unit_prices {PMwR}

Compute Prices for Portfolio Based on Units

Description

Compute prices for a portfolio based on outstanding shares.

Usage

unit_prices(NAV,
            cashflows,
            initial.price, initial.shares = 0,
            cf.included = TRUE)

Arguments

NAV

a dataframe of two columns: timestamp and net asset value

cashflows

a data.frame of two or three columns: timestamp, cashflow and (optionally) an id

initial.price

initial price

initial.shares

number of outstanding shares for first NAV

cf.included

logical

Details

This function is experimental, and its interface is not stable yet.

The function may be used to compute the returns for a portfolio with external cashflows, i.e. what is usually called time-weighted returns.

Valuation (i.e. the computation of the NAV) must take place before external cashflows. Fairness suggests that: what price would you give an external investor if you had not valued the positions? And even if fairness mattered not: suppose we traded on a specific day, had a positive PL, and ended the day in cash. We could then not differentiate any more between a cash increase because of an external inflow and a cash increase because of a profitable trade.

Value

A data.frame

timestamp

the timestamp

NAV

total NAV

price

NAV per share

units

outstanding units (i.e. shares) after cashflows

Attached as an attribute is a data.frame transactions.

Author(s)

Enrico Schumann

References

Schumann, E. (2023) Portfolio Management with R. https://enricoschumann.net/PMwR/

See Also

returns, pl

Examples

NAV <- data.frame(timestamp = seq(as.Date("2017-01-01"),
                                  as.Date("2017-01-10"),
                                  by = "1 day"),
                  NAV = c(100:104, 205:209))

cf <- data.frame(timestamp = c(as.Date("2017-01-01"),
                               as.Date("2017-01-06")),
                 cashflow = c(100, 100))

unit_prices(NAV, cf, cf.included = TRUE)
##     timestamp NAV    price    units
## 1  2017-01-01 100 100.0000 1.000000
## 2  2017-01-02 101 101.0000 1.000000
## 3  2017-01-03 102 102.0000 1.000000
## 4  2017-01-04 103 103.0000 1.000000
## 5  2017-01-05 104 104.0000 1.000000
## 6  2017-01-06 205 105.0000 1.952381
## 7  2017-01-07 206 105.5122 1.952381
## 8  2017-01-08 207 106.0244 1.952381
## 9  2017-01-09 208 106.5366 1.952381
## 10 2017-01-10 209 107.0488 1.952381