PeerPerformance: Luck-Corrected Peer Performance Analysis in R

PeerPerformance is an R package for the peer-performance evaluation of financial investments with luck-correction. In particular, it implements the peer performance ratios of Ardia and Boudt (2018) which measure the percentage of peers a focal fund outperforms and underperforms, after correction for luck. It is useful for fund or portfolio managers to benchmark their investments or screen a universe of new funds. In addition, it implements the testing framework for the Sharpe and modified Sharpe ratios, described in Ledoit and Wolf (2008) and Ardia and Boudt (2015). See also Ardia et al. (2022,2023) for applications in sustainable finance.

Installation

The stable version is on CRAN:

install.packages("PeerPerformance")

The development version can be installed from GitHub:

# install.packages("remotes")
remotes::install_github("ArdiaD/PeerPerformance")

Features

• Peer performance screening of a fund universe with luck correction: alphaScreening() (risk-adjusted alphas, optionally with factor exposures via screen_beta = TRUE), sharpeScreening() and msharpeScreening() (Sharpe / modified Sharpe). Each returns the out-/equal-/under-performance ratios (pi+, pi0, pi-).
• Cross-group screening: the Y argument screens each fund (or a single focal fund) against a separate peer group; targetPeerPerformance() is a convenience wrapper for screening a chosen subset against the whole universe.
• Pairwise testing: alphaTesting(), sharpeTesting(), msharpeTesting().
• Methods for screening results: print(), summary(), plot() (the Ardia and Boudt 2018 screening plot), confint() (bootstrap confidence intervals for the ratios), and as.data.frame() (tidy output).
• Dynamic and factor analyses: rollScreening() (rolling-window ratios) and exposureHeterogeneity() (factor exposure heterogeneity of Ardia et al. 2023).
• A vignette (vignette("PeerPerformance")) and a reproducible Monte-Carlo validation script (system.file("scripts", "validation.R", package = "PeerPerformance")).

Quick start

library("PeerPerformance")
data("hfdata")

## screen a universe of funds, luck-corrected
sc <- alphaScreening(hfdata[, 1:30], control = list(nCore = 1))
summary(sc)                    # ranked table with win/loss counts
plot(sc)                       # peer performance screening plot
confint(sc, parm = "pipos")    # bootstrap CIs for the outperformance ratios

Please cite the package in publications!

By using PeerPerformance you agree to the following rules:

1. You must cite Ardia and Boudt (2018) in working papers and published papers that use PeerPerformance.
2. You must place the following URL in a footnote to help others find PeerPerformance: https://CRAN.R-project.org/package=PeerPerformance
3. You assume all risk for the use of PeerPerformance.

Ardia, D., Boudt, K. (2018).
The peer performance ratios of hedge funds.
Journal of Banking and Finance, 87, 351-368.
https://doi.org/10.1016/j.jbankfin.2017.10.014
https://doi.org/10.2139/ssrn.2000901

Other references

Ardia, D., Boudt, K. (2015). Testing equality of modified Sharpe ratios.
Finance Research Letters, 13, 97-104.
https://doi.org/10.1016/j.frl.2015.02.008
https://doi.org/10.2139/ssrn.2516591

Ardia, D., Bluteau, K., Tran, D. (2022). How easy is it for investment managers to deploy their talent in green and brown stocks? Finance Research Letters, 48, 102992. https://doi.org/10.1016/j.frl.2022.102992
https://doi.org/10.2139/ssrn.4009286

Ardia, D., Bluteau, K., Lortie-Cloutier, G., Tran, D. (2023). Factor exposure heterogeneity in green and brown stocks. Finance Research Letters, 55, Part A, pp.103900. https://doi.org/10.1016/j.frl.2023.103900
https://doi.org/10.2139/ssrn.4362696

Ledoit, O., Wolf, M. (2008).
Robust performance hypothesis testing with the Sharpe ratio.
Journal of Empirical Finance, 15(5), 850-859.