Stock Recommendations Research Articles

Stock Recommendations from Stochastic Discounted Cash Flows

We present two stock recommendation systems based on stochastic characterization of firm present value that extends the conventional discounted cash flow analysis. The single-stock quantile recommendation system compares the market price of a company's stocks with the estimated distribution of the company's fair value to obtain an individual measure of mispricing, while the cross-sectional quantile system builds a relative measure of mispricing using the fair-value distribution of all firms. Both systems use mispricing information to build sell side and buy side portfolios. Statistical tests show that these portfolios consistently deliver significant excess returns even when rebalancing costs are accounted for. Moreover, we show how analysts' indications can be improved by using the first two moments of their recommendations following the same procedure of the cross-sectional quantile system.

The market reaction to analyst stock recommendation and earnings forecast consistency: International evidence

Using a large international sample from 81 counties, this paper examines the incremental effects of recommendation–forecast consistency on the market reactions to revisions in stock recommendations and earnings forecasts. We define analysts’ stock recommendations and earnings forecasts as consistent if both are above or below their respective consensus, and inconsistent otherwise. We hypothesize and find that recommendation-forecast consistency adds incremental positive effects to the market reaction to analysts’ stock recommendation revisions and their earnings forecast revisions for our international sample. Moreover, the incremental positive effects exist in both strong and weak investor protection countries, with the effects being greater in strong-investor protection countries than in weak-investor protection countries. Finally, International Financial Reporting Standards (IFRS) adoption significantly enhances these incremental positive effects.

Does Reciprocity Affect Analysts’ Incentives to Release Timely Information? Evidence from Syndication Relationships in Securities Underwriting

Reciprocity is a subtle but influential factor in maintaining business relationships. We examine the stock recommendations, earnings forecasts, and target prices issued by “reciprocity-pressured” analysts affiliated with securities underwriters who rely on other major underwriters’ invitations to be in syndicates. Consistent with the reciprocal pressure hypothesis, we find that reciprocity-pressured analysts delay releasing negative information on other major underwriters’ clients despite the fact that these analysts’ affiliated underwriters do not receive fees from those clients. We also document temporary retaliation by major underwriters when reciprocity-pressured analysts deviate from such expected reciprocity. The results suggest that reciprocal pressure in the investment banking industry has a real effect on analyst information dissemination. This paper was accepted by Suraj Srinivasan, accounting.

The Macbeth Factor: The Dark Side of Achievement‐driving Analysts

He et al. (2018) find achievement‐striving analysts exert more effort and exhibit better performance. In this study, we also employ the facial width‐to‐height ratio (fWHR) of analysts as a proxy for achievement drive to re‐examine the performance of achievement‐striving analysts. We argue that achievement‐striving analysts’ superior performance can be alternatively explained by their opportunistic forecast issuances. We find analysts with greater fWHR tend to issue downwardly biased last forecasts before firms’ announcement of actual earnings to curry favour with corporate managers. We also find that analysts with greater fWHR issue more optimistic stock recommendations, especially to underwriting clients of their affiliated brokerage firms, and thus exhibit lower stock picking ability. High‐fWHR analysts are also more likely to issue bold positive forecasts but less likely to convey bold negative news. The market shows its concern regarding such opportunistic forecasting behaviour and penalizes analysts with greater fWHR by reacting less to the forecast revisions they issue.

Financial restatements and sell-side analysts' stock recommendations: evidence from Malaysia

PurposeThe purpose of this paper is to examine the influence of financial restatements on the sell-side analysts' stock recommendations.Design/methodology/approachThe 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.FindingsThis 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.Practical implicationsThe 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.Originality/valueThis 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.

Bearish Analysts and the Issuance of 'Unbeatable' Earnings Forecasts

We examine a sample of analysts who, despite having a bearish stock recommendation for a covered firm, issue an unusually optimistic earnings forecast at the end of the year. For the period 2004 through 2018, among analysts with an unfavorable stock recommendation, we find that about 5% issue a final earnings forecast prior to the earnings announcement that exceeds even the most optimistic earnings forecast issued by any other analyst covering the stock, presumably in an effort to increase the likelihood that the firm misses earnings expectations. More than 11% of all firm-year observations are subject to at least one such forecast at the hands of a bearish analyst. We find that analysts with pessimistic stock recommendations are more likely to issue these “difficult-to-beat” earnings forecasts when they have greater pressure to justify their negative view of the stock and when the earnings forecast has greater potential to revise investor beliefs. Nevertheless, stock returns associated with these optimistic forecast revisions are muted. We also find that although Thomson Reuters often excludes these forecasts from the consensus earnings estimate, the majority remain in the consensus and increase the likelihood that the firm misses the consensus forecast by about 21%. However, the market reaction to a negative earnings surprise is less severe when a bearish analyst has issued a difficult-to-beat forecast for the firm. Our study sheds light on one perverse strategy analysts use to increase the likelihood of an earnings miss.

Asymmetric Information: Tinjauan Berdasarkan Rekomendasi Analis Sekuritas dan Revisi Earning Forecast

Trading in the stock market occur due to differences in opinion on the expected value of the securities. In the other side, asymmetric information between investors and companies caused the stock price does not reflect the real price. So, asymmetric information should be reduced. Information by securities analysts is an information that is expected to reduce that. This study investigates the performance of securities analysts through its role in reducing asymmetric information. This is motivated by some previous studies that stated that the Indonesian capital market is inefficient, because high levels of asymmetric information. Analysts is considered as inform market participant who can reduce the asymmetric information so as to make capital market to be efficient. The role of securities analysts is seen through the product. There are stock recommendation and earnings forecast revision. Testing the consistency of the analyst's stock recommendations and earnings forecast revision before testing their impact on asymmetric information. The results showed that output in the form of stock securities analysts and earnings forecast recommendation are consistent but has not been able to reduce the asymmetry of information that occurs between investors and companies.

Underlying risk preferences and analyst risk‐taking behavior

AbstractWe investigate the relationship between underlying risk preferences on analysts’ work‐related decisions. Specifically, we examine whether facial width‐to‐height ratio (fWHR), an innate personal characteristic that has been linked to financial risk tolerance, is associated with analysts’ stock coverage decisions and the boldness of their earnings forecasts and stock recommendations. We find that high‐fWHR analysts cover firms with lower earnings predictability, and issue bolder forecasts and recommendations. Our findings shed new light on the black box of analyst decision making, assisting investment practitioners in evaluating the information content produced by different types of analysts and understanding the observed dispersion in analyst forecasts.

The association of analysts’ cash flow forecasts with stock recommendation profitability

PurposeThe 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.Design/methodology/approachTo 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.FindingsThe 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.Research limitations/implicationsFuture 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.Practical implicationsThese findings may also help investors to better assess the quality of analysts’ research outputs and to identify superior stock recommendations.Originality/valueThis 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.

The effect of affiliated analysts on stock recommendations: Evidence from share pledges in China

We use the share pledge context in China to examine how affiliated analysts whose securities companies are pledgees of share pledge firms issue stock recommendations on these listed firms. We find that their recommendations are more optimistic than those of non-affiliated analysts, and they are more likely to issue Buy and Add recommendations, suggesting that they issue optimistic rating reports for share pledge firms due to their conflicts of interest. We also find a dynamic adjustment in the stock recommendation behavior of these analysts, and their probability after issuing optimistic stock recommendations is significantly reduced before and after the years that the affiliation relationship between them and share pledge firms both began and ended. These affiliated analysts continue to issue optimistic stock recommendations after visiting the share pledge firms if they work in the same location as the firms, or if they are star analysts among New Fortune’s “top five analysts,” and when the information transparency of the share pledge firms is higher. In addition, the optimistic stock recommendation behavior of affiliated analysts is more significant in our sample of firms with high share pledge ratios and downward stock price pressure. The earnings forecast quality of affiliated analysts is also found to be lower, and they are less inclined to downgrade stock recommendations for these share pledge firms. Buy recommendations issued by both non-affiliated and affiliated analysts can bring cumulative excess returns in the short event window, but those issued by affiliated analysts are significantly negative in the long-term event window, and significantly lower than those issued by non-affiliated analysts. Overall, our study shows that affiliated analysts issue optimistic rating reports on share pledge firms due to conflicts of interest, which leads to decision-making bias in investors and thus decreases the stock price crash risk of the firms. Our findings further reveal the economic consequences of share pledging and extend our understanding of the behavior of analysts in a conflict of interest situation from the share pledge perspective.

Stock Selection Model Based on Machine Learning with Wisdom of Experts and Crowds

Both stock recommendations from sell-side analysts and online user generated content from crowds have great significance in the stock market. We examine and compare different effects of analyst attitude and crowd sentiment on stock prices in this article with data from CSMAR. By estimating a multivariate linear regression model, we find that although the wisdom of both experts and crowds has impact on stock prices, the latter's impact on stock prices prevails. We also adopt LightGBM, a novel machine learning model, to predict stock trends based on empirical results. Portfolio returns of different models also suggest that crowd wisdom is more valuable for creating investment strategy than expert wisdom. And it is necessary to take the wisdom of both experts and crowds into consideration when making investment decision.

Individual Analysts' Stock Recommendations, Earnings Forecasts, and the Informativeness of Conference Call Question and Answer Sessions

ABSTRACT This paper deepens our understanding of the anatomy of an earnings conference call. Prior research indicates that, on average, analysts providing bullish stock recommendations or beatable earnings forecasts benefit from greater access to corporate management. Therefore, we analyze whether and to what extent individual analysts' ex ante stock recommendations and earnings forecasts affect the information content of analyst-manager conversations. Using intraday absolute stock price reactions around specific analyst-manager dialogs to measure informativeness, we find that manager dialogs with bearish analysts whose forecasts are missed are more informative. Such analysts engage in longer conversations with more back-and-forth iterations and exhibit a more negative tone, relative to bullish analysts that provide beatable forecasts. Stock prices directionally respond to both the analyst's linguistic tone and the manager's voice pitch. In sum, the capital market effects during an earnings conference call are far more nuanced than previously documented.

An abstract argumentation approach for the prediction of analysts’ recommendations following earnings conference calls

Financial analysts constitute an important element of financial decision-making in stock exchanges throughout the world. By leveraging on argumentative reasoning, we develop a method to predict financial analysts' recommendations in earnings conference calls (ECCs), an important type of financial communication. We elaborate an analysis to select those reliable arguments in the Questions Answers (QA) part of ECCs that analysts evaluate to estimate their recommendation. The observation date of stock recommendation update may variate during the next quarter: it can be either the day after the ECC or it can take weeks. Our objective is to anticipate analysts' recommendations by predicting their judgment with the help of abstract argumentation. In this paper, we devise our approach to the analysis of ECCs, by designing a general processing framework which combines natural language processing along with abstract argumentation evaluation techniques to produce a final scoring function, representing the analysts' prediction about the company's trend. Then, we evaluate the performance of our approach by specifying a strategy to predict analysts recommendations starting from the evaluation of the argumentation graph properly instantiated from an ECC transcript. We also provide the experimental setting in which we perform the predictions of recommendations as a machine learning classification task. The method is shown to outperform approaches based only on sentiment analysis.

The optimism and accuracy of bank-affiliated analysts’ forecasts

ABSTRACT This paper studies the effect of commercial bank affiliation on analysts’ forecasts using the Korean data over the 2000–2015 period. We find that the median EPS forecast error of 2.88% made by independent analysts is significantly larger than the median error of 2.47% made by bank-affiliated analysts. The difference in the optimism and accuracy of independent vs. bank-affiliated analysts is significant only for non-chaebol companies. We also find that capital markets respond to more positively (or more negatively) to stock recommendation changes that bank-affiliated analysts make and that the responses are larger for non-chaebol companies. The results are consistent with our conjecture that bank-affiliated analysts make more informative forecasts by sharing the information generated by commercial banks under bank conglomerates, their information advantage is salient for non-chaebol companies, and capital markets give value to the information advantage.

The Usefulness of Analysts’ Target Prices to Foreign Institutional Investors: U.S. Evidence

This paper investigates whether foreign institutional investors in the United States earn future returns by responding to analysts target price revisions. to examine this issue, this study is using firm fixed effect and industry fixed effect regression in order to examine the effect of using analyst’s target price revisions on future abnormal return for foreign institutional investors. We used 51,427 firm-quarter observations between 2003 and 2013 in the U.S. equity market. Different robust approaches were used to proxy foreign institutional trading. We find a positive and significant increase in foreign institutional ownership in response to a positive change in analysts’ target prices, which predict positive stock returns. The results are robust to controlling for other analysts’ outputs, such as revisions to their earnings’ forecasts and stock recommendations, in addition to other determinants of institutional trading. These results are also robust using different measures of institutional trading. In addition, the results show that foreign institutional trading based on target prices’ revisions is more pronounced in firms with high information asymmetry. The results show that foreign institutional investors rely more on analysts in small firms and firms with low analyst coverage.

Does Algorithmic Trading Affect Analyst Research Production?

We document a causal negative relationship between algorithmic trading (AT) and analyst research production, as captured by a decreased frequency of earnings forecasts and stock recommendations and lower analyst coverage. This is consistent with AT increasing the speed of price discovery, reducing the profitability of trades on analyst research by non-algorithmic traders and, consequently, their demand for analyst investment advice. Supporting evidence shows that the effect of AT on analyst research production is stronger for stock recommendations, which institutions follow primarily for investment decisions, and for forecasts issued before earnings announcements when analysts’ information discovery dominates the information interpretation role. We also find a negative relationship between AT and investment-focused institutional investors such as transient and non-monitoring investors. Our analysis demonstrates that AT can have long-lasting consequences on capital markets, beyond microstructure effects, through its negative effect on firm’s information environment.

Analyzing the Analysts: The Effects of Technical and Social Skills on Analyst Performance and Careers

This paper investigates the effects of financial analysts’ technical and social skills on their performance and career advancement. Using financial analysts’ LinkedIn profiles, we find that analysts with strong technical skills endorsed by LinkedIn connections generate more accurate earnings forecasts and more profitable stock recommendations. Analysts with better social skills, proxied by the number of LinkedIn connections, have better communications with corporate management during conference calls and also produce more accurate earnings forecasts. Their stock recommendations are not more profitable but receive stronger market reactions. Further, these sociable analysts are more likely to be voted as All-Star Analysts and to move to high-status brokerage firms when they change jobs. These findings provide the first large-sample evidence on the different roles of technical and social skills in the financial industry.

Analysts’ Cultural Attitudes to Time Orientation

We study how analysts’ cultural attitudes to time orientation affect their production of long-term earnings forecasts, the profitability of their stock recommendations, and managerial myopia for the firms they cover. We find that analysts from a long-term oriented culture produce more long-term earnings forecasts, issue more timely long-term forecasts and more profitable stock recommendations. These results are more pronounced among firms with more long-term investments, for smaller firms, and during periods of higher economic uncertainty. Exploring the quasi-natural experiments of brokerage houses’ mergers and closures, we find a positive and plausibly causal effect of the coverage by long-term oriented analysts on firm innovation. Contrary to extant research finding that analysts’ coverage in general fosters managerial myopia, our paper shows that the coverage of long-term oriented analysts ameliorates managerial myopia.

Origin of Outperformance for Stock Recommendations by Sell-Side Analysts

We examine the structure of portfolios built on sell-side analysts’ recommendations and show that those portfolios’ abnormal returns are explained primarily by the analysts’ stock picking ability and only partially by the effect of overweight in small-cap stocks, given that more than 80% of the studied portfolios are concentrated in the three smallest Size Deciles. We document the portfolios’ abnormal returns by examining the number of stocks in the portfolios and the weights assigned to market-cap Size Deciles and Global Industry Classification Standard (GICS) sectors and perform an attribution analysis that allows us to identify the sources of overall value-added performance. We find that the average monthly added value of 0.46 (0.34) percent obtained on Strong Buy and Buy recommendations from Stars (Non-Stars) is primarily explained by the analysts’ intra-sector stock-picking skills and that the monthly added value of 0.16 (0.18) percent obtained from Stars (Non-Stars) is related to the portfolios’ allocation among size-specific deciles.

Does Time-Space Compression Affect Analysts’ Forecast Performance?

Through the difference-in-differences (DID) methodology, we find that the connection of China’s high-speed railway (HSR) as an exogenous shock could improve analysts’ forecast performance, leading to more accurate forecasts, decrease the dispersion between analysts, stimulate more forecast revisions with less revision volatility, evidenced by analyst site visits to firms. The results are robust with a battery of robustness checks such as 2SLS regression and so on. And the counterfactual relations caused by the lightning accident in 2011 also confirm our previous assumption. Furthermore, when the local economic development is weak, the trips by HSR is more convenient, or the information environment of firms are weak, the correlations will be stronger. Moreover, the connection of HSR also improves the information availability of stock recommendations issued by analysts. Overall, our study contributes to the relevant study of sell-side analysts’ performance and has an important impact on studying the role of geographic proximity on information efficiency.