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Compute Portfolio Returns

Source: R/compute_portfolio_returns.R
compute_portfolio_returns.Rd

Computes individual portfolio returns based on specified sorting variables and sorting methods. The portfolios can be rebalanced every period or on an annual frequency by specifying a rebalancing month, which is only applicable at a monthly return frequency. The function supports univariate and bivariate sorts, with the latter supporting dependent and independent sorting methods.

Usage

compute_portfolio_returns(
  data,
  sorting_variables,
  sorting_method,
  rebalancing_month = NULL,
  breakpoint_options_main,
  breakpoint_options_secondary = NULL,
  breakpoint_function_main = compute_breakpoints,
  breakpoint_function_secondary = compute_breakpoints,
  min_portfolio_size = 1L,
  cap_weight = 0.8,
  data_options = NULL,
  quiet = FALSE
)

Arguments

data

A data frame containing the dataset for portfolio assignment and return computation. The panel data must include individual stock identifiers and the time point. It must contain columns for the sorting variables and excess returns. Additionally, lagged market capitalization is required for value-weighted returns (see parameter data_options).

sorting_variables

A character vector specifying the column names in data to be used for sorting and determining portfolio assignments. For univariate sorts, provide a single variable. For bivariate sorts, provide two variables, where the first string refers to the main variable and the second string refers to the secondary ("control") variable.

sorting_method

A string specifying the sorting method to be used. Possible values are:

  • "univariate": For a single sorting variable.

  • "bivariate-dependent": For two sorting variables, where the main sort depends on the secondary variable.

  • "bivariate-independent": For two independent sorting variables.

For bivariate sorts, the portfolio returns are averaged over the controlling sorting variable (i.e., the second sorting variable), and only portfolio returns for the main sorting variable (given as the first element of sorting_variables) are returned.

rebalancing_month

An integer between 1 and 12 specifying the month in which to form portfolios that are held constant for one year. For example, setting it to 7 creates portfolios in July that are held constant until June of the following year. The default NULL corresponds to periodic rebalancing.

breakpoint_options_main

A named list of breakpoint_options() passed to breakpoint_function_main for the main sorting variable.

breakpoint_options_secondary

An optional named list of breakpoint_options() passed to breakpoint_function_secondary.

breakpoint_function_main

A function to compute the breakpoints for the main sorting variable. The default is set to compute_breakpoints().

breakpoint_function_secondary

A function to compute the breakpoints for the secondary sorting variable. The default is set to compute_breakpoints().

min_portfolio_size

An integer specifying the minimum number of firms required in the reported portfolio cross-section on a given date (default 1L, i.e. at least one observation per reported portfolio). For both univariate and bivariate sorts the threshold is applied to the firm count per (portfolio, date) of the reported cross-section: for univariate sorts that is firms per portfolio-date; for bivariate sorts that is firms per main-portfolio-date summed across the secondary buckets. Cross-sections below the threshold have their returns set to NA. A typical value is 5L (the Fama-French convention). Set to 0L to deactivate the check entirely.

cap_weight

A numeric value between 0 and 1 specifying the percentile at which market capitalization is capped per date when computing capped value-weighted excess returns (i.e., ret_excess_vw_capped). Defaults to 0.8.

data_options

A list of class tidyfinance_data_options (created via data_options()) specifying column name mappings. The id element is used to specify the entity (i.e., firm), date is used to specify the date column, ret_excess is used to specify the excess return column, and mktcap_lag is used to specify the market capitalization. Uses data_options() default if NULL: "id" = "permno", "date" = "date", "ret_excess" = "ret_excess", and "mktcap_lag" = "mktcap_lag".

quiet

A logical value indicating whether to suppress informational messages about missing values in the output panel (default is FALSE).

Value

A data frame with computed portfolio returns as a complete panel (all portfolio-date combinations), containing the following columns:

  • portfolio: The portfolio identifier.

  • date: The date of the portfolio return.

  • ret_excess_vw: The value-weighted excess return of the portfolio (only computed if data contains a lagged market capitalization column specified by data_options(), which defaults to mktcap_lag). NA if insufficient observations for that portfolio-date.

  • ret_excess_ew: The equal-weighted excess return of the portfolio. NA if insufficient observations for that portfolio-date.

  • ret_excess_vw_capped: The capped value-weighted excess return of the portfolio (only computed if data contains a lagged market capitalization column specified by data_options(), which defaults to mktcap_lag). Weights are computed using market capitalization capped at the cap_weight percentile per date. NA if insufficient observations for that portfolio-date.

Details

The function checks for consistency in the provided arguments. For univariate sorts, a single sorting variable and a corresponding number of portfolios must be provided. For bivariate sorts, two sorting variables and two corresponding numbers of portfolios (or percentiles) are required. The sorting method determines how portfolios are assigned and how returns are computed. The function handles missing and extreme values appropriately based on the specified sorting method and rebalancing frequency.

Note

Ensure that data contains all required columns: the specified sorting variables and excess returns (see options and defaults set in data_options). The function will stop and throw an error if any required columns are missing.

See also

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

Examples

set.seed(42)
# Univariate sorting with periodic rebalancing
data <- data.frame(
  permno = 1:500,
  date = rep(
    seq.Date(
      from = as.Date("2020-01-01"),
      by = "month",
      length.out = 100
    ),
    each = 10
  ),
  mktcap_lag = runif(500, 100, 1000),
  ret_excess = rnorm(500),
  size = runif(500, 50, 150)
)

compute_portfolio_returns(
  data, "size", "univariate",
  breakpoint_options_main = breakpoint_options(n_portfolios = 5)
)
#> # A tibble: 500 × 5
#>    portfolio date       ret_excess_vw ret_excess_ew ret_excess_vw_capped
#>        <int> <date>             <dbl>         <dbl>                <dbl>
#>  1         1 2020-01-01       -1.08         -1.11                -1.08  
#>  2         1 2020-02-01        1.02          1.02                 1.02  
#>  3         1 2020-03-01        0.949         0.678                0.949 
#>  4         1 2020-04-01        0.0696        0.821                0.0696
#>  5         1 2020-05-01       -0.250        -0.489               -0.250 
#>  6         1 2020-06-01        0.307         0.239                0.307 
#>  7         1 2020-07-01        0.196        -0.0990               0.191 
#>  8         1 2020-08-01       -0.0112       -0.0345              -0.0112
#>  9         1 2020-09-01       -0.154        -0.577               -0.171 
#> 10         1 2020-10-01       -1.26         -1.06                -1.26  
#> # ℹ 490 more rows

# Bivariate dependent sorting with annual rebalancing
compute_portfolio_returns(
  data, c("size", "mktcap_lag"), "bivariate-dependent", 7,
  breakpoint_options_main = breakpoint_options(n_portfolios = 5),
  breakpoint_options_secondary = breakpoint_options(n_portfolios = 3),
)
#> Warning: There were 24 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `portfolio_main = assign_portfolio(...)`.
#> ℹ In group 1: `date = 2020-07-01`, `portfolio_secondary = 1`.
#> Caused by warning:
#> ! The number of portfolios differs from the specified parameter due to clusters
#> in the sorting variable.
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 23 remaining warnings.
#> Returning a complete panel with 430 missing values in factor returns due to
#> insufficient observations (fewer than 1 firm per (portfolio, date)
#> cross-section).
#> # A tibble: 470 × 5
#>    portfolio date       ret_excess_vw ret_excess_ew ret_excess_vw_capped
#>        <int> <date>             <dbl>         <dbl>                <dbl>
#>  1         1 2020-07-01        -0.276        -0.276               -0.276
#>  2         1 2020-08-01        NA            NA                   NA    
#>  3         1 2020-09-01        NA            NA                   NA    
#>  4         1 2020-10-01        NA            NA                   NA    
#>  5         1 2020-11-01        NA            NA                   NA    
#>  6         1 2020-12-01        NA            NA                   NA    
#>  7         1 2021-01-01        NA            NA                   NA    
#>  8         1 2021-02-01        NA            NA                   NA    
#>  9         1 2021-03-01        NA            NA                   NA    
#> 10         1 2021-04-01        NA            NA                   NA    
#> # ℹ 460 more rows