Risk-Aware Financial Forecasting Enhanced by Machine Learning and Intuitionistic Fuzzy Multi-Criteria Decision-Making

Battle Count: 94

The paper is primarily focused on financial statement forecasting (balance sheets, income statements, cash flows) rather than direct trading signal generation. However, it provides valuable risk-aware outputs (VaR, confidence intervals, Sharpe/Sortino ratios, scenario analysis) that are directly applicable to quantitative risk management and portfolio construction. The sentiment analysis component and macroeconomic sensitivity analysis are relevant for systematic trading strategies. The framework is more suited for fundamental analysis and risk governance than high-frequency or algorithmic trading.

High complexity due to: (1) multiple heterogeneous ML models requiring different frameworks (PyTorch, Scikit-learn, Optuna); (2) NLP pipeline with FinancialBERT fine-tuning for Turkish financial text; (3) GNN graph construction with dynamic temporal edges; (4) BNN with Monte Carlo Dropout for uncertainty quantification; (5) Intuitionistic fuzzy MCDM with entropy weighting, EDAS, and MARCOS; (6) Bayesian hyperparameter optimization with TPE; (7) Multi-source data integration (structured, unstructured, macroeconomic). Requires expertise in deep learning, NLP, graph learning, Bayesian methods, and fuzzy decision theory.

The paper provides detailed hyperparameter tables (Table 2), describes data sources (KAP, TÜİK, TCMB), preprocessing steps, and model architectures. However, no code repository is provided, the dataset is proprietary (defense company financials), and some implementation details (e.g., exact FinancialBERT fine-tuning procedure, GNN graph construction specifics) are described at a high level. The MCDM methodology is fully specified with equations.