CRONOLOGÍA DEL RECIPIENTE
9. PLATOS/FUENTES
Literature investigating the different properties of analysts’ earnings forecasts include Malkiel and Cragg (1968), who examined the accuracy of different sources of earnings estimate predictors, including analysts’ earnings forecasts, market price to earnings ratios and past growth rates. Malkiel and Cragg (1968) used individual analysts’ earnings forecasts to manually compute an unweighted consensus mean that is similar to the consensus produced by IBES. The use of an unweighted mean as the best estimator of the distributions of individual analysts’ earnings forecasts presupposes distribution normality. This assumption was specifically noted by the authors in their comparison of earnings predictors between different industry categories. It was found that analysts’ forecasts of the earnings growth of firms were no better than using past growth rates to make forecasts. However, both analysts’ earnings forecasts and past growth rates were found to be more accurate than using market price to earnings ratios as estimates of future earnings growth. Thus evidence suggested analysts’ earnings forecasts as a possible but not the only source of corporate earnings prediction. The accuracy of analysts’ earnings forecasts over all other sources of earnings prediction remained an area to be investigated.
The study of the accuracy of the consensus of analysts’ earnings forecasts progressed to accuracy comparisons against benchmarks such as time series models in the late 1970s. Proponents included Brown and Rozeff (1978) and Brown and Rozeff (1980) in which analysts’ forecast performance was measured against the Box and Jenkins time series models (Box, Jenkins and Reinsel, 1994). During this period Thomson Financial’s IBES forecasts publication service was not yet established and researchers resorted to earnings forecast sources such as the Value Line Investment Survey. This differs from the IBES consensus in that the Value Line publishes a single, independent analyst earnings forecast per firm per period versus the IBES which generates its consensus forecasts by aggregating individual analysts’ earnings forecasts on a per firm per period basis.
The prior study by Brown and Rozeff (1978) utilised Value Line Investment Survey forecasts from 1972-1975 and compared its performance against three other forecast methods, namely seasonal martingale (random walk), seasonal sub-martingale (random walk with or without a drift) and the Box-Jenkins. It was found the Value Line Investment Survey consistently made significantly better earnings forecasts than the Box and Jenkins and other types of time series models.
The latter study by Brown and Rozeff (1980) evaluated the forecast accuracy (percentage error from actual earnings) of 11 Value Line security analysts over the period 1973-1976 and compared this to the Box Jenkins ARIMA model. It was found 10 of the 11 analysts outperformed the ARIMA benchmark on the average, again pointing towards the general conclusion that analysts forecast earnings were more accurate than the Box Jenkins model.
The two studies above presented evidence substantiating the improvement in analyst forecasts accuracy over Box Jenkins models. It must be noted for studies carried out in the 1970s, it was necessary to use Value Line Investment Survey data due to the non-availability of the IBES forecasts data for that time period.
Philbrick and Ricks (1991), in their comparison of the Value Line Investment Survey and the IBES, concluded that the two analyst forecasts sources are “comparable in terms of their forecast data, but Value Line is a better source of actual EPS (earnings per share) data for the purpose of measuring earnings surprise.’’ However, Ramnath, Rock and Shane (2005), using a more recent sample period indicated just the opposite, namely “Value Line and IBES are comparable as sources of actual EPS data, but consensus forecasts derived from IBES outperform Value Line forecasts, both in accuracy and as proxies for market expectations.”
Additionally, the explanation for IBES consensus forecasts’ improved predictive accuracy over the Value Line forecasts was due to the reduction in idiosyncratic errors from the aggregation12 of individual analysts’ earnings forecasts. Thus based on the studies examined above by Brown and Rozeff (1978), Brown and Rozeff (1980) and Ramnath et al. (2005), IBES analysts’ earnings forecasts are expected to be more accurate than mechanical models and the Value Line Investment Survey forecasts.
The above studies are some examples of studies published in the 1970s, 1980s and the 1990s showing that analysts’ earnings forecasts are significantly more accurate than forecasts of mechanical models (for example, Barefield and Comiskey, 1975; Brown and Rozeff, 1978; Collins and Hopwood, 1980; Fried and Givoly, 1982; Brown, Hagerman, Griffin and Zmijewski, 1987; O’Brien, 1988, Hopwood and McKeown, 1990 and Branson, Lorek and
12 The aggregation principle suggests aggregation improves predictive accuracy by reducing
Pagach, 1995). According to the Thomson Scientific Social Science Citation Index13, of this
list the three most cited papers Fried and Givoly (1982), Brown et al. (1987), O’Brien (1988) were cited 13, 15, 16 times respectively during the 1992-1994 period.
Fried and Givoly (1982) compared analysts’ earnings forecasts from the Earnings Forecaster14 against the earnings forecast accuracy of extrapolative techniques, namely a
univariate time-series model15 and an index model16, from 1969 through 1979. It was found
analysts’ earnings forecasts were significantly more accurate than time series model earnings forecasts and the errors of analysts’ earnings forecasts were relatively more likely to explain stock price returns than forecasts from time-series models. Fried and Givoly (1982) attribute these results to two reasons: 1) analysts use a broader information set that includes company information sourced outside of financial reports; and 2) analysts have a timing advantage because their forecasts are published after the generation of time-series forecasts.
Brown et al. (1987) compared the forecast accuracy of Value Line Investment Survey analysts’ earnings forecasts with three Box and Jenkins times-series models17 for 233 firms
for 20 quarters from 1975 through 1979. They found that the analysts’ quarterly earnings forecasts were significantly more accurate than those from the three Box and Jenkins models with evidence robust to year, forecast horizon, forecast error definition, outlier treatment
13 The Social Sciences Citation Index (SSCI) provides access to current and retrospective
bibliographic information, author abstracts, and cited references found in over 1,700 of the world's leading scholarly social sciences journals covering more than 50 disciplines. It also covers individually selected, relevant items from approximately 3,300 of the world's leading science and technology journals. See http://scientific.thomson.com/products/ssci/ for more details.
14 Earnings Forecaster is a weekly publication by Standard and Poor (S&P) that first appeared in 1967.
The Earnings Forecaster lists the outstanding EPS forecasts for about 1500 companies. The forecasts are those made by S&P and by about 70 other security analysts and brokerage houses who agreed to submit their forecasts, upon release, to the publication.
15 This is a submartingale model of the form P
t = At-1 + Ct, where Ct is the (arithmetic) average growth
in EPS computed over the years t-6 to t-1.
16 The index model is based on the relationship found between the first differences in individual
company earnings and an economy-wide index of earnings such as the differences in earnings across all firms.
17 The time-series models are Box-Jenkins ARIMA models in the (p, d, q)( P, D, Q) notation, with the
three models being (l, 0, 0)(0, 1, l), analysed by Brown and Rozeff (1979), (l, 0, 0)(0, 1, 0) plus a constant, investigated by Foster (1977) and (0, l, l)(0, 1, l), examined by Griffin (1977).
method, fiscal quarter used and the type of significant tests used (parametric and non- parametric). The reasons Brown et al. (1987) provided to explain the superior analysts’ forecast performance were consistent with the two reasons for the analyst advantage proposed by Fried and Givoly (1982).
O’Brien (1988) compared the performance of IBES analysts’ quarterly earnings forecasts with those of two quarterly time-series models from Foster (1977), namely a naïve quarterly random walk model with drift and an autoregressive quarterly model with drift, for 184 firms for 26 quarters from 1969 through 1975. Four forecast horizons (see the definition of the Forecast Horizon in the Glossary of Terms) were used: 240, 180, 120 and 60 trading days prior to annual earnings announcement with the three longest horizons exhibiting significantly smaller mean error in analysts’ earnings forecasts than both time-series models. For the shortest horizon, the author found the error in analysts’ earnings forecasts to be at least as accurate as those generated by the time-series models.
In a more recent study in the Australian context, Ho (1996) examined the bias and accuracy of Australian IBES analysts’ earnings consensus forecasts over the period 1987-1990 and showed that analysts in general provide more accurate forecasts than naïve time-series models within the constraints of shorter forecast horizons. Similarly, Brown (1996), in a study using US IBES forecast data from 1974 through 1991, showed analysts’ earnings forecast errors were “within 3% of an appropriate benchmark (namely, stock price), that their forecasts generally were significantly more accurate than forecasts by naive or sophisticated times-series models, and that analyst forecast errors have not been increasing over time.”
Evidence indicates that for over 3 decades of analyst forecast accuracy research established the predictive superiority of analysts’ earnings forecasts over mechanical models. The evidence of analysts’ earnings forecasts as a more accurate proxy of market expected earnings over other types of mechanical forecast models have provided researchers the impetus to branch out to other forecast domains such as: (a) properties of analysts’ earnings forecasts (bias, revisions, following/ neglect and dispersion); (b) the relationship of the properties of analysts’ earnings forecasts with other components of the earnings forecast research framework such as behaviour of analysts (Clement and Tse, 2003, 2005); and (c) market response such as share price return and share price volatility (Johnson, 2004). Only the first point is examined in the context of this thesis because of the reasons discussed in 2.3.
The next section examines the different definitions of accuracy and how the most appropriate one is chosen and utilised within the context of this thesis.
2.4.3 Definition of Accuracy: Consensus and Individual Analysts’ Earnings