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Review of Accounting Studies

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01.12.2013

Do sell-side analysts exhibit differential target price forecasting ability?

verfasst von: Mark T. Bradshaw, Lawrence D. Brown, Kelly Huang

Erschienen in: Review of Accounting Studies | Ausgabe 4/2013

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Abstract

We examine the overall and individual analyst performance of 12-month-ahead target price forecasts over the 10 years from 2000 through 2009. Implied target price-based returns exceed actual returns by an average of 15 %, and absolute target price forecast errors average 45 %. At the end of the 12-month forecast horizon, only 38 % of target prices are met, but 64 % are met at some time during the forecast horizon. We find statistically significant but economically weak evidence of persistent differential abilities by sell-side analysts to forecast target prices. Target price announcement period return analyses indicate no differential market reactions to analysts’ target price revisions conditional on their recent target price forecast performance. This finding is consistent with the market understanding that analysts have, at best, limited abilities to persistently provide accurate target price forecasts.

Vorheriger Artikel Dynamic risk, accounting-based valuation and firm fundamentals

Nächster Artikel Management forecast credibility and underreaction to news

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In their examination of 1,126 analyst reports written from 1997 through 1999 by 56 different sell-side analysts who were members of Institutional Investor’s All-American Research Team, Asquith et al. (2005) find that all the reports contained recommendations, 99.1 % contained earnings forecasts, and 72.6 % contained target price forecasts.

See Brown (2007) for over 1,400 abstracts of studies using Thomson Financial data, many of which are devoted to sell-side analysts’ earnings forecasts and stock recommendations. See Ramnath et al. (2008) for a recent literature review examining analysts’ earnings forecasts and stock recommendations.

Our inferences align with the conclusions in Marras (2011), who examines the level of return predictability, its source, and its persistence. As he notes, relative to statistical significance, “economic measures … weigh more heavily on investors’ minds.” Although Marras (2011) considers a standard asset pricing framework that is only indirectly related to analysts’ target price forecasts, our conclusions regarding the lack of economic significance conform with his conclusion that “estimation risk and transaction costs substantially reduce the economic value of predictability.”

Our results are also consistent with the notion that analysts do possess persistent abilities to forecast stock prices but the market does not properly recognize their abilities, but we are inclined to give little weight to this interpretation. Several conversations with quantitative investment professionals support our inference that analysts have at best limited target price forecasting abilities, and the market seems to understand this.

The 3 years, 1997 through 1999, were a raging bull market. The closing prices for the Dow Jones on December 31 were 6,448.27 in 1996, 7,908.25 in 1997, 9,181.43 in 1998, and 11,497.12 in 1999. Thus the market returns based on this popular index were 22.6, 16.1, and 25.2 % for 1997 through 1999, respectively.

Prior research shows that mutual fund managers have limited abilities to consistently beat benchmark returns (e.g., Carhart 1997; Chen et al. 2000) and that market experts fail to persistently accurately forecast macroeconomic variables such as interest rates (Belongia 1987), gross domestic product (Loungani 2001), recessions (Fintzen and Stekler 1999), and business cycle turning points (Zarnowitz 1992).

The best-known case is Henry Blodget’s $400 target price for Amazon, issued in December 1998 when Amazon was trading at approximately $240. Amazon’s price surpassed the $400 target price within 2 months, which led to Blodget’s rise as one of the preeminent analysts in the internet industry.

Brav and Lehavy (2003) analyze market reactions to target prices and find they are immediate, substantial, and permanent. Asquith et al. (2005) focus on All-American analysts, and provide a comprehensive analysis of a sample of their research reports. They find that the market reacts more to target price forecast revisions than to earnings forecast revisions of the same magnitude and that it reacts to target price forecast revisions in cases where it does not react to earnings forecast revisions.

For example: “Institutional investors pay no attention to analysts’ targets, preferring to figure out on their own what a stock’s value is. Because, let’s face it, target prices are a marketing tool.” (Maiello 2000). Likewise: “Those price targets were the equivalent of snake oil being sold off the back of covered wagons in the Wild West,” and “[i]ndividual investors should do as their institutional brethren do and pay no heed to target prices” (Morgenson 2001).

We ignore dividends in both realized and forecasted returns for simplicity. We acknowledge that error may be introduced when analysts’ forecasted dividends and realized dividends differ substantially.

Using signed TPERROR poses difficulties of interpretation because it is unclear whether negative TPERROR is better or worse than positive TPERROR. Intuitively, TPERROR >0 indicates a target price that was surpassed by actual prices, but if a hypothetical investor relied on the target price forecast to allocate capital, a large positive error would likely be associated with an inefficient under-allocation of capital to the security ex ante.

We also construct a variable, TPMETDAYS, formed by summing the days during the forecast horizon on which the trading price closes at or above the target price and dividing by the number of trading days, generally 252. This measure quantified the fraction of trading days during the forecast horizon the stock closes at or above the target price. The results are similar to those for TPMETEND and TPMETANY, so we do not report them for brevity.

For TPRANK, we calculate one rank correlation for each analyst in each semi-annual period.

We do not estimate a multivariate specification for TPRANK because it is measured at the analyst level causing it to lack firm-specific attributes.

This research design choice admittedly removes factors that skilled analysts would likely incorporate into their target price forecasts. Hence their inclusion detracts from our primary question. However, we include these other factors to provide insight into the determinants of target price accuracy. We thus report univariate regressions (with the LagTPPERF variables only) in addition to model (1).

Aboody et al. (2010) argue that strong prior returns heighten small investors’ demands immediately before earnings announcements, leading to post-earnings announcement return reversals. We expect this effect to also be applicable to the release of analyst reports.

“cumulative factor to adjust stock prices” on CRSP to convert stock prices to the same-split adjusted basis. We use stock-split adjusted target prices on I/B/E/S, so all target prices are based on the same-split adjusted basis.

Our sample starts with year 2000 as I/B/E/S provides target price data from late 1999. Our sample ends with year 2009, as we require one-year-ahead stock prices data to measure ex post target price performance.

I/B/E/S also identifies target prices with forecast horizons of 3, 6, 9, 18, 24, and 36 months. Our Table 1 shows that 2.3 percent of (14,603 out of 639,406) the individual target prices available in our original download do not have a 12-month forecast horizon.

We delete observations with TP/P ratio greater than four because we would still have extreme outliers due to data errors if we only deleted observations with TP/P ratio falling into the top percentile of the distribution. Our review of outliers suggests that miscoded or misaligned split factors are the primary explanation for most extreme values. After truncating the data, TP/P ratio has a minimum of 0.54 and a maximum of four (not tabulated).

The overall negative correlation between TP/P and target price performance illustrates the importance of the magnitude of the TP/P benchmark when measuring analysts’ performance. Our results are consistent with LaPorta (1996), who shows that the probability of analysts’ long-term earnings growth forecasts being achieved decreases as their expected earnings growth increases.

Using data from 1997 through 1999 up markets, Asquith et al. (2005) find that target prices are met 54.3 % during the 12-month forecast horizon.

Imbalances in the number of observations across portfolios reflect (1) the manner that SAS handles ties in its ranking procedure and (2) loss of observations when we impose the subsequent target price requirement.

The LagTPPERF variables are transformed to range between 0 and 1 (i.e., [Quintile-1]/4).

Note that TPMETEND, TPMETANY, and |TPERROR| are measured at the target price level. In contrast, TPRANK is measured at the analyst level. We do not estimate a multivariate specification for TPRANK because firm-specific attributes are not applicable.

Specifically, as illustrated in Table 4, the spread of differential performance between the top and bottom quintile performers falls from 1.12 in the measurement period to 0.01 in the test period when target price performance is measured as TPRANK. Similarly, the spread drops from 63, 62, and 66 % in the measurement period to 24, 2, and 11 % in the test period when target price performance is measured as |TPERROR|, TPMETEND, and TPMETANY, respectively.

As noted earlier, TPMETEND and TPMETANY are relegated to descriptive status, so are excluded from this analysis.

Using analyst-level analysis, we find that the spread reduces from 63, 62, and 66 % in the measurement periods to 24, 2, and 11 % in the test periods when target price performance is measured as |TPERROR|, TPMETEND, and TPMETANY, respectively.

When LagTPPERF is measured as TPMETEND, the coefficient becomes significantly negative (zero) after controlling for lagged TPERROR (contemporaneous TP/P). Results are available from the authors.

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Titel: Do sell-side analysts exhibit differential target price forecasting ability?
verfasst von: Mark T. Bradshaw
Lawrence D. Brown
Kelly Huang
Publikationsdatum: 01.12.2013
Verlag: Springer US
Erschienen in: Review of Accounting Studies / Ausgabe 4/2013
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-012-9216-5

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