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Shooting Stars: The Value of Ranked Analysts' Recommendations
KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Financial analysts play a key role in collecting, processing and disseminating information for the stock market. Selecting the best analysts among thousands of analysts is an important task for investors that determines future investment profitability. Extensive research has been dedicated to finding the best analysts of the market based on various criteria for different clienteles. The state of the art approach in this process has developed into so-called Star Rankings with lists of top analysts who have previously outperformed their peers. How useful are such star rankings? Do the recommendations of stars have higher investment value than the recommendations of non-stars (i.e., recommendations of Stars “shoot” more precisely before and after selection)? Or do star rankings simply represent the past performance that will regress to the mean in the future (i.e., in reality, Shooting Stars are not stars and quickly disappear from the sky)?

The aim of this Ph.D. thesis is to empirically investigate the performance of sell-side analysts’ recommendations by focusing on a group of star analysts. This thesis comprises four papers that address two overarching questions. (1) Do star rankings capture any true skill, and, thus, can investors rely on the rankings? (Papers I and II) (2) How do market conditions impact star analysts? (Papers III and IV)

Paper I examines the profitability persistence of the investment recommendations from analysts who are listed in the four different star rankings of Institutional Investor magazine, StarMine’s “Top Earnings Estimators”, “Top Stock Pickers” and The Wall Street Journal and shows the predictive power of each evaluation methodology. By investigating the precision of the signals that the various methodologies use in determining who the stars are, the study distinguishes between the star-selection methodologies that capture short-term stock-picking profitability and the methodologies that emphasize the more persistent skills of star analysts. As a result, this study documents that there are star-selection methods that select analysts based on more enduring analyst skills, and, thus, the performance of these methods’ stars persists even after ranking announcements. The results indicate that the choice of analyst ranking is economically important in making investment decisions.

Paper II investigates the structure of the portfolios that are built on the recommendations of sell-side analysts and confirms that the abnormal returns are explained primarily by analysts’ stock-picking ability and only partially by the effect of over-weight in small-cap stocks. The study examines the number of stocks in the portfolios and the weights that are assigned to market-cap size deciles and GICS sectors and performs an attribution analysis that identifies the sources of overall value-added performance.

Paper III examines the differences in seasonal patterns in the expected returns on target prices between star and non-star analysts. Although the market returns in the sample period do not possess any of the investigated seasonal effects, the results show that both groups of analysts, stars and non-stars, exhibit seasonal patterns and issue more optimistic target prices during the summer, with non-stars being more optimistic than stars. Interestingly, the results show that analysts are highly optimistic in May, which contradicts the adage “Sell in May and go away” but is consistent with the notion of a trade-generating hypothesis: since analysts face a conflict of interests, they may issue biased recommendations and target prices to generate a trade. A detailed analysis reveals that the optimism cycle is related to the calendar of companies’ earnings announcements rather than the market-specific effects.

Paper IV discusses how a shift in economic conditions affects the competitiveness of sell-side analysts. The focus is on the changes that were triggered by the financial crisis of 2007-2009 and a post-crisis “uncertainty” period from 2010-2013. The study follows Bagnoli et al. (2008) in using a change in the turnover of rankings as a measure of a transformation in analysts’ competitive advantages. Paper IV extends their research and documents how different ranking systems capture analysts’ ability to handle changes in the economic environment. The results show that market conditions impact analyst groups differently, depending on the group’s competitive advantages.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2017. , p. 61
Series
TRITA-IEO, ISSN 1100-7982 ; 2017:04
Keywords [en]
Alpha; analysts’ recommendations; Institutional Investor; sell-side analysts; star analysts; StarMine; The Wall Street Journal
National Category
Business Administration
Research subject
Industrial Engineering and Management; Economics
Identifiers
URN: urn:nbn:se:kth:diva-205284ISBN: 978-91-7729-344-6 (print)OAI: oai:DiVA.org:kth-205284DiVA, id: diva2:1088235
Public defence
2017-05-12, E3, Osquars backe 14, E-huset, KTH Campus, Stockholm, 10:00 (English)
Opponent
Supervisors
Projects
European Doctorate in Industrial Management
Note

QC 20170412

Available from: 2017-04-12 Created: 2017-04-11 Last updated: 2017-04-13Bibliographically approved
List of papers
1. Do Stars Shine? Comparing the Performance Persistence of Star Sell-Side Analysts Listed by Institutional Investor, the Wall Street Journal, and StarMine
Open this publication in new window or tab >>Do Stars Shine? Comparing the Performance Persistence of Star Sell-Side Analysts Listed by Institutional Investor, the Wall Street Journal, and StarMine
2017 (English)In: Journal of financial services research, ISSN 0920-8550, E-ISSN 1573-0735, Vol. 52, no 3, p. 277-305Article in journal (Refereed) Published
Abstract [en]

We investigate the profitability persistence of the investment recommendations from analysts listed in four different star rankings, Institutional Investor magazine, StarMine's "Top Earnings Estimators" and "Top Stock Pickers", and The Wall Street Journal, and show the predictive power of each evaluation methodology. We found that only Buy and Strong Buy recommendations from the entire group of Star analysts outperform those of the Non-Stars in the year after election, while Sell and Strong Sell recommendations performed as those of the Non-Stars. We document that the highest average monthly abnormal return of holding a long-short portfolio, 0.97 %, is obtained by following the recommendations of the group of star sell-side analysts rated by StarMine's "Top Earnings Estimators" during the period from 2003 to 2014. Since earnings are one of the main drivers of stock prices, the results obtained are in line with the notion that focusing on superior earnings forecasts is one of the top requirements for successful stock picks.

Place, publisher, year, edition, pages
Springer, 2017
Keywords
Star analysts, Analyst recommendations, StarMine, Institutional Investor, TheWall Street Journal
National Category
Business Administration
Identifiers
urn:nbn:se:kth:diva-205275 (URN)10.1007/s10693-016-0258-x (DOI)000413302700005 ()2-s2.0-84984916938 (Scopus ID)
Note

QC 20170412

Available from: 2017-04-11 Created: 2017-04-11 Last updated: 2017-11-06Bibliographically approved
2. The origin of outperformance for stock recommendations by sell-side analysts.
Open this publication in new window or tab >>The origin of outperformance for stock recommendations by sell-side analysts.
2017 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Since Barber et al. (2006, JAE) reported a methodology for measuring the investment value of sell-side analysts' recommendations by constructing a "paper portfolio", this method has become the standard approach in the related academic literature. In this paper, we replicate this portfolio methodology and investigate whether the portfolios' outperformance is explained by the analysts' stock picking skills or it is an artifact of the portfolio construction approach. We examine 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 show that the portfolios' abnormal returns are explained primarily by the analysts' stock picking ability and only partially by the effect of an overweight in small-cap stocks, given that more than 80% of the studied portfolios are concentrated in the three smallest size deciles.

Keywords
Alpha, Sell-side analyst recommendations, Attribution analysis, Institutional Investor, StarMine, The Wall Street Journal
National Category
Business Administration
Identifiers
urn:nbn:se:kth:diva-205278 (URN)
Note

QC 20170412

Available from: 2017-04-11 Created: 2017-04-11 Last updated: 2017-04-26Bibliographically approved
3. The seasonality in sell-side analysts’ recommendations
Open this publication in new window or tab >>The seasonality in sell-side analysts’ recommendations
2017 (English)Article in journal (Refereed) Submitted
Abstract [en]

In this paper, we examine whether highly reputed sell-side analysts (stars) account for seasonality in their forecasts. We find that both star and non-star analysts are highly optimistic in May, which contradicts the adage “Sell in May and go away”. Detailed analyses reveal that optimism cycles are related to the calendar of companies’ earnings announcements rather than to market-specific effects. We show this by measuring the differences in seasonality patterns in expected returns on target prices by star and non-star sell-side analysts. Although the market returns in our sample period, 2003-2014, do not show any statistically significant patterns in the three investigated seasonal effects, our results show that Non-Stars issue more optimistic target prices than Stars.

Keywords
Seasonality in stock prices, Target prices, Sell-side analyst recommendations, Star analysts
National Category
Business Administration
Research subject
Industrial Engineering and Management
Identifiers
urn:nbn:se:kth:diva-205281 (URN)
Note

QC 20170412

Available from: 2017-04-11 Created: 2017-04-11 Last updated: 2017-04-20Bibliographically approved
4. What trend is an analysts' friend? How overall market conditions affect the competitiveness of financial analysts
Open this publication in new window or tab >>What trend is an analysts' friend? How overall market conditions affect the competitiveness of financial analysts
2017 (English)Report (Other academic)
Abstract [en]

This study documents how a shift in economic conditions triggered by the financial crisis of 2007-2009 and a post-crisis “uncertainty” period of 2010-2013 impacts the competitiveness of sell-side security analysts. I use a change in turnover of rankings as a measure of transformation in analysts’ competitive advantages in order to show how different ranking systems capture analysts’ ability to handle the changes in the economic environment. The results show that Institutional Investor’s stars, elected based on buy-side votes and valued primarily for the “helpfulness” of those analysts, experience an increase in turnover after the financial crisis and that they issue significantly less-profitable investment recommendations during high uncertainty periods. At the same time, objectively selected analysts in rankings based on either accuracy of earnings forecasts or profitability of recommendations, do not show any clear pattern of an impact of the crisis (or uncertainty) on the turnover of stars and on the profitability of their recommendations. The study concludes that the stock-picking skill of analysts is independent of market conditions, while buy-side clients view the “helpfulness” of analysts differently during different economic conditions.

Publisher
p. 21
National Category
Business Administration
Identifiers
urn:nbn:se:kth:diva-205283 (URN)
Note

QC 20170412

Available from: 2017-04-11 Created: 2017-04-11 Last updated: 2017-04-27Bibliographically approved

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