# portfolio.frequency: Calculate Sub-Portfolio Concentration In portsort: Factor-Based Portfolio Sorts

## Description

Computes the frequency that an asset appears in each sub-portfolio based on its rank.

## Usage

 `1` ```portfolio.frequency(sort.output, rank) ```

## Arguments

 `sort.output` object returned from either the conditional.sort or unconditional.sort function. `rank` input the rank of the security you would like to return the frequency for.

## Details

Returns the frequency that the security appears in each sub-portfolio based on the rank input.

## Author(s)

Alexander Dickerson and Jonathan Spohnholtz

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```# Load the included data library(portsort) data(Factors) # Specifiy the sort dimension - in this case, a double-sort on lagged returns and Bitcoin volumes dimA = 0:3/3 dimB = 0:3/3 # Specify the factors # Lagged returns, lagged volumes are stored in the Factors list R.Forward = Factors[[1]]; R.Lag = Factors[[2]]; V.Lag = Factors[[3]] # Subset the data from late 2017 R.Forward = R.Forward["2017-12-01/"] R.Lag = R.Lag["2017-11-30/2018-09-05"] V.Lag = V.Lag["2017-11-30/2018-09-05"] Fa = R.Lag Fb = V.Lag # Conduct an unconditional sort (in this case) or a conditional sort sort.output = unconditional.sort(Fa = Fa, Fb = Fb , R.Forward = R.Forward, dimA = dimA, dimB = dimB) # We want to see which security appeared the most in each sub-portfolio, # i.e the secruity with a rank of 1. rank = 1 portfolio.frequency(sort.output,rank) ```

portsort documentation built on May 2, 2019, 6:36 a.m.