arXiv Machine Learning
STOCK-JEPA: Prior-Anchored Latent Revision Representation Learning in Equity Markets
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Oct 8
Towards Financial World Modeling
The paper introduces Market‑1T, a massive dataset of nearly one trillion 1‑Hz observations of U.S. equities spanning 2008‑2025, and establishes a rigorous evaluation protocol for financial representation learning. It conducts a large‑scale comparison of 18 encoder‑training strategies across two decades of market regimes, assessing their predictive performance on common finance tasks and probing latent structure. The study reveals that encoders with similar predictive accuracy can organize market state in markedly different ways, providing a foundation for training and evaluating representations for financial world models such as DINO‑WM, V‑JEPA 2, and LeWM.
arXiv AI
Sep 10
EXAONE Finance 1.0: An Attention-free Time Series Foundation Model for Financial Time Series
arXiv:2609.04239v2 Announce Type: replace Abstract: This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series foundation model (TSFM) tailored to financial...
arXiv AI
Sep 7
EXAONE Forecast for Finance
EXAONE Forecast for Finance (EXAONE Finance) is a financial time‑series foundation model designed to overcome the limitations of existing models that rely on self‑attention and assume fully observed data. It replaces self‑attention with a causal 1D convolution for temporal mixing and a group‑aware pooling MLP for variate mixing, achieving linear‑time complexity. The model is pretrained on a large, diverse financial corpus and, through masked context augmentation, learns to handle missing data, ultimately topping the FinVerse benchmark across accuracy, ranking, and profitability metrics.
arXiv Machine Learning
Sep 23
Financially Guided Deep Portfolio Optimization
arXiv:2605.28853v2 Announce Type: replace-cross Abstract: Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction cos...
arXiv AI
Jun 9
Addressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman
arXiv:2606. 09104v1 Announce Type: cross Abstract: Deep reinforcement learning (DRL) frameworks for portfolio optimization have shown promise for their ability to learn allocation rules dynamically from market data.
arXiv AI
Sep 18
Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning
The paper introduces Future‑Aligned Soft Contrastive Learning (FASCL), a representation learning framework that uses pairwise future return correlations as continuous supervision to improve asset retrieval. FASCL’s soft contrastive loss aligns retrieved assets with correlated future returns, and the authors propose a new evaluation protocol to directly assess future trajectory similarity. Experiments on 5,631 US‑listed securities outperform 14 baselines in future return correlation, rank information coefficient, trend consistency, and gross Sharpe ratio across various retrieval depths and basket sizes.