outOFsamp: Compare out-of-sample portfolio choice algorithms by a leave-percent-out method.

Description

This function randomly leaves out 5 percent (`pctOut'=5 by default) data and finds portfolio choice by seven different portfolio selection algorithms using the data on the remaining 95 percent (say). The randomization removes any bias in time series definitions of `out-of-sample' data. For example, the input to outOFsamp(.) named `mtx' is a matrix with p columns for p stocks and n returns. Also, let the maximum number of stocks admitted to belong in the portfolio be four, or `maxChosen=4'. Now outOFsamp function computes the returns earned by the seven portfolio selection algorithms, called "SD1", "SD2", "SD3", "SD4", "SDAll4", "decile," and "moment," where SDAll4 refers to a weighted sum of SD1 to SD4 algorithms. Each algorithm provides a choice ranking of p stocks with choice values 1,2,3,..,p where stock ranked 1 should get the highest portfolio weight. The outOFsamp function then calls the function `rank2return,' which uses these rank choice numbers to the selected `maxChosen' stocks. The allocation is linearly declining. For example, it is 4/10, 3/10, 2/10, and 1/10, with the top choice stock receiving 4/10 of the capital. Each choice of `pctOut' rows of the `mtx' data yields an outOFsamp return for each of the seven portfolio selection algorithms. These outOFsamp return computations are repeated reps times. A new random selection of `pctOut' rows (must be 2 or more) of data is made for each repetition. We set reps=20 by default. The low default is set to save processing time in early phases, but we recommend reps=100+. The final choice of stock-picking algorithm out of seven is suggested by the one yielding the largest average out-of-sample return over the `reps' repetitions.`Its standard deviation measures the variability of performance over the `reps' repetitions.

Usage

outOFsamp(mtx, pctOut = 5, reps = 10, seed = 23, maxChosen = 2, verbo = FALSE)

Value

a matrix called `avgRet' with seven columns for seven stock-picking algorithms "SD1","SD2","SD3","SD4","SDAll4","decile",and "moment," containing out-of-sample average returns for linearly declining allocation in a portfolio. The user needs to change rank2return() for alternate portfolio allocations.

Arguments

mtx: matrix size n by p of data on n returns from p stocks
pctOut: percent of n randomly chosen rows left out as out-of-sample, default=5 percent. One must leave out at least two rows of data
reps: number of random repetitions of left-out rows over which we average the out-of-sample performance of a stock-picking algorithm, default reps=20
seed: seed for random number generation, default =23
maxChosen: number of stocks (out of p) with nonzero weights in the portfolio
verbo: logical, TRUE means print details, default=FALSE

Author

Prof. H. D. Vinod, Economics Dept., Fordham University, NY

Examples

Run this code

if (FALSE) {
x1=c(2,5,6,9,13,18,21,5,11,14,4,7,12,13,6,3,8,1,15,2,10,9)
x2=c(3,6,9,12,14,19,27,9,11,2,3,8,1,6,15,10,13,14,5,7,4,12)
x3=c(2,6,NA,11,13,25,25,11,9,10,12,6,4,3,2,1,7,8,5,15,14,13)
mtx=cbind(x1,x2,x3)
mtx=mtx[complete.cases(mtx),]
os=outOFsamp(mtx,verbo=FALSE,maxChosen=2, reps=3)
apply(os,2,mean)}