Package {CamelRatiosIndex}

CamelRatiosIndex: Multivariate-Weighted Indexing of CAMEL Ratios for Bank Performance

Description

Computes a composite year-on-year index for bank performance assessment using the CAMEL framework (Capital Adequacy, Asset Quality, Management Efficiency, Earnings, Liquidity). The multivariate weighting scheme employs factor analysis with robust covariance estimation to derive communality-based weights from the correlation matrix of CAMEL ratios. Provides functions for index computation, visualization, and comparison across banks and time periods.The methodology is described in Ayimah et al. (2023a) doi:10.9734/bpi/mono/978-81-19315-32-1 and Ayimah et al. (2023b) https://ajtem.com/index.php/ajtem/article/view/53.

Author(s)

Maintainer: John Coker Ayimah jayimah@htu.edu.gh [copyright holder]

Authors:

• John Coker Ayimah jayimah@htu.edu.gh [copyright holder]

• George Kyei Agyen gkagyen@live.com [copyright holder]

• Raymond Achiyaale rachiyaale@htu.edu.gh [copyright holder]

See Also

Useful links:

• https://github.com/JC-Ayimah/CamelRatiosIndex

• https://JC-Ayimah.github.io/CamelRatiosIndex/

• Report bugs at https://github.com/JC-Ayimah/CamelRatiosIndex/issues

CAMEL Ratio Data for Ghanaian Banks (2015)

Description

CAMEL ratio data for 21 Ghanaian commercial banks as of 2015. Data sourced from Bank of Ghana publications.

Usage

camel_2015

Format

A data frame with 21 rows and 6 columns:

Bank

Bank name (character)

Ca1

Capital Adequacy ratio

Aq1

Asset Quality ratio

Me1

Management Efficiency ratio

Eq1

Earnings ratio

Lm1

Liquidity ratio

Source

Bank of Ghana and Ghana Stock Exchange

CAMEL Ratio Data for Ghanaian Banks (2016)

Description

CAMEL ratio data for 21 Ghanaian commercial banks as of 2016. Data sourced from Bank of Ghana publications.

Usage

camel_2016

Format

A data frame with 21 rows and 6 columns:

Bank

Bank name (character)

Ca2

Capital Adequacy ratio

Aq2

Asset Quality ratio

Me2

Management Efficiency ratio

Eq2

Earnings ratio

Lm2

Liquidity ratio

Source

Bank of Ghana and Ghana Stock Exchange

CAMEL Ratio Data for Ghanaian Banks (2017)

Description

CAMEL ratio data for 21 Ghanaian commercial banks as of 2017. Data sourced from Bank of Ghana publications.

Usage

camel_2017

Format

A data frame with 21 rows and 6 columns:

Bank

Bank name (character)

Ca2

Capital Adequacy ratio

Aq2

Asset Quality ratio

Me2

Management Efficiency ratio

Eq2

Earnings ratio

Lm2

Liquidity ratio

Source

Bank of Ghana and Ghana Stock Exchange

CAMEL Ratio Data for Ghanaian Banks (2018)

Description

CAMEL ratio data for 21 Ghanaian commercial banks as of 2017. Data sourced from Bank of Ghana publications.

Usage

camel_2018

Format

A data frame with 21 rows and 6 columns:

Bank

Bank name (character)

Ca2

Capital Adequacy ratio

Aq2

Asset Quality ratio

Me2

Management Efficiency ratio

Eq2

Earnings ratio

Lm2

Liquidity ratio

Source

Bank of Ghana and Ghana Stock Exchange

CAMEL Ratio Data for Ghanaian Banks (2019)

Description

CAMEL ratio data for 21 Ghanaian commercial banks as of 2019. Data sourced from Bank of Ghana publications.

Usage

camel_2019

Format

A data frame with 21 rows and 6 columns:

Bank

Bank name (character)

Ca2

Capital Adequacy ratio

Aq2

Asset Quality ratio

Me2

Management Efficiency ratio

Eq2

Earnings ratio

Lm2

Liquidity ratio

Source

Bank of Ghana and Ghana Stock Exchange

CAMEL Ratio Data for Ghanaian Banks (2020)

Description

CAMEL ratio data for 21 Ghanaian commercial banks as of 2020. Data sourced from Bank of Ghana publications.

Usage

camel_2020

Format

A data frame with 21 rows and 6 columns:

Bank

Bank name (character)

Ca2

Capital Adequacy ratio

Aq2

Asset Quality ratio

Me2

Management Efficiency ratio

Eq2

Earnings ratio

Lm2

Liquidity ratio

Source

Bank of Ghana and Ghana Stock Exchange

CAMEL Ratio Data for Ghanaian Banks (2021)

Description

CAMEL ratio data for 21 Ghanaian commercial banks as of 2021. Data sourced from Bank of Ghana publications.

Usage

camel_2021

Format

A data frame with 21 rows and 6 columns:

Bank

Bank name (character)

Ca2

Capital Adequacy ratio

Aq2

Asset Quality ratio

Me2

Management Efficiency ratio

Eq2

Earnings ratio

Lm2

Liquidity ratio

Source

Bank of Ghana and Ghana Stock Exchange

CAMEL Ratio Data for Ghanaian Banks (2022)

Description

CAMEL ratio data for 21 Ghanaian commercial banks as of 2022. Data sourced from Bank of Ghana publications.

Usage

camel_2022

Format

A data frame with 21 rows and 6 columns:

Bank

Bank name (character)

Ca2

Capital Adequacy ratio

Aq2

Asset Quality ratio

Me2

Management Efficiency ratio

Eq2

Earnings ratio

Lm2

Liquidity ratio

Source

Bank of Ghana and Ghana Stock Exchange

Compute Multivariate-Weighted CAMEL Index

Description

Computes a composite year-on-year index for bank performance assessment using the CAMEL framework. The multivariate weighting scheme employs robust factor analysis to derive communality-based weights from the correlation matrix of CAMEL ratios.

Usage

camel_index(
  base_data,
  current_data,
  bank_names = NULL,
  n_factors = 3,
  scale_data = TRUE,
  cov_control = rrcov::CovControlOgk(),
  method = "pca",
  scores_method = "regression"
)

Arguments

Details

The index is computed as the arithmetic mean of two multivariate-weighted Laspeyres-type and Paasche-type indices, scaled to a base of 100. The percentage difference (PD) from the base year is also reported.

Value

A list of class "camel_index" containing:

index_table

A tibble::tibble() with columns bank, I_mw (composite index, base = 100), and PD (percentage difference from base).

mw_lasp

Numeric vector of multivariate-weighted Laspeyres indices.

mw_pash

Numeric vector of multivariate-weighted Paasche indices.

I_mw

Numeric vector of composite indices (base = 100).

PD

Numeric vector of percentage differences from base year.

weights_base

Numeric vector of communality-based weights from base year factor analysis.

weights_current

Numeric vector of communality-based weights from current year factor analysis.

eigenvalues_base

Numeric vector of eigenvalues from base year correlation matrix.

eigenvalues_current

Numeric vector of eigenvalues from current year correlation matrix.

n_factors_base

Integer, number of eigenvalues > 1 in base year.

n_factors_current

Integer, number of eigenvalues > 1 in current year.

fa_base

The fitted robustfa::FaCov() object for base year.

fa_current

The fitted robustfa::FaCov() object for current year.

relativity_data

Matrix of current-to-base ratios for each CAMEL variable and bank.

base_data

The processed base year data (matrix, no bank names).

current_data

The processed current year data (matrix, no bank names).

bank_names

Character vector of bank identifiers.

n_factors

Integer, number of factors used.

call

The matched call.

Data Format

When supplying data frames, the first column must be the bank identifier (character or numeric), and the remaining columns must be the five CAMEL ratios in the standard order:

1. Capital Adequacy (Ca)

2. Asset Quality (Aq) – inverted internally

3. Management Efficiency (Me) – inverted internally

4. Earnings (Eq)

5. Liquidity (Lm) – inverted internally

The inversion of Aq, Me, and Lm is handled automatically because higher values of these ratios indicate worse bank performance.

Examples

# Using the built-in example data
base_year <- camel_2015
current_year <- camel_2022

result <- camel_index(base_year, current_year)
result$index_table

# Access individual components
result$mw_lasp
result$mw_pash
result$weights_base

# Using matrices with explicit bank names
base_mat <- as.matrix(camel_2015[, -1])
curr_mat <- as.matrix(camel_2022[, -1])
banks <- camel_2015$Bank

result2 <- camel_index(base_mat, curr_mat, bank_names = banks)
result2$index_table

Plot CAMEL Index Percentage Differences

Description

Creates a ggplot2 line graph showing the percentage difference (PD) from the base year for each bank, enabling visual comparison of bank performance across the CAMEL framework.

Usage

plot_camel_index(
  x,
  highlight_banks = NULL,
  add_reference_line = TRUE,
  point_size = 3,
  line_size = 0.8,
  colour_palette = NULL,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  theme_fn = ggplot2::theme_minimal,
  ...
)

## S3 method for class 'camel_index'
autoplot(object, ...)

Arguments

Value

A ggplot object.

Examples

# Basic plot
result <- camel_index(camel_2015, camel_2022)
plot_camel_index(result)

# Highlight specific banks
plot_camel_index(result, highlight_banks = c("Absa", "Ecobank", "GCB"))

# Custom styling
plot_camel_index(
  result,
  highlight_banks = c("Absa", "Ecobank"),
  title = "Bank Performance: 2015 vs 2022",
  subtitle = "Percentage difference from base year",
  colour_palette = c("#E41A1C", "#377EB8"),
  theme_fn = ggplot2::theme_bw
)

Print Method for camel_index Objects

Description

Print Method for camel_index Objects

Usage

## S3 method for class 'camel_index'
print(x, ...)

Arguments

Value

Invisibly returns x.

Examples

base_year <- camel_2015
current_year <- camel_2022

result <- camel_index(base_year, current_year)
result

Summary Method for camel_index Objects

Description

Provides a detailed summary of the CAMEL index computation, including eigenvalues, factor loadings, and weight attribution.

Usage

## S3 method for class 'camel_index'
summary(object, ...)

Arguments

Value

Invisibly returns object.

Examples

result <- camel_index(camel_2015, camel_2022)
summary(result)