# Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis
## 4 Results and Discussion
In the training phase, the most appropriate parameters for the success of the model are determined, and in the testing phase, the success of the model is measured according to statistical metrics [40]. Different ratios were tested in this study, and it was decided that the most appropriate ratio was 70% training and 30% testing. In this study, a feedback neural network model was used for the ANN. Thus, the error was minimized. It was observed that the model with four hidden layers and three neurons in each layer gave the most successful results. The learning coefficient (\(\lambda\)) is a factor that determines the speed of updating the weights in artificial neural networks. While a high value allows the weights to be updated quickly, very large values can lead to instability during the training of the network. The momentum coefficient (\(\alpha\)) is a coefficient that increases the effect of previous updates in updating the weights. This helps to prevent the network from becoming stuck at local minima and can speed up the learning process. The learning coefficient (\(\lambda\)) was chosen to be 0.6, and the momentum coefficient (\(\alpha\)) was 0.8. The number of iterations was set as 1000. In multiple linear regression, there is more than one independent variable in the system. For this reason, the effects of variables on the system may be different from each other. In this study, the forward-elimination method was used for the selection of variables. The model initially contained a single variable, and the number of variables increased with each step. In the decision tree method, which was another method used in this study, the Gini index technique was used as a quality measure. The Gini index is calculated for each class, and the sum of the squares of the results is subtracted from 1 [41]. The Gini value takes a result between 0 and 1, and the closer the result is to 0, the better the discrimination. If the number of records in the model is less than or equal to 2, the tree is designed to grow no further. The results of the models using machine learning methods for ROA are presented in Table 4.  
The numerical results are presented in Figure 2 (R2 values). \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ANN**} & \multicolumn{4}{c}{**MLR**} & \multicolumn{4}{c}{**DTR**} \\ \hline  & **R2** & **MAE** & **MSE** & **RMSE** & **R2** & **MAE** & **MSE** & **RMSE** & **R2** & **MAE** & **MSE** & **RMSE** \\ \hline ALCTL & 0.836 & 0.124 & 0.025 & 0.159 & 0.778 & 0.114 & 0.018 & 0.136 & 0.683 & 0.253 & 0.097 & 0.312 \\ \hline ARENA & 0.707 & 0.095 & 0.014 & 0.119 & 0.88 & 0.056 & 0.007 & 0.082 & 0.958 & 0.258 & 0.119 & 0.345 \\ \hline DESPC & 0.878 & 0.077 & 0.01 & 0.101 & 0.774 & 0.169 & 0.038 & 0.196 & 0.626 & 0.252 & 0.126 & 0.355 \\ \hline DGATE & 0.79 & 0.103 & 0.022 & 0.149 & 0.753 & 0.07 & 0.009 & 0.094 & 0.917 & 0.265 & 0.094 & 0.306 \\ \hline FONET & 0.969 & 0.063 & 0.007 & 0.082 & 0.919 & 0.078 & 0.013 & 0.112 & 0.816 & 0.196 & 0.085 & 0.292 \\ \hline INDES & 0.654 & 0.072 & 0.008 & 0.088 & 0.84 & 0.059 & 0.005 & 0.073 & 0.189 & 0.023 & 0.078 & 0.28 \\ \hline INGRM & 0.745 & 0.115 & 0.022 & 0.147 & 0.662 & 0.117 & 0.035 & 0.186 & 0.54 & 0.316 & 0.118 & 0.343 \\ \hline KAREL & 0.869 & 0.11 & 0.015 & 0.123 & 0.924 & 0.091 & 0.013 & 0.116 & 0.814 & 0.303 & 0.157 & 0.397 \\ \hline KRONT & 0.723 & 0.103 & 0.015 & 0.124 & 0.857 & 0.093 & 0.019 & 0.136 & 0.487 & 0.218 & 0.077 & 0.278 \\ \hline LINK & 0.973 & 0.061 & 0.007 & 0.086 & 0.937 & 0.063 & 0.007 & 0.085 & 0.983 & 0.336 & 0.24 & 0.49 \\ \hline LOGO & 0.961 & 0.081 & 0.009 & 0.095 & 0.886 & 0.094 & 0.014 & 0.118 & 0.657 & 0.365 & 0.165 & 0.407 \\ \hline NETAS & 0.913 & 0.019 & 0.001 & 0.03 & 0.773 & 0.044 & 0.003 & 0.056 & 0.484 & 0.2 & 0.096 & 0.311 \\ \hline PKART & 0.806 & 0.082 & 0.019 & 0.137 & 0.947 & 0.09 & 0.012 & 0.111 & 0.725 & 0.241 & 0.13 & 0.361 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Estimation results for ROA. Figure 2: R2 Values of models for ROA.