On the investment value of sell-side analyst recommendation revisions in the UK

Sell-side analysts working for brokerage houses play an important role in the capital markets by collecting and analyzing a variety of market, industry, and firm-specific information and then making stock recommendations.¹ These stock recommendations, disseminated through electronic and print media, have been widely used by investors in their investment decisions. Whether sell-side analyst recommendations can truly create investment value and promote market efficiency has been of great interest to financial academics. The related literature can stretch as back as the pioneering study of Cowles (1933), which shows that investors are not able to add value to the market when they follow analyst recommendations. The inability of financial analysts to predict stock price movements is confirmed by numerous following studies (see, e.g., Colker 1963; Logue and Tuttle 1973; Groth et al. 1979; among others), in spite of a substantial amount of money spent by brokerage houses on security analysis (Grossman and Stiglitz 1980).² In contrast, Stickel (1995) and Womack (1996) report that upgrades (downgrades)—favorable (unfavorable) changes in analyst recommendations—are accompanied by significantly positive (negative) returns at the time of their announcements. Barber et al. (2001) prove the existence of profitable investment strategies based on publicly available analyst recommendations, presenting a challenge to the semi-strong form of efficient market hypothesis (EMH). However, these investment strategies require a great deal of trading and generate considerable transaction costs, suggesting that the observed market inefficiencies are not easily exploitable by investors (see, also, Jegadeesh et al. 2004; Mikhail et al. 2004). Altinkiliç and Hansen (2009) further call into question the information role played by financial analysts in that their stock recommendation revisions often piggyback on public information (e.g., corporate events and news), thus providing investors with little incremental information (see, also, Altinkiliç et al. 2013).

Despite the existence of extensive sell-side analyst research in the US, empirical evidence on the investment value of analyst recommendations remains mixed. Jegadeesh and Kim (2006, p. 275) argue that an in-depth examination in other developed markets “will give us a more comprehensive picture of the extent to which the unique skill of analysts are useful for investors”. The UK stock market, a highly developed and sophisticated market, provides us with an appropriate setting to shed additional light on the existing controversy within this area of research, while it has received very scant attention in the financial literature. For example, Dimson and Fraletti (1986) examine an unpublished sample of 1649 telephone recommendations made by a leading UK brokerage house in 1983, but they find no significant abnormal returns for the recommended stocks. Ryan and Taffler (2006, p. 372) argue that the study of Dimson and Fraletti (1986) examines stock recommendations made by “a single UK brokerage house only and is biased towards large capitalization stocks”. Ryan and Taffler (2006) investigate a sample of 2506 changes in analyst recommendations made by six London-based brokerage houses from December 1993 to June 1995, showing that stock prices are significantly affected by analyst recommendation revisions.

Given the limited observations and short sample periods examined in prior UK studies, which generally suffer from small sample bias, this study aims to conduct a comprehensive investigation into a novel real-time database, uniquely created by Morningstar Company Intelligence. Our final sample consists of 70,220 analyst recommendation revisions for stocks either on the main market of the London Stock Exchange (LSE) or on the Alternative Investment Market (AIM) from January 1995 to June 2013; as such, our dataset is much larger than has been employed in prior UK studies. With the Morningstar database, we take an investor-oriented, calendar-time perspective,³ to assess the performance of investment strategies based on UK sell-side analyst recommendation revisions. Our study is close to Barber et al. (2001) and Barber et al. (2007), but differs from them in several major respects. For example, Barber et al. (2007) include initiations, resumptions, and reiterations when constructing their portfolios. However, sell-side analysts often leave their stock recommendations unchanged for long periods of time; as a result, these stock recommendations become stale and less informative over time (see, Jegadeesh et al. 2004; Boni and Womack 2006; Jegadeesh and Kim 2006, 2010). Therefore, unlike Barber et al. (2007), we construct two upgrade and downgrade portfolios by exclusively focusing on analyst recommendation revisions. Specifically, the upgrade portfolio includes all stocks with upward revisions to Strong Buys or Buys from previous Strong Sells, Sells, or Holds, while the downgrade portfolio includes all stocks with downward revisions to Strong Sells, Sells, or Holds from previous Strong Buys or Buys.

In addition, different from Barber et al. (2001) and Barber et al. (2007), we evaluate the performance of the upgrade and downgrade portfolios on a 1-year rolling window basis, using the intercepts derived from the single-factor capital asset pricing model (CAPM), the Fama and French (1993) three-factor model, the Carhart (1997) four-factor model, and the Fama and French (2015) five-factor model. The rolling window analysis, which has not been used in prior sell-side analyst research, to the best of our knowledge, enables us to more effectively capture the time-varying portfolio performance over the long sample period, while the use of various single- and multi-factor models helps rule out the concern on the poor model of asset pricing raised by Barber et al. (2001).

Our empirical investigation proceeds in three main parts, showing some interesting results that are robust to alternative model specifications. In the first part, we examine the time-varying daily abnormal returns (both gross and net of transaction costs) to the upgrade and downgrade portfolios using the whole sample of analyst recommendation revisions. Specifically, on average, the upgrade portfolio generates no significantly positive abnormal gross (or net) returns in any period of time, suggesting that upgrades are in general valueless. In contrast, the downgrade portfolio generates significantly negative abnormal gross returns in several periods of time. For example, the negative abnormal gross returns to the downgrade portfolio are statistically significant, at least at the 5% level, from April 2001 to January 2003, ranging from − 3.50 basis points to − 6.40 basis points, and from March 2009 to June 2010, ranging from − 3.45 basis points to − 8.59 basis points. However, given the existence of massive portfolio rebalancing, the downgrade portfolio does not generate significant abnormal net returns after taking a reasonable level of transaction costs into account.

Although a large number of investment strategies based on analyst recommendation revisions fail to make profits after transaction costs, Barber et al. (2001, p. 537) argue that “it remains an open question whether other types of trading strategies could be profitable”. Inspired by Barber et al. (2001), we further employ two alternative investment strategies to assess: (1) whether analyst recommendation revisions made by high-ranking brokerage houses are more valuable in the UK; and (2) whether UK analyst recommendation revisions within specific industry sectors can make large enough profits to offset transaction costs.

On the one hand, it has been well reported that brokerage houses and their analysts, as repetitive players in the capital markets, obtain and accumulate their reputation capital, which is directly affected by the performance of stocks they recommend (see, Womack 1996; Brown et al. 2009). Therefore, it is possible that the superior performance made by some star sell-side analysts (e.g., those working for high-ranking brokerage houses) is balanced by the inferior performance made by their non-star counterparts (Fama and French 2010). In the second part of our empirical investigation, we replicate all analyses in the first part using a subsample of 11,016 analyst recommendation revisions exclusively made by the top 5 brokerage houses, measured by their positions on the annual All-Europe Research Team published by Institutional Investor (see “Appendix 1”). The subsample results, however, are qualitatively the same as those using the whole sample, suggesting that reputation of brokerage houses does not play a significant role in enhancing the investment value of sell-side analyst recommendation revisions in the UK.

On the other hand, prior sell-side analyst research controls for a wide range of stock characteristics (see, e.g., Stickel 1995; Womack 1996; Jegadeesh et al. 2004; Anderson et al. 2017), other than for industry factor. Jegadeesh et al. (2004, p. 1118) point out that “it is possible that analyst recommendation revisions reflect news about a firm’s competitive position in its industry”. Boni and Womack (2006) argue that upgrades and downgrades, aggregated across all sell-side analysts for stocks within each industry, might generate significant abnormal net returns, though they do not explicitly examine the transaction costs. Motivated by Jegadeesh et al. (2004) and Boni and Womack (2006), we conduct an industry-based analysis in the third part of our empirical investigation on the performance of the upgrade and downgrade portfolios within each industry sector, identified by the two-digit Industry Classification Benchmark (ICB) codes (see “Appendix 2”). Specifically, we find no significantly positive abnormal net returns to the upgrade portfolio within any industry sector in any period of time. However, within two high-tech industry sectors, i.e., Health Care (ICB 45) and Technology (ICB 95) sectors, the downgrade portfolio generates significantly negative abnormal net returns, at least at the 5% level, in several periods of time. For example, within Technology sector, the significantly negative abnormal net returns vary from − 3.21 basis points to − 6.44 basis points from September 2001 to March 2003, and vary from − 2.44 basis points to − 7.45 basis points from December 2006 to May 2010; similar evidence is found for the downgrade portfolio within Health Care sector. The reported incremental investment value is not only statistically significant, but also economically meaningful, suggesting the importance of controlling for the industry effect, which has never been explored in prior UK sell-side analyst research, to the best of our knowledge.

Thus far, our empirical investigation not only provides important evidence to complement the financial literature, but has significant implications for market participants. A natural question to ask is whether the reported portfolio performance in our empirical investigation is simply as a result of random chance, i.e., sell-side analysts’ luck. Another major contribution of this study is for the first time to introduce a rolling window-based time-series bootstrapping simulation method (see details in “Appendix 3”), to distinguish sell-side analysts’ luck from their skill (Fama 1998; Barber et al. 2001). Specifically, our simulated results confirm that, on average (1) the observed insignificantly positive abnormal returns to the upgrade portfolio could be attributed to sell-side analysts’ lack of skill in making valuable upward revisions (even before transaction costs), rather than their bad luck; and (2) sell-side analysts have certain skill in making valuable downward revisions (before transaction costs) over some periods of time, while such skill is not sufficient to cover the size of transaction costs, irrespective of whether these revisions are made by high-ranking brokerage houses or not. More importantly, our simulated results confirm that sell-side analysts do possess superior skill in making downward revisions for stocks with high-tech related characteristics over some periods of time, even after transaction costs.

Overall, this study contributes to sell-side analyst research in several important respects. First, this study employs a calendar-time approach to assess the performance of investment strategies based on UK sell-side analyst recommendation revisions, using a uniquely insightful database, Morningstar Company Intelligence, which has never been examined before. In addition, our industry-based analyses show that it is likely to make profits by short selling stocks with downward revisions within two high-tech industry sectors, i.e., Health Care and Technology sectors, over some periods of time even after transaction costs, the results of which could be mainly attributed to the greater coverage and efforts made by sell-side analysts on high-tech firms with substantial intangible assets (Barth et al. 2001; Barron et al. 2002). Finally, we develop a time-series bootstrapping simulation method to test whether the observed time-varying investment performance is due to random chance (i.e., sell-side analysts’ luck), though our simulation method can be applied to distinguish luck from skill when evaluating the calendar-time portfolio performance in other types of investments.

The remainder of this paper is organized as follows. Sections 2 and 3 describe data and research design, respectively. Section 4 presents empirical and simulated results, while the final section concludes.

We obtain the real-time sell-side analyst recommendations from the Morningstar Extracted Data File: Historic Broker Recommendations for UK Registered and UK Listed Companies, uniquely created by Hemscott Company Guru, now part of Morningstar Company Intelligence. Each stock recommendation record contains information on the name of the covered firm, the name of the brokerage house issuing the recommendation, the recommendation starting and expiration dates, and a rating between 1 and 9. A rating of 1 reflects a strong buy, 2 a buy, 3 a weak buy, 4 a weak buy/hold, 5 a hold, 6 a hold/sell, 7 a weak sell, 8 a sell, and 9 a strong sell. We exclude stock recommendations that omit the name of brokerage houses, those without releasing the expiration dates, and/or those with data errors. Also, we require that (1) the gap between the starting and expiration dates of each recommendation is less than 1 year to ensure that the brokerage house is actively following the recommended stock; and (2) the relevant financial and accounting data of the covered firms are available from the London Share Price Database (LSPD).

Our initial sample is comprised of 384,165 publicly available analyst recommendations made by 144 brokerage houses on 2905 distinct firms listed either on the main market of the LSE or on the AIM from January 1995 to June 2013. To allow for an easy and intuitive comparison with prior US studies, we reclassify all original analyst recommendations into five categories: Strong Buys (1 and 2; 44.99%), Buys (3 and 4; 10.66%), Holds (5; 32.43%), Sells (6 and 7; 3.68%), and Strong Sells (8 and 9; 8.24%). Brown et al. (2009) argue that prior sell-side analyst research is generally hampered by a very low proportion of negative recommendations (Sells and Strong Sells), e.g., 3.0% in the US (see, Barber et al. 2003). Table 1 shows that our Morningstar database overcomes this deficiency with much more negative recommendations, e.g., 11.92% of Sells and Strong Sells.

Sell-side analysts, however, often leave their stock recommendations unchanged for long periods of time; as a result, these stock recommendations become stale and less informative over time, potentially resulting in poor portfolio performance (see, Jegadeesh et al. 2004; Boni and Womack 2006; Jegadeesh and Kim 2006 and 2010; Hobbs et al. 2012). Therefore, our empirical investigation exclusively focuses on analyst recommendation revisions that convey more valuable information and have more predictive power over time. Table 2 presents the matrix of our final sample of 70,220 UK analyst recommendation revisions, i.e., 45.04% are Strong Buys and Buys, 38.73% are Holds, and 16.23% are Sells and Strong Sells, similar to those reported elsewhere (see, e.g., Stickel 1995; Barber et al. 2001; Boni and Womack 2006). The inclusion of 23,235 (33.09%) analyst recommendation revisions for 1042 dead firms in our sample also helps avoid the potential survivorship bias.

In this section, we focus on presenting the empirical and simulated results under the Fama and French (2015) five-factor model, though our results remain qualitatively unchanged under the CAPM, the Fama and French (1993) three-factor model, and the Carhart (1997) four-factor model. These results are not presented for the sake of brevity, but are available on request.

The purpose of this inquiry is to conduct an in-depth investigation into the investment value of sell-side analyst recommendation revisions in the UK from January 1995 to June 2013, using a uniquely insightful database that has never been examined before. We take an investor-oriented, calendar-time perspective, to assess the time-varying performance of the upgrade and downgrade portfolios. Our empirical investigation shows that, on average, sell-side analysts’ upward revisions are valueless over the whole sample period; it is likely for investors to make profits by short selling stocks with downward revisions (before transaction costs) over some periods of time, while these profits disappear after transaction costs. Our results do not alter when we construct two alternative upgrade and downgrade portfolios exclusively using a subsample of analyst recommendation revisions made by the top 5 brokerage houses. This suggests that the reputation of brokerage houses does not play an important role in making valuable analyst recommendation revisions in the UK, inconsistent with that reported in the US. One possible explanation on the discrepancy could be that the annual All-European Research Team published by Institutional Investor has less influence on UK sell-side analysts’ career concern, compared with the influence of its US counterpart. Further research might focus on the development of more appropriate proxies for the reputation of brokerage houses and sell-side analysts in the UK (see, e.g., Nolte et al. 2014).

Our time-series bootstrapping simulations confirm that, on average, (1) the observed insignificantly positive abnormal returns (both gross and net of transaction costs) to the upgrade portfolio could be attributed to sell-side analysts’ lack of skill in making valuable upward revisions, rather than their bad luck; and (2) sell-side analysts have certain skill in making valuable downward revisions over some periods of time (at least before transaction costs), though such skill is not sufficient to cover the size of transaction costs. More importantly, our simulated results confirm that sell-side analysts do possess superior skill in making valuable downward revisions for stocks with high-tech related characteristics, even after transaction costs are taken into account.

Finally, it is worth mentioning that there is some slight differences between the investment value and profitability of sell-side analyst recommendations.¹⁵ On the one hand, analyst recommendations could be worthless, but the portfolio might be profitable. For example, Green (2006) reports that early access to stock recommendations provides investors with incremental investment value, which could be attributed to the existence of momentum traders in the market (Hong and Stein 1999). These momentum traders have a tendency to exchange aggressively and deviate a stock’s price beyond its fundamental value, thus leading to a market overreaction in the short term. On the other hand, analyst recommendations are in fact valuable, but the portfolio might end up not being profitable, probably due to the crowd effect when the information is widely and publicly broadcast. According to Hong and Stein (1999), the market firstly underreacts to the release of analyst recommendations, as news watchers tend to react to information with a delay. The initial underreaction is generally followed by overreaction, since investors may chase fad, which drives stock prices overshoot their long-term equilibrium, and eventually causes the portfolio not profitable in the long term. In this manner, the key implication of the Hong and Stein (1999) model on sell-side analyst research is the speed of reaction to stock recommendations (Green 2006; Jegadeesh and Kim 2006), which could be left for future research.