Analyzing Imputed Financial Data: A New Approach to Cluster Analysis
Halima Bensmail and Ramon P. DeGennaro
Working Paper 2004-20
The authors introduce a novel statistical modeling technique to cluster analysis and apply it to financial data. Their two main goals are to handle missing data and to find homogeneous groups within the data. Their approach is flexible and handles large and complex data structures with missing observations and with quantitative and qualitative measurements. The authors achieve this result by mapping the data to a new structure that is free of distributional assumptions in choosing homogeneous groups of observations. Their new method also provides insight into the number of different categories needed for classifying the data. The authors use this approach to partition a matched sample of stocks. One group offers dividend reinvestment plans, and the other does not. Their method partitions this sample with almost 97 percent accuracy even when using only easily available financial variables. One interpretation of their result is that the misclassified companies are the best candidates either to adopt a dividend reinvestment plan (if they have none) or to abandon one (if they currently offer one). The authors offer other suggestions for applications in the field of finance.
JEL classification: G20, G29, G35
Key words: dividend reinvestment, Bayesian analysis, Gibbs sampler, clustering
The authors gratefully acknowledge the support of a University of Tennessee Finance Department Summer Faculty Research Award and a College of Business Scholarly Research Grant. The views expressed here are the authors’ and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the authors’ responsibility.
Please address questions regarding content to Halima Bensmail, Statistics Department, the University of Tennessee, Knoxville, Tennessee 37996, 865-974-8325, firstname.lastname@example.org and Ramon P. DeGennaro, SunTrust Professor of Finance, The University of Tennessee, Knoxville, Tennessee 37996, 865-974-3216, and Visiting Scholar, Federal Reserve Bank of Atlanta, 1000 Peachtree Street, N.E., Atlanta, Georgia 30309-4470, email@example.com.