What factors determine the quality of securities analysts’ earnings estimates?

Discussion in 'Stocks' started by ASusilovic, Aug 18, 2009.

  1. 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 – in the
    case of an earnings estimate – 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 analysts’ 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 analysts’ 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 “learning by doing” 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 analyst’s 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 companies’
    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 analysts’ 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 analyst’s 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 peers’ decision-making
    process. Unlike other market players, however, the
    analyst’s own forecast gives him an idea of how the consensus
    estimate could change.

    According to Banerjee’s definition (1992), individual analysts’
    behaviour is classified as non-rational herd behaviour
    if they base their forecast exclusively on other analysts’
    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 analysts’ 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 – if analysts use the
    consensus forecast as one of several sources of information
    – 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).
    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.

  2. an old man said that an estimate is good when the prediction is right
  3. a financial analyst’s real achievement
    is to collect, weight and compress existing information.

    His real "achievement" is to pick the correct shade of lipstick.
  4. Suss----you can sell "that" as a cure for insomnia to Bayer or Pfizer! :D