The influence of analysts' reports on investors

As key information intermediaries, sell-side financial analysts have a significant influence on financial disclosure decisions as well as financial reporting choices. But how do they influence investors?

Sell-side financial analysts are one of the cornerstones of financial markets and they have a significant influence on corporate financial disclosure decisions. As key information intermediaries, analysts facilitate the operation of well-functioning financial markets and contribute to pricing accuracy. Analysts both provide new information to the market and help investors interpret existing information, such as financial statements (Schipper 1991, Bradshaw 2011). Apart from informing investors, analysts also potentially influence firms' real operating decisions as well as financial reporting choices. For example, through their issuance of quarterly and annual earnings forecasts, analysts can contribute to an over-emphasis of specific earnings targets and thus possibly increase firms' earnings management (and/or expectations management) so they can meet or beat those targets. At the same time, evidence exists that analysts also help monitor firms' accounting information and increase the quality of the financial reports. Given analysts' important roles in influencing financial disclosure and providing information to investors, it is important to understand how analyst reports influence investors.

The importance of sell-side analysts is also apparent from the large literature in academic accounting studying these analysts. In fact, there are hundreds of accounting-related studies on analysts in accounting (Brown 1993 and Bradshaw 2011 provide overviews of this literature). However, the majority of this research centers on earnings forecasting. Although clearly important, some researchers argue that earnings forecasts are less important than other activities, or even just one of the inputs into analysts' other activities (Schipper 1991; Bradshaw 2011).1 Moreover, in a survey reported in Institutional Investor magazine (October 2010), investors were asked to rate the importance they place on a dozen sell-side equity analyst research attributes. The highest ranked attribute was industry knowledge (whereas earnings estimates were ranked 12th), which is imparted to investors through analysts' written reports. These written reports are the focus of the studies described below.

In work with colleagues and former PhD students at the Rotman School of Management, I have conducted research that examines the content of analysts' written reports. Specifically, we investigate (1) the readability of the reports and (2) how analysts choose peer firms for use in their reports, respectively.

What Explains the Readability of Analysts' Reports and does it Matter?

In De Franco, Hope, Vyas, and Zhou (2013), we examine both the factors that explain variations in how "readable" analysts' written reports are, and whether investors view the ease of comprehension (readability) as important. Our tests use an extensive database of 356,463 analysts' reports for US public companies from 2002 to 2009. We study two aspects of readability: straightforward language and concise reports. To measure the former, we use different versions of the Fog Index, which indicate the level of formal education a reader of average intelligence would need to understand a piece of writing clearly after one reading. Following Li (2008) and Lawrence (2013), we measure conciseness using the length of the report.2

The research begins with the prediction that readability is associated with the ability of the writer. This hypothesis relies on the idea that innate ability (ability acquired through education or experience) and the specific ability to write easier-to-read reports are likely to be jointly determined. We employ five indicators of high-ability analysts and expect each one to be positively correlated with analysts' report readability. All tests take into account other analyst, firm and industry characteristics. Our results suggest that high-ability analysts issue reports that are easier to read. Specifically, analysts with more experience write more readable reports, as do analysts who provide more timely earnings forecasts, analysts who revise their earnings forecast more frequently, analysts who are ranked by Institutional Investor magazine, and analysts who are consistent in their earnings forecast-stock recommendation revisions.

Now that we know which analysts write reports that are easier to read, we turn our attention to the question of whether investors care about the readability of these reports. Our prediction is that greater report readability leads to increased trading volume (and greater abnormal stock returns). Our hypothesis has support from theoretical work, such as Kim and Verrecchia (1991), which shows that more precise information leads to greater trading volume. The intuition behind these models is that more informative signals cause investors to initiate trades.

We measure abnormal trading volume over a three-day window centered on the analysts' report and regress this variable on the two readability measures as well as numerous control variables to account for other determinants of abnormal trading volume (including determinants of analyst report readability). As this is a short-window event study, any observed effect is unlikely to be driven by other (omitted) variables. The results show that trading volume reactions are higher for both measures of readability of analysts' reports, which is consistent with the idea that readability affects investors' decisions and that the readability of analyst reports is important for the users of the reports.

Do Analysts Select the Peer Firms for their Reports with Bias?

In the second study by De Franco, Hope, and Larocque (2013), we examine the peer firms analysts use in their reports. Analysts provide earnings estimates, valuations (including target prices), and stock recommendations for the firms they cover. They often use peer firms to compare performance and valuations across firms, as well as to estimate the valuation of the firms they cover. In our study, we hand-collect analysts' reports and manually extract the information on comparable or peer companies from the reports. Thus, we are able to examine the peers used by analysts in their research reports. We are particularly interested in studying the relation between peer valuation and peer choice by analysts. Existing evidence suggests that in issuing their reports, analysts have incentives to obtain investment banking business and thus may not provide estimates that always maximize investor wealth (Barniv et al. 2009, 2010). In other words, given the discretion in peer selection (which we document in the study), there is a potential for analysts to choose peers in a biased manner. In particular, if an analyst is induced to estimate a high valuation for a firm he or she covers, one possible strategy would be to choose peer companies with high valuation multiples so as to make the firm in question appear relatively undervalued.

As we expect analysts to pick peers primarily based on economic determinants such as industry and firm size, we include numerous control variables to account for these determinants in our tests. These control variables represent the normal (or unbiased) explanatory variables for peer choice. Generally speaking, we consider comparable firms to be those firms in the same industry that have similar size, similar profitability drivers and similar share characteristics. Our tests include both the level of these variables as well the similarity between the firms being analyzed and their potential peers.

Hand-collecting data on peer firms is costly, and for that reason we limit our sample to analyst reports from 2005 and 2008. Our final sample comprises 2,547 report-year observations (for 1,368 unique firms) and 13,575 peers (3,165 unique peers). We test for the effect of peer valuation on peer choice using four valuation multiples: price to earnings, price to book, enterprise value to sales, and enterprise value to EBITDA.

Our regression models have high explanatory power for how analysts' actually pick peers (i.e., the number of companies correctly classified as chosen peers is about 88%). More importantly, across all four valuation metrics, we find strong evidence that, even after considering a large number of other effects, our results are consistent with analysts choosing peers with higher valuation multiples and thus support the idea that analysts choose peers in an optimistically-biased manner.

In additional analyses, we examine whether the documented bias is stronger for conditions for which we have reasons to expect a more pronounced effect. We first test whether high-ability analysts, defined to include those analysts who are ranked by Institutional Investor (II) magazine, exhibit less bias than other analysts. Prior research suggests that II-ranked analysts exhibit higher ability as evidenced by their greater forecasting accuracy, stock recommendation profitability and report readability. In addition, it is likely that institutional investors (who are the primary audience of II-ranked analysts) demand less biased (or more neutral) advice. Consistent with these expectations, we find that the relation between peer valuation and peer choice is reduced (and in fact disappears for two of the four valuation multiples we consider) for II-ranked analysts.

We further investigate whether the effect is greater for stronger analyst incentives. In line with our interpretation of analysts choosing peer firms with high valuation as "bias" (rather than some other explanation), we find that the effect is greater for analysts with investment banking affiliations, suggesting analysts choose peers strategically to increase the chances of attracting investment banking business.

Finally, we explore whether the observed relation between peer valuation and peer choice could partially explain the mechanism through which analysts justify their recommendations or target prices. A large literature finds that equity analysts' buy recommendations are much more prevalent than hold or sell recommendations. Similarly, Brav and Lehavy (2003) find that analysts set target prices that are, on average, 28% higher than contemporaneous stock prices. Our empirical results suggest that the known positive bias in stock recommendations, as well as increases in target prices, may be justified by analysts selecting peers with higher valuations.

Overall, we conclude that while analysts primarily consider valid economic factors when selecting peer firms to benchmark against, there does seem to be some bias in the choice of peers. In particular we find that on average analysts tend to pick peers with high valuations.

Technical editor: Karim Jamal, FCA, PhD, chair of the department of accounting, operations and information systems, School of Business, University of Alberta

Footnotes

1 Presumably, analysts use their own publicly issued earnings forecasts to derive intrinsic value estimates. If so, one should expect these estimates to relate positively to analysts' stock recommendations. However, Bradshaw (2004) and Barniv, Hope, Myring, and Thomas (2009; 2010) find that residual income valuations, developed using analysts' earnings forecasts, do not relate as expected with analysts' recommendations.

2 Conciseness comprises the number of words and the number of characters in the report. The argument is that longer reports are more difficult to understand due to higher information processing costs. As an alternative explanation for a lengthy document is greater complexity, our multivariate tests control for several factors related to complexity (i.e., we're capturing "excessive" length).

References

Barniv, R., O.-K. Hope, M.J. Myring, and W.B. Thomas. 2009. Do Analysts Practice What They Preach? The Effects of Regulations on How Analysts Use Earnings Forecasts in Setting Stock Recommendations. The Accounting Review 84 (4): 1015-1039.

Barniv, R., O.-K. Hope, M.J. Myring, and W.B. Thomas. 2010. International Evidence on Analyst Stock Recommendations, Valuations, and Returns. Contemporary Accounting Research 27 (4): 1131-1167.

Bradshaw, M.T. 2004. How do analysts use their earnings forecasts in generating stock recommendations? The Accounting Review 79 (1): 25-50.

Bradshaw, M.T. 2011. Analysts' forecasts: What do we know after decades of work? Working paper, Boston College.

Brav, A., and R. Lehavy. 2003. An empirical analysis of analysts' target prices: Short-term informativeness and long-term dynamics. Journal of Finance 58 (5): 1933-1967.

Brown, L. 1993. Earnings forecasting research: Its implications for capital markets research." International Journal of Forecasting 9: 295-320.

De Franco, G., O.-K. Hope, and S. Larocque. 2013. Analysts' Choice of Peer Companies. Working paper, University of Toronto and University of Notre Dame.

De Franco, G., O.-K. Hope, D. Vyas, and Y. Zhou. 2013. Analyst report readability. Forthcoming, Contemporary Accounting Research. Institutional Investor. 2010 (October). The 2010 All-America Research Team (page 52).

Kim, O., and R.E. Verrecchia. 1991. Trading volume and price reactions to public announcements. Journal of Accounting Research 29: 302–321.

Lawrence, A. 2013. Individual Investors and Financial Disclosure. Forthcoming, Journal of Accounting and Economics.

Li, F. 2008. Annual report readability, current earnings, and earnings persistence. Journal of Accounting and Economics 45 (2-3): 221-247.

Schipper, K. 1991. Commentary on analysts' forecasts. Accounting Horizons 5 (4): 105-121.

About the Author

Ole-Kristian Hope


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