Abstract

http://ssrn.com/abstract=765745
 


 



Cluster Analysis of Imputed Financial Data Using an Augmentation-Based Algorithm


Ramon P. DeGennaro


University of Tennessee, Knoxville - Department of Finance

Halima Bensmail


University of Tennessee, Knoxville


STATISTICAL DATA MINING AND KNOWLEDGE DISCOVERY, Bensmail, Halima and Romon P. DeGennaro, eds., CRC Press, pp. 513-528, 2003

Abstract:     
We introduce a novel statistical modeling technique to cluster analysis and apply it to financial data. Our main goals are to handle missing data and to find homogeneous groups within the data. Our approach is flexible and handles large and complex data structures with missing observations and with quantitative and qualitative measurements. We achieve this by mapping the data to a new structure that is free of distributional assumptions in choosing homogeneous groups of observations. Our new method also provides insight into the number of different categories needed for classifying the data. We use this approach to partition a matched sample of stocks. One group offers dividend reinvestment plans, and the other does not. Our approach partitions this sample with almost 97 percent accuracy even when using only easily available financial variables. One interpretation of this 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). We offer other suggestions for applications in the field of finance.

Keywords: cluster analysis, missing data, dividend reinvestment plan

JEL Classification: C00, G35

Accepted Paper Series


Not Available For Download

Date posted: August 4, 2005  

Suggested Citation

DeGennaro, Ramon P. and Bensmail, Halima, Cluster Analysis of Imputed Financial Data Using an Augmentation-Based Algorithm. Available at SSRN: http://ssrn.com/abstract=765745

Contact Information

Ramon P. DeGennaro (Contact Author)
University of Tennessee, Knoxville - Department of Finance ( email )
423 Stokely Management Center
Knoxville, TN 37996
United States
865-974-1726 (Phone)
865-974-1716 (Fax)
Halima Bensmail
University of Tennessee, Knoxville ( email )
Knoxville, TN 37996
United States
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