Fractile graphical analysis in finance: A new perspective with applications

• Main Authors: BERA, Anil K., GHOSH, Aurobindo
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: non-parametric regression; Fractile Graphical Analysis; rank regression; quantile regression; smooth test; F-tests; bootstrap tests; mutual fund returns; private equity returns; Finance and Financial Management
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/7196; https://ink.library.smu.edu.sg/context/lkcsb_research/article/8195/viewcontent/jrfm_15_00412_v2.pdf
• Institution: Singapore Management University

Fractile Graphical Analysis (FGA) was proposed by Prasanta Chandra Mahalanobis in 1961 as a method for comparing two distributions at two different points (of time or space) controlling for the rank of a covariate through fractile groups. We use bootstrap techniques to formalize the heuristic method used by Mahalanobis for approximating the standard error of the dependent variable using fractile graphs from two independently selected “interpenetrating network of subsamples.” We highlight the potential and revisit this underutilized technique of FGA with a historical perspective. We explore a new non-parametric regression method called Fractile Regression where we condition on the ranks of the covariate and compare it with existing regression techniques. We apply this method to compare mutual fund inflow distributions after conditioning on ranks or fractiles of pre-tax and post-tax returns and compare distributions of private and public equity returns after controlling for fractiles of assets under management size using the two sample smooth test.