and extends the model of Jaffee and Russell and then claims results that differ from Jaffee and Russell, neither is credit rationing necessary in a single contract equilibrium, nor is an unstable equilibrium possible in the multiple-contract case. While we have no quarrel with such a result, it appears irrelevant for our model. In particular, in our model borrowers have differing default propensities known only to themselves, while the lender must act on the aggregate average default propensity. The comment by Hess claims that we confused competitive supply curves with zero profit curves, and that our conclusion of profitable credit rationing in a competitive model is wrong. We strongly deny these claims, but do acknowledge a value in the Hess comment in drawing attention to questions of the definition of competition and equilibrium in a moral hazard, credit rationing model.

Portfolio diversification of firms' controlling owners influences their firms' capital investment. Empirically, the effect of owners' portfolio diversification on their firms' investment levels is positive for publicly traded firms and tends to be negative for privately held ones. These findings are consistent with predictions of a model in which a risk-averse investor simultaneously chooses her portfolio structure, and both the level and riskiness of capital investment of the firm she controls, and in which the firm can be potentially constrained in its capital investment choices. Overall, our results indicate that owners' portfolio underdiversification and firms' financial constraints can affect firms' resource allocation. Received May 3, 2017; editorial decision March 8, 2019 by Editor Francesca Cornelli. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

We examine real outcomes for biotech startups going public around the Jumpstart Our Business Startups (JOBS) Act. Reduced compliance costs associate with greater innovation capital formation as biotech IPO volume and proceeds increase after the JOBS Act. Biotechs, which conduct over 30% of IPOs since 2012, go public with products earlier in the FDA approval process and more frequently target rare diseases and cancer. Consistent with our survey evidence that managers use compliance savings to invest in R&D, we link the JOBS Act to post-IPO increases in project-level development, such as new patents, clinical trials, and staffing of laboratories. Post-JOBS Act product candidates are more likely to reach key milestones in the FDA approval process and these startups fail at lower rates. Benefits accrue to shareholders without sacrificing financial reporting quality. Our results demonstrate how tailoring regulations for startups can provide economic and societal benefits. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

The International Criminal Court (ICC) initiated criminal proceedings against Ahmad Al Faqi Mahdi in 2016 for the war crime of directing the destruction of religious monuments in Timbuktu, Mali, in the context of a non-international conflict. The monuments included nine mausoleums of Sufi saints that Islamist groups regarded as idolatrous, one mosque, and approximately four thousand ancient manuscripts. The case, which successfully ended in a guilty plea, marks the first time that the ICC charged an individual with a 'cultural heritage' crime. The prosecution's argument during the hearing relied heavily on an interactive digital platform that facilitated the organization, analysis, and presentation of the evidence to the court. SITU, an applied research organization, created the platform in collaboration with the ICC's Office of the Prosecutor. It combined geospatial information, historic satellite imagery, photographs, open-source videos, and other types of site documentation to help the ICC judges visualize what was no longer physically there. The result was a digital reconstruction featuring structures and their contents lost to explosives, pickaxes, and hammers. The theoretical, methodological, and ethical issues that arose out of the collaboration, the focus of this article, highlights the multiple ways in which such platforms fashion 'facts' rather than objectively present them. A close analysis of the design decisions made, visualization techniques employed, and presentation strategies used to support the case against the defendant highlights both the possibilities and limits of such platforms as part of future international criminal proceedings. [ABSTRACT FROM AUTHOR]

This paper uses option prices to learn about the equity price uncertainty surrounding information released on earnings announcement dates. To do this, we introduce reduced-form models and estimators to separate price uncertainty about earnings announcements from normal day-to-day volatility. Empirically, we find strong support for the importance of earnings announcements. We find that the anticipated price uncertainty is quantitatively large, varies across time, and is informative about the future return volatility. Finally, we quantify the impact of earnings announcements on formal option pricing models. Received April 13, 2017; editorial decision February 5, 2018 by Editor Stijn Van Nieuwerburgh. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

I explore the relationship between theory and prediction in political economy and organisational economics, employing the works of Keith Cowling and Stephen Hymer as case examples of prediction-aiding good theory. I develop the insights of the two scholars by leveraging key ideas from classical economics and applying the result to the platform-enabled, market-assisted organisational economy. Based on that I suggest that the emergence and rise of platform-enabled Big Tech, unicorns and the ‘sharing economy’ are both aligned with and partly predictable. I go on to hazard some further predictions about the future of the corporation and capitalism and discuss research opportunities. [ABSTRACT FROM AUTHOR]

We exploit a natural experiment in the largest online consumer lending platform to provide the first evidence that loan terms, in particular maturity choice, can be used to screen borrowers based on their private information. We compare two groups of observationally equivalent borrowers who took identical unsecured 36-month loans; for only one of the groups, a 60-month loan was also available. When a long-maturity option is available, fewer borrowers take the short-term loan, and those who do default less. Additional findings suggest borrowers self-select on private information about their future ability to repay. Received December 27, 2016; editorial decision December 12, 2017 by Editor Philip Strahan. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web Site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

The return premium associated with the Amihud (2002) measure is generally considered a liquidity premium that compensates for price impact. We find that the pricing of theAmihud measure is not attributable to the construction of the return-to-volume ratio intended to capture price impact, but is driven by the trading volume component. Additionally, the high-frequency price impact and spread benchmarks are priced only in January and do not explain the pricing of the trading volume component of the Amihud measure. Additional analyses suggest that the volume effect on stock return is likely caused by mispricing, not by compensation for illiquidity. [ABSTRACT FROM AUTHOR]

We take up Cochrane's (2011) challenge to identify the firm characteristics that provide independent information about average U.S. monthly stock returns by simultaneously including 94 characteristics in Fama-MacBeth regressions that avoid overweighting microcaps and adjust for data-snooping bias. We find that while 12 characteristics are reliably independent determinants in non-microcap stocks from 1980 to 2014 as a whole, return predictability sharply fell in 2003 such that just two characteristics have been independent determinants since then. Outside of microcaps, the hedge returns to exploiting characteristics-based predictability also have been insignificantly different from zero since 2003. [ABSTRACT FROM AUTHOR]

I study asset price amplification in an asymmetric information model. Entrepreneurs issue debt to finance investments in a physical asset. They have private information about their success probabilities. For a given debt level, higher asset prices require entrepreneurs to invest more of their own funds. This makes bad entrepreneurs more reluctant to mimic good ones; as a result, good entrepreneurs increase their equilibrium leverage and invest more and this amplifies the initial asset price increase. This model generates predictions about the credit market that are qualitatively consistent with existing evidence. [ABSTRACT FROM AUTHOR]

In the analysis of gene expression data, network approaches take a system perspective and have played an irreplaceably important role. Gaussian graphical models (GGMs) have been popular in the network analysis of gene expression data. They investigate the conditional dependence between genes and "transform" the problem of estimating network structures into a sparse estimation of precision matrices. When there is a moderate to large number of genes, the number of parameters to be estimated may overwhelm the limited sample size, leading to unreliable estimation and selection. In this article, we propose incorporating information from previous studies (for example, those deposited at PubMed) to assist estimating the network structure in the present data. It is recognized that such information can be partial, biased, or even wrong. A penalization‐based estimation approach is developed, shown to have consistency properties, and realized using an effective computational algorithm. Simulation demonstrates its competitive performance under various information accuracy scenarios. The analysis of TCGA lung cancer prognostic genes leads to network structures different from the alternatives. [ABSTRACT FROM AUTHOR]

Uncertainty about future policy rates plays a crucial role for the transmission of monetary policy to financial markets. We demonstrate this using event studies of FOMC announcements and a new model-free uncertainty measure based on derivatives. Over the 'FOMC uncertainty cycle' announcements systematically resolve uncertainty, which then gradually ramps up again. Changes in monetary policy uncertainty around FOMC announcements—often due to forward guidance—have pronounced effects on asset prices that are distinct from the effects of conventional policy surprises. The level of uncertainty determines the magnitude of financial market reactions to surprises about the path of policy rates. [ABSTRACT FROM AUTHOR]

We document a little-known source of information exploited by sophisticated institutional investors: the Freedom of Information Act (FOIA), a law that allows for the disclosure of previously unreleased information by the U.S. Government. Through FOIA requests, we identify several sophisticated institutional investors, chiefly hedge funds, that request information from the FDA. We explore the type of information commonly requested by funds and the types of firms that are targets of FDA-FOIA requests and show that FOIA requests allow these investors to generate abnormal returns. Thus, we illustrate a detailed mechanism through which costly information becomes incorporated into market prices. [ABSTRACT FROM AUTHOR]

Frontline workers bring different forms of knowledge to bear on decisions and actions. Even so, knowledge has so far received limited attention in the street-level literature. This article develops a nuanced understanding of what constitutes knowledge in frontline work and shows how different forms of knowledge are mobilized on the ground. Taking a practice-based and abductive approach, the article draws on qualitative data from a multi-sited organizational ethnography in three Danish child welfare agencies as well as insights from a broad range of literature to build a conceptual framework for studying knowledge mobilization in frontline work. The framework delineates three interdependent forms of knowledge— knowledge-that, knowledge-how, and knowledge-by-acquaintance—that are all essential in frontline work. Knowledge-that is explicit and includes research evidence. Knowledge-how is rooted in experience and acquired through practice. Knowledge-by-acquaintance is rooted in encounters and denotes frontline workers’ “sense” of a case or situation. The empirical work shows how each form of knowledge is mobilized in practice. The findings yield important insights into the dynamics of knowledge mobilization at the frontlines, including the detrimental effects of rapid turnover, the conditions for realizing ideals such as evidence-based practice and data-driven decision-making, and the potential implications of digitalization and algorithmization. [ABSTRACT FROM AUTHOR]

This paper examines the effects of foreign electoral shocks on currency markets. I develop a theory of signaling and uncertainty to explain why elections in countries with close economic ties should affect exchange rates. Methodologically, this paper focuses on several case studies, with the 2016 US election as a central case. I utilize an event analysis framework to measure the impact of the election on the Mexican peso by exploiting the plausible exogeneity of Donald Trump's tweets. I also measure changes in the peso using Trump's predicted chance of winning the election and show that the peso is weakest when Trump has the highest chance of winning the election. In addition, I include a series of robustness checks and analyses of other notable recent cases when electoral uncertainty affected currencies in other countries, including the 2018 Brazilian election. The results quantify the effect of foreign elections on exchange rates, building on the existing literature that focuses on how domestic elections shape currency markets. I conclude with a discussion of the external validity of the phenomenon demonstrated by the cases in the paper, charting future research on the topic and outlining ways to extend the findings. [ABSTRACT FROM AUTHOR]

Hundreds of papers and factors attempt to explain the cross-section of expected returns. Given this extensive data mining, it does not make sense to use the usual criteria for establishing significance. Which hurdle should be used for current research? Our paper introduces a new multiple testing framework and provides historical cutoffs from the first empirical tests in 1967 to today. A new factor needs to clear a much higher hurdle, with a t-statistic greater than 3.0. We argue that most claimed research findings in financial economics are likely false. [ABSTRACT FROM AUTHOR]

We investigate whether and how an exogenous and unprecedented improvement in non-U.S. firms' financial-reporting quality affects post-earnings-announcement drift (PEAD).We find that PEAD declines after the information shock, and this decrease is more pronounced for firms with fewer concurrent earnings announcements, greater institutional holdings, and lower limits to arbitrage. In addition, the decrease in PEAD is driven by firms with greater changes in financial reporting, an increase in analyst forecast accuracy and institutional ownership, and a decrease in limits to arbitrage. These findings support the mispricing explanation of PEAD, in particular the limited attention hypothesis, in an international setting. [ABSTRACT FROM AUTHOR]

We develop a strategic trading model in which an insider exploits noise traders' overreaction. A feedback effect arises from the insider's trading on fundamental information (the expected growth rate of dividends) and nonfundamental information (insider's inventory or noise supply). We find that the stock price is not fully revealing; a faster mean-reverting noise supply leads to a more volatile price; the price impact can increase with insider's risk-aversion; and a risk-averse insider can trade more aggressively on fundamental information than a risk-neutral one does. Insider's current trade and his previous inventory exhibit simultaneously positive forecasting powers for future stock returns. [ABSTRACT FROM PUBLISHER]

Relying on 2.2 million articles from forty-five national and local U.S. newspapers between 1989 and 2010, we find that firms particularly covered by the media exhibit, ceteris paribus, significantly stronger momentum. The effect depends on article tone, reverses in the long run, is more pronounced for stocks with high uncertainty, and is stronger in states with high investor individualism. Our findings suggest that media coverage can exacerbate investor biases, leading return predictability to be strongest for firms in the spotlight of public attention. These results collectively lend credibility to an overreaction-based explanation for the momentum effect. [ABSTRACT FROM PUBLISHER]

Existing scholarship on police decision-making notes the importance of categories and 'governing mentalities' in shaping front-line discretionary practices. Much of this work explores categories of race and ethnicity. Important questions remain regarding how micro-level practices connect to organizational dynamics and why ethnic profiling endures despite attempts to counter such practices. Drawing on critical approaches to uncertainty and risk, not least Mary Douglas's cultural theory, we analyse data drawn from an ethnographic study of police work in a large city in the Netherlands. Our analysis emphasizes the multiple lines of accountability that render officers vulnerable in different ways, officers' combining of different rationalities of decision-making and the influence of everyday rituals that cultivate and reinforce particular organizational thought styles and discretionary practices. [ABSTRACT FROM AUTHOR]

Researchers working with digital evidence must be aware of its vulnerabilities if they hope to maximize its value for justice and accountability. In this paper, we highlight two important vulnerabilities: the impermanent nature of digital evidence, and its susceptibility to attack from online dis/misinformation campaigns. We argue that to overcome the challenge that the impermanent nature of digital evidence presents, researchers can implement rapid response archiving techniques that, while not as rigorous as protocols set out for the collection of evidence in legal cases, could leave them with digital breadcrumbs to aid with the recovery of deleted digital evidence. We further propose that researchers consider implementing a stance of radical transparency in the collection, synthesis, and presentation of digital evidence with the goal of fortifying it against dis/misinformation campaigns. [ABSTRACT FROM AUTHOR]

We test a frog-in-the-pan (FIP) hypothesis that predicts investors are inattentive to information arriving continuously in small amounts. Intuitively, we hypothesize that a series of frequent gradual changes attracts less attention than infrequent dramatic changes. Consistent with the FIP hypothesis, we find that continuous information induces strong persistent return continuation that does not reverse in the long run. Momentum decreases monotonically from 5.94% for stocks with continuous information during their formation period to –2.07% for stocks with discrete information but similar cumulative formation-period returns. Higher media coverage coincides with discrete information and mitigates the stronger momentum following continuous information. [ABSTRACT FROM PUBLISHER]

Studies of eusocial insects have extensively investigated two components of task allocation: how individuals distribute themselves among different tasks in a colony and how the distribution of labor changes to meet fluctuating task demand. While discrete age- and morphologically-based task allocation systems explain much of the social order in these colonies, the basis for task allocation in non-eusocial organisms and within eusocial castes remains unknown. Building from recent advances in the study of among-individual variation in behavior (i.e. animal personalities), we explore a potential mechanism by which individuality in behaviors unrelated to tasks can guide the developmental trajectories that lead to task specialization. We refer to the task-based behavioral syndrome that results from the correlation between the antecedent behavioral tendencies and task participation as a task syndrome. In this review, we present a framework that integrates concepts from a long history of task allocation research in eusocial organisms with recent findings from animal personality research to elucidate how task syndromes and resulting task allocation might manifest in animal groups. By drawing upon an extensive and diverse literature to evaluate the hypothesized framework, this review identifies future areas for study at the intersection of social behavior and animal personality. [ABSTRACT FROM AUTHOR]

The ability to quantify structural uncertainty in geological models that incorporate geophysical data is affected by two primary sources of uncertainty: geophysical parameter uncertainty and uncertainty in the relationship between geophysical parameters and geological properties of interest. Here, we introduce an open-source, trans-dimensional Bayesian Markov chain Monte Carlo (McMC) algorithm GeoBIPy—Geophysical Bayesian Inference in Python—for robust uncertainty analysis of time-domain or frequency-domain airborne electromagnetic (AEM) data. The McMC algorithm provides a robust assessment of geophysical parameter uncertainty using a trans-dimensional approach that lets the AEM data inform the level of model complexity necessary by allowing the number of model layers itself to be an unknown parameter. Additional components of the Bayesian algorithm allow the user to solve for parameters such as data errors or corrections to the measured instrument height above ground. Probability distributions for a user-specified number of lithologic classes are developed through posterior clustering of McMC-derived resistivity models. Estimates of geological model structural uncertainty are thus obtained through the joint probability of geophysical parameter uncertainty and the uncertainty in the definition of each class. Examples of the implementation of this algorithm are presented for both time-domain and frequency-domain AEM data acquired in Nebraska, USA. [ABSTRACT FROM AUTHOR]