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Application of Multimodal Financial Data Fusion Analysis in Corporate Strategic Management

Yujie Yan

Yujie Yan

School of Economics and Management, Beijing Jiaotong UniversityBeijing, China
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Yan, Yujie
  
11 avr. 2025
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Publié en ligne: 11 avr. 2025
Reçu: 14 nov. 2024
Accepté: 13 mars 2025
DOI: https://doi.org/10.2478/amns-2025-0842
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© 2025 Yujie Yan, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Multimodal Fusion Framework for Corporate Strategic Decision-Making
Multimodal Fusion Framework for Corporate Strategic Decision-Making

Figure 2.

Comparison of MAE, RMSE, and R2 for different models in financial risk assessment. Lower is better for MAE/RMSE, higher is better for R2.
Comparison of MAE, RMSE, and R2 for different models in financial risk assessment. Lower is better for MAE/RMSE, higher is better for R2.

Figure 3.

provides a heatmap visualization of the precision, recall, and F1-score for each model.
provides a heatmap visualization of the precision, recall, and F1-score for each model.

Figure 4.

Comparison of execution time (in seconds) for different financial decision models. Lower values indicate faster processing times.
Comparison of execution time (in seconds) for different financial decision models. Lower values indicate faster processing times.

Performance of Investment Recommendation Models

Model Precision Recall F1-score
Time-Series Deep Learning 78.2% 72.5% 75.3%
Sentiment-Based Model 81.4% 78.1% 79.7%
Knowledge Graph-Based Approach 85.0% 80.3% 82.6%
Multimodal Fusion 92.1% 91.2% 91.7%

Performance Comparison of Financial Risk Prediction Models

Model MAE RMSE R2
Numerical Data Only 0.182 0.245 0.78
Sentiment Analysis 0.174 0.239 0.81
Knowledge Graph 0.161 0.223 0.86
Multimodal Fusion 0.122 0.198 0.91
Langue:
Anglais
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Sujets de la revue:
Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres
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