A Kalman filter control technique in meanvariance portfolio management
 James DiLellio
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This article develops and tests a methodology for rebalancing the meanvariance optimized portfolio through the use of a Kalman filter. The approach combines information from a meanvariance (MV) optimization technique along with a three factor regression model that includes market capitalization, book to market ratio, and the market index. We demonstrate empirically using 46 years of daily returns from 17 industrial sectors that a Kalman filter model can be an effective approach under the conditions of minimum variance and low expected risktoreturn for both a constrained and unconstrained MV technique. Enhancements to returns are largely due to the ability to reduce turnover for the unconstrained case, and the ability to maintain portfolio exposure to cap and value weighted positions in the constrained case. Statistical significance is demonstrated to show return improvements of the Kalman filter model over a comparable MV technique, where the greatest statistical benefit at the 0.05 and 0.10 level is shown under the minimum variance objective. Additionally, the KF applied to the constrained optimization case always provides a Sharpe ratio higher than the Naïve portfolio, after transactions costs are taken into account.
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 Title
 A Kalman filter control technique in meanvariance portfolio management
 Journal

Journal of Economics and Finance
Volume 39, Issue 2 , pp 235261
 Cover Date
 20150401
 DOI
 10.1007/s1219701292449
 Print ISSN
 10550925
 Online ISSN
 19389744
 Publisher
 Springer US
 Additional Links
 Topics
 Keywords

 Kalman Filter
 Three Factor Model
 Optimization
 Investments
 C13
 G11
 Industry Sectors
 Authors

 James DiLellio ^{(1)}
 Author Affiliations

 1. Pepperdine University, 24255 Pacific Coast Highway, Malibu, CA, 90263, USA