outOFsell {generalCorr}

Compare out-of-sample (short) selling algorithms by a leave-percent-out method.

Description

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().

Usage

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

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

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. User needs to change rank2sell() for alternate portfolio allocations.

Note

The traditional time-series out-of-sample leaves out the last few time periods, and estimates the stock-picking model using part of the data time periods. The pandemic of 2019 has revealed that the traditional out-of-sample would have a severe bias in favor of pessimistic stock-picking algorithms. The traditional method is fundamentally flawed since it is sensitive to the trends (ups and downs) in the out-of-sample period. The method proposed here is free from such biases. The stock-picking algorithm recommended by our outOFsamp() is claimed to be robust against such biases.

Author(s)

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

See Also

rank2sell

Examples

## Not run: 
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)
## End(Not run)