Dynamics Of Recommendations By Financial Analysts – The Reluctant Analyst
University of Illinois at Urbana-Champaign – Department of Economics
Historically, the Chinese market has been relatively isolated from international investors, but much is changing there now, making China virtually impossible for the diversified investor to ignore. Earlier this year, CNBC pointed to signs that Chinese regulators may start easing up on their scrutiny of companies after months of clamping down on tech firms. That Read More
Boston College – Department of Finance and Department of Economics
University of Massachusetts Boston – Department of Accounting and Finance
January 13, 2012
We estimate the dynamics of recommendations by financial analysts, uncovering the determinants of inertia in their recommendations. We provide overwhelming evidence that analysts revise recommendations reluctantly, introducing frictions to avoid frequent revisions. More generally, we characterize the sources underlying the infrequent revisions that analysts make. Publicly-available data matter far less for explaining recommendation dynamics than do the recommendation frictions and the long-lived information that analysts acquire that the econometrician does not observe. Estimates suggest that analysts structure recommendations strategically to generate profitable order flow from retail traders. We provide extensive evidence that our model describes how investors believe analysts make recommendations, and that investors value private information revealed by analysts’ recommendations.
Dynamics Of Recommendations By Financial Analysts – The Reluctant Analyst – Introduction
One of the most important services that financial analysts provide is to make recommendations to retail and institutional customers about which stocks to purchase, and which ones to sell. Brokerage houses want to employ financial analysts who provide recommendations on which investors can profit, thereby generating profitable trading activity for the brokerage house. Many researchers (e.g., Womack, 1996; Francis and Soffer, 1997; Barber et al., 2001 and 2006; Jegadeesh et al., 2004; Ivkovic and Jegadeesh, 2004; Howe et al., 2009; and Bradley et al., 2014) have documented the profitability and informativeness of various measures of recommendations and recommendation changes.
In this line, one can contemplate an “idealized” financial analyst who first gathers and evaluates information from public and private sources about a set of companies to form assessments about their values, and then compares his value assessment with the stock’s price, issuing recommendations to his investor audience on that basis. Thus, an idealized analyst employing a five-tier rating system would issue “Strong Buy” recommendations for the most under-valued stocks, whose value-price differentials, aaaaaaaaaa, exceeded a high critical cutoff, 5. The analyst would establish progressively lower cutoffs, 4, 3 and 2, that determine “Buy”, “Hold”, “Sell” and “Strong Sell” recommendations, so that, for example, the analyst would issue Buy recommendations for value-price differentials between 5 and 4, and strongly advise customers to sell stocks with the worst value-price differentials below 2.
Analysts do not form recommendations in this way. To understand why, observe that sometimes a stock’s value-price differential will be close to a cutoff, in which case slight fluctuations in price relative to value lead to repeated recommendation revisions. In practice, analysts infrequently revise recommendations|customers would question the ability of an analyst who repeatedly revised recommendations on which they based investments.
We develop and estimate a model of a “reluctant” financial analyst. The analyst assesses value just like an idealized analyst, and when initiating coverage, he makes an initial recommendation on the same basis. However, the analyst only downgrades a recommendation if the value-price differential falls far enough below the critical cutoff, and only upgrades a recommendation if the value-price differential rises far enough above the cutoff. Thus, a reluctant analyst downgrades a recommendation from a Buy only if a stock’s value-price differential falls below , and he upgrades from a hold only if the differential rises above are stickiness parameters that measure an analyst’s strategic “reluctance” to revise recommendations.
The contributions of our paper are first to identify the drivers and determinants of stickiness in analyst recommendations. We distinguish the relative importance of recommendation revision frictions, persistent analyst information and public information available to an econometrician for explaining the dynamics of analyst recommendations. In turn, these drivers provide insights into the strategic considerations and information of analysts. We uncover how incorporating strategic behavior and analyst information alters our understanding of how various public information characteristics of a firm (e.g., size, past performance) enter an analyst’s assessment of firm value. We show how our model informs about the returns of firms following recommendation revisions inside and out of earnings announcement and guidance windows. Finally, we show that our model provides a measure of the “surprise” associated with a recommendation revision or initiation that explains the magnitudes of returns.
There are many different sources of stickiness in recommendations, and the econometric model must account for each of them to avoid biasing estimates that lead to mistaken inferences about their relative importance. One source of stickiness is simply that much of the public information that analysts receive arrives in lumps. Concretely, earnings announcements arrive quarterly, and earnings guidance is given sparingly. An unsurprising consequence, for example, is that recommendation revisions are more likely inside announcement and guidance windows, generating “stickiness” outside these windows.
See full PDF below.