From Headlines to Holdings: Deep Learning for Smarter Portfolio Decisions

Battle Count: 235

Directly addresses portfolio optimization, a core problem in quantitative trading. The end-to-end approach eliminating the two-step forecast-then-optimize pipeline is practically relevant. Integration of sentiment signals and dynamic graph structures reflects real-world trading considerations. However, the small 9-stock universe, absence of transaction costs, and proof-of-concept nature limit immediate practical deployment. The Sharpe ratio optimization objective aligns with institutional portfolio management goals. The framework's architecture (LSTM + GAT + sentiment) is directly applicable to multi-asset allocation strategies.

The framework integrates multiple complex components: LSTM for temporal modeling, GAT for relational modeling, graph construction (static and dynamic), sentiment feature engineering, PCA for dimensionality reduction, and Sharpe ratio optimization. Requires familiarity with PyTorch, graph neural networks, financial data processing, and hyperparameter tuning (Optuna). The 5 model variants add complexity. However, the architecture is well-described with clear workflow steps. No code is provided, increasing implementation burden. CPU-only training suggests moderate computational requirements but limits scalability.

The paper provides detailed hyperparameter tables (Table 8), feature descriptions (Tables 4-5), library listings (Table 9), and specifies a fixed random seed (42). However, no code repository is provided. Data sources (AlphaVantage API, MarketAux API) are publicly accessible but require API keys. The exact preprocessing steps and graph construction logic are described but not fully reproducible without code. CPU-only execution is noted, which aids reproducibility but limits scalability.