We examine Twitter discussion of sell-side analysts’ stock recommendation revisions. While many investors lack direct access to analyst research, we observe revision-related Twitter discussion associated with approximately 90 percent of the revisions in our sample, usually within three hours of their announcement. Revision-related Twitter discussion is greater for upgrades and for analysts from larger brokerages. Examining within-revision intraday price discovery, we observe increased price discovery during intraday windows with more revision-related tweets, especially for tweets that have more user engagement, are posted by more influential authors, or involve stocks with more intense retail trading volume. We find that revision-related retail trading is more intense and better predicts future returns for revisions with more revision-related Twitter discussion. We observe no such evidence for institutional investors who have direct access to sell-side research. Our results suggest that Twitter is an important channel in facilitating price discovery following analyst revisions, particularly among retail investors.

We compare the earnings information produced by the five largest forecast data providers (FDPs)-Bloomberg, Capital IQ, FactSet, I/B/E/S, and Zacks-and observe substantial differences across FDPs in both forecasted and actual street earnings values, and thus the earnings surprise, for the same firm-quarter. We provide evidence that differences in the earnings surprise across FDPs for the same firm-quarter (i.e., "FDP differences") have economically meaningful implications for price responsiveness and liquidity around earnings announcements. We also find that, for announcements where FDPs disagree about whether the announcing firm missed or beat earnings expectations, investors are more likely to side with higher-quality FDPs but may not fully impound the implications of FDP quality differences during the announcement window. On average, relative to the other FDPs, I/B/E/S ranks highly in our measure of FDP quality, such that investor reactions are likely to align with I/B/E/S earnings information, validating its use as a representative FDP in academic research. Taken together, our results are consistent with FDPs pursuing differentiated information production strategies that generate capital market frictions when these strategies lead to material FDP differences.

Using a large sample of analyst reports (~215,000), we examine whether two textual attributes, tone and uncertainty, provide incremental information to investors beyond quantitative summary measures. We find that tone (uncertainty) is positively (negatively) associated with short-term abnormal returns around the report's release date conditional on forecast and recommendation revisions. These results are consistent with our hypothesis that analysts provide additional information in the text of their reports that is not captured in the summary quantitative measures. The results also suggest that investors place significantly more weight on tone information when the report contains "bad" news, although the evidence is mixed w.r.t. uncertainty. We also examine the effects of consistency between textual and quantitative report attributes. We find that investors place more weight on report tone when tone and forecast (recommendation) revisions provide consistent signals, but not vice-versa. ⊗ We appreciate the comments and suggestions of seminar participants at INSEAD.

Darrough and Russell (2002) find that top-down S&P 500 EPS forecasts made by market strategists are more pessimistic, on average, than comparable bottom-up S&P 500 EPS forecasts aggregated from sell-side analysts’ firm-level forecasts. We find that strategists’ relative pessimism varies systematically with macroeconomic conditions. In turn, strategists’ relative pessimism has predictive value for future aggregate earnings surprises and aggregate stock returns, consistent with sell-side analysts and investors underreacting to the information contained in strategists’ index forecasts. We find that the predictive ability is concentrated during periods of macroeconomic uncertainty. Strategists’ relative pessimism also predicts future firm-level earnings surprises and their market reaction. Finally, we support our conclusion by demonstrating that top-down forecasts exhibit superior quality over bottom-up forecasts. Overall, investors and policy makers can benefit when they use both bottom-up and top-down forecasts in combination when forming expectations for aggregate earnings.

We examine whether plaintiffs’ attorney marketing releases are useful in predicting the incidence and severity of securities litigation. Examining a large sample of negative stock return events (“stock drops”) from 2010–2023, we find that plaintiffs’ attorneys selectively announce corporate “investigations” soliciting potential plaintiffs around a small percentage of stock drops. These marketing releases strongly predict future litigation controlling for known determinants of litigation risk, including characteristics of the stock drop and associated trigger event. Plaintiffs’ attorney marketing is more predictive of litigation when multiple law firms or larger plaintiffs’ firms announce investigations, and for companies with lower institutional ownership. Similarly, we find that investigation announcements around the announcement of material financial restatements are predictive of litigation risk controlling for restatement characteristics. We also find evidence of incremental intraday negative returns associated with the first investigation announced after a stock drop or restatement, consistent with investors responding to the increased litigation risk signaled by an initial investigation announcement. Lastly, examining a broad sample of securities class action lawsuits filed over our sample period, we find that lawsuits followed by more extensive post-filing marketing by plaintiffs’ attorneys, particularly by star plaintiffs’ firms, are more likely to settle and settle for higher amounts. Taken together, our results are consistent with attorneys’ pre-filing marketing efforts providing a timely public signal of future litigation, and their post-filing marketing investments reflecting informed assessments of case merits.

ABSTRACT We use a simple k-nearest neighbors algorithm (hereafter, k-NN*) to forecast earnings. k-NN* forecasts of one-, two-, and three-year-ahead earnings are more accurate than those generated by popular extant forecasting approaches. k-NN* forecasts of two- and three-year (one-year)-ahead EPS and aggregate three-year EPS are more (less) accurate than those generated by analysts. The association between the unexpected earnings implied by k-NN* and the contemporaneous market-adjusted return (i.e., the earnings association coefficient (EAC)) is positive and exceeds the EAC on unexpected earnings implied by alternate approaches. A trading strategy that is long (short) firms for which k-NN* predicts positive (negative) earnings growth earns positive risk-adjusted returns that exceed those earned by similar trading strategies that are based on alternate forecasts. The k-NN* algorithm generates an empirically reliable ex ante indicator of forecast accuracy that identifies situations when the k-NN* EAC is larger and the k-NN* trading strategy is more profitable. Data Availability: Data are available from the public sources described in the text. JEL Classifications: C21; C53; G17; M41.

We examine Twitter discussion of analysts' stock recommendation revisions. While many investors lack direct access to analyst research, approximately 90% of the revisions in our sample (2013 to 2020) are discussed on Twitter, with the majority of revisions receiving Twitter coverage within three hours of their announcement. We find that revision-related Twitter discussion is more extensive for upgrades and for analysts from larger brokerages. Examining within-revision intraday price discovery, we also observe increased levels of price discovery during intraday windows with more revision-related tweets, especially for tweets with more user engagement and those posted by more influential authors. Finally, our evidence suggests revision-related Twitter discussion improves retail investors’ response to revision news, and we observe no such evidence for institutional investors who typically have direct access to sell-side research. Overall, our results indicate that Twitter is an important channel in facilitating price discovery following analyst revisions, particularly among retail investors.

In September 2009, Thomson Reuters (TR) discontinued its practice of relying on analysts to determine the treatment of unexpected charges and gains in favor of their immediate exclusion from GAAP earnings. Adopting a difference-in-differences approach, we show that this plausibly exogenous change in TR's methodology resulted in street earnings that are more predictive of future performance; and timelier, more accurate, and less dispersed analyst forecasts of future earnings, consistent with TR enhancing the properties of street earnings and analyst forecasts. Finally, using path analysis we show that a significant portion of TR's effect on price discovery is through its effect on analysts; and that the change in TR's treatment of unexpected items increased (decreased) the relative influence of TR (analysts) on the pricing of street earnings. We conclude that forecast data providers like TR are more than a conduit of information from analysts to investors.

Investors rely on “street” earnings – earnings adjusted for consistency with analysts’ consensus forecasting basis and disseminated by forecast data providers (FDPs) – to measure quarterly earnings news. We examine an exogenous 2009 shock that significantly improved the timeliness of street earnings dissemination for a prominent FDP. We present evidence that this exogenous improvement in FDP timeliness increased the timeliness of investors’ response to earnings news. In the post-shock period, we find that, although both FDP dissemination and price discovery are concentrated to an intraday timescale, predictable information processing frictions explain the timeliness of the market reaction to street earnings, both directly and indirectly through their effect on FDPs. These frictions, along with after-hours liquidity constraints, limit investors’ ability to immediately self-process and trade on street earnings news.

Using both investor- and stock-level data, I examine the relation between stockholders’ unrealized returns since purchase and the market response to earnings announcements. I demonstrate that stockholders’ unrealized gain/loss position moderates their trading behavior in response to earnings announcements. I also find that this behavior generates a short-window return underreaction to earnings news. My results are generally consistent with predictions from prospect theory regarding the manner in which stockholders’ unrealized returns moderate their trading response to belief shocks. However, my results also suggest that an emotional component (i.e., regret avoidance/pride seeking) is necessary to explain the observed investor behavior.

ABSTRACT Under the Italian statutory audit regime, three individual accountants are jointly appointed to audit each client's annual financial statements and sign off on the tax return. These individuals can belong to the same or different accounting firms and through multiple and repeated collaborations they form a professional network. We use network measures of centrality to capture individuals' ability to acquire and apply tax expertise across clients. We demonstrate that clients engaging better-connected individual auditors have comparatively lower effective tax rates. Our results are robust to controlling for a number of client, individual, and accounting firm characteristics, as well as for alternative network connections between clients. We also use instrumental variables, individual fixed effects, and matching to mitigate the effect of endogenous pairing of clients and auditors. Our findings demonstrate that in a joint audit environment, individual auditor professional networks have consequences for tax outcomes. Data Availability: Data are obtainable from the public sources cited in the text and are available upon request.

Our analyses are based on the observation that the portion of change in enterprise value captured in earnings differs according to the source of value change. These sources are: (1) cash flows to/from debt holders, equity holders, and/or cash reserves; and, (2) changes in value of assets in place (i.e., enterprise returns). We demonstrate that the effect on earnings of each source of growth captures distinct aspects of accounting conservatism. Although the effect of cash flows on earnings has received little attention in the extant literature, we show that it explains more of earnings than is explained by enterprise returns. We show that the difference in the portion of negative cf. positive enterprise returns captured in current earnings is similar to the portion of negative cf. positive equity returns reported in the extant literature. We also demonstrate an interaction between the sign of returns and cash flows.

We present new evidence that highlights the role of information intermediaries in the distribution and processing of earnings estimates in capital markets. We find that the time taken to activate an analyst's earnings forecast in the Thomson Reuters Institutional Brokers’ Estimate System is related to measures of investor demand for timely information processing, processing difficulty, and limited attention. Furthermore, we find that forecast announcement returns are muted and post-announcement drift is magnified for forecasts with longer unexpected activation delay and that market inefficiency is concentrated in neglected stocks and potentially exploitable. Finally, analyzing intraday returns, we find that activations facilitate price discovery.

We suggest and show the efficacy of two fundamental changes to the methodology at the core of the vast empirical literature examining the extent to which accounting captures concurrent changes in market value. First, we focus on the part of the earnings/returns relation that is not dollar-for-dollar because, at best, the part that is recorded dollar-for-dollar is uninteresting empirically and, at worst, including this part may lead to incorrect inferences. Second, we suggest the inclusion of an omitted variable, capturing transactions with owners, in the earnings/change in value relation. Absent unconditional conservatism, this additional variable should provide no explanatory power in the model we propose. However, our empirical analyses demonstrate that this added variable contributes similar explanatory power for earnings to that provided by cum-distribution value change, and that the sign of this variable also affects estimates of conditional conservatism derived from either our model or that of Basu [1997].

We empirically analyze the relation between enterprise profit as recorded in corporate income statements and two sources of growth; growth via investments by the owners in new assets and growth that is generated from assets already in the enterprise. We argue and show that accounting for growth depends on both the direction (expansion vs. contraction) and source of the growth. Our analyses of accounting for growth due to investments by the owners of the enterprise adds a dimension to the relation between earnings and change in value that is statistically and economically significant and one that has not been considered in the extant literature. Our analyses of the accounting due to growth generated from assets in place rather than the accounting for change in equity value (as in the extant literature) brings the focus to the mapping between change in value and earnings that is not simply dollar-for-dollar.

We investigate the incremental market reaction to first-time going concern audit reports (GCARs) relative to similarly distressed non-GCAR firms. We utilize a matched-sample research design to show that first-time GCARs are associated with incremental negative abnormal returns and increases in market-adjusted share turnover at the annual report filing date. Moreover, our results indicate that greater net selling by institutional investors (i.e., institutional flight) during the fiscal year increases the magnitude of these associations. We also find that first-time GCARs signal an increased likelihood of bankruptcy and weaker operating performance in the subsequent year, and that institutional flight prior to the GCAR moderates the severity of these signals. Taken together, our findings provide new evidence that first-time GCARs are incrementally informative beyond other financial statement information, and that the informativeness of the auditor’s GCAR decision is moderated by the observed trading decisions of institutional investors.