CamelRatiosIndex

Overview

CamelRatiosIndex implements the multivariate-weighted indexing method for bank performance assessment using the CAMEL framework. The package computes composite year-on-year indices that enable:

Comparison across multiple banks
Assessment of bank health relative to a base year
Evaluation of the overall banking industry health

Based on the methodology proposed by Ayimah et al. (2023a, 2023b). This composite index is intended to offer regulators and policymakers a standardised, objective for monitoring bank performance over time and across institutions. Its ability to benchmark banks against a common base year enhances early-warning capabilities, enabling supervisory authorities to identify emerging weaknesses individual banks as well as systemic vulnerabilities within the industry.

Installation

You can install the released version from CRAN with:

install.packages("CamelRatiosIndex")

Or the development version from Github using:

# install.packages("remotes")
remotes::install_github("JC-Ayimah/CamelRatiosIndex")

Quick Start

library(CamelRatiosIndex)

# inspect example datasets
head(camel_2015)   # used as base year's data
head(camel_2022)   # used as current year's data

# Compute CAMEL index
result <- camel_index(camel_2015, camel_2022)

# View results
result$index_table
#> # A tibble: 21 x 3
#>    bank      I_mw    PD
#>    <chr>    <dbl> <dbl>
#>  1 Absa     102.5  2.52
#>  2 AB        98.3 -1.72
#>  3 ADB      101.8  1.78
#>  ...

# Visualize
plot_camel_index(result, highlight_banks = c("Absa", "Ecobank", "GCB"))

Features

Tidyverse-native: Built on dplyr, ggplot2, and tibble
Flexible input: Accepts data frames or matrices with bank names
Robust statistics: Uses OGK robust covariance estimation via robustfa
Rich output: Returns index table, weights, eigenvalues, and factor analysis objects
Publication-ready plots: ggplot2-based visualization with customizable themes
Built-in data: Example datasets from 21 Ghanaian commercial banks

The CAMEL Framework

Dimension	Description	Direction
Capital Adequacy	Ca	Higher = better
Asset Quality	Aq	Higher = worse (auto-inverted)
Management Efficiency	Me	Higher = worse (auto-inverted)
Earnings	Eq	Higher = better
Liquidity	Lm	Higher = worse (auto-inverted)

Functions

Function	Description
`camel_index()`	Compute composite CAMEL index
`plot_camel_index()`	Plot percentage differences across banks
`print.camel_index()`	Print method for index results
`summary.camel_index()`	Detailed summary of factor analysis
`autoplot.camel_index()`	ggplot2 autoplot method

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

This package is released under the MIT License. See LICENSE.md for details.

References

Ayimah, J. C., et al. (2023a). A Robust Multivariate Weighting Technique for Computing a Measure for Inflation. African Journal of Technical Education and Management, 3(1), 1-15. Retrieved from https://ajtem.com/index.php/ajtem/article/view/53.

Ayimah, J.C. (2023b). Computing Multivariate-Weighted Consumer Price Index: An Application Manual in R. B P International. DOI:http://dx.doi.org/10.9734/bpi/mono/978-81-19315-32-1