Math @ Duke

Publications [#287188] of Ingrid Daubechies
Papers Published
 Brodie, J; Daubechies, I; De Mol, C; Giannone, D; Loris, I, Sparse and stable Markowitz portfolios.,
Proceedings of the National Academy of Sciences of the United States of America, vol. 106 no. 30
(July, 2009),
pp. 1226712272 [19617537], [doi]
(last updated on 2019/05/25)
Abstract: We consider the problem of portfolio selection within the classical Markowitz meanvariance framework, reformulated as a constrained leastsquares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (stabilizes) the optimization problem, encourages sparse portfolios (i.e., portfolios with only few active positions), and allows accounting for transaction costs. Our approach recovers as special cases the noshortpositions portfolios, but does allow for short positions in limited number. We implement this methodology on two benchmark data sets constructed by Fama and French. Using only a modest amount of training data, we construct portfolios whose outofsample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve evenly weighted portfolio.


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