**logopt: a journey in R, finance and open source**, and kindly contributed to R-bloggers)

Before tackling the main subject, two quick notes:

- I did not post for quite a while in part because I followed the Coursera online course Introduction to Computational Finance and Financial Econometrics. It was a nice refresher, extremely well presented, and including some R. This did consume enough time to make posting cumbersome though.
- I started using R studio, and I am quite happy with it under Windows. This has also impacted my code, as R studio automatically stacks your plots.

The main topic of this post is to motivate an escape from the simplex, i.e. allow for weights either larger than 1 or smaller than 0. Generally, this implies going short on one asset deemed less favorable and shift the short proceed to a deemed more favorable asset.

Note that in the past Syntax Highlighter had problems with R-bloggers, you might need to go to the original page to view the code.

# assume the use of RStudio (no explicit management of graphic devices)

library(logopt)

data(nyse.cover.1962.1984)

x <- coredata(nyse.cover.1962.1984)

nStocks <- dim(x)[2]

EvaluateOnAllTuples <- function(ListName, TupleSizes, fFinalWealth, ...) {

if (exists(ListName) == FALSE) {

LocalList <- list()

for (i in 1:length(TupleSizes)) {

TupleSize <- TupleSizes[i]

ws <- combn(x=(1:nStocks), m=(TupleSize), FUN=fFinalWealth, simplify=TRUE, ...)

LocalList[[i]] <- ws

}

assign(ListName, LocalList, pos=parent.frame())

}

}

TupleSizes <- c(2,3,4,5)

# evaluate the sorted coefficients for best CRP

SortedOptB <- function(cols, ...) {

x <- list(...)[[1]] ;

x <- x[,cols]

b <- bcrp.optim(x)

return(sort(b))

}

TupleSizes <- c(2,3,4,5)

EvaluateOnAllTuples("lSortedOptBSmall", TupleSizes, SortedOptB, x)

TupleSizes <- c(nStocks-3,nStocks-2,nStocks-1,nStocks)

EvaluateOnAllTuples("lSortedOptBLarge", TupleSizes, SortedOptB, x)

Colors <- c("red", "green", "blue", "brown", "cyan", "darkred","darkgreen")

for (iL in 1:length(lSortedOptBSmall)) {

nCoeff <- nrow(lSortedOptBSmall[[iL]])

E <- ecdf(lSortedOptBSmall[[iL]][1,])

Title <- sprintf("Cumulative PDF for all sorted weights for BCRP of %d assets", nCoeff)

plot(E, col=Colors[1], xlim=c(0,1), pch=".", main = Title, xlab = "relative weight")

cat(sprintf("Combinations of %d assets\n", nCoeff))

for (iB in 1:nCoeff) {

E <- ecdf(lSortedOptBSmall[[iL]][iB,])

lines(E, col=Colors[iB], pch=".")

if (iB < nCoeff) {

cat(sprintf("Percent of portfolio with %d weight(s) smaller than 0.001: %f\n", iB, E(0.001) ))

}

}

}

SmallOf2 <- lSortedOptBSmall[[1]][1,]

hist(SmallOf2,n=25,probability=TRUE, main="Histogram of smallest weight for two assets",

xlab="Smallest coefficient")

lines(density(SmallOf2, bw=0.02),col="blue")

# for the large ones, we show only the largest coefficients and show them in

# opposite order to find how many coefficients are not always insiginifcant

for (iL in 1:length(lSortedOptBLarge)) {

nCoeff <- nrow(lSortedOptBLarge[[iL]])

E <- ecdf(lSortedOptBLarge[[iL]][nCoeff,])

Title <- sprintf("Cumulative PDF for largest sorted weights for BCRP of %d assets", nCoeff)

plot(E, col=Colors[1], xlim=c(0,1), pch=".", main = Title, xlab = "relative weight")

cat(sprintf("Combinations of %d assets\n", nCoeff))

for (iB in 1:nCoeff) {

iCoeff <- nCoeff-iB+1

E <- ecdf(lSortedOptBLarge[[iL]][iCoeff,])

if (E(0.001) < 0.9999) {

lines(E, col=Colors[iB], pch=".")

cat(sprintf("Percent of portfolio with %d weight(s) smaller than 0.001: %f\n", iCoeff, E(0.001) ))

}

}

}

Only a subset of the results are shown below, first the cumulative probability function for the weights of the coefficients for all combinations of 5 assets. The graph shows that the smallest coefficient is almost always 0 and the second coefficient is also very small all of the time. The textual output shows that the second coefficient is insignificant 91% of the time or equivalently 91% of the best portfolio only uses 3 of the 5 possible assets.

Combinations of 5 assets

Percent of portfolio with 1 weight(s) smaller than 0.001: 0.996499

Percent of portfolio with 2 weight(s) smaller than 0.001: 0.914086

Percent of portfolio with 3 weight(s) smaller than 0.001: 0.499448

Percent of portfolio with 4 weight(s) smaller than 0.001: 0.067975

The density for the two asset case shows clearly that there is a high peak at zero.

Finally the textual output and the ECDF shows that for 33 possible assets, only up to 7 are present in the BCRP

Combinations of 33 assets

Percent of portfolio with 33 weight(s) smaller than 0.001: 0.000000

Percent of portfolio with 32 weight(s) smaller than 0.001: 0.000000

Percent of portfolio with 31 weight(s) smaller than 0.001: 0.000000

Percent of portfolio with 30 weight(s) smaller than 0.001: 0.000000

Percent of portfolio with 29 weight(s) smaller than 0.001: 0.065126

Percent of portfolio with 28 weight(s) smaller than 0.001: 0.707843

Percent of portfolio with 27 weight(s) smaller than 0.001: 0.947059

All this points to the fact that most of the weights of the BCRP end up on the boundary of the simplex, and that removing that specific constraint would get an even better solution, at least in term of terminal wealth. We’ll investigate further this in future posts.

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**logopt: a journey in R, finance and open source**.

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