Apply a market model and return a list of returns objects.

The function applies a given market model to securities' rates of returns and returns a list of returns objects for each security, which can be passed directly to a whole battery of tests.

apply_market_model(
  rates,
  regressors,
  same_regressor_for_all = TRUE,
  market_model = c("mean_adj", "mrkt_adj", "sim"),
  estimation_method = c("ols"),
  estimation_start,
  estimation_end
)

Arguments

rates	an object of list, data.frame, zoo containing rates of returns of securities.
regressors	an object of the same class as rates containing regressors. The argument can be omitted, if market model is mean_adj. regressors must have the same number of components as rates except cases when the same regressor is used for all securities.
same_regressor_for_all	logical. Should the same regressor be used for each security? The default value is TRUE.
market_model	a character indicating the market model among mean_adj, mrkt_adj, and sim.
estimation_method	a character specifying an estimation method for sim model.
estimation_start	an object of Date class giving the first date of the estimation period.
estimation_end	an object of Date class giving the last date of the estimation period.

Value

A list of returns objects.

Details

The generic function is dispatched for such classes as list, data.frame, and zoo. If same_regressor_for_all is TRUE, and regressors has the length greater than one, the first element of regressors will be applied for each security in rates.

References

Brown S.J., Warner J.B. Using Daily Stock Returns, The Case of Event Studies. Journal of Financial Economics, 14:3-31, 1985.

See also

returns

Examples

## 1. Mean-adjusted-returns model
if (FALSE) {
library("magrittr")
tickers <- c("AMZN", "ZM", "UBER", "NFLX", "SHOP", "FB", "UPWK")
securities_returns <- get_prices_from_tickers(tickers,
                                              start = as.Date("2019-04-01"),
                                              end = as.Date("2020-04-01"),
                                              quote = "Close",
                                              retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous") %>%
    apply_market_model(market_model = "mean_adj",
                       estimation_start = as.Date("2019-04-01"),
                       estimation_end = as.Date("2020-03-13"))
}
## The result of the code above is equivalent to:
data(rates)
securities_returns <- apply_market_model(
    rates,
    market_model = "mean_adj",
    estimation_start = as.Date("2019-04-01"),
    estimation_end = as.Date("2020-03-13")
)

## 2. Market-adjusted-returns model
if (FALSE) {
library("magrittr")
rates_indx <- get_prices_from_tickers("^GSPC",
                                      start = as.Date("2019-04-01"),
                                      end = as.Date("2020-04-01"),
                                      quote = "Close",
                                      retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous")
tickers <- c("AMZN", "ZM", "UBER", "NFLX", "SHOP", "FB", "UPWK")
securities_returns <- get_prices_from_tickers(tickers,
                                              start = as.Date("2019-04-01"),
                                              end = as.Date("2020-04-01"),
                                              quote = "Close",
                                              retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous") %>%
    apply_market_model(regressor = rates_indx,
                       same_regressor_for_all = TRUE,
                       market_model = "mrkt_adj",
                       estimation_start = as.Date("2019-04-01"),
                       estimation_end = as.Date("2020-03-13"))
}
## The result of the code above is equivalent to:
data(rates, rates_indx)
securities_returns <- apply_market_model(
    rates = rates,
    regressor = rates_indx,
    same_regressor_for_all = TRUE,
    market_model = "mrkt_adj",
    estimation_start = as.Date("2019-04-01"),
    estimation_end = as.Date("2020-03-13")
)

## 3. Single-index market model
if (FALSE) {
library("magrittr")
rates_indx <- get_prices_from_tickers("^GSPC",
                                      start = as.Date("2019-04-01"),
                                      end = as.Date("2020-04-01"),
                                      quote = "Close",
                                      retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous")
tickers <- c("AMZN", "ZM", "UBER", "NFLX", "SHOP", "FB", "UPWK")
securities_returns <- get_prices_from_tickers(tickers,
                                              start = as.Date("2019-04-01"),
                                              end = as.Date("2020-04-01"),
                                              quote = "Close",
                                              retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous") %>%
    apply_market_model(regressor = rates_indx,
                       same_regressor_for_all = TRUE,
                       market_model = "sim",
                       estimation_method = "ols",
                       estimation_start = as.Date("2019-04-01"),
                       estimation_end = as.Date("2020-03-13"))
}
## The result of the code above is equivalent to:
data(rates, rates_indx)
securities_returns <- apply_market_model(
    rates = rates,
    regressor = rates_indx,
    same_regressor_for_all = TRUE,
    market_model = "sim",
    estimation_method = "ols",
    estimation_start = as.Date("2019-04-01"),
    estimation_end = as.Date("2020-03-13")
)