This function randomly leaves out 5 percent (`pctOut'=5 by default)
data and finds portfolio choice to sell 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 sell 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
p should get the highest portfolio weight. (worst is sold)
The outOFsamp function then calls the
function `rank2sell,' which uses these rank choice numbers to the selected
`maxChosen' stocks. The allocation is linearly declining. For example, it is
1/10, 2/10, 3/10, and 4/10, with the worst return stock
(top choice for selling) receiving highest proportion of the capital
designated for selling.
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-selling algorithm out of seven
is suggested by the average out-of-sample return over the `reps' repetitions.
This function is sell version of outOFsamp().
outOFsell(mtx, pctOut = 5, reps = 10, seed = 23, maxChosen = 2, verbo = FALSE)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. User needs to change rank2sell() for alternate portfolio allocations.
matrix size n by p of data on n returns from p stocks
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
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 for random number generation, default =23
number of stocks (out of p) with nonzero weights in the portfolio
logical, TRUE means print details, default=FALSE
Prof. H. D. Vinod, Economics Dept., Fordham University, NY
rank2sell
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=outOFsell(mtx,verbo=FALSE,maxChosen=2, reps=3)
apply(os,2,mean)}
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