Abstract
We investigate the information-dissemination role of the business press by examining the coverage of analyst recommendation revisions. Consistent with the press providing wider dissemination of analyst reports, we find evidence that coverage of analyst recommendation revisions significantly increases the initial market reaction to these revisions and decreases the subsequent price drift. Furthermore, we find that news flash coverage, rather than in-depth coverage, of a recommendation revision drives both the initial market reaction results and drift results. Finally, we show that broader press coverage influences the activities of large-trade institutional investors but not high-frequency traders. Overall, our findings suggest a complementary role between analysts and the business press: increased dissemination of recommendation revisions, rather than information creation on the part of the business press, serves to better inform the market about analyst recommendation revision decisions.
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Notes
Miller and Skinner (2015) discuss the importance of media and technology in the dissemination of information, the disclosure of firm-specific news, and the interaction between various information intermediaries.
We acknowledge that this has been done previously, most notably by Rees et al. (2015). We discuss this study in more detail later in the paper.
For example, on Aug 7, 2018, Tesla CEO Elon Musk tweeted his intention to take Tesla private at a price of $420 per share. This announcement prompted an immediate double-digit increase in the firm’s stock price. He was subsequently removed as chairman of Tesla but allowed to stay on as CEO. In contrast, any number of analysts have made statements of similar magnitude with no SEC involvement. Note that Mr. Musk later recanted this intention.
We acknowledge that managers also disseminate what could be viewed as opinions when they issue guidance; however, these disclosures are more costly than a recommendation revision released by an analyst and should therefore be given more weight by the market.
See Appendix B for examples of news flashes and full-length articles.
Untabulated results indicate that full coverage is more likely when a recommendation revision differs greatly from the consensus recommendation. In our manual inspection of full-length articles, we observe that these articles generally agree with the analysts’ recommendations but provide more in-depth explanations of why the analysts chose to revise their recommendations.
We also acknowledge that analysts face clear career incentives to issue useful research (Hong et al. 2000; Hong and Kubik 2003). However, these career concerns appear to be secondary to characteristics like optimism and industry knowledge (more optimistic analysts are more likely to be promoted). These studies further highlight the conflicts analysts face when making their recommendations, and they make it less clear whether broader dissemination of these recommendations will be a net benefit for the pricing of securities. Furthermore, Brown et al. (2015) demonstrate that mutual fund managers herd on recommendations, and this herding has proved to be price destabilizing as mutual funds hold a higher level of stock.
In fact, the business press does not cover all the recommendations issued. Table 2 Panel A shows that only about 25% of recommendation revisions receive coverage.
Inferences remain unchanged when we use probit regression, instead of logistic regression.
As a robustness test, we also use the sample means/medians in computing the relative analyst characteristics and obtain similar results (untabulated).
Following the recommendations of Lennox et al. (2012), we conduct additional tests to determine the validity of this instrument. In untabulated results, we test the exclusion restriction. First, we verify that our instrument (coverage of the prior period earnings announcement) is associated with press coverage of current analyst recommendation revisions but not with current stock returns on the date of the revisions and the post-recommendation revision return drift. Second, we also sequentially estimate the logit model in Panel B of Table 3, with one control variable being omitted each time, and find that our results are unaffected. Third, following Bushee et al. (2003), we remove all control variables from the model (except the instrumental variable) and again find that our inferences are unaffected.
In all regressions, we use fiscal quarter fixed effects. The results hold when we use calendar quarter fixed effects.
This identification is complicated by the fact that RavenPack does not include the level of recommendation or the identity of the brokerage or analyst, so we cannot match the article to each recommendation in cases where there are multiple recommendations by different brokerages for a given firm within a short period. However, RavenPack includes the direction of the recommendation revision and provides the article’s headline, which includes a brokerage name. We manually read the headline to extract a brokerage name and use the name as well as the direction of the revision to identify the article that actually covered the revision.
Bradley et al. (2014) show that, for a significant portion of recommendations covered in newswires, the reported I/B/E/S time stamp is delayed, relative to the newswire stamp, on average, by about 3.7 h with a median of 1.6 h. We therefore make the simplifying assumption that any article that mentions an analyst’s recommendation revision for a firm within two days of the revision date relates to that revision. To ensure an unambiguous match, we manually read all the headlines of articles matched to each recommendation in the second step.
This task was accomplished with the help of four research assistants, who manually read and matched (confirmed) each headline.
Drake et al. (2014) note that about 68% of earnings announcements are covered in news flashes, which is lower than the news-flash coverage of analyst recommendation revisions. This finding suggests that for earnings announcements (as opposed to analysts’ recommendations), the media is more likely to immediately produce and disseminate additional related content than to simply repeat basic facts about earnings.
The size of these returns is comparable to that found previously. For example, Womack (1996) finds 3.3% (−4.3%) of three-day abnormal returns for upgrades (downgrades). Jegadeesh and Kim (2010) find 2.29%, −3.40%, and − 0.12% of two-day abnormal returns following recommendation revisions for upgrades, downgrades, and reiterations, respectively.
It is hard to find prior studies that allow us to directly compare the size of the drift, given that prior studies use different samples, conditioning variables, and event windows. It is worth pointing out that the drift is bigger for older samples. Our sample period is between 2000 and 2015. Mikhail et al. (2004) use a sample between 1985 and 1999 and show that the one-month drift for upgrades (downgrades) is between 0.51% and 0.87% (between −0.62% and − 0.86%).
We also note that the coefficient on the triple interaction among EA_Dummy, Recommendation_Change, and Press_Cover is significantly negative. This negative coefficient is consistent with our concern that the stock market reaction to recommendation revisions is contaminated by the reaction to earnings announcements.
There is no conclusive evidence on the informativeness of analysts’ recommendations. Some studies, including those by Womack (1996), Bradley et al. (2014), and Li et al. (2015), provide evidence for the informational role of the recommendations. On the other hand, other papers, including those by Altinkilic and Hansen (2009) and Chen et al. (2005), provide conflicting evidence. Loh and Stulz (2011) also show that only about 12% of recommendation revisions in their sample are influential in returns and that influential recommendation changes come only from a subset of skilled analysts.
The results are similar when we use our alternative specifications.
Higher odd lot and cancel-to-trade ratios indicate more algorithmic trading, and higher trade-to-order volume and trade sizes indicate less algorithmic trading. Accordingly, we multiply the trade-to-order volume and trade size variables by negative one before entering them into the factor analysis. We note that the four proxies for algorithmic trading converge to a single factor that explains 65% of the underlying variation with an Eigenvalue of 2.61.
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Acknowledgements
We thank Artur Hugon, Derrald Stice, Jake Thornock, Brady Twedt, Andrew Van Buskirk, Roger White, and workshop participants at Arizona State University. All mistakes are our own.
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Appendices
Appendix 1. Variable Definitions
Variable | Definition |
|---|---|
Press_Cover | An indicator variable equal to 1 if an analyst recommendation is covered by the business press and 0 otherwise |
Stock_Recommendation | Stock_Recommendation is coded as follows: Strong Sell = 1 Sell = 2 Hold = 3 Buy = 4 Strong Buy = 5 |
Recommendation_Change | The difference between current and previous recommendations issued by an analyst for a firm |
Abs_Recommendation_Change | The absolute value of Recommendation_change |
AllStar | An indicator variable equal to 1 if an analyst was recognized as a member of Institutional Investor’s All-America Research Team as of the most recent prior year and 0 otherwise |
Forecast_Accuracy | A rank of the absolute forecast error of the analyst’s earnings forecast for the most recent quarter prior to the recommendation date, multiplied by −1. The absolute forecast error is calculated as the absolute value of an analyst’s earnings forecast for a quarter minus actual earnings for the quarter divided by the stock price measured at the beginning of the fiscal quarter. Relative forecast accuracy is calculated as the forecast accuracy of analyst i following firm j in quarter t minus the lowest forecast accuracy by any analyst following firm j in quarter t, with this difference scaled by the range in forecast accuracy for all analysts following firm j in quarter t. |
Recommendation_Horizon | The difference between the current and previous recommendation announcement dates (in days) |
Firm_Experience | The number of years an analyst has covered a firm. Relative firm experience is calculated as the firm experience for analyst i following firm j in quarter t minus the smallest firm experience by any analyst following firm j in quarter t, with this difference scaled by the range in the firm experience for all analysts following firm j in quarter t. |
General_Experience | General experience is defined as the number of years an analyst has covered any firm. Relative general experience is calculated as the general experience for analyst i following firm j in quarter t minus the smallest general experience by any analyst following firm j in quarter t, with this difference scaled by the range in the general experience for all analysts following firm j in quarter t. |
BrokerSize | Broker size is defined as the total number of analysts employed by a brokerage for which an analyst has worked as of the most recently completed calendar year prior to the recommendation date. Relative broker size is calculated as the broker size for analyst i following firm j in quarter t minus the smallest broker size of any analyst following firm j in quarter t, with this difference scaled by the range in broker size for all analysts following firm j in quarter t. |
Industry_Coverage | Industry coverage is defined as the number of industries covered by an analyst during the most recently completed calendar year prior to the recommendation date. Relative industry coverage is calculated as the industry coverage of analyst i following firm j in quarter t minus the smallest industry coverage by any analyst following firm j in quarter t, with this difference scaled by the range in industry coverage for all analysts following firm j in quarter t. |
Firm_Coverage | The number of firms covered by an analyst during the most recently completed calendar year prior to the recommendation date. Relative firm coverage is calculated as the firm coverage of analyst i following firm j in quarter t minus the lowest firm coverage by any analyst following firm j in quarter t, with this difference scaled by the range in firm coverage for all analysts following firm j in quarter t. |
Prior_Press_Cover_Analyst | An indicator variable equal to 1 if any recommendation issued by an analyst is covered by the business press at least once during the 30 days prior to the recommendation date and 0 otherwise |
ABS_ABN_RET | The absolute value of the difference between a firm’s raw stock returns and the value-weighted market return on the recommendation date |
ABN_TURN | The difference between a firm’s share turnover and the market’s share turnover on the recommendation date, where a firm’s turnover is computed as daily CRSP trading dollar volumes divided by the market value of shares outstanding on the recommendation date and the market share turnover is calculated as the average daily turnover for all stocks listed on CRSP on the recommendation date |
SUE | The difference between the current quarter’s earnings per share and analysts’ consensus earnings per share forecasts, scaled by the standard deviation of this difference during the last eight quarters, including the current quarter |
LNMVE | The log of outstanding shares times closing stock price on the fiscal quarter end date |
MTB | Book assets minus book equity plus market equity, all divided by book assets. Market equity is calculated as the fiscal-quarter closing price times the shares outstanding. Book equity is defined as stockholder’s equity minus preferred stock plus balance-sheet deferred taxes and investment tax credit. If balance-sheet deferred taxes and investment tax credit is missing, it is set to zero. If stockholder’s equity is not available, it is replaced by common equity plus preferred stock par value, or assets minus liabilities. Preferred stock is preferred stock redemption value, or preferred stock par value. |
LNANALYST | The log of 1 plus the number of analysts providing one-quarter-ahead earnings forecasts at least once during the fiscal quarter |
INST_HOLD | The percentage of institutional ownership on the fiscal quarter-end date |
LNEMPLOYEE | The log of 1 plus the number of employees (Compustat Annual #29) |
LNOWN | The log of 1 plus the number of shareholders (Compustat Annual #100) |
Qt1_TURN | Quarter t-1’s share turnover minus quarter t-1’s market share turnover |
Qt1_VOLAT | Quarter t-1’s return volatility computed as the standard deviation of the log of 1 plus daily return, multiplied by the square root of 252 |
Prior_Press_Cover_Firm | An indicator variable equal to 1 if a firm is covered by the business press at least once during the 30 days prior to the date of the recommendation revision and 0 otherwise |
SP1500 | An indicator variable equal to 1 if a firm is a member of the S&P 1500 stock index in year t and 0 otherwise |
Abn_Return (−1, +1) | Raw buy-and-hold stock return over (t = −1, +1) minus the return to a benchmark portfolio formed based on size, book-to-market, and momentum over the same three-day period (t = 0 is the date of the recommendation revision) |
Abn_Return (+2, +20) | Raw buy-and-hold stock return over (t = +2, +20) minus the return to a benchmark portfolio formed based on size, book-to-market, and momentum over the same three-day period (t = 0 is the date of the recommendation revision) |
Abn_Return (+2, +60) | Raw buy-and-hold stock return over (t = +2, +60) minus the return to a benchmark portfolio formed based on size, book-to-market, and momentum over the same three-day period (t = 0 is the date of the recommendation revision) |
EA_Dummy | An indicator variable equal to 1 if a firm made an earnings announcement within one month around the date of the recommendation revision and 0 otherwise |
Prior_Press_Cover_EA | An indicator variable equal to 1 if the prior year’s earnings announcement for firm i is covered by the business press and 0 otherwise |
Flash_Cover | An indicator variable equal to 1 if an analyst recommendation is covered in a news flash disseminated by the business press and 0 otherwise |
Full_Cover | An indicator variable equal to 1 if an analyst recommendation is covered in a full article disseminated by the business press and 0 otherwise |
ABN_LARVOL | A firm’s daily average shares traded over days [0, +2] for large trades, divided by total shares outstanding, minus the firm’s trading average over days [−41, −11]. We define large trades as trades greater than or equal to $50,000. |
High_Algorithm | An indicator variable equal to 1 if a firm’s factor score on the date of a press release of a recommendation revision is greater than a median factor score on the same date or if a firm’s average factor score over three days around the press release is greater than a median average factor score over the same period, and 0 otherwise. Factor score is created from a factor analysis of four algorithmic trading proxies developed by Weller (2017) (i.e., the log of odd lot volume ratio, the log of trade-to-order volume ratio, the log of cancel-to-trade ratio, and the average trade size). |
Appendix 2. Example Articles
This appendix contains news flashes (B.1) and excerpts from full articles (B.2) about analysts’ recommendation revisions. The excerpts from the full articles illustrate the potential role of the press in producing information regarding analysts’ recommendation revisions and conveying this information to the market.
1.1 News Flashes
-
The following news flash was published in Dow Jones Newswires on January 15, 2004:
DJ UBS Upgrades Apple Computer To Buy From Neutral >AAPL.
(END) Dow Jones Newswires.
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The following news flash was published in Dow Jones Newswires on December 19, 2008:
eBay Cut To Mkt Perform From Outperform By Bernstein.
(END) Dow Jones Newswires
-
The following news flash was published in Dow Jones Newswires on September 24, 2012:
United States Steel Corp Cut To Neutral From Buy By Citigroup.
(END) Dow Jones Newswires.
1.2 Excerpts from Full Articles
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The following article excerpt was published in Dow Jones Newswires on February 15, 2001:
Prudential Cuts Amazon −2: Sell Ratings Extremely Rare.
By Ross Snel Of DOW JONES NEWSWIRES.
NEW YORK (Dow Jones)—Prudential Securities analyst Mark Rowen has lowered his investment rating on shares of Amazon.com Inc. (AMZN) to sell from hold, declaring that there is greater downside risk for holders of the online retailer’s shares than there is upside potential.
In a research note Thursday, Rowen wrote that he was prompted to re-examine Amazon’s stock valuation by “anemic” growth in the Seattle company’s core book, music and video business.
Rowen lowered his price target on Amazon’s shares to $9 from $20.
Sell and strong-sell ratings from Wall Street analysts are extremely rare. They account for only 1% of all outstanding stock ratings, according to First Call/Thomson Financial.
Faye Landes, an analyst at Sanford C. Bernstein, has an underperform rating on Amazon, her firm’s lowest rating. Sanford Bernstein, however, does not have an investment banking business, so its analysts are perceived to be free of the pressure that investment banking firm analysts sometimes face.
Holly Guthrie, Rowen’s counterpart at Janney Montgomery Scott, had cut her rating on Amazon shares to sell last October but raised it back to hold in late December.
Rowen’s move is the latest blow for Amazon, which is coming under increasing pressure to show it can make it to profitability before it runs out of cash.
Last week, Lehman Brothers’ convertible debt analyst Ravi Suria, who for some time has been sharply critical of Amazon, issued a scathing report that questioned Amazon’s levels of working capital and its ability to continue operating through the remainder of the year.
Amazon’s shares traded lower on the news and were recently changing hands at $14.13, down 31 cents, or 2.2%. Volume was 2.2 million shares, compared with daily average volume of 9.4 million shares.
(MORE TO FOLLOW) Dow Jones Newswires.
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The following article excerpt was published in Dow Jones Newswires on April 24, 2007:
Merrill Cuts Wendy’s Intl To Sell, Questioning 2007 Targets.
By Richard Gibson Of DOW JONES NEWSWIRES.
Merrill Lynch & Co. (MER), challenging management’s performance forecasts, downgraded shares of Wendy’s International Inc. (WEN) Tuesday to sell from neutral.
“Expect more bad news than good,” restaurant analyst Rachael Rothman wrote in a 24-page report explaining her action.
Lowering her earnings estimates for this year and next, she said, “We believe Wendy’s is likely to miss several of its 2007 guidance targets,” among them per-share earnings, same-store sales, restaurant margin expansion and earnings before interest, taxes, depreciation and amortization, or Ebitda.
“This is the fourth year in a row that management has targeted 3% to 4% same-store sales growth. In each of the last three years, management has missed its target by between 1% and 7%,” Rothman said.
She also said the hamburger chain’s planned rollout of breakfast, combined with recent increases in minimum wages, “will make it difficult for WEN to achieve significant labor leverage” this year.
The analyst lowered her current-quarter earnings estimate to 33 cents a share, compared with Street expectations of 40 cents. She expects year earnings of $1.12 compared with the analyst average of $1.27, and she sees fiscal 2008 earnings of $1.40, compared with the average of $1.65, according to Thomson Financial.
Wendy’s had no immediate comment on the report.
Shares of Wendy’s were trading recently at $32.14, down 71 cents, or 2.2%, on the New York Stock Exchange. Volume was 2.2 million compared with average daily volume of 1.6 million.
-By Richard Gibson, Dow Jones Newswires; 515–282-6830; dick.gibson@dowjones.com
(END) Dow Jones Newswires.
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The following article excerpt was published in Dow Jones Newswires on June 12, 2012:
Bernstein upgrades Boeing to outperform.
Bernstein Research on Tuesday said it is upgrading Boeing Co. (BA) to outperform from market perform, and revising its target price to $92, up considerably from $85 previously. In its report, Bernstein said it improved its outlook on the production rate and delivery of the aircraft maker’s 787 commercial model, and that it believes the recent share price decline at Boeing “is an overreaction to macroeconomic difficulties”. Bernstein also noted that it will retain its below-consensus EPS estimates for the company through 2012 and 2013, but recommends that investors “ignore” negative earnings revisions and focus on the outlook and deliveries for the 787 program. Boeing shares closed at $70.11 on Monday.
(END) Dow Jones Newswires.
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Ahn, M., Drake, M., Kyung, H. et al. The role of the business press in the pricing of analysts’ recommendation revisions. Rev Account Stud 24, 341–392 (2019). https://doi.org/10.1007/s11142-019-9485-3
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DOI: https://doi.org/10.1007/s11142-019-9485-3