Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
The origin of outperformance for stock recommendations by sell-side analysts.
KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).
KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).ORCID iD: 0000-0001-7402-0096
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.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Alpha, Sell-side analyst recommendations, Attribution analysis, Institutional Investor, StarMine, The Wall Street Journal
National Category
Business Administration
Identifiers
URN: urn:nbn:se:kth:diva-205278OAI: oai:DiVA.org:kth-205278DiVA: diva2:1088232
Note

QC 20170412

Available from: 2017-04-11 Created: 2017-04-11 Last updated: 2017-04-26Bibliographically approved
In thesis
1. Shooting Stars: The Value of Ranked Analysts' Recommendations
Open this publication in new window or tab >>Shooting Stars: The Value of Ranked Analysts' Recommendations
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. 61 p.
Series
TRITA-IEO, ISSN 1100-7982 ; 2017:04
Keyword
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:nbn:se:kth:diva-205284 (URN)978-91-7729-344-6 (ISBN)
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

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Kucheev, YurySorensson, Tomas
By organisation
Industrial Economics and Management (Dept.)
Business Administration

Search outside of DiVA

GoogleGoogle Scholar

Total: 40 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf