Compute Multivariate-Weighted CAMEL Index — camel

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

base_data: A data frame or matrix containing the base year CAMEL ratios. If a data frame, the first column must contain bank identifiers (character or numeric). If a matrix, bank identifiers must be supplied separately via bank_names.
current_data: A data frame or matrix containing the current year CAMEL ratios, in the same format and order as base_data.
bank_names: A character or numeric vector of bank identifiers. Required when base_data and current_data are matrices. Must be the same length as the number of rows in the data. Ignored when inputs are data frames.
n_factors: Integer specifying the number of factors to extract in the robust factor analysis. Default is 3.
scale_data: Logical indicating whether to standardize the data before factor analysis. Default is TRUE.
cov_control: A control object for robust covariance estimation, passed to robustfa::FaCov(). Default is rrcov::CovControlOgk().
method: Character specifying the factor analysis method. Default is "pca" (principal component analysis). See robustfa::FaCov() for options.
scores_method: Character specifying the method for computing factor scores. Default is "regression". See robustfa::FaCov() for options.

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.

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.

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:

Capital Adequacy (Ca)
Asset Quality (Aq) – inverted internally
Management Efficiency (Me) – inverted internally
Earnings (Eq)
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)
#> ℹ Using 3 factors (Kaiser criterion suggests 2 for base year).
result$index_table
#> # A tibble: 21 × 3
#>    bank      I_mw      PD
#>    <chr>    <dbl>   <dbl>
#>  1 Absa    116.    16.1  
#>  2 AB      327.   227.   
#>  3 ADB     154.    54.1  
#>  4 BA       99.6   -0.420
#>  5 CB        3.72 -96.3  
#>  6 Ecobank 549.   449.   
#>  7 FBN     171.    70.9  
#>  8 FB      151.    50.6  
#>  9 FAB     204.   104.   
#> 10 FNB     144.    43.7  
#> # ℹ 11 more rows

# Access individual components
result$mw_lasp
#>  [1]  1.17068455  3.24528378  1.52780746  0.98655880  0.01257239  6.02659502
#>  [7]  1.65444746  1.54951394  2.03302237  1.49816252  2.98825411  1.28723657
#> [13]  1.13849623 -1.97551778  0.82971916  0.84601356  0.94124611  1.70736177
#> [19]  1.43859304  0.28238957  1.40351131
result$mw_pash
#>  [1]  1.15082501  3.30155142  1.55371945  1.00501635  0.06177212  4.94882308
#>  [7]  1.76427243  1.46318577  2.05241470  1.37566218  2.51395785  1.28567395
#> [13]  1.14279086 -1.80089258  0.87655232  0.79703627  0.89022374  1.65663138
#> [19]  1.43712269  0.17659450  1.42483169
result$weights_base
#>        X1        X2        X3        X4        X5 
#> 0.7903609 0.7050024 0.8849089 0.8942996 0.8265737 

# 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)
#> ℹ Using 3 factors (Kaiser criterion suggests 2 for base year).
result2$index_table
#> # A tibble: 21 × 3
#>    bank      I_mw      PD
#>    <chr>    <dbl>   <dbl>
#>  1 Absa    116.    16.1  
#>  2 AB      327.   227.   
#>  3 ADB     154.    54.1  
#>  4 BA       99.6   -0.420
#>  5 CB        3.72 -96.3  
#>  6 Ecobank 549.   449.   
#>  7 FBN     171.    70.9  
#>  8 FB      151.    50.6  
#>  9 FAB     204.   104.   
#> 10 FNB     144.    43.7  
#> # ℹ 11 more rows