Abstract

This study conducts a comprehensive investigation into the investment value of sell-side analyst recommendation revisions in the UK, using a unique dataset from 1995 to 2013. Our rolling window analysis shows that, on average, upgrades fail to generate any significantly positive abnormal returns in any period of time, even before transaction costs. In addition, although downgrades could generate significantly negative abnormal gross returns over some periods of time, these observed significant returns disappear after accounting for transaction costs. Overall, our bootstrapping simulations confirm sell-side analysts’ lack of skill in making valuable up/downward revisions to cover the size of transaction costs, irrespective of whether these revisions are made by high-ranking brokerage houses or not. However, an industry-based analysis shows that, within two high-tech industry sectors, i.e., Health Care and Technology sectors, sell-side analysts possess certain skill in making valuable downgrades over some periods of time and, in particular, such skill is sufficient to offset transaction costs.

Highlights

  • 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 making stock recommendations.1 These stock recommendations, disseminated through electronic and print media, have been widely used by investors in their investment decisions

  • This study focuses on stock recommendation revisions, exclusively made by sell-side analysts working for brokerage houses in the UK. 2 Grossman and Stiglitz (1980) argue that if prices fully reflect all available information, the use of analyst recommendations cannot generate superior returns, and brokerage houses should not spend large sums of money on security analysis, nor should market participants have any incentive to pay for such costly information

  • We report the average rating for analyst recommendation revisions based on the nine-point rating scale, while in square brackets, we report the average rating based on the five-point rating scale ωi,t−1 represents the weight of stock i in the portfolio p on date t − 1, that is, the market value of stock i as of the close of trading on date t − 1, divided by the aggregate market value of all stocks in the portfolio as of the close of trading on date t − 1

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Summary

Introduction

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 making stock recommendations. These stock recommendations, disseminated through electronic and print media, have been widely used by investors in their investment decisions. 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. 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. The inclusion of 23,235 (33.09%) analyst recommendation revisions for 1042 dead firms in our sample helps avoid the potential survivorship bias

Portfolio construction
Portfolio performance evaluation
Transaction costs
Abnormal gross returns
Abnormal net returns
High‐ranking brokerage houses
Industry‐based investment strategies
Bootstrapping simulations
Simulated results based on the whole sample
Simulated results based on the subsample made by the top 5 brokerage houses
Simulated results within two high‐tech industry sectors
Findings
Conclusions

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