From Headlines to Holdings: Deep Learning for Smarter Portfolio Decisions

Paper: arXiv 2509.24144

Authors: Yun Lin, Jiawei Lou, Jinghe Zhang

Abstract

Deep learning offers new tools for portfolio optimization. We present an end-to-end framework that directly learns portfolio weights by combining Long Short-Term Memory (LSTM) networks to model temporal patterns, Graph Attention Networks (GAT) to capture evolving inter-stock relationships, and sentiment analysis of financial news to reflect market psychology. Unlike prior approaches, our model unifies these elements in a single pipeline that produces daily allocations. It avoids the traditional two-step process of forecasting asset returns and then applying mean–variance optimization (MVO), a sequence that can introduce instability. We evaluate the framework on nine U.S. stocks spanning six sectors, chosen to balance sector diversity and news coverage. In this setting, the model delivers higher cumulative returns and Sharpe ratios than equal-weighted and CAPM-based MVO benchmarks. Although the stock universe is limited, the results underscore the value of integrating price, relational, and sentiment signals for portfolio management and suggest promising directions for scaling the approach to larger, more diverse asset sets.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper employs advanced deep learning architectures (LSTMs, GATs) with mathematical formulations and a custom Sharpe-ratio loss function, but its empirical validation is limited to a small universe of nine stocks without robust out-of-sample testing or data availability details.

Research Flowchart

  flowchart TD
  A["Research Goal: End-to-End<br>Portfolio Optimization"] --> B["Data Inputs:<br>Price, News, Relations"]
  B --> C["Methodology Pipeline:<br>LSTM + GAT + Sentiment"]
  C --> D["Computational Process:<br>Direct Weight Generation"]
  D --> E["Key Findings:<br>Higher Returns & Sharpe Ratio"]
  E --> F["Outcome:<br>Proof of Concept for<br>Integrated Signal Processing"]

Cited References (16)

1. Deep learning for portfolio optimization (2020) — link
2. Large-scale time-varying portfolio optimisation using graph attention networks — → 2407.15532
3. Graph neural networks for asset management (2021) — link
4. Graph neural networks for deep portfolio optimization (2023) — link
5. HGFormer: A hierarchical graph transformer for portfolio optimization (2024) — link
6. Deep learning in finance (2016) — link
7. Leveraging BiLSTM-GAT for enhanced stock market prediction: a dual-graph approach to portfolio optimization (2025) — link
8. Effects of Daily News Sentiment on Stock Price Forecasting — → 2308.08549

2 of 16 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.