# Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis
## 4 Results and Discussion
\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  & **R\({}^{2}\)** & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** & **MAE** & **MSE** & **RMSE** \\ \hline ALCTL & 0.847 & 0.292 & 0.143 & 0.179 & 0.503 & 0.178 & 0.045 & 0.212 & 0.913 & 0.17 & 0.05 & 0.224 \\ \hline ARENA & 0.945 & 0.086 & 0.015 & 0.123 & 0.89 & 0.084 & 0.01 & 0.099 & 0.654 & 0.298 & 0.152 & 0.39 \\ \hline DESPC & 0.95 & 0.094 & 0.014 & 0.118 & 0.908 & 0.108 & 0.018 & 0.133 & 0.998 & 0.269 & 0.132 & 0.363 \\ \hline DGATE & 0.714 & 0.155 & 0.037 & 0.193 & 0.967 & 0.045 & 0.003 & 0.255 & 0.906 & 0.317 & 0.163 & 0.404 \\ \hline FONET & 0.875 & 0.097 & 0.012 & 0.108 & 0.879 & 0.096 & 0.012 & 0.107 & 0.716 & 0.28 & 0.145 & 0.381 \\ \hline INDES & 0.674 & 0.151 & 0.038 & 0.196 & 0.561 & 0.154 & 0.049 & 0.22 & 0.753 & 0.286 & 0.149 & 0.386 \\ \hline INGRM & 0.437 & 0.177 & 0.043 & 0.207 & 0.837 & 0.273 & 0.113 & 0.336 & 0.916 & 0.291 & 0.118 & 0.343 \\ \hline KAREL & 0.938 & 0.058 & 0.005 & 0.071 & 0.376 & 0.122 & 0.021 & 0.144 & 0.537 & 0.267 & 0.09 & 0.301 \\ \hline KRONT & 0.925 & 0.059 & 0.005 & 0.07 & 0.651 & 0.112 & 0.027 & 0.164 & 0.991 & 0.25 & 0.126 & 0.356 \\ \hline LINK & 0.912 & 0.096 & 0.022 & 0.127 & 0.947 & 0.112 & 0.017 & 0.132 & 0.897 & 0.218 & 0.104 & 0.323 \\ \hline LOGO & 0.995 & 0.076 & 0.008 & 0.09 & 0.993 & 0.084 & 0.008 & 0.091 & 0.797 & 0.206 & 0.058 & 0.241 \\ \hline NETAS & 0.411 & 0.109 & 0.045 & 0.153 & 0.252 & 0.131 & 0.038 & 0.196 & 0.451 & 0.111 & 0.047 & 0.216 \\ \hline PKART & 0.984 & 0.051 & 0.004 & 0.065 & 0.977 & 0.049 & 0.005 & 0.067 & 0.613 & 0.156 & 0.056 & 0.236 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Estimation results for ROE. more consistent and reliable, while the DTR model may outperform them in certain cases. DTR is effective in capturing non-linear relationships in the data set. If the relationship between the dependent variable and independent variables is non-linear, DTR models can better capture this complexity and perform better. For example, the high R\({}^{2}\) values of DTR models for DESPC and KRONT (0.998 and 0.991) may indicate the presence of such non-linear relationships. Figure 5 shows the RMSE values of companies' ROE values according to three different machine learning methods. The results obtained reveal that the overall performance of the methods is effective. When the ROE RMSE results are analyzed, the MLR method performed the best by reaching the lowest RMSE values in most firms. For example, in ARENA (0.099), FONET (0.107) and LOGO (0.091), the MLR method had significantly lower error rates than other methods. This superior performance of the MLR method shows that it works effectively especially in data sets where linear relationships are dominant. On the other hand, the ANN method outperforms MLR in some firms by providing lower RMSE values compared to MLR. For example, the ANN method achieved the lowest error rates in KAREL (0.071) and KRONT (0.07). The success of ANN in these firms reveals that it is more effective in data sets with complex and non-linear relationships. However, in some cases, the ANN method may have higher error rates, especially due to the complexity of the data sets. The DTR method, on the other hand, generally yielded the highest RMSE values and underperformed the ANN and MLR methods in most firms. The DTR method caused high error rates, especially in cases of noise and extreme complexity in the data sets. This may be due to the fact that the decision trees of the DTR method are prone to overfitting. In conclusion, the MLR method generally performed the best with the lowest RMSE values and stands out as the most appropriate forecasting method for many firms. The ANN method, on the other hand, outperformed others for some firms and is particularly effective in data sets with nonlinear relationships.