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3 papers in the last four weeks, down 57% on the four weeks before. 0.0% of all new papers.

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Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning
Feb 11, 2026Hyeongmin Lee, Chanyeol Choi, Jihoon Kwon +8Contrastive Learning Information Retrieval
Seoul National University of Science and Technology Seoul, Republic of Korea · LinqAlpha Cambridge, MA, United States · Massachusetts Institute of Technology Cambridge, MA, United States +7
KAN vs LSTM Performance in Time Series Forecasting
Nov 23, 2025Tabish Ali Rather, S M Mahmudul Hasan Joy, Nadezda Sukhorukova +1Time Series Forecasting Non-Stationary Time Series Forecasting
Department of Mathematics, Swinburne University of Technology, Hawthorn, 3122, Victoria, Australia.
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding
Aug 1, 2021Eghbal Rahimikia, Stefan Zohren, Ser-Huang PoonFinancial Forecasting Time Series Forecasting
Alliance Manchester Business School at the University of Manchester · Department of Engineering Science, University of Oxford · Alliance Manchester Business School, University of Manchester

Feb 11, 2026, cs.CEJ/K move · Enter open · S save

Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning

Hyeongmin Lee, Chanyeol Choi, Jihoon Kwon, Yoon Kim+7

Seoul National University of Science and Technology Seoul, Republic of Korea · LinqAlpha Cambridge, MA, United States · Massachusetts Institute of Technology Cambridge, MA, United States+7

Contrastive Learning Information Retrieval Predictive Representation Learning Financial Time Series Finance Algorithmic Trading

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Asset retrieval (finding similar assets in a financial universe) is central to quantitative investment decision-making. Existing approaches define similarity through historical price patterns or sector classifications, but such backward-looking criteria provide no guarantee about future behavior. We argue that effective asset retrieval should be future-aligned: the retrieved assets should be those most likely to exhibit correlated future returns. To this end, we propose Future-Aligned Soft Contrastive Learning (FASCL), a representation learning framework whose soft contrastive loss uses pairwise future return correlations as continuous supervision targets. We further introduce an evaluation protocol designed to directly assess whether retrieved assets share similar future trajectories. Experiments on 5,631 US-listed securities against 14 baselines show that FASCL attains the best future return correlation at every retrieval depth and the best rank information coefficient at every depth and horizon, leads on trend consistency in 13 of 16 cells, and gives the highest gross Sharpe ratio in a spread trading backtest at every basket size. Code is available at https://github.com/HyeongminLEE/fascl .

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Latest papers 63

Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning

KAN vs LSTM Performance in Time Series Forecasting

Realised Volatility Forecasting: Machine Learning via Financial Word Embedding