Motivated by methods used to evaluate the quality of data, we create a novel firm-year measure to estimate the level of error in financial statements. The measure, which has several conceptual and statistical advantages over available alternatives, assesses the extent to which features of the distribution of a firm's financial statement numbers diverge from a theoretical distribution posited by Benford's Law. After providing intuition for the theory underlying the measure, we use numerical methods to demonstrate that certain error types in financial statement numbers increase the deviation from the theoretical distribution. We corroborate the numerical analysis with simulation analysis that reveals that the introduction of errors to reported revenue also increases the deviation. We then provide empirical evidence that the measure captures financial statement data quality. We first show the measure's association with commonly used measures of accruals-based earnings management and earnings manipulation. Next, we demonstrate that (1) restated financial statements more closely conform to Benford's Law than the misstated versions in the same firm-year and (2) as divergence from Benford's Law increases, earnings persistence decreases. Finally, we show that our measure predicts material misstatements as identified by SEC Accounting and Auditing Enforcement Releases and can be used as a leading indicator to identify misstatements.

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This study proposes models that can be used as shorthand analysis tools for CDS spreads and CDS spread changes. For this purpose we examine the determinants of CDS spreads and spread changes on a broad database of 718 US firms during the period from early 2002 to early 2013. Contrary to previous studies, we discover that market variables still have explanatory power after controlling for firm-specific variables inspired by structural models. Three explanatory variables appear to overshadow the other variables examined in this paper: Stock Return, ∆Volatility (the change in stock return volatility) and ∆MRI (change in the median CDS spread in the rating class). We also discover that models used in the event study literature to explain spread changes can be improved by using additional market variables. Further, we show that ratings explain cross-section variation in CDS spreads even after controlling for structural model variables.

This paper investigates the association between the adoption of international accounting standards and foreign investment decisions. Prior research suggests that information asymmetries between local and foreign investors and behavioral biases caused by unfamiliarity of the foreign markets contribute to investors preferring to invest in their home markets. Because one of the goals of the adoption of international accounting standards is to establish a high-quality, internationally familiar set of accounting standards, I predict that foreign investments will increase in countries that adopted International Financial Reporting Standards (IFRS) after the adoption and that this increase is driven by the familiarity of IFRS. I find that foreign equity portfolio investments (FPI) increase in countries that adopt IFRS. More importantly, I find that this relation is driven by foreign investors from countries that also use IFRS. Moreover, the effect of accounting familiarity is more pronounced when investor and investee countries share language, legal origin, culture, and region. I also find that countries with lower corruption and better investor protection experience larger increases in FPI after they adopt IFRS relative to other IFRS users. These findings are consistent with the hypothesis that familiar accounting information drives foreign investment decisions.

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This study examines whether key characteristics of analysts' forecasts-timeliness, accuracy, and informativeness-change when investor demand for information is likely to be especially high, i.e., during periods of high uncertainty. Findings reveal that when uncertainty is high, analysts' forecasts are more timely but less accurate.However, analysts' forecasts are also more informative to the market, which is consistent with investors' demand for timely information, even if it is less accurate. We observe these findings when market prices are increasing and decreasing, consistent with the findings resulting from uncertainty in general rather than just uncertainty associated with market declines. We also examine how timeliness, accuracy, and informativeness change in response to elevated levels of three sources of uncertainty-market, industry, and firm level. We predict and find that analysts are better able to deal with heightened industry uncertainty, as reflected by greater timeliness with no loss in forecast accuracy. In contrast, analysts have greater difficulty dealing with heightened market uncertainty, as both timeliness and forecast accuracy decline. K E Y W O R D Searnings forecasts, financial analysts, uncertainty INTRODUCTIONEquity analysts play an important role in capital markets by interpreting public information relating to companies, industries, and the economy, and by developing private information to forecast future earnings and stock prices and to make stock recommendations. Investors rely on analysts' research reports, stock recommendations, and forecasts of earnings and other financial statement items as a source of information that provides a basis for investment decisions.The role of analysts is likely to be even more important during times of heightened uncertainty, when investors may have more difficulty interpreting public information relating to companies, industries, and the economy. High uncertainty implies there is less precise public information, which drives investor demand for information from analysts.Investors demand timely and accurate forecasts for use in valuation and investment decisions; however, uncertainty J Bus Fin Acc. 2018;45:295-318. wileyonlinelibrary.com/journal/jbfa

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