Return.calculate    package:PerformanceAnalytics    R Documentation

_c_a_l_c_u_l_a_t_e _s_i_m_p_l_e _o_r _c_o_m_p_o_u_n_d _r_e_t_u_r_n_s _f_r_o_m _p_r_i_c_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     calculate simple or compound returns from prices

_U_s_a_g_e:

     Return.calculate(prices, method=c("compound","simple"))
     CalculateReturns(prices, method=c("compound","simple"))

_A_r_g_u_m_e_n_t_s:

  prices: data object containing ordered price observations 

  method: calculate "simple" or "compound" returns, default compound 

_D_e_t_a_i_l_s:

     Two requirements should be made clear.  First, the function
     'Return.calculate' assumes regular price data.  In this case, we
     downloaded monthly close prices.  Prices can be for any time
     scale, such as daily, weekly, monthly or annual, as long as the
     data consists of regular observations.  Irregular observations
     require time period scaling to be comparable.  Fortunately,
     'to.period' in the 'xts' package, or the 'aggregate.zoo' in the
     'zoo' package supports supports management and conversion of
     irregular time series.

     Second, if corporate actions, dividends, or other adjustments such
     as time- or money-weighting are to be taken into account, those
     calculations must be made separately. This is a simple function
     that assumes fully adjusted close prices as input.  For the IBM
     timeseries in the example below, dividends and corporate actions
     are not contained in the "close" price series, so we end up with
     "price returns" instead of "total returns".  This can lead to
     significant underestimation of the return series over longer time
     periods.  To use adjusted returns, specify 'quote="AdjClose"' in
     'get.hist.quote', which is found in package 'tseries'.

_V_a_l_u_e:

     vector or matrix of simple or compound returns

_A_u_t_h_o_r(_s):

     Peter Carl

_R_e_f_e_r_e_n_c_e_s:

     Bacon, C. _Practical Portfolio Performance Measurement and
     Attribution_. Wiley. 2004. Chapter 2 

_S_e_e _A_l_s_o:

     'Return.cumulative'

_E_x_a_m_p_l_e_s:

     require(tseries)
     prices = get.hist.quote("IBM", start = "1999-01-01", end = "2007-01-01", quote = "AdjClose", compression = "d")
     R.IBM = Return.calculate(prices, method="simple")
     R.IBM = as.xts(R.IBM)
     colnames(R.IBM)="IBM"
     chart.CumReturns(R.IBM,legend.loc="topleft", main="Cumulative Daily Returns for IBM")