Sparse and stable Markowitz portfolios

We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares 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 to account for transaction costs. Our approach recovers as special cases the no-short-positions 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 out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naive evenly-weighted portfolio which constitutes, as shown in recent literature, a very tough benchmark.Comment: Better emphasis of main result, new abstract, new examples and figures. New appendix with full details of algorithm. 17 pages, 6 figure

A rapid procedure for the extraction of genomic DNA from intact Aspergillus spores

Genomic DNA of different species of Aspergillus was prepared from intact spores using the Nucleon MiY kit of Amersham. The method is rapid, does not involve mechanical disruption of the spores nor the use of phenol-chloroform extractions and yields DNA that is suitable for PCR amplification and Southern analysis. The method is also applicable to mycelium ground with glass beads