Forecast Revisions Research Articles

The impact of IFRS complexity on analyst forecast properties: The moderating role of high quality audit

AbstractThis paper examines the impact of International Financial Reporting Standards (hereafter IFRS) complexity on analyst forecast properties including forecast errors, forecast dispersion and forecast revision. Because high‐quality audit is likely to moderate the effect of complexity on analyst forecast properties, Industry Specialization (ISP) as a proxy for high quality audit is introduced as a moderating variable in this paper. The complexity of individual IFRS standards is measured based on IFRS reconciliation statements capturing the adjustments on accounts affected by the changes in accounting standards. We analyze 322 companies listed on the Australian Securities Exchange (ASX). Our results suggest that all IFRS standards are not equally complex. A few standards are relatively more complex. These standards have a positive effect on analyst forecast errors, forecast dispersion and forecast revision. In addition, we find that high quality audit (proxied by city‐level industry specialist auditors) can mitigate the adverse impact of IFRS complexity on analyst forecast errors but not on forecast dispersion and revision. More interestingly, the mitigating effect of high quality audit is restricted to only a group of companies exposed to a higher level of aggregate IFRS complexity. The findings of our study contribute to the IFRS literature by identifying the most complex accounting standards. Secondly, this study provides an ex‐ante experiment in IFRS adoption, which can be used by adopting countries to understand the impact of IFRS on the analyst forecasts. Finally, our study helps regulators to understand the effects of the complexity of accounting standards on users of financial statements.

Analyst Forecast Revision Consistency and Bias in Earnings Forecast Revisions

We address whether analysts bias earnings forecast revisions and convey the bias using forecast revision consistency, i.e., the extent to which analyst reports with earnings forecast revisions include stock recommendation and target price revisions consistent in sign with the earnings forecast revisions, the sign of which is the sign of earnings forecast revision. Incentives to curry favor with management can constrain analysts to issue biased earnings forecast revisions. As predicted, we find that forecast revision consistency is (i) positively associated with the market reaction to the analyst report; (ii) positively associated with consensus analyst forecast error (AFE), i.e., actual earnings minus the consensus of the analysts’ forecasts; and (iii) negatively associated with earnings announcement returns, incremental to AFE. Absolute forecast revision consistency is smaller (larger) when firms eliminate (initiate) guidance. These findings are consistent with analysts using forecast revision consistency to convey information about bias in their earnings forecast revisions.

Distress risk puzzle and analyst forecast optimism

A general consensus in the literature is that financial analysts make optimistic forecasts. That is, they tend to underreact to negative but overreact to positive information. In this study, we invoke this idea to provide an explanation for the distress risk puzzle, the phenomenon that high distress risk firms deliver anomalously low subsequent returns. We find that analysts underestimate the implication of the poor performance of higher distress risk firms, and thus make EPS and sales forecasts that are generally more optimistic than those for the lower distress risk firms. Because market respond to the analyst forecasts, investors initially overvalue the high distress risk firms; later on, when those firms report less than expected performance, analysts revise their forecasts downwards that in turn cause the high distress risk firms to earn low future returns composing of both immediate-forecast-revision responses and post-forecast-revision price drifts. We further document that (quarter) earnings announcements convey a substantial amount of information that roughly drives more than 60% of the analyst forecast revisions and 30% of the revision-related market responses.

Do managers mimic rivals' forecast revisions Evidence from Japan

When managers face uncertain business environments or enter a compensation contract that ties their firm to other's behaviours, their management forecast disclosure may be affected by the behaviours of others. This study examines management earnings forecast revisions in Japan from the standpoint of herding behaviour theory. The results reveal the following: 1) management earnings forecast revisions follow the mean values of preceding forecast revisions issued by firms in the same industry; 2) mimicking behaviours are weaker when the number of days from the management earnings forecast release to the closing date decreases or when the mean value of rivals' forecast revisions is negative; 3) bold management earnings forecast revisions (i.e., that deviate significantly from the mean value of rivals' forecast revisions) lead to improved forecast accuracy. The results suggest that analysts, policymakers, and investors should consider herding behaviours when they use management forecasts to make predictions.

Does forecast disaggregation facilitate organizational learning by doing? The effect of forecast disaggregation on the year-over-year improvement in demand forecast revisions

Demand forecast accuracy improves management earnings forecasts and is critical to organizational planning and coordination. Indeed, surveys indicate that CFOs name forecast error as their top internal concern and identify demand forecasting as one of their top organizational priorities. In this study, we examine organizational learning effects over time in forecast revisions. We first document that year-over-year experience in product level demand forecasting improves forecast revision quality; that is, we show forecast revision “learning by doing.” We further examine whether the disaggregation of a demand forecast into separate forecasts for each source of demand facilitates learning by doing. Exploiting proprietary data from a multinational manufacturing organization we show that disaggregation leads to higher quality forecast revisions that result in improved forecast accuracy earlier in the forecast cycle. Our study provides new insight into how organizations can begin to address the vexing problem of demand forecast error.

Contextualization of Numbers in Earnings Conference Calls

Prior research generally finds that using numbers in financial disclosure is beneficial. However, numbers can also be strategically proffered due to their perceived precision and informativeness. We hypothesize that firms use undercontextualized numbers to create a false impression of certainty and transparency. We find that the use of undercontextualized numbers in earnings conference calls is associated with short-term benefits but negative longer-term consequences. Specifically, undercontextualization is associated with a short-term positive market return, positive analysts’ forecast revisions, and more positive analysts’ tone on the call. Furthermore, undercontextualization is associated with a longer-term negative market return, a decrease in year-over-year earnings, and a higher incidence of restatements. In additional analyses, we find that undercontextualization appears to be intentional as it is associated with conference call casting and insider trading. Given the uniqueness of our undercontextualization measure, we also document its association with analyst scrutiny and other proxies for strategic call behavior.

Predisposed opportunities: incentives for earnings forecasts revision by management under the Japanese Timely Disclosure Rules

This study investigates the incentives on revisions of management earnings forecasts in Japan, where the disclosure of forecasts is effectively mandatory in quarterly financial reporting, but their revisions are required only when a certain threshold is exceeded. Under this unique Japanese system, which is different from the US-style voluntary disclosure, we hypothesise that predisposed opportunities provided in each quarterly financial reporting will encourage management to make voluntary disclosures. Our analysis shows that earnings forecasts revisions by management are lower on the earnings announcement date than during the course of a quarterly period. Our results also suggest that slight revisions made on the earnings announcement date contribute to increasing the frequency of revisions. In addition, our evidence shows relatively small errors when forecasts have been revised by narrow margins. These findings imply that the Japanese 'Timely Disclosure Rule' promotes the disclosure of timely revisions of forecasts and has the effect of disciplining management to monitor profit prospects more accurately. Our findings are also consistent with the Japanese management belief that forecast information is important, and timely disclosures should be voluntary rather than required by regulation.

The Unintended Benefits of Increased Disclosure Frequency: Evidence from the Brokerage House Industry

Using a plausibly exogenous shock to performance disclosure frequency by listed brokerage houses in China, we examine the impact of disclosure frequency on the quality of information production. Difference-in-difference estimation shows that analysts affiliated with listed brokerage houses exhibit lower forecast optimism in the post-regulation period, compared to their unlisted peers. Cross-sectional analyses suggest that the effect is more pronounced when the listed brokerage houses’ business relies less on their clients, when analysts have greater career concern, and when the covered firms exhibit lower valuation uncertainty/difficulty. Additional analysis indicates that forecast revisions by analysts affiliated with a listed brokerage house are more informative after the shock. Moreover, we find that analysts affiliated with listed brokerage houses issue more accurate and frequent forecasts and cover more companies than their peers do, which suggests that information production improves as a result of increased competition. Overall, our evidence suggests that increased performance disclosure by information intermediaries has positive externalities.

Patterns in IMF Growth Forecast Revisions: A Panel Study at Multiple Horizons

This paper investigates the performance of the IMF WEO growth forecast revisions across different horizons and country groups. We find that: (i) growth revisions in horizons closer to the actual are generally larger, more volatile, and more negative; (ii) on average, growth revisions are in the right direction, becoming progressively more responsive to the forecast error gap as horizons get closer to the actual year; (iii) growth revisions in systemic economies are relevant for growth revisions in all country groups; (iv) WEO and Consensus Forecast growth revisions are highly correlated; (v) fall-to-spring WEO revisions are more correlated with Consensus Forecasts revisions compared to spring-to-fall revisions; and (vi) across vintages, revisions for a given time horizon are not autocorrelated; within vintages, revisions tend to be positively correlated, suggesting perception of persistent short-term shocks.

How Do ESG Incidents Affect Firm Value?

We investigate how sell-side analysts adjust their earnings forecasts following ESG incidents. We find that following negative ESG news, analysts significantly downgrade their earnings forecasts at all horizons, including long-term. Forecast revisions account for all the negative impact of ESG incidents on firm values, implying no change in the discount rate. The negative revision of earnings forecasts reflects lower expectations on future sales (rather than higher future costs). In Europe, analysts who exhibit a stronger sensitivity to ESG news provide significantly more precise forecasts than their peers.

ВЛИЯНИЕ ДОЛГОСРОЧНЫХ УГРОЗ ПОТЕПЛЕНИЯ КЛИМАТА И ПАНДЕМИИ НА МИРОВОЙ РЫНОК НЕФТИ

The global oil market is currently being heavily impacted by the build-up of renewable energy power plants due to the threat of a warming climate. Many countries have decided to restructure the energy sector to minimize the burning of fossil fuels. This decision reduces the demand for oil and forces a revision of long-term oil market forecasts. The virulent COVID-2019 virus, which emerged in late 19, has caused a massive pandemic. The pandemic has markedly reduced human activity in all countries and, in the absence of effective drugs to deal with this virus and its many strains, poses additional long-term threats to both the global econ-omy and the oil market. This is confirmed by the forecasts of oil companies and think tanks discussed in this article.

The Debt-Equity Spread

We propose the debt-equity spread (DES) as a measure of the valuation gap between debt and equity at firm level. DES strongly predicts stock and bond returns in opposite directions. A strategy that takes long positions in firms with low DES, whose stocks are cheap relative to bonds, and short those with high DES generates an average stock return of 6% per annum and bond return of -3.3% per annum. The return predictability is consistently stronger among small, illiquid, and difficult-to-short stocks and bonds. In addition, higher debt-equity spreads are associated with (i) higher probability for negative revision of long-term earnings growth forecasts; (ii) more equity issuance and debt repurchase, resulting in a low leverage ratio; (iii) stronger preferences for stock payments in acquisitions; and (iv) more insider stock selling. Together, these results suggest that the return prediction of DES is likely driven by mispricing rather than risk exposures.

Information frictions in inflation expectations among five types of economic agents

We compare disagreement in expectations and the frequency of forecast revisions among five categories of agents: households, firms, professional forecasters, policymakers and participants to laboratory experiments. We provide evidence of disagreement among all categories of agents. There is however a strong heterogeneity across categories: while policymakers and professional forecasters exhibit low disagreement, firms and households show strong disagreement. This translates into a heterogeneous frequency of forecast revision across categories of agents, with policymakers revising more frequently their forecasts than firms and professional forecasters. Households last revise less frequently. We are also able to explore the external validity of experimental expectations.

Other Comprehensive Income, Its Components, and Analysts’ Forecasts

This paper examines how analysts incorporate other comprehensive income (OCI) and its components into their earnings forecasts. We first document that analysts’ one-year-ahead earnings forecasts are associated with OCI and OCI components having predictive ability; this suggests analysts (at least partially) incorporate this information into their forecasting. We then show that analysts are neither complete nor timely in incorporating OCI information into their forecasts, as several OCI components remain associated with analysts’ forecast errors. Further, we document that higher uncertainty in firm performance exacerbates analysts’ underreaction, evidencing a friction to full incorporation of OCI-related information. Finally, as evidence of where and when analysts derive OCI-related information, we document that analysts’ forecast revisions correlate with the release of firms’ 10-Ks and early 10-Qs (i.e., quarters one and two but not three).

Market Reaction to CEOs’ Dynamic Hemifacial Asymmetry of Expressions — A Machine-Learning Approach

Neuropsychological studies propose that listeners unconsciously assess speakers’ trustworthiness via their facial expressions. Building on this theory, we investigate how investors respond to CEOs’ dynamic hemifacial asymmetry of expressions (HFAsy) shown on CNBC’s video interviews about corporate earnings. We employ a machine-learning approach of face-detection and facial-expression-recognition based on conventional neural network to measure CEOs’ dynamic HFAsy. Consistent with the neuropsychological prediction that facial asymmetry induces distrust, we document that the stock market reacts negatively to the CEO’s HFAsy shown on the interview video. We also find that the abnormal bid-ask spread around the interview date is positively associated with the CEO’s HFAsy. We further show that these effects are more pronounced for firms with weaker information environments. Finally, we document that analyst forecast revisions are negatively associated with CEOs’ HFAsy. Overall, our study provides evidence that investor trust and trading behavior are affected by the dynamic hemifacial asymmetry of expressions appeared on CEOs’ faces.

Do managers mimic rivals' forecast revisions Evidence from Japan

Do managers mimic rivals' forecast revisions Evidence from Japan

Insider Trading Behavior Following Analyst Earnings Forecast Revisions

Insider Trading Behavior Following Analyst Earnings Forecast Revisions

Everyone Has an Opinion: The Informativeness of Social Media’s Response to Management Guidance

We examine whether the social media reaction to an important firm disclosure provides a signal of the quality of that disclosure and whether capital market participants’ reactions to the disclosure are consistent with the social media reaction. Specifically, we examine the sentiment of posts on StockTwits immediately following management forecasts and offer three main findings. First, the relation between StockTwits sentiment and forecast news is stronger when the forecast is later revealed to be more accurate and less biased, suggesting that StockTwits provides an early signal of forecast quality. Second, we find a positive association between the extent to which social media sentiment agrees with the forecast news and stock price reaction to the management forecast. This suggests that when social media sentiment agrees with management forecast news, investors do too. Finally, we find a positive association between the extent to which social media sentiment agrees with the forecast news and subsequent analyst forecast revisions, particularly when forecast news is positive. This suggests that when social media sentiment agrees with positive management forecast news, analysts do too. Intraday analysis provides preliminary evidence that investors appear to respond to sentiment, but long-run tests suggest that investors appear to underreact to the signal provided by social media sentiment, while analysts appear to overreact to the signal. Overall, our results suggest that the social media reaction to management forecasts provides a timely and accurate reflection of not only the forecast’s quality, but also of how the forecast will be received by important capital market participants.

Raiders of the Lost High-Frequency Forecasts: New Data and Evidence on the Efficiency of the Fed's Forecasting

<ns2:p>We introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of Governors of the Federal Reserve System. In contrast to the eight Greenbook forecasts a year the staff produces for Federal Open Market Committee (FOMC) meetings, our dataset has roughly weekly forecasts. We use these new data to study whether the staff forecasts efficiently and whether efficiency, or lack thereof, is time-varying. Prespecified regressions of forecast errors on forecast revisions show that the staff's GDP forecast errors correlate with its GDP forecast revisions, particularly for forecasts made more than two weeks from the start of a FOMC meeting, implying GDP forecasts exhibit time-varying inefficiency between FOMC meetings. We find some weaker evidence for inefficient inflation forecasts.</ns2:p>

COVID-19 and the Cross-Section of Equity Returns: Impact and Transmission

Using the first reported case of COVID-19 in a given U.S. county as the event day, we find that firms headquartered in an affected county experience, on average, a 27-bps lower return in the 10-day post-event window. This negative effect nearly doubles in magnitude for firms in counties with a higher infection rate (-50 bps). We test a number of transmission channels. Firms belonging to labor-intensive industries and those located in counties with a large mobility decline have worse stock performance. Firms sensitive to COVID-19-induced uncertainty also exhibit more negative returns. Finally, more negative stock returns are associated with downward revisions in earnings forecasts.