Yes / This study uses the quality of coverage decisions as a new metric to evaluate the performance of star and non-star analysts. We find that the coverage decisions of star analysts are better predictors of returns than those of non-star analysts. The return predictability of star analysts’ coverage decisions is stronger for informationally opaque stocks. We further exploit the staggered short selling deregulations, Google’s withdrawal, and the anti-corruption campaign as three quasi-natural experiments that create plausibly exogenous variations in the quality of information environment. These experiments show that the predictive power of star analysts’ coverage decisions strengthens (weakens) following a sharp deterioration (improvement) in firms’ information environment, consistent with the notion that star analysts possess superior ability to identify mispriced stocks. Overall, star analysts make better coverage decisions and play a superior role as information intermediaries, especially in poor information environment.

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. / <p>QC 20170412</p> / European Doctorate in Industrial Management