From the latest report of Deutsche Bundesbank ( Germany's central bank ): Securities analysts act, to a degree, as a link between the companies they cover and potential investors or market observers. They can therefore be regarded as information intermediaries. Their task is to collect and evaluate a wide range of information of varying quality and â in the case of an earnings estimate â condense it into a single figure. The result of their analysis is generally published prominently, but not any information on the preceding decision-making process. However, the quality and rationality of the forecasts can be properly assessed only if the factors influencing the decision are known. In the following, we will analyse various determinants that could have an impact on securities analystsâ decision-making process and the quality of their forecasts. We will study, first, what influence the individual environment has on forecast quality and, second, to what extent publicly available information is reflected in forecasts.1 Irrespectively of the determinants to be examined, any empirical study on forecast quality must control for the forecast horizon, which is defined as the period (generally measured in months) between the time the forecast is produced and the end of the business year for which the forecast is made. As expected, there is a negative correlation: forecast accuracy diminishes as the forecast horizon grows longer. The business year in question can also exert a specific influence, which has to be taken into account in any analysis. Analyst-specific and broker-specific factors Differences in individual analystsâ forecast quality can, in part, be explained by the individual environment or specific analyst characteristics.2 For instance, various studies show that long professional experience has a significant positive impact on forecast quality. One explanation is the âlearning by doingâ effect: the longer someone works as an analyst, the greater his experience in the field, which in turn leads to better forecast results. In addition, Hong and Kubik (2003) state that, for analysts working in the United States, continued employment in the industry is closely linked to forecast accuracy.3 In other words, analysts with comparatively many years of experience have undergone a selection process in which they were able to prevail over their rivals. However, the positive correlation between professional experience and forecast accuracy proved weak or even inexistent especially for European analysts, which is explained, inter alia, by differences in the incentive structure.4 The literature therefore makes a further distinction between general and company-specific professional experience. The latter relates exclusively to the period over which an individual analyst has covered a specific company. The positive correlation generally proves robust in empirical analysis; one possible reason is that communications between the analyst and the management of the covered company improve with years on the job. A further analyst-specific determinant whose potential impact is investigated in empirical studies is the number of forecasts that an analyst makes for a firm in a business year. If a large number of revisions are necessary, this points to difficulties in establishing an adequate assessment, which results in a negative correlation between this variable and forecast accuracy, particularly at the beginning of the business year. Conversely, at the end of the business year, the number of revisions should have ensured that the necessary adjustments have been made, which would mean a statistically significant difference can no longer be found. Broker-specific factors include the size of the portfolio an individual analyst covers. Here, a negative correlation is assumed in theory: the more enterprises or sectors an analyst covers, the less time he has to analyse a specific company, which is reflected in a significantly greater forecast error. Various studies also show that the size of an analystâs employer is statistically significant. US studies in particular demonstrate that analysts employed by larger brokerage houses make better forecasts than their peers at smaller houses. One possible explanation is that analysts with important brokerage houses have better access to companiesâ management. Similarly, they could have better resources at their disposal. Larger brokerage houses are regarded as the more attractive employers, partly for the reasons outlined above, potentially leading to them employing the better analysts. However, this argument does not necessarily apply to the European market, as brokerage houses do not hire staff based as exclusively on past forecast accuracy as in the United States.5 Although the above-mentioned variables are used to try to explain, as much as possible, the differences in forecast quality based on analyst-specific and broker-specific behaviour, a large part remains unexplained. As a result, prior analyst-specific forecast quality generally proves highly significant in addition to the above-mentioned determinants. For instance, Brown (2001) shows that a simple model containing only analystsâ individual prior forecast quality as an explanatory variable performs just as well as a model that contains the analyst characteristics described above.6 Processing publicly available information As mentioned above, a financial analystâs real achievement is to collect, weight and compress existing information. An important source of information is doubtless the current consensus forecast among other analysts, which is the subject of intense debate in the literature. The individual analyst starts out in the same situation as other uninvolved market players. While he is familiar with the result of the consensus estimate, he does not know what factors may have played a role in his peersâ decision-making process. Unlike other market players, however, the analystâs own forecast gives him an idea of how the consensus estimate could change. According to Banerjeeâs definition (1992), individual analystsâ behaviour is classified as non-rational herd behaviour if they base their forecast exclusively on other analystsâ consensus estimate and neglect their own information. 7 Such behaviour is, however, difficult to prove empirically. As different analysts usually respond to similar information signals, they will likely arrive at similar recommendations. In this case, it is therefore not clear whether synchronised analystsâ earnings revisions are due to herd behaviour or merely to the fact that they base their decisions on the same information. Clement, Hales and Xue (2007) demonstrate for the United States that the consensus forecast is used in a rational manner. They find that analysts are more likely to incorporate information from the consensus forecast into their own forecast the greater the number of analysts involved in the consensus forecast.8 In this case â if analysts use the consensus forecast as one of several sources of information â their own forecast accuracy may improve.9 It would therefore be premature to describe proof that the consensus forecast has an influence on analyst decisions as irrational behaviour. For Germany, Naujoks et al (2009) even show that analysts systematically go against the consensus forecast in order to raise their profile (antiherding). 10 Past stock market performance is similar to the consensus forecast. Even though this is publicly known and available information, taking share prices into account may well help improve the quality of earnings forecasts.11 Another source of information for which one would expect similar analyst behaviour is the macroeconomic outlook. Unexpected changes in and increased uncertainty about future macroeconomic developments are both likely to impact analyst-specific earnings forecasts. http://www.bundesbank.de/download/volkswirtschaft/monatsberichte/2009/200907mb_en.pdf
a financial analystâs real achievement is to collect, weight and compress existing information. His real "achievement" is to pick the correct shade of lipstick.