Stock Recommendations Research Articles

Seemingly Inconsistent Analyst Revisions

In about 20%-30% of cases where an analyst revises two outputs (namely, earnings estimates, target prices, or stock recommendations) simultaneously, the two estimates are revised in opposite directions. Existing literature notes that these inconsistent outputs are widespread, and concludes that they are lower-quality, driven by strategic bias, and are viewed as less valid by investors. We find that these characterizations are generally inaccurate. Apparent inconsistency is largely driven by accounting and economic factors, with only limited evidence that investment banking-related conflicts play a role. Moreover, inconsistent outputs are neither less accurate than consistent outputs nor do they resolve less investor uncertainty upon their release. Overall, our results suggest that researchers should be cautious in interpreting the correlation between analyst outputs as a measure of bias or quality, and in using a single analyst output as a proxy for an analyst’s overall views.

Do Financial Analysts Fully Incorporate the Future Earnings Implications of Really Dirty Surplus into Their Earnings Forecasts?

This paper investigates whether sell-side equity analysts fully incorporate the future earnings implications of really dirty surplus (RDS) into their earnings forecasts. RDS refers to gains or losses from contingent equity transactions settled at prices other than the fair value. We find that analysts’ earnings forecasts are over-optimistic for firms with large RDS losses, RDS over-optimism is lower for firms with higher analyst following, and the over-optimism carries over to stock recommendations. Our findings suggest that the lack of fair value information in accounting records about the off-market settlement drives the RDS-related analyst over-optimism.

Product Market Competition And Analysts’ Forecasting Properties: Evidence From Korea

In this paper, we examine the association between product market competition and financial analysts’ forecasting properties in a sample of 76,621 analyst-firm-year observations in South Korea between 2000 and 2014. Using the Herfindahl–Hirschman Index to proxy for product market competition, we document that financial analysts are likely to issue less accurate and more optimistic forecasts in highly competitive product markets. In addition, we find that analysts’ reports show more optimism in stock recommendations in firms with high market competition.

The reputational effects of analysts' stock recommendations and credit ratings: Evidence from operational risk announcements in the financial industry

This paper investigates whether more favorable stock recommendations and higher credit ratings serve as a reputational asset or reputational liability around reputation-damaging events. Analyzing the reputational effects of operational risk announcements incurred by financial institutions, we find that firms with a “Buy” stock recommendation or “Speculative Grade” credit rating are more likely to incur an equity-based reputational damage. In addition, firms with lower credit ratings incur a much more severe debt-based reputational damage. Moreover, credit ratings are more instrumental in mitigating the debt-based reputational damage caused by fraud incidents or incurred in non-banking activities. Furthermore, the misconduct of senior management could demolish the reputation of firms with less heterogeneous stock recommendations. Finally, credit ratings serve as an equity-based reputational asset in the short term but turn into an equity-based reputational liability in the long term. Overall, our analysis reveals that stock recommendations represent a reputational burden and credit ratings act as a reputational shield; however, the persistence and magnitude of such reputational effects are moderated by time and event characteristics.

Do Firm Size Influence Financial Analyst Research Reports and Subsequent Stock Performance

This study investigates whether analysts issue more favorable research reports for small stocks than for large stocks. Small stocks tend not to attract investors due to their size, bad liquidity, easily manipulated price, insufficient information, and high-uncertainty risk. If analysts follow a small stock, it might be because the firm is thought to have good prospects. This study finds that analysts report more positively on small stocks, including in their stock recommendations and earnings growth forecasts. The empirical results show that small stocks perform better in the following year than do other stocks but that this is not the case for operating performance. This finding suggests that analysts are more likely to recommend under-valued stocks, but this may not imply that the operating performance of these stocks will improve the following year.

Market Manipulation and Suspicious Stock Recommendations on Social Media

Social media can help investors gather and share information about stock markets. However, it also presents opportunities for fraudsters to spread false or misleading statements in the marketplace. Analyzing millions of messages sent on the social media platform Twitter about small capitalization firms, we find that an abnormally high message activity on social media is associated with a large price increase on the event day and followed by a sharp price reversal over the next week. Our findings are consistent with the patterns of a pump-and-dump scheme, where fraudsters use social media to temporarily inflate the price of small capitalization stocks. To differentiate between the effects of overoptimism by noise traders and the illegal gains of a pump-and-dump scheme, we investigate social interactions between Twitter users through the use of network theory. We identify several clusters of users with suspicious online activity (stock promoters, fake accounts, automatic postings), favoring the manipulation/promotion hypothesis over the behavioral hypothesis.

How Do Social Media Affect Analyst Stock Recommendations? Evidence from S&P 500 Electric Power Companies' Twitter Accounts

Research summary: The importance of firm‐stakeholder relationships is gaining increasing attention. Although a theory of the drivers and consequences of stakeholder pressure has been developing, it focuses on pressures from organized stakeholders such as shareholders, NGOs, and activists, and does not incorporate the emerging possibility that individual voices may matter. By exploring corporate Twitter, which facilitates movement of individual stakeholders such as customers to a higher stakeholder class by providing them with a greater sense of power and urgency, we study the circumstances under which customer voices significantly affect analyst stock recommendations. We find that favorable reactions to firm‐initiated messages matter, directly or indirectly, depending on the messages' growth implications. Customer‐initiated negative messages have a significant impact only with high volume and formal institutions that support customer opinions.Managerial summary: Social media is increasingly used by firms for disclosing information and engaging stakeholders. Yet, we know little about whether and how social media usage matters. We show how corporate Twitter usage may influence analyst stock recommendations. Our interviews of securities analysts suggest that social media is not institutionalized yet, but increasingly used as a source of channel checks, especially for vibes, validations, and so on. Our analyses of corporate Twitter accounts show that both firm‐initiated and customer‐initiated tweets can have significant impact on analyst recommendations under certain conditions. For firm‐initiated tweets, the extent of retweets is an important factor, along with the content of tweets, in particular, growth implications. For customer‐initiated tweets, negative tweets matter, but only with high volume and regulatory structure that supports customer protection. Copyright © 2017 John Wiley & Sons, Ltd.

Effects of ambiguity in market reaction to changes in stock recommendations

This study uses analyst recommendations and three ambiguity proxies, namely ambiguity in fundamentals, ambiguity in information and market ambiguity, to examine market reaction to recommendation changes in the Taiwanese stock market. The authors find that analysts’ recommendation changes have positive effects on subsequent buy-and-hold abnormal returns when market ambiguity is moderate. When ambiguity in fundamentals is low, recommendation changes have a positive influence on smaller firms. The effect of ambiguity in information on stock returns is associated with market ambiguity; market ambiguity is negatively associated with abnormal returns for firms with moderate ambiguity in fundamentals. Investors in a small firm rely more on analyst recommendations.

AN ANALYSIS OF BROKERAGE RECOMMENDATIONS FOR THE BIGGEST COMPANIES OF WIG20

The aim of the article is an analysis of stock recommendations. The article presents the measures that can be used for estimating the effectiveness of recommendations. The biggest companies of WIG20, the basic index of the Warsaw Stock Exchange, were analysed. Almost 600 recommendations issued by 40 brokerage offices and houses in the years 2010–2015 were analysed. Apart from the analysis of the rates of return realized on the basis of stock recommendations, attention was drawn to the issue of the investment risk.

The dispersion anomaly and analyst recommendations

The main objective of this study is to distinguish whether the forecast dispersion anomaly is due to Miller’s (J Finance 32(4):1151–1168, 1977) overpricing hypothesis or idiosyncratic risk, by conditioning the sample on “buy” and “sell” consensus recommendations. Observations on the long and short possibilities provided to the investors by the analyst stock recommendations can help us infer on the impact of short sale constraints even though they are not directly observed. This study provides strong evidence that the impact of analyst forecast dispersion is more pronounced in the group of stocks that receive the least favorable recommendations in a given period, even after controlling for the idiosyncratic risk, Fama–French factors (J Financ Econ 33(1):3–56, 1993; J Financ Econ 116(1):1–22, 2015) and even short-sale constraints. These results are consistent with Miller’s (1977) hypothesis, according to which if short-sale constraints bind, high opinion divergence stocks become overpriced and hence have low subsequent returns.

The Cost of Speaking in Two Tongues

Malmendier and Shanthikumar (2014) find that some analysts issue relatively higher stock recommendations and relatively lower earnings forecast of the same firm on the same day. They describe this behavior as speaking in two different tongues. In this paper, we explore the cost of this strategy on financial analysts. We show that when analysts employ the two-tongue strategy, they are sacrificing their forecasting accuracy on the target firms. Taking the two-tongue strategy in the previous year helps the analyst’s career in terms of staying in the same firm or avoiding being demoted to a smaller brokerage firm. However, this strategy reduces the analyst’s probability to move to a top ten brokerage house or to be nominated as an All-Star analyst. Finally, investors respond less positively to the higher stock recommendations when analysts issue high recommendation and low earnings forecast at the same time.

Do Earnings Estimates Add Value to Sell-Side Analysts’ Investment Recommendations?

Sell-side analysts change their stock recommendations when their valuations differ from the market’s. These valuation differences can arise from either differences in earnings estimates or the nonearnings components of valuation methodologies. We find that recommendation changes motivated by earnings estimate revisions have a greater initial price reaction than the same recommendation changes without earnings estimate revisions: about +1.3% (−2.8%) greater for upgrades (downgrades). Nevertheless, the postrecommendation drift is also greater, suggesting that investors underreact to earnings-based recommendation changes. Implemented as a trading strategy, earnings-based recommendation changes earn risk-adjusted returns of 3% per month, considerably more than non-earnings-based recommendation changes. Evidence from variation in firms’ information environment and analysts’ regulatory environment suggests that recommendation changes with earnings estimate revisions are less affected by analysts’ cognitive and incentive biases. This paper was accepted by Wei Jiang, finance.

Does the interaction between the accounting method choice and disclosure affect financial analysts’ information environment? The case of joint ventures under IAS 31

ABSTRACTIAS 31 allowed firms to choose between proportionate consolidation and the equity method to record joint ventures in the consolidated accounts of the venturer. Moreover, this election implied a decision about including information in the primary financial statements or in the notes. This paper investigates if financial analysts perceive accounting information differently depending on the method chosen conditioned to the disclosure of the required information in the notes. We analysed a sample of Spanish firms during 2005–2010. We not only considered earnings forecasts, but also examined target prices and stock recommendations. Furthemore, we look at how this accounting choice affects analysts’ information environment. Our results suggest that the choice of accounting regime does not affect the bias and accuracy of earnings forecasts, nor target prices nor stock recommendations, no matter if firms provide or not information in the notes. While the proportionate method implies lower dispersion in analysts’ forecasts than the equity method, our tests do not allow us to confirm that the information environment depends on the accounting method. These results support the decision adopted in IFRS 11 to impose a unique method for the accounting of joint ventures.

Do analysts who understand accounting conservatism exhibit better forecasting performance?

This study investigates the performance of analysts when they match the asymmetric timeliness of their earnings forecast revisions (i.e., asymmetric forecast timeliness) with the asymmetric timeliness of firms’ reported earnings (i.e., asymmetric earnings timeliness). We find that better timeliness-matching analysts produce more accurate earnings forecasts and elicit stronger market reactions to their forecast revisions. Further, better timeliness-matching analysts issue less biased earnings forecasts, more profitable stock recommendations and have more favorable career outcomes. Overall, our results indicate that analysts’ ability to incorporate conditional conservatism into their earnings forecasts is an important reflection of analyst expertise and professional success.

Stock Recommendations using Bio-Inspired Computations on Social Media

The tremendous growth of the social networks has paved way for social interactions of investing communities about a company’s stock performance. Investors are able to share their comments on stocks using social media platforms. These interactions are captured and mined to produce advice on investing which helps retail investors to do prospective investments to increase profits. In this paper, we propose a novel stock recommendation methodology using ant colony optimization (ACO). This method extracts sentiments from the investor’s stock reviews and performs the sentiment analysis, which is optimized by the ACO. This method helps to find the correlation between sentiments and stock values, to make future stock predictions and to give stock recommendations to the retail investor.

IFRS and Market Reactions to Analyst Stock Recommendations : Evidence from Korea

We examine changes in both short-term and long-term market reactions to financial analysts’ stock recommendations around the time of IFRS adoption. If...

Analyst’s ability, media selection and investor interests: evidence from China

PurposeThe purpose of this paper is to find out whether the election of star analysts leads to the conflict of interests between analysts\\institutional investors and individual investors. And then, further investigate how the election results to influence the individual investors’ decision making.Design/methodology/approachGiven the fact that earnings forecasts and stock ratings are the most important foundations for the investor’s investment decision, the authors investigate the relationship among the earnings forecasts, abnormal returns and the election of star analyst. This paper further analyzes the impact factors on investors’ decision. The data used in this paper for star analysts’ information, analysts’ forecast and recommendations, as well as stock performances-related data are from 2005 to 2012.FindingsThis paper finds that mass media cannot select analysts with high forecast accuracy, and then misleads investors. It demonstrates that the analysts with poorer forecast ability and more optimistic stock recommendations are more prone to be entitled as star analysts by mass media, and these titled star analysts tend to show a poorer performance. Therefore, the star analyst worsens investors’ cognition on analysts forecast ability and then misleads investors’ decision making.Social implicationsMedia plays a critical role in corporate governance, information collection and diffusion and reducing the information asymmetry, however, it is good to know the role of media in financial markets from a broader perspective. Because media may also bring negative factors to the financial markets such as misguiding the investors and intensify the conflict of interests between analyst and individual investors.Originality/valueThis paper supports a new perspective of the role of mass media in financial market, which is different from existing studies.

Does Monetary Incentive Lead to Better Stock Recommendations on Social Media?

Social media not only is a new channel to obtain financial market information but also becomes the venue for investors to share and exchange investment ideas. We examine the performance consequences of providing monetary incentive to both existing and new amateur analysts on social media and its implications for online investor communities. We find that monetary incentive is effective in increasing the amount of content output and generating more interest from the community as well, but it leads to neither better nor worse stock recommendations. Additional analysis suggests that monetary incentive results in wider stock and industry coverage, a sign of increased content diversity. This study contributes to the understanding of the role of monetary incentive in stimulating the sharing of value-relevant information by investors in social media communities.

The impact of equity incentive plans on analysts’ earnings forecasts and stock recommendations for Chinese listed firms: An empirical study

Using a sample of 932 listed firms and 2492 firm-year observations during the period of 2008–2014, this paper investigates the impact of equity incentive plans (EIPs) on the information environment of financial analysts, as reflected in the prominent analysts’ outputs – earnings forecasts and stock recommendations, in China’s unique corporate setting. It finds that analysts’ forecast accuracy is noticeably higher for listed firms with higher levels of management compensation in the form of EIPs. In addition, financial analysts are more likely to issue favourable stock recommendations for listed firms granting management stock options. Moreover, analysts’ forecast accuracy (dispersion/bias) appears to be considerably improved (reduced) when listed firms choose to use restricted stock units (RSUs), instead of stock options, in their EIPs, suggesting that the two types of equity incentives – stock options or RSUs – have different impacts on analysts’ earnings forecasts. Overall, the empirical results of this study are consistent with the alignment view of managerial incentives. This study also shows that not all equity incentives have the same alignment effect. Indeed, compared to RSUs, the alignment effect of stock options is likely to be offset by managers’ short-term opportunity behaviours.

Business Valuation – Practice of domestic WSE members in 2016

Goal – The aim of this paper is to check the popularity of various valuation methods in practice, as well as to present the statistics of parameters used in valuation processes and recommendations issued by domestic Warsaw Stock Exchange (WSE) members in the year 2016. Methodology – The study was prepared basing on 514 valuations of 118 companies gathered from 263 recommendations issued by 8 domestic WSE members. These input data were processed using statistical tools. Finally, they were aggregated and compared with textbooks and previous studies to present final conclusions. Results – Basing on the conducted analysis, the authors stated that broadly spread opinion about high popularity of DCF and relative model in business valuation is confirmed. More interestingly, it seems that practitioners often made valuations basing on the same or very similar assumptions, which is the most visible in the adopted period of detailed analysis. It should be noted that findings are based on historical data, which do not refer to the future and apply only to companies which are listed on the stock market. Originality – Authors’ analysis provides knowledge on how domestic WSE members apply the theory of business valuation in practice, which approaches are the most commonly used, and aims at explaining to answer why it is so. This knowledge can constitute a basis for reviewing and improving valuation tools used in stock recommendations. The authors encourage to conduct further discussion regarding the practice-theory gaps, and provide data to it.