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Nonfinancial information is becoming more readily available to investors, and thus, relative to annual financial reports, is having an increasing influence on investors' stock pricing decisions. Using Hogarth and Einhorn's (1992) belief-adjustment model, we examine how task familiarity (high, medium, and low) influences nonprofessional investor stock price decisions when these investors are presented with a stream of both positive and negative nonfinancial news. We find that task familiarity negatively correlates with reaction size for both positive and negative information, which creates arbitrage opportunities for those with more task familiarity. However, we find that assurance mitigates this effect, leveling the playing field for less task-familiar investors in most cases. These findings are important as the volume and variety of information types increase, and as more nonfinancial information enters the marketplace in discrete sound bites (e.g., social media, press releases, daily reports). Findings suggest that assurance is one way to lessen the biases exhibited by investors with less task familiarity. These results enhance our understanding of nonprofessional investor behavior through the lens of belief revision.

The purpose of this paper is to report on research concerning financial analysts’ activity surrounding profit warnings issued by listed companies in the four largest European stock exchanges (France, Germany, the Netherlands and the UK). The authors address three aspects of analysts’ forecasts: ex-post accuracy of forecasts, earnings forecast revisions, and consensus forecast dispersion. The goal of the analysis is to study the differences between financial analysts’ behavior within different regulatory settings, namely common law vs civil law countries.

The sample is composed of 1,330 profit warnings issued by listed European firms during the period 2000-2010. The authors apply event study methodology and OLS regressions to highlight the impact of the legal information environment on analysts’ reactions.

The empirical analysis reveals that analyst activity depends on each country’s legal context factors, such as the legal information environment of the firm and the index of investor protection. Accordingly, the authors show that both a richer legal information environment and stronger country-level investor protection substantially improve analyst accuracy around profit warnings.

The sample is only composed on firms from four European countries owing to a lack of firms from other European countries that disclosed PW during the period 2000-2010. It would be pertinent to conduct future research dealing with an international sample from different continents.

The paper contributes to a deeper understanding of analysts’ reactions to profit warnings. The findings can influence firms’ reporting practices and lead to future regulation policies.

This work is the first to examine the relationship between profit warning releases and the behavior of financial analysts in a pan-European context where there are different institutional levels of investor protection.

We conduct an experiment to analyze the impact of a well-established psychological construct, need for cognition, in an audit-related decision context. By simulating a basic audit sampling task, we determine whether the desire to engage in a cognitive process influences decisions made during that task. Specifically, we investigate whether an individual's need for cognition influences the quantity of data collected, the revision of a predetermined sampling plan, and the time taken to make a decision. Additionally, we examine the impact of cost constraints during the decision-making process.

Contrary to results in previous studies, we find those with a higher need for cognition sought less data than those with a lower need for cognition to make an audit sampling decision. In addition, we find that the need for cognition had no relationship to sampling plan revisions or the time needed to make an audit sampling decision. Previous studies regarding the need for cognition did not utilize incremental costs for additional decision-making information. Potentially, these costs provided cognitive challenges that influenced decision outcomes.

The problem of measurement errors in the national accounts has been recognized for a long time. The error chiefly arises from various source data and the timing of the flow of data received from providers. This chapter first discusses the type of measurement errors confronted by statistical agencies. Second, it presents a model of their behavior that illustrates the trade-offs that must be made in dealing with such errors. Third, the chapter discusses how the quality of the estimates can be gauged given measurement error and the inability to conduct standard statistical tests. Although the focus is on the production of U.S. Gross Domestic Product, the principles are applicable to all national statistical agencies.

This paper contributes to shed light on the quality and performance of US fiscal forecasts.

The first part inspects the causes of official fiscal forecasts revisions by Congressional Budget Office (CBO) between 1984 and 2016 that are due to technical, economic or policy reasons.

Both individual and cumulative means of forecast errors are relatively close to zero, particularly in the case of expenditures. CBO averages indicate net average downward revenue and expenditure revisions and net average upward deficit revisions. Focusing on the causes of the technical component, the authors uncover that its revisions are quite unpredictable, which cast doubts on inferences about fiscal policy sustainability that rely on point estimates. Comparing official with private-sector (consensus) forecasts, despite the informational advantages CBO might have, one cannot unequivocally say that one or the other is more accurate. Evidence also seems to suggest that CBO forecasts are consistently heavily biased toward optimism while this is less the case for consensus forecasts. Not only is the extent of information rigidity is more prevalent in CBO forecasts but also evidence seems to indicate that consensus forecasts dominate CBO in terms of information content.

The authors provide a detailed analysis on US fiscal forecasts both using revenue and expenditure and decomposing forecast errors into several explanatory components. Moreover, the authors compare official with private-sector (consensus) forecasts and assess which one is better or preferred.

In this volume, we have studied the cyclical behavior of numerous business cycle indicators from the U.S. transportation sector and studied how they are related to those of the overall economy. Our study began with the conceptualization of what constitutes the transportation services sector, identifying relevant monthly indicators from the private sector and the government, and finally putting them together to construct a monthly measure of output of the transportation services sector. The challenge was to develop an indicator that will be available promptly soon after a month with other widely reported monthly indicators such as the index of industrial production, Institute for Supply Management (ISM) surveys, CPI, index of leading indicators, etc. and is not subject to much data revisions. Since monthly activity measures of major transportation services sectors such as trucking and railroads are produced by private membership organizations, use of these data in the production of official statistics in the public sector needed skillful persuasion of government officials. Bureau of Transportation Statistics (BTS) releases the preliminary number for the latest month and replaces the number for the oldest preliminary month with a revised number. All other revisions are held until an annual comprehensive revision of the transportation services output (TSI). It is gratifying to see that the arrangement of cooperation between the transportation department and these private service organizations are working out seamlessly, and TSI continues to get the media attention.

The purpose of this paper is to detail a Library open source software (OSS) development project resulting in the launch of StatBase, a statistical gathering and data visualization tool, so that organizations can adopt a locally managed alternative to costly data aggregation tools.

This case study is based on a literature review, Agile development framework, and user experience modeling. The software solution features a Joomla framework with contributed modules and open source architecture.

This case study demonstrates the creation and practical implementation of a scalable OSS platform for data management and analysis.

Provides a frame of reference and methodology for libraries, both public and academic, seeking to implement a web-based resource to gather, organize, and interpret statistical metrics via a centralized, lightweight, open source architecture.

This case study provides a detailed scope and step-by-step technology process description by which an organization can adopt or model the StatBase solution for business metrics.

To add new empirical knowledge to debates about social practices of peer production communities, and to conversations about bias and its implications for democracy. To help identify Wikipedia (WP) articles that are affected by systematic bias and hopefully help alleviate the impact of such bias on the general public, thus helping enhance both traditional (e.g. libraries) and online information services (e.g. Google) in ways that contribute to democracy. This paper aims to discuss the aforementioned objectives.

Quantitatively, the authors identify edit-warring camps across many conflict zones of the English language WP, and profile and compare success rates and typologies of camp edits in the corresponding topic areas. Qualitatively, the authors analyze the edit war between two senior WP editors that resulted in imbalanced and biased articles throughout a topic area for such editorial characteristics through a close critical reading.

Through a large-scale quantitative study, the authors find that winner-take-all camps exhibit biasing editing behaviors to a much larger extent than the camps they successfully edit-war against, confirming findings of prior small-scale qualitative studies. The authors also confirm the employment of these behaviors and identify other behaviors in the successful silencing of traditional medicinal knowledge on WP by a scientism-biased senior WP editor through close reading.

WP sadly does, as previously claimed, appear to be a platform that represents the biased viewpoints of its most stridently opinionated Western white male editors, and routinely misrepresents scholarly work and scientific consensus, the authors find. WP is therefore in dire need of scholarly oversight and decolonization.

The authors independently verify findings from prior personal accounts of highly power-imbalanced fights of scholars against senior editors on WP through a third-party close reading of a much more power balanced edit war between senior WP editors. The authors confirm that these findings generalize well to edit wars across WP, through a large scale quantitative analysis of unbalanced edit wars across a wide range of zones of contention on WP.

This study aims to investigate the determinants of homeowners’ planned holding periods. Real estate market is known for displaying buying and selling behavior that does not conform to traditional economic theories such as rational expectation or expected utility. Mounting evidence of anomalous observations appear to be supported by other theories, such as prospect theory, which in particular helps explain the disposition effect – sellers are too quick to sell when prices are climbing and hold on to properties longer when prices are plummeting. While this evidence is widely documented in housing studies based on data on realized holding periods (i.e. ex post), this study explores factors that may motivate homeowners to alter their expected holding horizons (i.e. ex ante) to form new preferred holding periods that may be shorter or longer than those planned during house search.

The empirical study uses data collected from two cross-section surveys of recent homebuyers in rising and declining housing markets in Southern California in 2004-2005 and 2007-2008, respectively.

The empirical results demonstrate that in addition to the financial characteristics of the recent homebuyer, the characteristics of the buying experience – non-monetary, such as the realized search duration, and monetary, such as perception of negative or positive premium paid for the house relative to its market value – have a statistically significant effect on the holding horizon revision. The data strongly indicate that the perception of having overpaid increases the likelihood of upward revision of the original holding horizon. This effect is stronger in the declining than in the rising market – a crucial finding that mirrors the disposition effect.

This study sheds new light on what may contribute to the disposition effect in housing markets that has not yet been investigated in past literature. The novel approach here is to look at how different house price environments may affect homeowners’ holding periods ex ante when they begin, rather than ex post when already realized.

This study examines rating agency explanations that accompany changes in credit ratings for nonprofit hospitals. A national sample of Standard & Poor’s revision announcements is used to identify hospital characteristics that purportedly motivate credit rating changes. Significant differences in agency-cited performance dimensions, such as profitability, liquidity, service-mix, capital structure and market share, are observed across upward and downward revisions. The relative usefulness of these citations for explaining and classifying credit changes is also evaluated. The results suggest that agency explanations provide limited value relative to conventional, multivariate information sets.