Earnings Forecasts Research Articles

10‐K complexity, analysts' forecasts, and price discovery in capital markets

AbstractThis paper examines the behaviour of stock prices and analysts' earnings forecasts following firms' increasingly complex 10‐K filings. Our evidence suggests that greater information uncertainty outweighs greater analyst ability, and the net effect drives greater underreaction in analysts' earnings forecasts following the release of more complex 10‐Ks. Further analysis suggests that analyst underreaction mitigates market overreaction to the information in low‐complexity 10‐K filings, and overwhelms market overreaction to the information in high‐complexity 10‐K filings thus compromising price discovery of the earnings implications of more complex 10‐Ks.

“Superstitious” Investors

Abstract We reconsider the excess volatility puzzle through the lens of a model in which agents believe they can predict dividend growth when in fact they cannot. Besides excess volatility in the time series, the model explains the value premium, and the explanatory power of the value factor. In support of the model, we show that analysts’ earnings forecasts align with market valuation and that analysts are far more optimistic about growth stocks than they are about value stocks. Using both survey and price data, we show that the same mechanism can explain the excess returns earned by investing in high-interest rate currencies. JEL G12, G15, G41

Impact of equity pledges on analysts earnings forecasts

This study conducts an in-depth analysis of the relationship between the equity pledge phenomenon and analysts’ earnings forecast behavior using China's A-share listed companies from 2017 to 2022 as the research sample. Findings indicate that the existence of equity pledges increases the error range, optimism, and disagreement among analysts when making earnings forecasts. However, for non-state-owned enterprises, companies with a relatively high proportion of institutional shareholders, and those with better transparency, the impact of equity pledges on analysts’ forecasting behavior shows a weakening trend.

A New Measure for Differences of Opinions: Institutional Trade Dispersion

Using 13F quarterly holdings data of institutional investors, we introduce a novel measure for differences of opinions among investors by analyzing divergence in institutional investors’ trades. We find that increases in institutional trade dispersion predict a significant decline in future abnormal returns. Moreover, we find that this relationship persists after controlling for other proxies of divergence of investor opinions, such as the dispersion in analysts’ earnings forecasts, the change in the number of institutions holding the stock, and the dispersion in institutional investors’ holdings relative to their benchmarks. Consistent with Miller (1977), underperformance of high-dispersion stocks is found to be the strongest among stocks that experienced recent significant price increases, the stocks with the highest mispricing scores, and the stocks that face increases in short-interest positions after the onset of dispersion.

Corrigendum to “Environmental legal institutions and management earnings forecasts: Evidence from the establishment of environmental courts in China” [International Review of Economics and Finance 93 (2024) 545–573 IREF-D-23-00823]

Corrigendum to “Environmental legal institutions and management earnings forecasts: Evidence from the establishment of environmental courts in China” [International Review of Economics and Finance 93 (2024) 545–573 IREF-D-23-00823]

Does climate risk influence analyst forecast accuracy?

We examine how climate risk influences analyst forecast accuracy proxied by forecast error and dispersion. Using country-level climate risk estimated with time trends in droughts, we find that analyst forecasts are less accurate for firms in drought-prone countries. This effect of climate risk is stronger when climate risks are denoted in earnings forecasts, and when firms’ home countries have greater reliance on hydroelectric sources in electricity generation, more important agricultural and food industries, and active stances concerning climate change. Overall, our findings suggest noteworthy implications of climate risk on the financial markets via analyst forecast accuracy.

Government affiliation, analyst behavior and the economic consequences

AbstractA significant number of regional brokerage firms in China are owned by their respective local governments. We use this context to examine how local government affiliation affects brokerage firms’ research activities. Using a sample of analyst reports from 2013 to 2021, we find that analysts working at local government‐affiliated brokerage firms are more likely to cover firms within the local government's jurisdiction than non‐affiliated analysts. Local government‐affiliated analysts enjoy an information advantage, as evidenced by the issuance of more accurate earnings forecasts for local firms, compared to those of non‐affiliated analysts. The effect is stronger for local firms with lower information quality. Additionally, local government‐affiliated analysts show more optimism in stock recommendations, but not in earnings forecasts, for local firms, compared to non‐affiliated analysts. The effect is more pronounced for local firms that receive more local government subsidies, operate in regions with greater local government intervention or during the COVID‐19 pandemic and the National Congress conferences, and require more capital market support. The results of this study advance our understanding of the key determinants of analyst performance and provide new evidence in the debate on the role of government intervention in financial institutions.

Information or Noise? Examining the Effect of Discretionary Disclosure of Accounting Estimates on Financial Analyst Forecasts

Whether discretionary disclosure of accounting estimates increases or decreases the decision usefulness of financial information is a fundamental issue in accounting and finance. Our paper speaks to this issue by examining the effect of discretionary disclosure of accounting estimates on the forecasting behaviour of financial analysts, who are sophisticated users of financial information and play a crucial role as an information intermediary in the capital markets. We find that discretionary disclosure of accounting estimates (made in the 10‐K management discussion and analysis and financial statement footnotes) reduces analyst forecast errors and analysts’ tendency to round their annual earnings forecasts. These effects are more pronounced when analysts issue forecasts for companies with high accounting reporting complexity. Overall, the results suggest that discretionary disclosure of accounting estimates reduces information costs by providing relevant information for analysts’ forecasting decisions.

Does industry co-location improve corporate information environment?

This study examines how the co-location of firms within the same industry enhances their information environments. We hypothesize that benefits arise from two effects: the industry peer effect, involving intra-industry information spillovers, and the geographic peer effect, related to shared local economic conditions with nearby non-industry peers. Our analysis evaluates how these effects, both separately and jointly, impact analysts’ forecast properties and stock price informativeness regarding future earnings. We find that the industry peer effect is negatively related to forecast dispersion and positively related to forecast accuracy, while the geographic peer effect shows no significant correlation. In addition, stock price informativeness about future earnings is positively associated with the industry peer effect but not with the geographic peer effect. We also find that greater proximity to industry or local peers enables analysts to incorporate more private (relative to public) information into their earnings forecasts, thereby enhancing the firm’s information environment. JEL CLASSIFICATION: M41, D80, G10

Short-term momentum and reversals, turnover, and a stock’s price-to-52-week-high ratio

We show that short-term reversal behavior declines with a stock’s turnover and the prior month’s price-to-52-week-high ratio (PTH), shifting to momentum for stocks with both a relatively high turnover and PTH. This behavior of consecutive one-month individual stock returns is robust to subperiod analysis, risk adjustments, and alternative methodologies. Our findings suggest opposing channels. First, promoting short-term momentum, our evidence implies a PTH-anchoring underreaction to recent news, consistent with the short-term contrarian price-dampening channel of Atmaz et al. (2024) with higher turnover implying a stronger contrarian-induced underreaction. Second, promoting short-term reversals, our evidence reinforces the importance of the well-known liquidity-provision-compensation channel. Reversals are especially strong for low-PTH, low-turnover stocks, where the lower PTH implies a generally smaller-cap, less-liquid stock and the lower turnover implies a weaker contrarian-induced underreaction. We also find that the return behaviors vary with dispersion in analysts’ earnings forecasts and with market-wide sentiment, in a manner consistent with these channels.

The Use and Usefulness of Big Data in Finance: Evidence from Financial Analysts

Alternative data have become a new source of information for investors. This paper examines how this development is reshaping the role of sell-side analysts. Employing textual analysis of analysts’ written reports, we show that analysts themselves have begun to adopt alternative data in their analyses. Furthermore, we find that when analysts report drawing from alternative data they generate more accurate earnings forecasts, and their brokerages subsequently receive higher amounts in trading commissions from investors, suggesting that investors value the adoption of alternative data by analysts. This paper was accepted by Kay Gieseke, finance. Funding: This research is supported by NTU-Singapore under its startup grant program. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02659 .

Top management team incentive dispersion and management earnings forecasts

Top management team incentive dispersion and management earnings forecasts

Non-financial information farsightedness and capital market information efficiency

Non-financial information disclosed by firms is an important channel for investors and analysts to understand the operating conditions of firms and a major source of information other than financial data. This study examines the impact of disclosure of forward-looking non-financial information content by listed companies on the information efficiency of the capital market. Using data from A-share listed companies in China’s Shanghai and Shenzhen markets from 2007 to 2022, it is found that forward-looking non-financial information content has a positive impact on improving capital market information efficiency, while analysts’ attention plays a moderating role. The mechanism study finds that non-financial information disclosure with forward-looking content affects the accuracy of analysts’ earnings forecasts, the type of audit opinion, and the degree of financialisation of firms, which in turn have different impacts on the information efficiency of the capital market. The heterogeneity test finds that disclosure of non-financial information with forward-looking content will more significantly improve the information efficiency of the capital market if the listed company receives a high level of media attention, as well as listed companies with higher quality of internal control. This study explores the forward-looking characteristics of non-financial information in depth, enriches the study of information efficiency in the capital market, and provides theoretical suggestions for the content and direction of corporate non-financial information disclosure.

Passive Investment and Share Repurchases

SYNOPSIS We investigate the association between passive investment and share repurchases, both of which are at historically high levels and the subject of increased scrutiny. We first document that firms with more passive ownership exhibit higher levels of repurchase activity. Next, and more importantly, we find that firms with passive ownership conduct more “suspect” repurchases that result in reported earnings that meet or beat analysts’ earnings forecasts, and this effect is more pronounced for firms with performance-based equity compensation incentive plans. We also find that repurchase activity more negatively affects firms’ future employment, capital expenditures, and performance when the firm has more passive owners. We conclude that passive investment is associated with an increase in the quantity and a reduction in the quality of firms’ repurchase activities. Our study provides evidence that passive institutional investment may allow opportunistic management behavior rather than serving the monitoring role traditionally associated with institutional ownership. Data availability: Data are available from the public sources cited in the text. JEL Classifications: M41; G32; G35.

Digital Transformation and Management Earnings Forecast

This paper conducts an empirical examination using listed companies on China's Shanghai and Shenzhen A-share markets from 2010 to 2020. The findings indicate that digital transformation has a significant positive effect on the quality of management's earnings forecast. Mechanism analysis reveals that this effect is more pronounced in companies with lower internal control indices, higher levels of corporate governance, and higher agency costs. Digital transformation enhances the quality of corporate information disclosure and mitigates information asymmetry, further improving management's earnings forecast level. Through heterogeneity analysis, further research finds that companies lacking a technical background among senior executives, those in less competitive industries, those with poor growth, and non-state-owned enterprises, experience a more significant positive impact on the quality of management earnings forecasts from digital transformation. The conclusions provide new evidence for continuing to advance digital transformation and elucidate the benign cycle mechanism between the capital market and the national economy. This study also proposes new perspectives for the regulation of predictive information disclosure by enterprises.

Examining the impact of market uncertainty on earnings forecasts: an empirical study from Egypt

PurposeThis paper aims to conduct a comparative analysis of the impact of market uncertainty on the degree of accuracy and bias of analysts' earnings forecasts versus four model-based earnings forecasts.Design/methodology/approachThe study employs panel regression analysis on a sample of Egyptian listed companies from 2005 to 2022 to examine the impact of market uncertainty on the accuracy and bias of each type of earnings forecast.FindingsThe empirical analysis reveals that market uncertainty significantly affects analysts’ earnings forecast accuracy and bias, while model-based earnings forecasts are less affected. Furthermore, the Earnings Persistence and Residual Income model-based earnings were found to be superior in terms of exhibiting the least susceptibility to the impact of market uncertainty on their forecast accuracy and biasness levels, respectively.Practical implicationsThe findings have important implications for stakeholders within the financial realm, including investors, financial analysts, corporate executives and portfolio managers. They emphasize the importance of considering market uncertainty when formulating earnings forecasts, while concurrently highlighting the potential benefits of using alternative forecasting methods.Originality/valueTo our knowledge, the influence of market uncertainty on analysts' earnings forecast accuracy and bias in the MENA region, particularly in the Egyptian market, remains unexplored in existing research. Additionally, this paper contributes to the existing literature by pinpointing the forecasting method, specifically distinguishing between analysts-based and model-based approaches, whose predictive quality is less adversely impacted by market uncertainty in an emerging market.

Common institutional ownership and analyst earnings forecasts

This research investigates the impact of shared institutional ownership on the accuracy of analysts' predictions. Drawing from theoretical frameworks, we have formulated and empirically tested our research hypotheses. The results indicate a negative correlation between shared institutional ownership and the precision of analysts' forecasts. Additionally, peer pressure has been found to mediate this relationship. Furthermore, ownership type has been identified as an intermediary factor, with state-owned enterprises exhibiting a more significant negative effect.

Whether Managers Bias Fair Value Estimates for Investment Property to Meet/Beat Analysts’ Earnings Forecasts?

Whether Managers Bias Fair Value Estimates for Investment Property to Meet/Beat Analysts’ Earnings Forecasts?

The underwriter's conflict of interest and earnings forecast bias in prospectus: Evidence from Hong Kong

About 40% of the firms that go public on the Hong Kong Stock Exchange voluntarily include earnings forecasts in their prospectuses. These forecasts are on average biased downwards by 8% compared to the realized earnings. We hypothesize that the underwriter, in order to increase IPO underpricing and obtain private benefits, pressure managers to low-ball earnings forecasts in the prospectuses. We examine the effect of underwriting commission rate, reputation, and IPO trading commissions on the earnings forecast bias in the prospectuses. We find that underwriting commission rate is negatively associated with the forecast bias. Using the 90-day post-IPO trading volume as a proxy for future trading commissions and profits generated from institutional investors and aftermarket trading activities in general, we document that trading commissions are positively associated with the forecast bias. Further analysis shows that underpricing is positively associated with the forecast bias, but the effect of the forecast bias on underpricing disappears after controlling for the underwriter's trading commission. Overall, these results are consistent with predictions based on the agency theories of underwriter by Baron (1982) and Loughran and Ritter (2002).

Big Data Analytics and Management Forecasting Behavior

SYNOPSIS This paper investigates whether the use of Big Data analytics by firms has a spillover effect on management forecasting behavior. Insights provided by Big Data could potentially improve firms’ ability to forecast earnings (supply channel) and investor demand for earnings information is likely higher for firms engaging in data analytics (demand channel). Using a text-based measure of firms’ commitments to and usage of Big Data analytics, we find that Big Data analytics usage is positively associated with the propensity to issue management earnings forecasts. Consistent with the “supply channel” explanation, we find that Big Data analytics usage is positively associated with management forecast accuracy as well. Also, supporting the “demand channel” explanation, we find that Big Data analytics usage is associated with greater analyst following. Our findings of improved disclosure following commitments to Big Data analytics highlight a potentially unintended benefit of the Big Data revolution.