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Table 4 Out-of-sample performance of credit and profit scoring models—lending club

From: Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets

Model % invested loans Performance across invested loans Performance across all loans Total profit in mil. USD
   Average return SD Average return SD MCS  
Panel A: Credit scoring models        
 LR 60.8 8.66 18.20 5.27 14.81   1.42
 \(LR^{{\lambda_{\min } ,\alpha = 1}}\) 61.0 8.53 18.28 5.20 14.87   1.39
 \(LR^{{\lambda_{\min } ,\alpha = 0}}\) 61.4 8.57 18.15 5.26 14.81   1.40
 \(LR^{{\lambda_{\min } ,\alpha_{\min } }}\) 61.2 8.49 18.38 5.19 14.96   1.39
 RFC 78.5 7.46 21.46 5.86 19.26   1.62
 NNC 70.1 8.21 20.31 5.75 17.42   1.58
Panel B: Profit scoring models        
 LM 68.6 8.52 20.63 5.84 17.54   1.61
 \(LM^{{\lambda_{\min } ,\alpha = 1}}\) 72.5 8.94 20.43 6.48 17.85   1.84
 \(LM^{{\lambda_{\min } ,\alpha = 0}}\) 72.2 8.69 21.12 6.27 18.35   1.79
 \(LM^{{\lambda_{\min } ,\alpha_{\min } }}\) 72.8 8.93 20.42 6.50 17.87   1.84
 RFR 73.3 8.47 21.69 6.21 18.95   1.82
 NNR 80.1 7.83 22.76 6.27 20.61   1.79
  1. \(LR^{{\lambda_{\min } ,\alpha = 1}}\), \(LR^{{\lambda_{\min } ,\alpha = 0}}\), \(LR^{{\lambda_{\min } ,\alpha_{\min } }}\) are lasso, ridge and elastic net version of logistic regression, \(LM^{{\lambda_{\min } ,\alpha = 1}}\), \(LM^{{\lambda_{\min } ,\alpha = 0}}\), \(LM^{{\lambda_{\min } ,\alpha_{\min } }}\) are lasso, ridge and elastic net version of linear regression
  2. SD standard deviation, LR logistic regression-based models, LM linear regression-based models, RFC random forest classification, NNC neural network classification, RRR random forest regression, NNR neural network regression
  3. Denotes a model that belongs to the set of superior models