Forecasting Credit Ratings of EU Banks
Abstract
:1. Introduction
2. Literature Review
3. Data and Methodology
3.1. The Data
3.2. Support Vector Machines
4. Empirical Findings
4.1. Feature Selection
4.2. Ordered Probit Model Results
4.3. Support Vector Machines Model Results
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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1 | In the SVM jargon. |
2 | Our implementation of SVR models is based on LIBSVM (Chang and Lin 2011). The software is available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/. |
No | Abbreviation | Description |
---|---|---|
Panel A: Assets | ||
1 | TASSET | Total Assets |
2 | LO | Loans |
3 | GRLO | Gross loans |
4 | CBCB | Cash& Balances at Central Bank |
5 | LASSET | Liquid assets |
Panel B: Liabilities | ||
6 | DSF | Deposits and Short-term funding |
7 | EQ | Equity |
8 | TCDE | Total customer deposits |
9 | OIBL | Other interest-bearing liabilities |
10 | BDE | Bank deposits |
Panel C: Income and Expenses | ||
11 | NI | Net Income |
12 | NIM | Net interest margin |
13 | NIR | Net interest revenue |
14 | PBT | Profit before tax |
15 | OPIN | Operating income |
16 | ITEX | Income tax expense |
17 | OPPR | Operating profit |
18 | TOE | Total operating expenses |
19 | NOR | Net operating revenues |
20 | TIP | Total interest paid |
21 | TIR | Total interest received |
Panel D: Financial Ratios | ||
22 | NLTA | Net loans/Total assets |
23 | NLDSF | Net loans/Deposits and Short-Term funding |
24 | NLTDB | Liquid assets/Total deposits and borrowed |
25 | LADSF | Liquid assets/Deposits and Short-Term funding |
26 | LATDB | Liquid assets/Total deposits and borrowed |
27 | NIRAA | Net interest revenues/Average assets |
28 | OOPIAA | Other operating income/Average assets |
29 | NOEAA | Non-interest expenses/Average assets |
30 | ROAE | Return On Average Equity (ROAE) |
31 | ROAA | Return On Average Assets (ROAA) |
32 | ETA | Equity/Total assets |
33 | ENL | Equity/Net loans |
34 | EL | Equity/Liabilities |
Class Identification | Rating Category | Number of Banks | ||||
---|---|---|---|---|---|---|
3 | AAA | AA– | AA | A+ | 24 | |
2 | A– | A | ΒΒΒ+ | 34 | ||
1 | ΒΒΒ– | ΒΒΒ | 32 | |||
0 | ΒΒ+ | ΒΒ– | ΒΒ | Β+ | Β– | 22 |
Total | 112 |
Group 1 | Group 2 | Group 3 | Group 4 | Group 5 | Group 6 |
---|---|---|---|---|---|
18 variables | 20 variables | 30 variables | 30 variables | 10 variables | 136 variables |
Combinatorial 4 | Group 1 | Group 2 | Group 3 | Group 4 | Group 5 | Group 6 |
TASSET14 | TASSET14 | TASSET14 | TIR16 | TASSET14 | TIR16 | |
TIR16 | TIR16 | TASSET13 | DSF14 | TIR16 | DSF14 | |
NOEAA13 | NIRAA13 | OPPR13 | NIRAA13 | OIBL13 | TASSET15 | |
NIM13 | NOEAA13 | OIBL16 | NOEAA13 | TASSET13 | NOR14 | |
Combinatorial 8 | Group 1 | Group 2 | Group 3 | Group 4 | Group 5 | Group 6 |
TASSET14 | TASSET14 | TASSET14 | DSF14 | TASSET14 | TIR16 | |
TIR16 | TIR16 | TASSET13 | NIRAA13 | TIR16 | TASSET13 | |
TASSET13 | NIRAA13 | OIBL16 | EQ16 | OIBL13 | ETA16 | |
TIR14 | NOEAA13 | LADSF16 | TIR15 | TASSET13 | LO15 | |
NOEAA13 | TASSET13 | LADSF14 | GRLO14 | TIR14 | GRLO15 | |
NIM13 | TIR14 | LADSF13 | LO14 | NOEAA14 | NOEAA16 | |
NOEAA15 | NOEAA15 | OPPR14 | OOPIAA13 | TIR15 | OOPIAA13 | |
NOEAA16 | NOEAA16 | NI13 | OOPIAA16 | TASSET16 | NLTA14 | |
Stepwise-forward | Group 1 | Group 2 | Group 3 | Group 4 | Group 5 | Group 6 |
TASSET14 | TASSET14 | TASSET14 | TIR15 | TASSET14 | TASSET14 | |
TIR16 | TIR16 | TASSET13 | DSF14 | TIR16 | TIR16 | |
NIRAA14 | NIRAA14 | OPPR13 | NIRAA13 | OIBL13 | DSF14 | |
NOEAA14 | NOEAA14 | OIBL16 | NOEAA13 | TASSET13 | PBT13 | |
(4) | (4) | (4) | EQ16 | (4) | NIR13 | |
(5) | CBCB16 | |||||
CBCB13 | ||||||
OOPIAA14 | ||||||
TIR14 | ||||||
NIR14 | ||||||
(10) |
Regressor Selection | Group 1 | Group 2 | Group 3 | Group 4 | Group 5 | Group 6 |
---|---|---|---|---|---|---|
Combinatorial 4 | 57.14 | 54.46 | 50.89 | 49.11 | 51.79 | 54.46 |
Combinatorial 8 | 57.14 | 56.25 | 58.04 | 53.57 | 55.36 | 66.07 |
Stepwise-forward | 57.14 | 57.14 | 50.89 | 46.43 | 51.79 | 57.14 |
Correct | Incorect | % Correct | % Incorect | |
---|---|---|---|---|
predicted 0 | 15 | 7 | 68.18% | 31.82% |
predicted 1 | 20 | 12 | 62.50% | 37.50% |
predicted 2 | 24 | 10 | 70.59% | 29.41% |
predicted 3 | 15 | 9 | 62.50% | 37.50% |
Total | 74 | 38 | 66.07% | 33.93% |
Regressor Selection | Group 1 | Group 2 | Group 3 | Group 4 | Group 5 | Group 6 |
---|---|---|---|---|---|---|
Panel A: Linear kernel (k-fold cross validation) | ||||||
Combinatorial 4 | 62.50 | 63.39 | 60.71 | 63.39 | 61.61 | 60.71 |
Combinatorial 8 | 58.04 | 64.29 | 58.04 | 50.00 | 63.39 | 53.57 |
Stepwise-forward | 59.82 | 62.50 | 59.82 | 63.39 | 67.86 | 66.96 |
Panel B: nonlinear RBF kernel (k-fold cross validation) | ||||||
Combinatorial 4 | 64.29 | 63.39 | 61.61 | 62.50 | 63.39 | 61.61 |
Combinatorial 8 | 72.32 | 76.79 | 72.32 | 91.07 | 50.89 | 66.96 |
Stepwise-forward | 68.75 | 60.71 | 68.75 | 61.61 | 62.50 | 79.46 |
Correct | Incorrect | %Correct | %Incorrect | |
---|---|---|---|---|
predicted 0 | 22 | 0 | 100% | 0% |
predicted 1 | 26 | 6 | 81.25% | 18.75% |
predicted 2 | 31 | 3 | 91.18% | 8.82% |
predicted 3 | 23 | 1 | 95.83% | 4.17% |
Total | 102 | 10 | 91.07% | 8.93% |
Actual 0 | Actual 1 | Actual 2 | Actual 3 | |
---|---|---|---|---|
predicted 0 | 22 | 1 | 0 | 0 |
predicted 1 | 0 | 26 | 0 | 0 |
predicted 2 | 0 | 4 | 31 | 1 |
predicted 3 | 0 | 1 | 3 | 23 |
Regressor Selection | Group 1 | Group 2 | Group 3 | Group 4 | Group 5 | Group 6 |
---|---|---|---|---|---|---|
Panel A: Linear kernel (bootstrap) | ||||||
Combinatorial 4 | 64.29 [61.61, 66.96] | 70.54 [67.86, 73.21] | 63.39 [60.71, 66.52] | 64.29 [61.61, 66.96] | 70.54 [66.96, 73.21] | 63.40 [61.61, 66.96] |
Combinatorial 8 | 61.61 [58.04, 65.18] | 71.43 [68.75, 73.21] | 59.82 [57.15, 63.39] | 58.04 [55.36, 60.71] | 70.54 [67.86, 70.66] | 62.95 [60.27, 66.07] |
Stepwise-forward | 62.50 [59.82, 65.18] | 66.96 [64.29, 69.64] | 62.50 [59.82, 64.89] | 65.18 [62.95, 68.75] | 74.11 [71.43, 76.79] | 80.36 [77.68, 85.04] |
Panel B: nonlinear RBF kernel (bootstrap) | ||||||
Combinatorial 4 | 81.25 [79.46, 83.93] | 85.71 [84.82, 87.50] | 78.13 [75.90, 80.36] | 78.57 [75.89, 81.25] | 86.60 [83.93, 87.50] | 78.57 [75.89, 81.25] |
Combinatorial 8 | 82.15 [80.36, 83.93] | 91.96 [89.29, 93.75] | 82.14 [80.36, 83.93] | 91.52 89.29, 93.75] | 97.32 [96.43, 98.21] | 95.54 [94.64, 97.32] |
Stepwise-forward | 81.25 [79.46, 83.04] | 88.39 [84.82, 90.18] | 81.25 [79.46, 83.04] | 97.32 [95.54, 97.32] | 98.21 [97.32, 100] | 95.42 [92.11, 98.57] |
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Plakandaras, V.; Gogas, P.; Papadimitriou, T.; Doumpa, E.; Stefanidou, M. Forecasting Credit Ratings of EU Banks. Int. J. Financial Stud. 2020, 8, 49. https://doi.org/10.3390/ijfs8030049
Plakandaras V, Gogas P, Papadimitriou T, Doumpa E, Stefanidou M. Forecasting Credit Ratings of EU Banks. International Journal of Financial Studies. 2020; 8(3):49. https://doi.org/10.3390/ijfs8030049
Chicago/Turabian StylePlakandaras, Vasilios, Periklis Gogas, Theophilos Papadimitriou, Efterpi Doumpa, and Maria Stefanidou. 2020. "Forecasting Credit Ratings of EU Banks" International Journal of Financial Studies 8, no. 3: 49. https://doi.org/10.3390/ijfs8030049
APA StylePlakandaras, V., Gogas, P., Papadimitriou, T., Doumpa, E., & Stefanidou, M. (2020). Forecasting Credit Ratings of EU Banks. International Journal of Financial Studies, 8(3), 49. https://doi.org/10.3390/ijfs8030049