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The purpose of this paper is to examine whether analysts’ cash flow forecasts improve the profitability of their stock recommendations and whether the positive effect of cash flow forecasts on analysts’ stock recommendation performance varies with firms’ earnings quality.

To test the authors’ predictions, they identify a sample of 161,673 stock recommendations with contemporaneous earnings forecasts and/or cash flow forecasts and regress market-adjusted stock returns on a binary variable that proxies for the issuance of cash flow forecasts while controlling for contemporaneous earnings forecast accuracy, earnings quality, analysts’ forecast experience and capability and certain firm characteristics. The authors’ test results are robust to alternative measures of recommendation profitability, earnings quality and the use of recommendation revisions instead of recommendation levels.

The authors find that when analysts issue cash flow forecasts concurrently with earnings forecasts, their stock recommendations lead to higher profitability than when they only issue earnings forecasts, after controlling for analysts’ forecast capability. Moreover, the authors document that the contemporaneous positive relationship between cash flow forecasts and recommendations profitability is stronger for firms with low earnings quality than for firms with high earnings quality. The findings suggest that cash flow forecasts issued by analysts in response to market demand likely play a more important role in firm valuation than cash flow forecasts issued by analysts mainly because of supply-side considerations.

Future research could build on these findings to conduct further investigation on the alternative incentives for analysts’ forecasts of sales growth and long-term growth rates.

These findings may also help investors to better assess the quality of analysts’ research outputs and to identify superior stock recommendations.

This study provides insight into the role of cash flow forecasts in firm valuation and adds fresh evidence to the debate on the usefulness of cash flow forecasts. It extends the stream of research on the characteristics of analyst forecasts and increases our knowledge about the role of analysts in the financial market.

More studies have investigated the relation between option measures and stock returns during scheduled corporate events. This study adds to the literature and investigates the informational role of options concerning stock returns following unscheduled corporate news events. The authors focus on individual analysts' recommendation changes rather than consensus revisions, as the recommendation consensus might discard a large amount of potentially valuable information in the aggregation process.

Based on the econometric model, the authors follow Bakshi et al. (2003) to construct the model-free option implied measures. The authors further decompose the implied option variance into upside and downside components. In such a way, the different informational roles of call and put options can be distinguished. A variety of regression analyses are conducted to examine the predictive power of option implied measures, and the ordered probit model is used to test the tipping hypothesis of analyst recommendations.

This study’s results show that the option market impounds the “valuable” firm-specific news; thus, the pre-event option market is strongly related to stock returns around recommendations even though recommendation changes are largely “unscheduled”. At the same time, these results suggest that upside (good) and downside (bad) implied volatilities contain distinctive information on subsequent stock returns.

This study provides new evidence that an increase in upside (downside) volatility around analyst recommendation changes would increase the probability that analysts upgrade (downgrade) the stock. The findings provide implications for investors and risk managers in making investment decisions.

The purpose of this paper is to examine the influence of financial restatements on the sell-side analysts' stock recommendations.

The sample of this study is based on a dataset from a panel of 246 Malaysian public listed companies for the period 2008 to 2013 (651 company-year observations). This study employs feasible generalized least squares regression.

This study finds a negative and significant relationship between restated companies and sell-side analysts' stock recommendations, which means that sell-side analysts issue less favorable stock recommendations for restated companies.

The findings based on observations from an emerging economy complement the results of the US studies that analysts revise their earnings forecasts or recommendations downwards or drop coverage following financial restatements. The results of this study should be useful to capital market participants in understanding how analysts perceive and evaluate restated companies.

This paper expands the literature on financial restatements consequences in an emerging market which is largely unstudied. Prior research on analyst behavior towards restatements has focused on the consequences of restatements in terms of analyst following and forecast accuracy and dispersion. This study examines if and how the restatements affect the analysts' final output as reflected in the recommendation opinion, an area that has so far received little attention.

The recommendation of the analyst report is not only limited to a small number of ratings, but also biased toward a buy opinion with the absence of sell opinion. As an alternative to this, this paper aims to extract analysts' textual opinions embedded in the report body through text analysis and examine the profitability of investment strategies. Analyst opinion about a firm is measured by calculating the frequency of positive and negative words in the report text through the Korean sentiment lexicon for finance (KOSELF). To verify the usefulness of textual opinions, the author constructs a calendar-time based portfolios by the analysts' textual opinion variable of each stock. When opinion level is used, investment strategy has no significant hedged portfolio return. However, hedged portfolio constructed by opinion change shows significant return of 0.117% per day (2.57% per month). In addition, the hedged return increases to 0.163% per day (3.59% per month) when the opening price is used instead of closing price. This study show that the analysts’ opinion extracted from text analysis contains more detailed spectrum than recommendation and investment strategies using them give significant returns.

The purpose of this paper is to investigate what and how intellectual capital information (ICI) conveyed through analyst reports varies by the type of stock recommendation. It draws on the theory of impression management.

Content analysis is used to investigate ICI in the full text of sell‐side analysts’ initiating coverage reports. It categorises ICI by type and three qualitative characteristics: evidence; time orientation; and news‐tenor. It explores how the extent, types and qualitative characteristics of ICI found in analyst reports vary by the type of stock recommendation accompanying the analyst report.

Given the conflicting interests facing analysts and relative amenability of ICI, it was found that analysts use ICI to manage perceptions. In particular, analysts attempt to use ICI in their reports to subdue the pessimism associated with an unfavourable recommendation, increase credibility of favourable recommendations and distinguish sell from hold recommendations.

The paper contributes to the literature on impression management by extending its application to the study of sell‐side analysts’ decision processes and it alerts future researchers to the wider role played by ICI beyond its use in generation of forecasts and valuations. The paper's findings have implications for consumers of analyst reports, as the level of negativity/positivity of forecasts and recommendations may be altered as a result of the semantics associated with ICI.

This paper explores analysts’ use of ICI conditional on the type of stock recommendation accompanying the report. Findings are explained using the theory of impression management.

This paper provides evidence that will help stock market participants interpret sell‐side analyst buy/sell recommendations. We examine whether recommendation levels (e.g. buy) correspond with traditional predictors of the underlying stock's performance, and whether recommendation revisions (e.g. an upgrade) are consistent with news analysts receive. Consistent with theory, we find that more optimistic recommendations are associated with higher mean forecast errors, forecast revisions, and forecasted earnings‐to‐price ratios. However, contrary to expectations, they also have higher market‐to‐book ratios, higher market values, and lower ratios of value to price (Lee et al. 1999). These results are probably driven by specific differences between buys and the less optimistic recommendations, as holds and sells are rarely distinguishable from each other. Our recommendation revision findings are consistent with our expectations. Upgrades have significantly larger earnings forecast errors, earnings forecast revisions, and unexpected earnings growth than do reiterations or downgrades.

This study aims to examine whether the publication of analyst recommendations has reaction in the Russian stock market. This study also aims to determine the other factors that influence the reaction.

Event study analysis (ESA) and regression models are used in this study.

The study finds that Russian stock market significantly reacts to analyst recommendations publications. Then study deeply investigates about the influence of other factors on the Russian market when an analyst's recommendations are published such as changes in recommendation levels, companies' size and general economic situation. The analysis done in the context of three types of recommendations: “buy,” “hold” and “sell.” The study finds that the market reacts not only to separate forecasts and subsequent recommendations, but also to the changes in recommendations' levels as well. Interestingly, the study finds that the impact of crises is not found to be a significant factor in the context of the Russian market.

Analysts used to spend much more resources on conducting a fundamental analysis than ordinary investors do. Therefore, they usually possess valuable privileged information that is supposed to influence stock prices when published. However, the present study argues that the direction, extent and period of a reaction of an analyst's recommendations are highly complicated and depend on what factors are under consideration in a particular research. Very often, the authors who dedicate their papers to develop and study markets choose a couple of (or even one) factors and delve into them. Nevertheless, to the author's best knowledge, few frequently cited and well-conducted research focused on such an emerging market as the Russian one. Thus, it seems reasonable that there is a gap in the literature that needs to be filled while considering other important factors. The study findings have a significant investment policy content.

In several senses, the present study is unique. First, it investigates whether analyst recommendations sufficiently affect the Russian stock market; second, it determines whether the significant factors such as changes in recommendation levels, companies' size and general economic situation have influence on the reaction. Finally, the study discusses about whether there is an impact of crises in the present study findings.

This study aims to evaluate the short-term impact of brokerage analysts’ recommendations on abnormal returns using a sample selected from the S&P BSE 100 in the Indian context. The efficient market hypothesis, specifically, its semi-strong form, is tested for “Buy” stock recommendations published in the electronic version of Business Standard. The crucial issue is, are there any abnormal returns that can be earned following a recommendation? If so, how quickly do prices incorporate the information value of these recommendations? It tests the impact of analyst recommendations on average abnormal returns (AARs) and standardized abnormal returns (SRs) to determine their statistical significance.

Using a sample of stock recommendations published in the e-version of Business Standard, the event study methodology is used to determine whether AARs and SRs are significantly different from zero for the duration of the event window by using several significance tests.

The findings indicate a marginal opportunity for profit in the short term, restricted to the event day. However, the effect does not persist, i.e. the market is efficient in its semi-strong form implying that investors cannot consistently earn abnormal returns by following analysts’ recommendations. Post the event date, the market reaction to analyst recommendations becomes positive, however, insignificant until the ninth day after the recommendation providing support to the underreaction hypothesis given by Shliefer (2000) and post-recommendation price drift documented by Womack (1996). The study contributes by using different statistical tests to determine the significance of returns.

There are important implications for traders, investors and portfolio managers. The speed with which market prices incorporate publicly available information is useful in formulating trading strategies. However, stock characteristics such as market capitalization, volatility and level of analyst coverage need to be incorporated while making investment decisions.

The study contributes by using different statistical tests to determine the significance of returns.

The purpose of this paper is to document performance of analysts’ recommendations in the Middle East and North Africa (MENA) region during the period between 1999 and 2010.

This paper uses post-announcement market-adjusted returns as a measure of performance and computes returns for different holding periods. Significant positive (negative) returns following buy (sell) recommendation will indicate value relevance of these recommendations.

The authors show that analysts’ buy recommendations have significant information in them, while their sell recommendations contain no significant information. Significant positive returns are reported following analysts’ buy recommendations and insignificant returns following their sell recommendations. Furthermore, it is also shown that these results hold true only in markets where institutions are relatively strong (common law countries and countries with stronger property rights) and for firms which have lower agency conflicts (firms that pay dividends and have concentrated ownership). For markets where institutions are relatively weak and for firms which have greater agency conflicts, these results show no value in analysts’ recommendations.

These results imply that investors should not blindly follow analyst recommendations while making their investment decisions in the MENA region.

This paper makes detailed analysis of analyst recommendations in previously unexplored MENA region. Some conditions under which analyst recommendation have no value and some conditions under which they have partial value have also been identified.

The authors examine how financial analysts respond to online investor sentiment when updating recommendations for specific stocks in South Africa. The aim is to establish whether online sentiment contains significant information that can influence analyst recommendations. The authors follow up the above by examining when online investor sentiment is most associated with analyst recommendation changes.

For online investor sentiment proxies, the authors make use of the social media sentiment and news media sentiment scores provided by Bloomberg Inc. The sample size includes all companies listed on the Johannesburg Stock Exchange All Share Index. The study uses traditional ordinary least squares to examine the relation at the mean and quantile regression to identify the scope of the relationship across the distribution of the dependent variable.

The authors find evidence that pre-event news sentiment significantly influences analyst recommendation changes while no significant relationship is found with the Twitter sentiment. Further analysis shows that news sentiment is more influential when the recommendation changes are moderate (in the middle of the conditional distribution of the recommendation changes).

The study is the one of the first to examine the association between online sentiment and analyst recommendation changes in an emerging market using high frequency data. The authors also make a direct comparison between social media sentiment and news media sentiment, some of the most used contemporary investor sentiment proxies.