tidyfinance (version 0.4.3)

compute_portfolio_returns: Compute Portfolio Returns

Description

This function 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(
  sorting_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 = 0,
  data_options = NULL
)

Value

A data frame with computed portfolio returns, 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 the sorting_data contains mktcap_lag)

• ret_excess_ew: The equal-weighted excess return of the portfolio.

Arguments

sorting_data

A data frame containing the dataset for portfolio assignment and return computation. Following CRSP naming conventions, the panel data must identify individual stocks with permno and the time point with date. It must contain columns for the sorting variables and ret_excess. Additionally, mktcap_lag is needed for value-weighted returns.

sorting_variables

A character vector specifying the column names in sorting_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_variable) 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 for the main sorting variable.

breakpoint_options_secondary

An optional named list of breakpoint_options passed to breakpoint_function for the secondary sorting variable.

breakpoint_function_main

A function to compute the main sorting variable. The default is set to compute_breakpoints.

breakpoint_function_secondary

A function to compute the secondary sorting variable. The default is set to compute_breakpoints.

min_portfolio_size

An integer specifying the minimum number of portfolio constituents (default is set to 0, effectively deactivating the check). Small portfolios' returns are set to zero.

data_options

A named list of data_options with characters, indicating the column names required to run this function. The required column names identify dates, the stocks, and returns. Defaults to date=date, id=permno, and ret_excess = ret_excess.

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 returns are computed. The function handles missing and extreme values appropriately based on the specified sorting method and rebalancing frequency.

Examples

# 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)
)

# Bivariate dependent sorting with annual rebalancing
compute_portfolio_returns(
  data, c("size", "mktcap_lag"), "bivariate-independent", 7,
  breakpoint_options_main = breakpoint_options(n_portfolios = 5),
  breakpoint_options_secondary = breakpoint_options(n_portfolios = 3),
)