The paper examines whether benign overfitting—where highly overparameterized models still predict well—occurs in equity return prediction. It finds a double‑descent risk curve for ridgeless models and shows that while ridge regularization slightly improves performance, the advantage vanishes at high parameter‑to‑observation ratios. Ultimately, both models fail to beat a simple historical average, indicating that standard equity predictors lack genuine forecasting power even with flexible machine learning methods.
By Hui Guo, Jiawei Huang, Runze Li, Yan Yu
arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.
By Jiaze Sun, Kelvin J. L. Koa, Ruiyang Ni, Yize Liu, Haonan Chen, Ke-Wei Huang
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.
By Daniil Mikriukov (University of Liverpool, Xi'an Jiaotong-Liverpool University), Ruoyu Sun (Xi'an Jiaotong-Liverpool University), Angelos Stefanidis (Xi'an Jiaotong-Liverpool University), Jionglong Su (Xi'an Jiaotong-Liverpool University), Zhengyong Jiang (Xi'an Jiaotong-Liverpool University)
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
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.
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
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
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
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
arXiv:2606. 29347v1 Announce Type: cross Abstract: Adaptive Financial Transformer (AFT) is proposed for stock return prediction under non-stationary financial markets.
By Dishan Sarkar
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:2608. 14106v1 Announce Type: cross Abstract: When forecasting hourly returns for 1,000 US equities, we observe an unexpected phenomenon: predictions become nearly flat and show poor stock ranking, as measured by cross-sectional correlation.
By Shu Wan, Miles Ma, Hank Zhu, Guangqi Liu, Stephen Wang, Qingsong Wen, Huan Liu
arXiv:2608.30446v1 Announce Type: cross
Abstract: Small-cap-inclusive equity universes contain recently listed and intermittently traded securities, so enforcing a common look-back discards a substan...
By Christian Bongiorno, Lorenzo Villassero