PeerPerformance (version 2.4.0)

exposureHeterogeneity: Factor exposure heterogeneity from a beta screening

Description

Aggregates the per-fund equal-performance ratios of a screen_beta = TRUE alphaScreening into the factor-by-factor heterogeneity measure of Ardia et al. (2023). For each coefficient \(k\) (the alpha and each factor beta) it reports the average equal-exposure ratio \(\pi^0_k = \frac1N\sum_i \pi^0_{i,k}\) and the heterogeneity \(1-\pi^0_k\): the share of peers that are significantly differentiated on coefficient \(k\). A value close to one indicates large heterogeneity (much room to differentiate); close to zero, homogeneity.

Usage

exposureHeterogeneity(object)

Value

A data.frame of class exposureHeterogeneity with columns coefficient, equalExposure (\(\pi^0_k\)), and heterogeneity (\(1-\pi^0_k\)).

Arguments

object

A SCREENING object produced with screen_beta = TRUE.

Author

David Ardia and Kris Boudt.

References

Ardia, D., Bluteau, K., Lortie-Cloutier, G., Tran, D. (2023). Factor exposure heterogeneity in green and brown stocks. Finance Research Letters 55, Part A, 103900.

See Also

alphaScreening.

Examples

# \donttest{
data("hfdata")
set.seed(1234)
fac <- matrix(rnorm(nrow(hfdata) * 2), ncol = 2,
              dimnames = list(NULL, c("MKT", "SMB")))
sc <- alphaScreening(hfdata[, 1:20], factors = fac, screen_beta = TRUE,
                     control = list(nCore = 1))
exposureHeterogeneity(sc)
# }