1 Introduction
1.1 Background and motivation
“The greatest contribution to profitability, efficiency and reduced losses comes from the models’ powerful ability to rank-order firms by riskiness so that the bank can eliminate high risk prospects.”
1.2 Main findings
2 Data
2.1 Sample
Year | Bankrupt Firms | Non-Bankrupt Firms | Total | ||
---|---|---|---|---|---|
Active Firms | M&A’s | Others | |||
1990 | 22 | 3215 | 85 | 12 | 3334 |
1991 | 25 | 3208 | 57 | 6 | 3296 |
1992 | 17 | 3239 | 46 | 6 | 3308 |
1993 | 20 | 3295 | 62 | 2 | 3379 |
1994 | 10 | 3487 | 112 | 4 | 3613 |
1995 | 14 | 3873 | 144 | 4 | 4035 |
1996 | 14 | 4186 | 167 | 5 | 4372 |
1997 | 13 | 4520 | 233 | 4 | 4770 |
1998 | 19 | 4675 | 286 | 2 | 4982 |
1999 | 27 | 4554 | 351 | 2 | 4934 |
2000 | 20 | 4336 | 318 | 6 | 4680 |
2001 | 21 | 4317 | 236 | 28 | 4602 |
2002 | 14 | 4115 | 118 | 30 | 4277 |
2003 | 15 | 3790 | 135 | 38 | 3978 |
2004 | 13 | 3500 | 115 | 12 | 3640 |
2005 | 15 | 3433 | 148 | 18 | 3614 |
2006 | 10 | 3432 | 163 | 4 | 3609 |
2007 | 14 | 3298 | 215 | 6 | 3533 |
2008 | 20 | 3288 | 136 | 5 | 3449 |
2009 | 31 | 3196 | 94 | 6 | 3327 |
2010 | 6 | 3046 | 139 | 2 | 3193 |
2011 | 9 | 2985 | 128 | 8 | 3130 |
2012 | 12 | 2920 | 118 | 6 | 3056 |
2013 | 12 | 2873 | 106 | 7 | 2998 |
2014 | 12 | 2872 | 85 | 14 | 2983 |
2015 | 17 | 2900 | 114 | 10 | 3041 |
2.2 Variables construction
Panel A: Financial Ratios (Compustat) | ||
---|---|---|
Variable | Detailed Description | Compustat Item |
NITA | Net Income/Total Assets | NI/AT |
EBITTA | Earnings Before Interests and Taxes/Total Assets | EBIT/AT |
RETA | Retained Earnings/Total Assets | RE/AT |
CASHTA | Cash and Short-Term Investments/Total Assets | CHE/AT |
WCTA | Working Capital/Total Assets | WCAP/TA |
STDTA | Debt in Current Liabilities/Total Assets | DLC/AT |
TLTA | Total Liabilities/Total Assets | LT/AT |
CLCA | Current Liabilities/Current Assets | LCT/ACT |
EBITCL | Earnings Before Interests and Taxes/Current Liabilities | EBIT/LCT |
NICL | Net Income/Current Liabilities | NI/LCT |
CFOTA | Operating Cash Flows/Total Assets | OANCF/AT |
CFOTL | Operating Cash Flows/Total Liabilities | OANCF/LT |
SLTA | Sales/Total Assets | SALE/AT |
LOGASSETS | Natural logarithm of Total Assets | LOG(AT) |
Panel B: Market Variables (CRSP) | ||
VOLE | Annualized volatility of daily equity returns | |
EXRET | Annualized equity return minus the value-weighted return of NYSE, AMEX, NASDAQ stocks | |
LOGPRICE | Natural logarithm of the stock price, at the fiscal-year end | |
RSIZE | Natural logarithm of firm’s market capitalization over the total market capitalization of NYSE, AMEX, NASDAQ stocks | |
MB | Firm’s market capitalization over book value of equity (Market-to-Book ratio) | |
TLMTA | Total Liabilities/ (Market Capitalization + Total Liabilities) | |
NIMTA | Net Income/ (Market Capitalization + Total Liabilities) | |
CASHMTA | Cash and Short-Term Investments/ (Market Capitalization + Total Liabilities) |
2.3 Variables selection
2.4 Descriptive statistics
TLTA | STDTA | NITA | CASHTA | EBITCL | LOGPRICE | EXRET | CASHMTA | NIMTA | |
---|---|---|---|---|---|---|---|---|---|
Bankrupt Firms | |||||||||
Mean | 0.854 | 0.181 | − 0.382 | 0.107 | − 0.578 | 0.528 | − 0.224 | 0.0684 | − 0.250 |
Median | 0.825 | 0.099 | − 0.211 | 0.045 | − 0.153 | 0.560 | − 0.349 | 0.032 | − 0.172 |
St.Dev | 0.327 | 0.183 | 0.450 | 0.157 | 1.289 | 1.146 | 0.876 | 0.100 | 0.257 |
Healthy Firms | |||||||||
Mean | 0.486 | 0.048 | − 0.042 | 0.190 | 0.07 | 2.293 | 0.205 | 0.121 | − 0.022 |
Median | 0.478 | 0.014 | 0.031 | 0.101 | 0.153 | 2.474 | 0.106 | 0.063 | 0.022 |
St.Dev | 0.253 | 0.083 | 0.254 | 0.217 | 1.322 | 1.266 | 0.684 | 0.162 | 0.149 |
3 Methodology
3.1 Measuring discriminatory power
3.2 Maximizing discriminatory power
3.3 Information content tests
3.4 Economic analysis of bankruptcy models
3.4.1 Calculating credit spreads
3.4.2 Granting loans and measuring economic performance
4 Results
4.1 AUROC results
Panel A: AUROC estimation | ||
---|---|---|
Models | Private firms model | Public firms models |
Models with maximized AUROC | ||
Neural Network | 0.9332 | 0.9508 |
Logistic | 0.9221 | 0.9470 |
Models with maximized log-likelihood | ||
Neural Network | 0.9138 | 0.9440 |
Logistic | 0.8991 | 0.9425 |
Private Firms Model | Public Firms Model | |
Neural Network with max. AUROC vs Neural Network with max. LL | 2.04 | 1.72 |
Logistic model with max. AUROC vs Logistic model with max. LL | 2.43 | 1.68 |
4.2 Information content results
Private firms model | Public firms model | |||||||
---|---|---|---|---|---|---|---|---|
Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 1 | Model 2 | Model 3 | Model 4 |
Panel A: Logit models estimation | ||||||||
Prob1 | 0.069 (0.004) | 0.195 (0.012) | ||||||
Prob2 | 0.066 (0.004) | 0.182 (0.008) | ||||||
Prob3 | 0.695 (0.028) | 0.511 (0.055) | ||||||
Prob4 | 0.296 (0.041) | 0.271 (0.026) | ||||||
Rate | -1.120 (0.387) | -1.061 (0.387) | -0.879 (0.387) | -0.218 (0.371) | -1.070 (0.470) | -1.431 (0.454) | -0.789 (0.469) | -0.370 (0.402) |
Constant | -8.018 (0.306) | -8.553 (0.341) | -5.716 (0.183) | -5.521 (0.181) | -20.324 (1.085) | -17.456 (0.688) | -5.673 (0.220) | -5.540 (0.192) |
LL | -601.35 | -624.16 | -687.86 | -774.96 | -554.91 | -561.34 | -680.25 | -728.26 |
Pseudo-R2 | 29.05% | 26.36% | 18.84% | 8.56% | 34.53% | 33.77% | 19.74% | 14.07% |
Panel B: Vuong test statistics for differences in log-likelihoods | ||||||||
Vuong test stat | ||||||||
Private Firms Model | ||||||||
Model 1 vs Model 3 | 5.75 | |||||||
Model 2 vs Model 4 | 7.37 | |||||||
Public Firms Model | ||||||||
Model 1 vs Model 3 | 4.37 | |||||||
Model 2 vs Model 4 | 8.07 |
4.3 Economic performance results
Private firms model | Public firms model | |||||||
---|---|---|---|---|---|---|---|---|
Bank1 | Bank2 | Bank3 | Bank4 | Bank1 | Bank2 | Bank3 | Bank4 | |
Credits | 12,689 | 3,723 | 4,095 | 7,459 | 12,136 | 6,049 | 4,111 | 5,311 |
Market Share (%) | 44.20 | 12.97 | 14.26 | 25.98 | 42.27 | 21.07 | 14.32 | 18.50 |
Bankruptcies | 6 | 14 | 7 | 42 | 9 | 4 | 7 | 28 |
Bankruptcies/Credits (%) | 0.047 | 0.38 | 0.17 | 0.56 | 0.074 | 0.066 | 0.17 | 0.53 |
Average Spread (%) | 0.34 | 0.46 | 0.36 | 0.54 | 0.35 | 0.42 | 0.38 | 0.81 |
Revenues ($M) | 151.30 | 59.77 | 51.06 | 139.28 | 148.64 | 88.01 | 54.52 | 150.70 |
Loss($M) | 8.57 | 20.01 | 10.00 | 60.02 | 12.86 | 5.72 | 10.00 | 40.01 |
Profit($M) | 142.73 | 39.76 | 41.06 | 79.26 | 135.78 | 82.29 | 44.52 | 110.69 |
Return on Assets (%) | 0.32 | 0.31 | 0.29 | 0.31 | 0.32 | 0.39 | 0.31 | 0.60 |
Return on RWA (%) | 2.24 | 1.27 | 1.52 | 0.91 | 1.98 | 1.53 | 1.68 | 1.20 |
4.4 Forecasting bankruptcy two years ahead
Panel A: Performance, out-of-sample, 2007–2015 | ||||||
---|---|---|---|---|---|---|
Private Firms Model | Public Firms Model | |||||
Model | AUROC | Info. Cont | Econ. Ben | AUROC | Info. Cont | Econ. Ben |
Models with maximized AUROC | ||||||
Neural Network | 0.8678 | 16.20% | 1.36% | 0.8864 | 18.27% | 1.57% |
Logistic | 0.8503 | 13.83% | 0.82% | 0.8664 | 15.24% | 0.56% |
Models with maximized log-likelihood | ||||||
Neural Network | 0.8441 | 13.66% | 0.81% | 0.8571 | 9.53% | 0.90% |
Logistic | 0.8113 | 2.10% | 0.50% | 0.8558 | 4.33% | 0.97% |
Private Firms Model | Public Firms Model | ||||
Neural Network with max. AUROC vs Neural Network with max. LL | 2.18 | 3.17 | |||
Logistic model with max. AUROC vs Logistic model with max. LL | 7.15 | 1.28 |
Private Firms Model | Public Firms Model | ||||
Neural Network with max. AUROC vs Neural Network with max. LL | 2.34 | 6.13 | |||
Logistic model with max. AUROC vs Logistic model with max. LL | 7.15 | 7.33 |
4.5 Forecasting financial distress
Panel A: Performance, out-of-sample, 2007–2015 | ||||||
---|---|---|---|---|---|---|
Private firms model | Public firms model | |||||
Model | AUROC | Info. Cont | Econ. Ben | AUROC | Info. Cont | Econ. Ben |
Models with maximized AUROC | ||||||
Neural Network | 0.9175 | 32.82% | 1.14% | 0.9000 | 28.21% | 1.05% |
Logistic | 0.8982 | 26.84% | 0.36% | 0.8824 | 24.28% | 0.56% |
Models with maximized log-likelihood | ||||||
Neural Network | 0.8956 | 25.89% | 0.44% | 0.8870 | 22.23% | 0.97% |
Logistic | 0.8897 | 12.70% | -0.34% | 0.8753 | 12.60% | 0.28% |
Private Firms Model | Public Firms Model | ||||
Neural Network with max. AUROC vs Neural Network with max. LL | 5.42 | 5.99 | |||
Logistic model with max. AUROC vs Logistic model with max. LL | 3.23 | 2.24 |
Private Firms Model | Public Firms Model | ||||
Neural Network with max. AUROC vs Neural Network with max. LL | 7.62 | 7.32 | |||
Logistic model with max. AUROC vs Logistic model with max. LL | 10.27 | 9.31 |
4.6 Comparing our methodology with other methodologies
Panel A: Performance, out-of-sample, 2007–2015 | ||||||
---|---|---|---|---|---|---|
Private firms model | Public firms model | |||||
Model | AUROC | Info. Cont | Econ. Ben | AUROC | Info. Cont | Econ. Ben |
Our methodology | ||||||
Neural Network | 0.9332 | 29.05% | 2.21% | 0.9508 | 34.53% | 1.92% |
Alternative methodologies | ||||||
Miura et al. (2010) | 0.9188 | 17.59% | 1.55% | 0.9471 | 26.76% | 1.80% |
KK (2014) | 0.9136 | 8.57% | 1.23% | 0.9473 | 25.44% | 1.64% |
Private Firms Model | Public Firms Model | ||||
Our methodology vs Miura et al. (2010) | 1.55 | 1.15 | |||
Our methodology vs KK (2014) | 1.99 | 1.18 |
Private Firms Model | Public Firms Model | ||||
Our methodology vs Miura et al. (2010) | 5.72 | 5.24 | |||
Our methodology vs KK (2014) | 8.32 | 5.50 |
Panel A: Performance, out-of-sample, 2007–2015 | ||||||
---|---|---|---|---|---|---|
Private Firms Model | Public Firms Model | |||||
Model | AUROC | Info. Cont | Econ. Ben | AUROC | Info. Cont | Econ. Ben |
Our methodology | ||||||
Neural Network | 0.8678 | 16.76% | 1.42% | 0.8864 | 18.92% | 1.47% |
Alternative methodologies | ||||||
Miura et al. (2010) | 0.8479 | 10.97% | 1.39% | 0.8605 | 12.85% | 0.97% |
KK (2014) | 0.8488 | 5.08% | 0.87% | 0.8595 | 7.47% | 0.84% |
Panel B: DeLong (1988) test statistic for differences in AUROC | |||||
---|---|---|---|---|---|
Private Firms Model | Public Firms Model | ||||
Our methodology vs Miura et al. (2010) | 1.83 | 2.90 | |||
Our methodology vs KK (2014) | 1.75 | 3.01 |
Private Firms Model | Public Firms Model | ||||
Our methodology vs Miura et al. (2010) | 3.63 | 5.02 | |||
Our methodology vs KK (2014) | 6.69 | 7.27 |
Panel A: Performance, out-of-sample, 2007–2015 | ||||||
---|---|---|---|---|---|---|
Private Firms Model | Public Firms Model | |||||
Model | AUROC | Info. Cont | Econ. Ben | AUROC | Info. Cont | Econ. Ben |
Our methodology | ||||||
Neural Network | 0.9175 | 32.82% | 1.12% | 0.9000 | 28.21% | 1.07% |
Alternative methodologies | ||||||
Miura et al. (2010) | 0.8982 | 3.97% | 0.70% | 0.8825 | 10.53% | 0.71% |
KK (2014) | 0.8964 | 7.75% | 0.47% | 0.8827 | 9.89% | 0.71% |
Panel B: DeLong (1988) test statistic for differences in AUROC | ||||||
---|---|---|---|---|---|---|
Private Firms Model | Public Firms Model | |||||
Our methodology vs Miura et al. (2010) | 6.14 | 6.33 | ||||
Our methodology vs KK (2014) | 7.69 | 6.19 |
Private Firms Model | Public Firms Model | |||||
Our methodology vs Miura et al. (2010) | 15.55 | 13.93 | ||||
Our methodology vs KK (2014) | 14.39 | 12.65 |
4.7 Forecasting using quarterly data
Maximizing AUROC | Maximizing LL | |||
---|---|---|---|---|
Logistic | Neural Net | Logistic | Neural Net | |
Panel A: 1 quarter ahead | ||||
Private Firm Model | 0.9255 | 0.9201 | 0.9150 | 0.9111 |
Public Firm Model | 0.9589 | 0.9611 | 0.9513 | 0.9472 |
Panel B: 4 quarters ahead | ||||
Private Firm Model | 0.9040 | 0.9186 | 0.8927 | 0.9050 |
Public Firm Model | 0.9444 | 0.9456 | 0.9431 | 0.9300 |
Panel C: 8 quarters ahead | ||||
Private Firm Model | 0.8128 | 0.8280 | 0.7825 | 0.7918 |
Public Firm Model | 0.8485 | 0.8523 | 0.8398 | 0.8258 |
Panel D: Financial Distress Case | ||||
Private Firm Model | 0.8648 | 0.8871 | 0.8558 | 0.8797 |
Public Firm Model | 0.8566 | 0.8733 | 0.8544 | 0.8568 |