# Currency Crises Prediction Using Deep Neural Decision Trees
## 4 Results
### Estimated Models  
Table 3 shows the levels of precision (in percentage) reached in the classification of the currency crises of the methodologies applied in the present study for the three data sets: Training, validation, and testing. In greater order to less accuracy, it is shown that in all models, the DNDT method was the one that achieved greater classification capacity, followed by AdaBoost, MLP, SVM, and Logit. Figure 3 shows the results obtained using DNDTs for the models in each region and the global model. The classification accuracy obtained using the training data was 99.17%, 98.42%, 99.68%, 100%, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{4}{c}{**Dependent Variable**} \\ \cline{2-6}  & \multicolumn{2}{c}{**0**} & \multicolumn{2}{c}{**1**} \\ \cline{2-6}  & **Mean** & **SD 2** & **Mean** & **SD 2** \\ \hline REER Overall & 112.772 & 21.783 & 85.259 & 18.523 \\ Government Spending & 15.589 & 9.747 & 24.171 & 11.083 \\ Fixed Capital Formation 1 & 56,025 & 276.574 & 17,199 & 127.496 \\ Unemployment & 9.365 & 7.842 & 14.046 & 11.578 \\ Contagion & 0.138 & 0.035 & 0.192 & 0.370 \\ Soft Peg & 0.164 & 0.042 & 0.249 & 0.076 \\ Peg & 0.089 & 0.017 & 0.135 & 0.051 \\ Domestic Credit & 52.768 & 17.478 & 73.721 & 19.779 \\ Lending Interest Rate & 34.048 & 16.675 & 22.463 & 14.347 \\ Deposit Interest Rate & 37.617 & 18.428 & 42.843 & 23.362 \\ Polity & \(-1.000\) & 0.426 & \(-5.000\) & 0.618 \\ Durable & 24.000 & 2.165 & 11.000 & 1.482 \\ Persist & 17.000 & 2.478 & 9.000 & 1.247 \\ SFI & 5.000 & 1.822 & 12.000 & 2.151 \\ Left Government & 0.412 & 0.237 & 0.574 & 0.428 \\ Election & 0.096 & 0.057 & 0.126 & 0.084 \\ Turnover & 5.590 & 1.573 & 5.460 & 1.522 \\ Years & 7.693 & 2.582 & 2.942 & 2.165 \\ Economic Effectiveness & 2.000 & 1.562 & 1.300 & 1.257 \\ \hline \hline \end{tabular}  
* Variables expressed in millions of USD. 2 Standard deviation.  
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Logit**} & \multicolumn{2}{c}{**Multilayer**} & \multicolumn{2}{c}{**Support Vector**} \\  & & & **Perceptron** & **Machines** & **AdaBoost** & **DNDT** \\ \hline \multirow{3}{*}{Africa and Middle East} & Training & 91.52 & 94.44 & 93.38 & 95.25 & 99.17 \\  & Validation & 90.84 & 93.91 & 92.57 & 94.57 & 98.85 \\  & Testing & 90.25 & 93.62 & 92.18 & 94.11 & 98.24 \\ \hline \multirow{3}{*}{Latin America} & Training & 91.16 & 94.12 & 93.04 & 95.08 & 98.42 \\  & Validation & 90.72 & 93.37 & 92.68 & 94.21 & 97.79 \\  & Testing & 90.20 & 92.85 & 91.95 & 93.36 & 96.90 \\ \hline \multirow{3}{*}{South and East Asia} & Training & 91.64 & 95.06 & 93.47 & 96.17 & 99.68 \\  & Validation & 91.03 & 94.52 & 93.02 & 95.64 & 99.03 \\  & Testing & 90.62 & 94.13 & 92.61 & 95.19 & 98.54 \\ \hline \multirow{3}{*}{Europe} & Training & 92.19 & 95.43 & 93.81 & 96.86 & 100.00 \\  & Validation & 91.58 & 95.10 & 93.22 & 96.42 & 99.61 \\  & Testing & 90.88 & 94.46 & 92.93 & 95.73 & 99.07 \\ \hline \multirow{3}{*}{Global} & Training & 91.59 & 94.84 & 93.33 & 95.95 & 99.16 \\  & Validation & 90.94 & 94.27 & 92.65 & 95.34 & 98.87 \\ \cline{1-1}  & Testing & 90.37 & 93.76 & 91.83 & 94.28 & 98.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of accuracy ratios of deep neural decision trees (DNDTs) with other methodologies. the risk of currency crises. Finally, the most significant political variables in our models were SFI (Latin America and Global), which shows the capacity of the government to make and implement public policy, and Polity which indicates the level of democracy of a country. Existing literature has not found these political variables to be significant [39]. The results also differ in terms of the variables for the banking sector which have been significant in previous work [6; 7] but which did not exhibit a high level of sensitivity in our estimations. Figure 6: The RMSE scores of the test data for 500 iterations. Figure 7: Sensitivity analysis.