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Minimum sum regression as the optimum robust algorithm in the computation of financial beta


Manuel G. Russon
Ph.D., CFA, Associate Professor Tobin College of Business, St. Johns University, Jamaica, NY. 11439, USA
,
John J. Neumann
Associate Professor, Tobin College of Business, St. John's University. Jamaica, NY. 11439, USA
. (2016).


doi: http://dx.doi.org/10.21511/imfi.13(4-1).2016.09

Abstract
In the world of finance and portfolio management, beta refers to the sensitivity of a securitys return, to the sensitivity of the market portfolio and is an indication of the level of systematic risk, i.e., the amount of risk that a companys equity shares with the entire market. Correct values for beta are crucial for institutional portfolio managers, as the client contract almost always calls for a portfolio beta approximately equal to 1.0. Typically, beta is estimated using Ordinary Least Squares, but OLS is reliant on some very stringent assumptions. Here, betas are computed and compared using OLS and four robust regression algorithms. Minimum sum regression is identified as the superior robust regression algorithm to estimate beta.

Keywords: Financial Beta, Ordinary Least Squares, Robust Regression, Portfolio Management.
JEL Classification: C21, G11.