R/constant_mean_return.R

In wbach12/p9eventstudy: Eventstudy functions.

#' Constant Mean Return Model
#'
#' This function calculates the normal returns using the constant-mean-return model.
#' Returns either the residuals or the constant means.
#' @param return.data should be a data frame containing dates (Date) in the first column and returns in the rest.
#' @param residuals Do you want the residuals? Defaults to TRUE. If FALSE the constan means are returned.
#' @keywords Keywords
#' @export
#' @examples
#' ConstantMeanReturn(return.data = ReturnData, residuals = TRUE)
#' ConstantMeanReturn(return.data = ReturnData, residuals = FALSE)

# This function calculates the normal returns using the constant-mean-return model
# Input:
# return.data should be a data frame containing dates (Date) in the first column and returns in the rest.
# Output:
# If residuals = TRUE the residuals from the constant-mean-return model are returned. If FALSE the constant mean is returned.

ConstantMeanReturn <- function(return.data, residuals = TRUE) {
  # Set number of observations and assets in return.data and set date vector
  observations <- nrow(return.data)
  assets <- ncol(return.data) - 1
  returns <- return.data[,-1]

  # Construct result containers
  constant.mean <- as.data.frame(setNames(replicate(assets, 1, simplify = F), colnames(returns)))
  resids <- return.data

  # If only one asset is included in the study
  if(assets == 1){
    constant.mean <- mean(returns, na.rm = TRUE)
    if (residuals == TRUE) {
      resids[,2] <- returns - constant.mean
      result <- resids
    } else {
      result <- constant.mean
    }
  } else {
    # If multiple assets are included in the study
    for (i in 1:assets) {
      constant.mean[i] <- mean(returns[,i], na.rm = TRUE)
      if (residuals == TRUE) {
        resids[,i+1] <- returns[,i] - as.numeric(constant.mean[i])
      }
    }
    if (residuals == TRUE) {
      result <- resids
    } else {
      result <- constant.mean
    }
  }
  return(result)
}