What're the returns (XIRR) for my CPFIS Portfolio

How did this strategy fare thus far?

XIRR- a metric used in assessing rate of returns with a given set of cashflows - came up to be around 5.7%! Note: This figure here accounts for the dividends received over the years.

Hurray! It’s more than twice the 2.5% hurdle rate in CPF OA. And even the 4% rate from SA. One could voluntarily transfer OA to SA, but you will lose the flexibility of using the OA for serving mortage in future

My thoughts

This is still an ongoing experiment in my ‘lab’. Apparently it seems to be working well! Going forward, I will continue this strategy.

If you are keen in the technicalities of computing XIRR (applied the function developed by someone else here –>#https://github.com/SunilVeeravalli/xirr)…

suppressMessages(source("./CPFOA/F_xirr.R"))
suppressMessages(source("./CPFOA/another_irr_eg.R"))
suppressPackageStartupMessages(library(tvm))

#Reading my dataset
cpf_contrib <- read_csv(file = "./CPFOA/jr_cpfis_contrib.csv", col_names = TRUE)

## Parsed with column specification:
## cols(
##   Date = col_character(),
##   num_stocks_cpf_port = col_double(),
##   sti_px = col_double(),
##   cpf_oa_port_val = col_double(),
##   Dividends_per_share = col_double()
## )

#Formattting date
cpf_contrib$Date = as.Date(cpf_contrib$Date, format = "%m/%d/%y")
names(cpf_contrib)[which(names(cpf_contrib) == "Date")] = "dates" 

#including ss
cpf_contrib$net_stocks_purchase = lead(cpf_contrib$num_stocks_cpf_port - lag(cpf_contrib$num_stocks_cpf_port))

#Contributions
cpf_contrib$stock_amt_purchase = cpf_contrib$net_stocks_purchase * cpf_contrib$sti_px

#Dividends amount
cpf_contrib$dividends = cpf_contrib$num_stocks_cpf_port * cpf_contrib$Dividends_per_share 
cpf_contrib$dividends = ifelse(is.na(cpf_contrib$dividends), 0, cpf_contrib$dividends)

#Net cashflow
cpf_contrib = cpf_contrib %>%
                mutate(net_cash_flow = dividends - stock_amt_purchase)

#Setting final cashflow  
cpf_contrib$net_cash_flow[nrow(cpf_contrib)] = cpf_contrib$cpf_oa_port_val[nrow(cpf_contrib)]

#Computing the xirr
xirr(cpf_contrib[, c("dates","net_cash_flow")])

## [1] "XIRR is 5.775%"

print(xirr2(cpf_contrib$net_cash_flow, cpf_contrib$dates))

## [1] 0.05777714

Final dataset on dividends, purchases, cashflows, portfolio values

suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))

kable(cpf_contrib, caption = "Final data-frame of dividends, purchase, cashflows, portfolio values")