# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 4 Results and Discussion
### Performance Analysis  
The objective of this experiment is to validate and showcase the performance of the Merkle-Transformer model in processing financial data, especially in comparison with current mainstream deep learning models. Through this experiment, the superiority of the Merkle-Transformer in key performance metrics such as precision, recall, accuracy, and F1 score is intended to be demonstrated, thereby proving its potential application in the field of financial security. The results in Table 1 indicate that the Merkle-Transformer outperforms all other models in all evaluation metrics, followed by RoBERTa, BERT, Transformer, BERT-wwm, and SpanBERT. These results reflect the performance differences of various models in financial behavior detection tasks, where the Merkle-Transformer's superior performance is attributed to its unique design and algorithms.  
Firstly, the Merkle-Transformer model integrates the data integrity verification mechanism of Merkle trees with the efficient data processing capabilities of the Transformer model. The Merkle tree structure provides a mechanism for verifying data integrity, ensuring that data remains unaltered or undamaged during processing, which is especially important in financial data processing. The Transformer model, known for its efficient self-attention mechanism, excels in handling complex sequential data. This combination not only improves the accuracy of data processing but also enhances the robustness of the model when facing challenges unique to financial data. Models like RoBERTa, BERT, and SpanBERT, although performing well in natural language processing tasks, are limited in their performance in financial data processing due to their original design focus. These models primarily concentrate on semantic understanding of text data, not on the specific characteristics of financial data. Additionally, they lack a mechanism for verifying data integrity, an important consideration in financial data processing. The Transformer model, as the originator of the self-attention mechanism, has certain advantages in processing sequential data but its application in the financial domain is limited by its generality. Without customized processing for the specific characteristics of financial data, it may not achieve optimal performance in financial behavior detection tasks. The BERT-wwm model, despite its advantages in processing Chinese text, is limited in this specific task of financial behavior detection due to its understanding of financial-specific contexts.  
The superior performance of the Merkle-Transformer model can be attributed to its integration of the data integrity verification mechanism of Merkle trees with the efficient data processing capabilities of the Transformer model. In contrast, models like RoBERTa, BERT, Transformer, and BERT-wwm, despite their exemplary performance in natural language processing, showed slightly inferior results in this specific task of stock price prediction. This is mainly because these models, while capable of processing sequential data, lack customized processing abilities for financial characteristics. For instance, they might be insufficient in capturing complex market trends and subtle financial signals, resulting in lesser predictive performance compared to the Merkle-Transformer, which is specifically designed for financial data. The SpanBERT model performed the worst across all metrics, possibly due to its focus on the span representation of text during pre-training, rather than the time dependencies in sequential data, which may not be optimal for dealing with highly time-sensitive data like stock prices.