  Much of the trading activity in Equity markets is directed to brokerage
houses. In exchange they provide so-called "soft dollars," which basically are
amounts spent in "research" for identifying profitable trading opportunities.
Soft dollars represent about USD 1 out of every USD 10 paid in commissions.
Obviously they are costly, and it is interesting for an institutional investor
to determine whether soft dollar inputs are worth being used (and indirectly
paid for) or not, from a statistical point of view. To address this question,
we develop association measures between what broker--dealers predict and what
markets realize. Our data are ordinal predictions by two broker--dealers and
realized values on several markets, on the same ordinal scale. We develop a
structural equation model with latent variables in an ordinal setting which
allows us to test broker--dealer predictive ability of financial market
movements. We use a multivariate logit model in a latent factor framework,
develop a tractable estimator based on a Laplace approximation, and show its
consistency and asymptotic normality. Monte Carlo experiments reveal that both
the estimation method and the testing procedure perform well in small samples.
The method is then used to analyze our dataset.
