## 4 Result and Discussion  
Our proposed model worked best on twenty different companies' datasets of a frontier market. Using our dataset, we have trained four machine learning ensemble models, such as Random Forest, AdaBoost, GradientBoosting, Xgboost, and ML stacking ensemble, then three deep learning models such as CNN, LSTM, and BiLSTM, and finally, with our proposed stacking ensemble neural network model. The results in Table 2 have shown that the machine learning ensemble models have provided substantial RMSE errors. We have achieved the highest RMSE error of 15.489 and the highest MAE error value of 15.416 using the machine learning models. The machine learning models' error rates are higher than the neural network and our proposed models' error rates. CNN works well on some of the data patterns, but not all. For instance, companies such as ACIBANK, BANKASIA, DESCO, DHAKABANK, EXIMBANK, GP, IBNSINA, IFIC, JAMUNABANK and UTTARABANK have lower RMSE error percentages of 2.3866, 0.6713, 1.5500, 0.8770, 0.8822, 1.6824, 1.4132, 1.1520, 1.0167, and 1.3121, respectively. On the other hand, companies such as ABBANK, APEXFOOD, BATASHOE, BERGERPBL, BEXIMCO, BRACBANK, CITYBANK, DUTCHBANGLABANK, FUWANGFOOD and KEYACOSMET have higher RMSE error percentages of 3.6829, 5.4682, 6.3256, 5.4822, 5.8887, 4.3199, 3.2923, 11.5777, 3.6536, and 6.8706, respectively. LSTM and BILSTM models also show inconsistent results over the datasets. LSTM performed better on ACIBANK, BANKASIA, BRACKBANK, CITYBANK, DESCO, DHAKABANK, EXIMBANK, GP, IBNSINA, IFIC, JAMUNABANK and UTTARABANK with an error rate of 1.2314, 0.4191, 2.7113, 1.8324, 0.9894, 1.0917, 1.04872, 0.9660, 1.6709, 1.8123, 0.4649 and 0.9504, respectively, and performed poorly on ABBANK, APEXFOOT, BATASHOE, BERGERPBL, BEXIMCO, DUTCHBANGLABANK, FUWANGFOOD and KEYACOSMET with RMSE error rates of 3.8222, 4.7450, 7.7107, 5.4605, 5.3503, 15.2332, 4.4977, and 5.8391. BiLSTM provides satisfactory results on ACIBANK, APEXFOOT, BANKASIA, BATASHOE, BEXIMCO, CITYBANK, DESCO, DHAKABANK, EXIMBANK, FUWANGFOOD, GP, IBNSINA, IFIC, JAMUNABANK, and UATTARABANK with the RMSE error rates of 0.2280, 1.3357, 0.4956, 1.5567, 2.5656, 2.7964, 0.7367, 1.6444, 0.4895, 2.2982, 1.0760, 1.0107, 1.8714, 0.7879 and 0.6997, respectively, and unsatisfactory result on ABBANK, BERGERPBL, BRACBANK, DUTCHBANGLABANK and KEYACOSMET with RMSE error rates of 3.9660, 4.4781, 4.0452, 10.1262 and 8.4249, respectively. These results show that BiLSTM performs comparatively better than CNN and LSTM. The main problem is that none of the traditional models confirm that they can precisely predict every dataset. The randomness in the result proves that we cannot rely on those  
Figure 3: Modified stacking ensemble of neural network model. models. However, our proposed model provides a satisfactory result for every company: ABBANK, ACIBANK, APEXFOOT, BANKASIA, BATASHOE, BERGERPBL, BEXIMCO, BRACBANK, CITYBANK, DESCO, DHAKABANK, DUTCHBANGLABANK, EXIMBANK, FUWANGFOOD, GP, IBNSINA, IFIC, JAMUNABANK, KEYACOSMET and UTTARA-BANK had RMSE error rates of 0.6766, 1.1880, 1.0634, 0.8139, 0.9859, 0.4721, 0.6300, 2.1705, 0.7283, 0.8349, 0.5206, 1.0317, 1.0317, 2.5696, 2.5696, 0.9153, 0.3626, 0.5614, 1.0614 and 0.6741, respectively, which clearly evidence that it maintains consistent results on every dataset. Moreover, our model can maintain high accuracy on multiple data patterns as we have achieved the RMSE and MAE error values between \(0<result<3\) for all the datasets. Our proposed model has shown a consistent low error rate and outstanding accuracy as the RMSE value has remained below 2.