ractive: Generate random active portfolios
In rportfolios: Random Portfolio Generation

Description Usage Arguments Details Value Author(s) References See Also Examples

This function generates m random actively managed portfolios relative to a given benchmark portfolio. Each portfolio is the combination of a benchmark portfolio and a notional neutral long short portfolio with given gross notional exposure. The number of non zero positions in the long short portfolios is k.

1 2	ractive(m, x.b, x.g, k = length(x.b), segments = NULL, max.iter = 2000, eps = 0.001)

`m`	A positive integer value for the number of portfolios in the sample
`x.b`	A numeric vector with the investment weights in the benchmark portfolio
`x.g`	A positive numeric value for the gross notional exposure in the long short portfolio
`k`	A positive integer value for the number of non zero positions in the long short portfolio
`segments`	A vector or list of vectors that defines the portfolio segments
`max.iter`	A positive integer value for the maximum iterations for the long short portfolio
`eps`	A small positive real value for the convergence criteria for the gross notional exposure

The function executes the function random.active using the R function sapply. The result returned is the transpose of the matrix generated in the previous step.

A numeric m \times n matrix. The rows are the portfolios and the columns are the investment weights for each portfolio

Frederick Novomestky fn334@nyu.edu

Grinold, R. C. and R. H. Kahn, 1999. Active Portfolio Management: Quantitative Approach for Providing Superior Returns and Controlling Risk, Second Edition, McGraw-Hill, New York, NY.

Qian, E. E., R. H. Hua and E. H. Sorensen, 2007. Quantitative Equity Portfolio Management, Chapman \& Hall, London, UK.

Scherer, B., 2007. Portfolio Construction and Risk Budgeting, Third Edition, Risk Books, London, UK.

random.active

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### benchmark consists of 20 equally weighted investments
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x.b <- rep( 1, 30 ) / 30
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### the gross notional exposure of the long short portfolio is a benchmark weight
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x.g <- 1 / 30
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### generate 100 random active portfolios with 30 non zero positions in the long short portfolios
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x.matrix <- ractive( 100, x.b, x.g )
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### generate 100 random active portfolios with 10 non zero positions in the long short portfolios
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y.matrix <- ractive( 100, x.b, x.g, 10 )