Earnings Announcements Research Articles

Media Tone and Earnings Management before the Earnings Announcement: Evidence from China

Abstract This article examines the relationship between media tone and earnings management before the earnings announcement. Using data from China, I discover a positive relationship between media tone before the earnings announcement and earnings management. For companies whose managers sell their ownership in the next time and have prior media exposure, the beneficial relationship is more pronounced. The relationship between abnormal media tone before results declaration and earnings management appears to be weakened by external oversight. According to the findings, managers can use media slant in addition to earnings management as a way to increase stock price following an earnings announcement. I also find that managers may use two public channels to influence media tone before the earnings announcement: the distribution of preliminary accounting data and earnings pre-announcement. Our study expands on the media’s involvement in earnings management and offers proof of the concurrent use of multiple management techniques to artificially raise stock prices.

Do Unexpected Earnings of Industry Leaders Affect the Discretionary Reporting Behavior of Followers? Evidence From China

ABSTRACTThis study examines how the unexpected earnings of industry leaders influence the discretionary reporting behavior of industry followers. Chinese firms are required to disclose their annual earnings announcement (EA) dates before the fiscal year ends. Leveraging this unique setting, we demonstrate that followers delay their EAs in response to earnings surprises reported by industry leaders. This effect is more pronounced for followers who face lower costs of delaying (i.e., those with weak corporate governance and who face no penalties for delaying EAs) and those who receive greater benefits from delaying (i.e., those in more competitive industries and in industries where leaders meet or beat market expectations). We also find that, compared with other followers, those that delay EAs are more likely to engage in last‐minute earnings management by reducing their effective tax rates. Furthermore, our findings suggest that when industry leaders report good news, followers who delay EAs are more likely to do the same. However, we find no positive market reaction to these delayed EAs. Overall, this study provides new evidence that industry leaders’ earnings surprises significantly impact their followers’ decisions about discretionary financial reporting, including the delay of EAs and last‐minute earnings management.

Examining the Information Efficiency of Indonesia Equity Market: Evidence from Earnings Announcements

The main objective of this study is to examine the semi-strong form of market efficiency in the Indonesia Equity Market on publicly available information. The study uses a market model to analyze the significance of abnormal returns before and after earnings releases. It analyzes the cumulative average abnormal returns of the daily trades before and after the earnings release. The study reveals that there are no significant abnormal returns during the pre-announcement periods, suggesting that there is no significant leakage of information before the disclosures. An analysis of the post-announcement documents reveals a delay in the market response, indicating that the market is inefficient in rapidly and accurately transmitting the information from earnings releases to investors. It also observes significant abnormal returns within the initial week of the release periods and a post-earnings announcement drift of up to 30 days. The findings indicate that the market does not possess the attributes of a semi-strong form of market efficiency. The results imply that the market’s financial reporting processes have the potential for digital technology improvement to enhance transparency, accountability, and information efficiency within the market.

A rating system to evaluate non-GAAP exclusion quality

AbstractWe develop a rating system to evaluate the quality of individual non-GAAP exclusions. Our perspective is that high-quality exclusions reflect nonrecurring economic transactions, are transitory accounting adjustments, or have little usefulness in forecasting cash flows. We use four approaches to rate exclusions. We evaluate the serial correlation of the exclusion, survey accounting academics’ views, obtain practitioner ratings from the CFA Institute, and identify the exclusions approved by the Chinese securities regulator. A firm’s exclusion quality score is the weighted average rating of its individual exclusions. For our sample of S&P 500 firms, we document that exclusion quality varies by industry, captures trends in non-GAAP reporting, and is reasonably stable at the firm level. To validate the rating, we show that firms with lower exclusion quality scores receive more SEC comment letters, incur more Regulation G violations, exhibit greater analyst forecast dispersion, and have slower price discovery following earnings announcements.

Aggregate tone and gross domestic product

AbstractWe examine whether the change in earnings announcement textual tone, aggregated across individual publicly traded firms, helps predict gross domestic product (GDP) growth. The literature finds that changes in aggregate accounting earnings do help predict GDP growth, but only when aggregate earnings changes are negative. Because conservative accounting rules limit managers' ability to communicate positive news promptly, we examine the tone of quarterly corporate earnings announcements as a possible source of timely positive information provided by firms. We find that the change in aggregate tone in the earnings announcements from the same quarter in the previous year predicts one‐quarter‐ahead GDP growth, but only when the change is positive. Our study contributes to the literature by investigating the relation between aggregate corporate disclosure tone and macroeconomic outcomes.

Asymmetric Uncertainty Around Earnings Announcements: Evidence from Options Markets

We use the Indian stock options market to study the evolution of uncertainty and asymmetric uncertainty around earnings announcements (EAs). We find that uncertainty (implied volatility) and asymmetric uncertainty (options skew) increase monotonically before the EA day and decrease after EA. Options volume (relative to spot and to futures) also exhibits similar behavior, suggesting that informed investors prefer options markets to spot and futures markets. Both options skew and put-to-call volume ratio can predict the sign of the EA surprise one day before EA, indicating that price discovery and information assimilation happen in the options market.

Beyond the Event Window: Earnings Horizon and the Informativeness of Earnings Announcements

ABSTRACT The impact of earnings announcements (EAs) on investor uncertainty depends not only on how much new information they contain but also on how long it would take comparable information to arrive in the future through alternative sources, which I term “earnings horizon.” Using a structural model of periodic EAs, I show that earnings horizon is not captured by standard empirical measures of earnings informativeness or timeliness based on the event-study approach. However, earnings horizon can be estimated using patterns in return volatility over firms’ reporting cycles, which indicate that EAs have a short horizon and thus reduce investor uncertainty by one-third the amount suggested by event studies. Moreover, these patterns indicate that it takes investors considerably longer than the three- to five-day windows commonly applied in event studies to fully process EAs and that more frequent financial reporting may significantly enhance EAs’ informativeness. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: C58; D82; D84; G10; G12; G14; G30.

Discretionary dissemination on Twitter

AbstractThe study provides large‐scale descriptive evidence on the timing and nature of corporate financial tweeting. Using an unsupervised machine learning approach to analyze 24 million tweets posted by S&P 1500 firms from 2012 to 2020, we find that firms are more likely to tweet financial information around significantly negative or positive news events, such as earnings announcements and the filing of financial statements. This convex U‐shaped relation between the likelihood of posting financial tweets and the materiality of accounting events becomes stronger over time. Whereas research based on early samples concludes that firms are less likely to disseminate financial information on Twitter when the news is bad and material, the symmetric dissemination behavior we find suggests that these conclusions should be revised. We also show that a machine learning algorithm (Twitter‐Latent Dirichlet Allocation) is superior to a dictionary approach in classifying short messages like tweets.

Beyond the street EPS surprise – when ‘other surprises’ matter in explaining earnings announcement returns

This study examines when accounting surprises other than the street EPS surprise, i.e. ‘other surprises’, are useful to investors. First, ‘other surprises’ are incrementally important when companies use earnings management to beat the analyst consensus EPS forecasts and when earnings quality is low. Second, ‘other surprises’ are useful in interpreting quarterly results for (i) growth stocks, (ii) stocks with high institutional ownership (particularly by active institutions and blockholders), and (iii) in interpreting the quality of the street EPS surprise. Third, the street EPS surprise shows no association with earnings announcement returns controlling for ‘other surprises.’ This result reflects that some ‘other surprises’, such as the net income surprise, are better predictors of future earnings surprises than the street EPS surprise.

Retail Trading Around Earnings Announcements: Evidence from Robinhood Traders

Retail investors from the discount broker Robinhood swarm into stocks with pending earnings announcements and stay away from them immediately after the announcements. We study four competing explanations for this phenomenon: liquidity provision, informed trading, lottery preference, and attention-induced herding by noise traders. We find strong evidence that, immediately around earnings announcements, Robinhood investors’ behavior is primarily driven by attention-induced noise trading. Our results offer new insights into retail traders’ motivation for trading when they face heightened uncertainty from earnings announcements. We also find corroborative evidence from a proprietary data that tracks retail trading activities from NASDAQ.

The Determinants and Information Effects of Earnings Announcement Date Variability

Prior research finds mixed evidence that firms strategically manage their earnings announcement timing to either highlight or obscure financial information. While most prior studies focus on the specific timing and the nature of individual earnings announcements, we instead focus on the variability of firms’ annual earnings announcement dates (hereafter referred to as EADs) over a span of time. Using archival data collected from I/B/E/S and Compustat, we find that firms with fewer resources, weaker internal monitoring systems, and greater financial uncertainty are much more likely to exhibit increased EAD variability. Furthermore, we provide substantial evidence that the capital market’s response to earnings is noticeably weaker when a firm’s EAD variability is higher. Additional in-depth analysis reveals that firms exhibiting higher EAD variability tend to report significantly lower future performance in both the short- and long-term horizons. Consequently, while managers might intentionally alter an earnings announcement date to exploit variations in investor attention, this comprehensive study provides significant evidence that they should also consider how the market perceives and interprets the overall EAD variability. This understanding is crucial to improve strategic financial communication and maintain investor trust.

The relevance of dark trading for information acquisition in the German stock market

The implementation of the European double volume cap (DVC) mechanism acts as an exogenous shock to help understand how restrictions on dark trading affect the price discovery process for German stocks. Using the price jump ratio as a measure of information acquisition prior to earnings announcements, we show that dark pool trading aids information acquisition. This relationship is stronger for negative earnings news, for stocks that are hard to value, and when there is uncertainty around earnings. The introduction of the DVC mechanism weakens the relationship between dark trading and pre-announcement information acquisition.

Common Media Holding Companies and the Uniqueness of Business Press Content

ABSTRACT We examine how common media holding companies impact the uniqueness of business press content. Consistent with common media holding companies reducing the diversity of perspectives among journalists, we find that media outlets are more likely to cover the same earnings announcement and utilize more similar tone and content when they belong to a common holding company. We provide evidence that these effects are enhanced by outlet reach and economic incentives to share content. Finally, we provide evidence consistent with coverage by common media holding companies impeding price formation. Overall, our findings suggest that content within common media holding companies is less diverse and that this may have negative implications for markets. Data Availability: Data are available from the sources cited in the text. JEL Classifications: M40; M41; M49; G10; G14; L82.

Research and Empirical Evidence of Machine Learning based Financial Statement Analysis Methods

This study presents a novel approach called FinAnalytix which merging machine learning’s prowess in pattern recognition with financial statement analysis. This integrated algorithm combines deep neural networks and recurrent neural networks for predictive accuracy in stock return analysis, alongside logistic regression and random forest models for robust fraud detection in financial statements. The empirical evidence demonstrates FinAnalytix effectiveness in identifying abnormal financial patterns and predicting market reactions to earnings announcements. The study utilizes extensive data from listed companies, ensuring a comprehensive and practical application. FinAnalytix represents a significant advancement in the field, providing a dual approach to financial analysis for enhancing investment strategies through accurate stock return forecasts and bolstering financial integrity by detecting fraudulent activities. The simulation of the study based on the financial data of 100 sample listed companies. This research not only bridges the gap between traditional financial analysis and modern machine learning techniques but also offers a powerful tool for investors and regulatory bodies in navigating the complex financial landscape.

Book Value Forecast Quality: Financial Versus Nonfinancial Firms

ABSTRACTThis study investigates the quality and usefulness of analysts’ book value forecasts for financial firms relative to nonfinancial firms. We examine whether forecast properties (accuracy, bias, and dispersion) of book value forecasts and the market reaction to unexpected book value surprises differ between financial and nonfinancial firms. We find that book value forecasts are more accurate, less biased, and less dispersed for financial firms compared to nonfinancial firms. In addition, we show that these results are not simply a result of differences in earnings per share (EPS) forecast quality. We also provide evidence that book value forecasts are incrementally helpful to analysts in forecasting earnings for financial firms. In addition, we find evidence that the stock price drift following an earnings announcement is more pronounced when book value per share (BPS) surprises are considered in addition to EPS surprises. However, this finding only holds for financial firms. We find that this delayed reaction can form the basis for a profitable trading strategy.

Stock Splits Puzzle: An Additional Answer From Pre‐Split Earnings Announcements

ABSTRACTIf stock splits are used by managers to convey private information to the public, we hypothesize that the information content of splits should be related to the information managers reveal during quarterly earnings announcements. Supporting this hypothesis, we find that post‐split stock returns and operating earnings growth are positively correlated with the 5‐day returns around the last earnings announcement prior to the splits. This relationship holds even after controlling for different characteristics, including size, book‐to‐market, and momentum. These findings add new evidence to the rich literature on the signaling hypothesis regarding the motivation behind this corporate event.

The Brand Premium

Abstract We highlight the limitations of using cumulative advertising expenses as an input measure of brand value. Using two output measures—Interbrand’s data and a novel text-based measure—we find that an equal-weighted portfolio of top brands yields an annual abnormal return of 3%. The excess returns are driven by companies that develop their brands internally. Intangible factors proposed in the literature have no explanatory power for the premium. Analysts underestimate the future earnings of top brands, leading to significant excess returns following earnings announcements. We find no abnormal returns associated with the input measure of brand value. (JEL G4, G12, G14, M41)

Market reaction on earnings announcement information contents: Analysis from book-to-market

Market reaction on earnings announcement information contents: Analysis from book-to-market

Financial Reporting and Consumer Behavior

ABSTRACT We show that financial reporting influences consumer behavior by drawing consumer attention to announcing firms. Analyzing global positioning system (GPS) data, we document upticks in foot traffic to firms’ commercial locations immediately following their earnings announcements. This increase is more pronounced for announcements with substantial media attention, fewer concurrent announcements, heightened internet search volume, and extreme stock price jumps and earnings surprises—indicating that announcement coverage impacts consumer behavior by capturing attention. Furthermore, foot traffic increases with positive earnings for firms offering durable goods, suggesting consumers respond to news about firms’ financial prospects. Consumer attention patterns increase revenues and advertising effectiveness, ultimately suggesting that financial reporting serves a marketing function. Data Availability: All other data are available from the public sources cited in the text. JEL Classifications: G10; G11; G12; G14; G40; G41.

The effect of ASC 606 adoption on value relevance of revenues: Early evidence

This study examines the effect of adopting the principle-based revenue recognition standard, i.e., ASC 606, on the value relevance of reported revenues. We hand-collect data on the cumulative effects of ASC 606 adoption on retained earnings to identify materially impacted firms. We find that, compared to firms not materially impacted by ASC 606 adoption, the value relevance of reported revenues has improved for firms materially impacted based on long-window tests, while the informativeness of reported revenues has not improved for firms materially impacted based on short-window tests around quarterly earnings announcements. We also find that analysts' revenue forecast revisions have increased incrementally for firms materially impacted by ASC 606 adoption. Overall, our results suggest that ASC 606 adoption has generally increased the value relevance and informativeness of reported revenues.