Single Factor Asset-Pricing Model Summary: Statistics and Stylized Facts

Source: R/table.CAPM.R

Takes a set of returns and relates them to a benchmark return. Provides a set of measures related to an excess return single factor model, or CAPM.

table.SFM(Ra, Rb, scale = NA, Rf = 0, digits = 4)

Arguments

Ra

a vector of returns to test, e.g., the asset to be examined

Rb

a matrix, data.frame, or timeSeries of benchmark(s) to test the asset against.

scale

number of periods in a year (daily scale = 252, monthly scale = 12, quarterly scale = 4)

Rf

risk free rate, in same period as your returns

digits

number of digits to round results to

Details

This table will show statistics pertaining to an asset against a set of benchmarks, or statistics for a set of assets against a benchmark.

Author

Peter Carl

Examples

 # CRAN does not allow examples to load suggested packages in one of its tests
data(managers)
table.SFM(managers[,1:3], managers[,8], Rf = managers[,10])
#>                     HAM1 to SP500 TR HAM2 to SP500 TR HAM3 to SP500 TR
#> Alpha                         0.0058           0.0091           0.0062
#> Beta                          0.3901           0.3384           0.5523
#> Alpha Robust                  0.0061           0.0042           0.0050
#> Beta Robust                   0.3315           0.2629           0.6064
#> Beta+                         0.3005           0.5227           0.4858
#> Beta-                         0.4264           0.0698           0.5067
#> Beta+ Robust                  0.3753           0.5738           0.7045
#> Beta- Robust                  0.4166           0.0298           0.4634
#> R-squared                     0.4339           0.1673           0.4341
#> R-squared Robust              0.3257           0.1211           0.5591
#> Annualized Alpha              0.0715           0.1147           0.0772
#> Correlation                   0.6587           0.4090           0.6589
#> Correlation p-value           0.0000           0.0000           0.0000
#> Tracking Error                0.1132           0.1534           0.1159
#> Active Premium                0.0408           0.0776           0.0545
#> Information Ratio             0.3604           0.5060           0.4701
#> Treynor Ratio                 0.2428           0.3883           0.1956

result = table.SFM(managers[,1:3], managers[,8], Rf = managers[,10])
require(Hmisc)
textplot(result, rmar = 0.8, cmar = 1.5,  max.cex=.9, 
         halign = "center", valign = "top", row.valign="center", 
         wrap.rownames=15, wrap.colnames=10, mar = c(0,0,3,0)+0.1)
title(main="Single Factor Model Related Statistics")