Finance

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Finance research examines how assets are priced, risks measured, and capital allocated across markets, firms, households, and institutions. It combines econometrics and factor models with machine learning, time-series and graph models, reinforcement learning, and language-model tools for news, filings, and financial workflows. Applications range from return forecasting and portfolio construction to credit assessment, fraud detection, market microstructure, and automated financial agents. A central challenge is whether apparent gains survive point-in-time testing, transaction costs, regime shifts, and independent audits, so realistic benchmarks, calibration, and explainability are increasingly important alongside performance.

Latest papers

281 papers11 papers in the last four weeks, down 48% on the four weeks before. 0.1% of all new papers.

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
DeXposure-FM: A Time-series, Graph Foundation Model for Credit Exposures and Stability on Decentralized Financial Networks
Feb 3, 2026Aijie Shu, Wenbin Wu, Gbenga Ibikunle +1Financial Services Decentralized Finance
School of Informatics, University of Edinburgh, United Kingdom · Cambridge Centre for Alternative Finance, Judge Business School, University of Cambridge, United Kingdom · Business School, University of Edinburgh, United Kingdom +1
Are Whitepaper Claims Reflected in Market Structure? A Contamination-Aware Pipeline and a Power-Limited Null
Jan 28, 2026Murad FarzullaData Contamination Representation Alignment
Dissensus, London, UK · King’s College London, London, UK
BizFinBench.v2: Towards Reliable LLMs in Finance via Real-User Data and Offline/Online Bilingual Evaluation
Jan 10, 2026Xin Guo, Rongjunchen Zhang, Guilong Lu +4Financial Services Multilingual Language Model Evaluation
HiThink Research · Shanghai University of Finance and Economics
Generative AI for Analysts
Dec 12, 2025Jian Xue, Qian Zhang, Wu ZhuFinancial Services Financial Forecasting
School of Economics and Management, Tsinghua University · School of Management, Xiamen University
TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding
Dec 7, 2025Zifan Peng, Jingyi Zheng, Yule Liu +8Financial Services Grounded Text Generation
1Hong Kong University of Science and Technology (Guangzhou) · 3Independent Researcher · University of North Texas +2
NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance
Jul 13, 2025Hanwool Lee, Sara Yu, Yewon Hwang +4Multilingual Language Models Text Embeddings
FinancialNLPLab, MODULABS Shinhan Securities Seoul, Republic of Korea · FinancialNLPLab, MODULABS KT Seoul, Republic of Korea · FinancialNLPLab, MODULABS EMRO Seoul, Republic of Korea +4
No Free Labels: Limitations of LLM-as-a-Judge Without Human Grounding
Mar 7, 2025Michael Krumdick, Charles Lovering, Varshini Reddy +2LLM Evaluation LLM-as-a-Judge
Kensho Technologies · MIT
Classification of Financial Data Using Quantum Support Vector Machine
Dec 14, 2024Seemanta Bhattacharjee, MD. Muhtasim Fuad, A. K. M. Fakhrul HossainQuantitative Finance Hybrid Quantum-Classical ML
Shahjalal University Of Science and Technology, Sylhet-3114, Bangladesh
Large and Deep Factor Models
Jan 20, 2024Bryan Kelly, Boris Kuznetsov, Semyon Malamud +1Quantitative Finance Neural Tangent Kernel
Yale School of Management, AQR Capital Management, and NBER · AQR Capital Management · NBER +5
Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?
Aug 4, 2021Yang Bai, Kuntara PukthuanthongQuantitative Finance Classification
College of Business and Economics, California State University, Fullerton · Trulaske Sr. College of Business, University of Missouri
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, 2026J/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 .