The paper introduces Forecast-Augmented Relative Policy Optimization (FRPO), a method that enhances portfolio management by jointly supervising asset opportunities and account-level trading decisions. FRPO employs a Financial State Head to learn future relative-return targets and a Same-Path Group-Relative Optimization to compare trading trajectories, with an Adaptive Gate coordinating auxiliary supervision. Experiments on DJIA30, TOPIX Core30, and three investment-style portfolios show that FRPO outperforms competing methods in mean cumulative and annualized returns across all settings.
By Zeyu Liu, Xuanzhi Feng, Haoran Xu, Lai Sing Kwong, Yang Li, Hualei Zhang, Hao Chen, Jie Zhang, Xiaoyi Pang, Song Guo
arXiv:2502. 18834v3 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investment strategies.
By Yifan Hu, Yuante Li, Peiyuan Liu, Yuxia Zhu, Naiqi Li, Tao Dai, Shu-tao Xia, Dawei Cheng, Changjun Jiang
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.
By Hyeongmin Lee, Chanyeol Choi, Jihoon Kwon, Yoon Kim, Alejandro Lopez-Lira, Wonbin Ahn, Justin Xu, Srijan Sood, Qingsong Wen, Chun-Li Yang, Yongjae Lee
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...
By Rahul Fernandes, Travis Desell
arXiv:2608.03259v3 Announce Type: replace-cross
Abstract: Time-series foundation models (TSFMs) are increasingly evaluated on broad benchmarks that apply uniform conventional forecast evaluation metr...
By Jaehoon Lee, Jun Seo, Seunghan Lee, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Minjae Kim, Sungdong Yoo, Junhyeok Kang, Sangjun Han, Soonyoung Lee, Hao Ni, Srijan Sood, Stefan Zohren, Chanyeol Choi, Yongjae Lee, Zhangyang Wang, Lukasz Szpruch, Benjamin Bowring, Wonbin Ahn
arXiv:2609.37715v1 Announce Type: new
Abstract: Time-series foundation models are increasingly adapted to new domains through fine-tuning on target data, under the implicit assumption that more targe...
By Manh Nguyen, Minh Hoang Nguyen, Huu Hiep Nguyen, Van Dai Do, Hung Le
arXiv:2606. 04576v1 Announce Type: cross Abstract: Learning Value-at-Risk (VaR) and Expected Shortfall (ES) is important for managing financial risks effectively.
By Yichi Zhang, Ke Zhu, Zhoufan Zhu
arXiv:2610.07006v1 Announce Type: new
Abstract: Learning effective representations helps characterize the structure and dynamics of equity markets from financial data with a low signal-to-noise ratio...
By Yizhi Luo, Jiahe Yi, Jianhui Zhang, Shuo Sun
VertiFuseX is a hybrid LSTM architecture that fuses multi‑scale temporal representations at the penultimate layer, stacking features from LSTM, Bi‑LSTM, and St‑LSTM branches and a parallel DNN stream. On 15 years of global equity index data, it reduces MAPE by 30‑54% and improves MAE and RMSE by over 40% compared to LSTM baselines, outperforming seven state‑of‑the‑art models across 33 metric‑dataset comparisons. The model is lightweight (675k parameters, 2.6 MB footprint) with 1.5 ms/sample inference latency and demonstrates robust, interpretable forecasting with reduced drawdowns in algorithmic trading simulations.
By Aashish Bohra, Vivek Vijay
FinForesight is a diagnostic backtesting benchmark designed to evaluate deep research systems for financial forecasting. It tests four forecasting tasks—valuation, return ranking, event direction, and future financial fundamentals—across Chinese, U.S., and Hong Kong markets, while also including two diagnostic tasks for counterfactual reasoning and financial calculation. The framework limits evidence to what was available at the forecast date and compares pretraining-era, post-cutoff, and live evaluations to assess how well controlled backtests predict future performance.
By Fengyuan Liu, Mukai Li, Jieshun You, Yilin Zheng, Chenjia Min, Yizhou Zhang, Harper Chen, Qi Liu
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.
By Humzah Merchant, Alec Guthrie, Simon Mahns, Randall Balestriero, Bradford Levy
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...
By Seunghan Lee, Jaehoon Lee, Jun Seo, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Minjae Kim, Sungdong Yoo, Junhyeok Kang, Sangjun Han, Soonyoung Lee, Wonbin Ahn