Assessing multivariate predictors of financial market movements: A latent factor framework for ordinal data

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.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS213 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Small Cities Blues: Looking for Growth Factors in Small and Medium-Sized Cities

The purpose of this exploratory study is to attempt to identify particular public policies which have the potential to increase the economic viability of smaller metropolitan areas and cities. We identify characteristics associated with smaller metro areas that performed better-than-expected (winners) and worse-than-expected (losers) during the 1990s, given their resources, industrial mix, and location as of 1990. Once these characteristics have been identified, we look for evidence that public policy choices may have promoted and enhanced a metro area's ability to succeed and to regain control of its own economic destiny. Methodologically, we construct a regression model which identifies the small metro areas that achieved higher-than-expected economic prosperity (winners) and the areas that saw lower-than-expected economic prosperity (losers) according to the model. Next, we explore whether indications exist that winners and losers are qualitatively different from other areas in ways that may indicate consequences of policy choices. A cluster analysis is completed to group the metro areas based on changes in a host of social, economic, and demographic variables between 1990 and 2000. We then use contingency table analysis and ANOVA to see if "winning" or "losing," as measured by the error term from the regression, is related to the grouping of metro areas in a way that may indicate the presence of deliberate and replicable government policy.economic, development, growth, factors, erickcek, mckinney, incentives, local, regional, small, medium, cities

Hierarchical Co-Clustering: Off-line and Incremental Approaches

International audienceClustering data is challenging especially for two reasons. The dimensionality of the data is often very high which makes the cluster interpretation hard. Moreover, with high-dimensional data the classic metrics fail in identifying the real similarities between objects. The second challenge is the evolving nature of the observed phenomena which makes the datasets accumulating over time. In this paper we show how we propose to solve these problems. To tackle the high-dimensionality problem, we propose to apply a co-clustering approach on the dataset that stores the occurrence of features in the observed objects. Co-clustering computes a partition of objects and a partition of features simultaneously. The novelty of our co-clustering solution is that it arranges the clusters in a hierarchical fashion, and it consists of two hierarchies: one on the objects and one on the features. The two hierarchies are coupled because the clusters at a certain level in one hierarchy are coupled with the clusters at the same level of the other hierarchy and form the co-clusters. Each cluster of one of the two hierarchies thus provides insights on the clusters of the other hierarchy. Another novelty of the proposed solution is that the number of clusters is possibly unlimited. Nevertheless, the produced hierarchies are still compact and therefore more readable because our method allows multiple splits of a cluster at the lower level. As regards the second challenge, the accumulating nature of the data makes the datasets intractably huge over time. In this case, an incremental solution relieves the issue because it partitions the problem. In this paper we introduce an incremental version of our algorithm of hierarchical co-clustering. It starts from an intermediate solution computed on the previous version of the data and it updates the co-clustering results considering only the added block of data. This solution has the merit of speeding up the computation with respect to the original approach that would recompute the result on the overall dataset. In addition, the incremental algorithm guarantees approximately the same answer than the original version, but it saves much computational load. We validate the incremental approach on several high-dimensional datasets and perform an accurate comparison with both the original version of our algorithm and with the state of the art competitors as well. The obtained results open the way to a novel usage of the co-clustering algorithms in which it is advantageous to partition the data into several blocks and process them incrementally thus "incorporating" data gradually into an on-going co-clustering solutio