Skip to contents

Compute Long-Short Returns

Source: R/compute_long_short_returns.R
compute_long_short_returns.Rd

Calculates long-short returns based on the returns of portfolios. The long-short return is computed as the difference between the returns of the "top" and "bottom" portfolios. The direction of the calculation can be adjusted based on whether the return from the "bottom" portfolio is subtracted from or added to the return from the "top" portfolio.

Usage

compute_long_short_returns(
  data,
  direction = "top_minus_bottom",
  data_options = NULL
)

Arguments

data

A data frame containing portfolio returns. The data frame must include columns for the portfolio identifier, date, and return measurements (as specified in data_options).

direction

A character string specifying the direction of the long-short return calculation. It can be either "top_minus_bottom" or "bottom_minus_top". Default is "top_minus_bottom". If set to "bottom_minus_top", the return will be computed as (bottom - top).

data_options

A list of class tidyfinance_data_options (created via data_options()) specifying column name mappings. The date element is used to specify the date column, the ret_excess element is used to specify the excess return column, and portfolio is used to specify the assigned portfolio. Uses data_options() default if NULL: "date" = "date", "ret_excess" = "ret_excess", and "portfolio" = "portfolio".

Value

A data frame with columns for date, return measurement types (from the "ret_measure" column), and the computed long-short returns. The data frame is arranged by date and pivoted to have return measurement types as columns with their corresponding long-short returns.

See also

Other portfolio functions: assign_portfolio(), breakpoint_options(), compute_breakpoints(), compute_portfolio_returns(), data_options(), filter_options(), filter_sorting_data(), implement_portfolio_sort(), portfolio_sort_options()

Examples

set.seed(42)
data <- data.frame(
  permno = 1:100,
  date = rep(
    seq.Date(from = as.Date("2020-01-01"), by = "month", length.out = 100),
    each = 10
  ),
  mktcap_lag = runif(100, 100, 1000),
  ret_excess = rnorm(100),
  size = runif(100, 50, 150)
)

portfolio_returns <- compute_portfolio_returns(
  data, "size", "univariate",
  breakpoint_options_main = breakpoint_options(n_portfolios = 5)
)

compute_long_short_returns(portfolio_returns)
#> # A tibble: 100 × 4
#>    date       ret_excess_vw ret_excess_ew ret_excess_vw_capped
#>    <date>             <dbl>         <dbl>                <dbl>
#>  1 2020-01-01     -0.833          -0.650               -0.833 
#>  2 2020-02-01      0.0996         -0.0632               0.0996
#>  3 2020-03-01      0.658           0.687                0.673 
#>  4 2020-04-01      0.998           1.05                 1.01  
#>  5 2020-05-01      0.567           0.747                0.567 
#>  6 2020-06-01     -1.13           -0.817               -1.05  
#>  7 2020-07-01      1.66            1.27                 1.66  
#>  8 2020-08-01      0.922           1.23                 0.948 
#>  9 2020-09-01     -0.488           0.137               -0.488 
#> 10 2020-10-01     -0.000934        0.0858               0.0389
#> # ℹ 90 more rows