The Origin Of Outperformance For Stock Recommendations By Sell-Side AnalystsVW Staff
The Origin Of Outperformance For Stock Recommendations By Sell-Side Analysts
Royal Institute of Technology (KTH) – Industrial Engineering and Management; Universidad Politécnica de Madrid
KTH Royal Institute of Technology – Industrial Engineering and Management; Swedish House of Finance
March 18, 2016
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.
The Origin Of Outperformance For Stock Recommendations By Sell-Side Analysts – Introduction
An investment portfolio’s performance can be explained in terms of both selection and allocation effects. In this research, we measure whether the outperformance of portfolios constructed using sell-side analysts’ recommendations previously reported in the academic literature is caused by the analysts’ selection skills or their allocation skills. We focus on the structure of portfolios constructed using investment recommendations from sell-side analysts. There is limited discussion in the extant literature about the actual structure of the dynamic portfolios used in research and how the observed abnormal returns are explained by the portfolios’ holdings, which is surprising considering the number of studies showing that a portfolio constructed by investing one dollar in each new Strong Buy and Buy recommendation generates significant abnormal returns. Additionally, it is important for investors to understand which type of portfolio output they can expect if they follow analysts’ recommendations. Knowing a portfolio’s content also helps in assessing which classes of stocks are recommended, their contribution to the portfolio’s risk and return, and how the portfolio is positioned in relation to the market portfolio.
Our study fills a gap between earlier research showing high abnormal returns for dynamic portfolios constructed using sell-side analysts’ recommendations and the lack of detailed knowledge about the actual content of these portfolios. We conduct a holdings-based analysis that allows us to compare the size and market sector weights in the dynamic portfolios for Star analysts and for Non-Star analysts within the overall market structure.
Literature on the investment value of recommendations from sell-side analysts documents the likelihood of generating excess returns by constructing a dynamic portfolio based on analysts’ recommendations (Barber et al., 2006, 2007; Fang and Yasuda, 2014; Kucheev et al., 2015). These studies reported not only high abnormal returns found to be linked to overall firm-level or analyst-group characteristics but also the statistical features of the database used and recommendations (such as the frequency and the magnitude of recommendation levels and recommendation changes and the timing of recommendations). However, the actual portfolio holdings obtained by following the dynamic portfolio methodologies used by academics remain uninvestigated. Focusing on actual holdings enables an exploration of whether the overall portfolio’s performance is driven by analysts’ stock-picking skills (the selection effect) or by an overweight in either sectors that are more profitable or size-specific market deciles (the allocation effect).
Although most researchers attempt to measure stock-picking skill, the sector rotation (market timing) in constructed portfolios is not a conscious decision by analysts but instead an artifact of the methodology and/or nature of the market explained by analysts’ attention to a particular sector or size-specific market decile. Analysts do have a choice of stocks within the market size in which they recommend stocks to invest in; however, the portfolio sector weights that we investigate are driven by the number of analysts who follow the sector and the frequency of recommendation changes for the sector. Thus, it is important to clarify the extent to which outperformance is explained by the selection and allocation effects to discuss whether analysts possess any significant stock-picking skills that allow them to beat industry- and size-specific benchmarks.
Our study investigates the source of the abnormal returns reported in a large number of studies performed on the investment value of analysts’ stock recommendations by focusing on the actual stock holdings in the constructed portfolios used in these studies. Our primary methodological approach is that of an attribution analysis based on holdings that reveals how value-added performance is attributed to analysts’ stock selection and market-timing skills.
We expect this study to be of interest to both academics and practitioners. From an academic perspective, our study contributes to a deeper understanding of how the abnormal returns of portfolios constructed based on analysts’ recommendations are obtained. From an investor’s perspective, our research strengthens our knowledge about analysts’ ratings, the investment value of their recommendations and the portfolio characteristics that will result from their advice. Finally, for sell-side analysts, our research provides decision support for making better recommendations in terms of understanding the importance of choice of industry and size of recommended firms.
Our study uses investment recommendations from The Thomson Financials Institutional Brokers’ Estimate System (I/B/E/S) Detail Recommendations File and manually collected lists of star analysts from Institutional Investor magazine (October 2003-October 2013), The Wall Street Journal (May 2003-April 2013), and StarMine (October 2003-August 2013). We follow the methodology of Barber et al. (2006) and construct portfolios based on the recommendations of Star and Non-Star analysts by investing one dollar into each new recommendation (excluding reiterations) and then holding the stocks either for one year or until the recommendation changes, whichever comes first.
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