arXiv Machine Learning

Bitcoin Price Direction Prediction via Regime-Aware Multi-Modal Fusion of Social Sentiment and Technical Features

arXiv:2607. 23370v1 Announce Type: new Abstract: Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance.

arXiv AI

22h ago

MoFE: A Novel Mixture-of-Experts Framework with Fourier Neural Operators for Cryptocurrency Forecasting

arXiv:2608. 17342v1 Announce Type: cross Abstract: Forecasting cryptocurrency prices remains a formidable challenge due to inherent non-stationarity, abrupt regime shifts, and multi-scale stochastic dependencies.

By Bowen Liu, Mingming Sun

benchmarks

arXiv Machine Learning

Jul 17

Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier

arXiv:2607. 15258v1 Announce Type: new Abstract: The growing use of Bitcoin as a decentralized digital asset and investment tool has sparked strong interest in understanding its market behavior.

By Arthur G. Bubolz, Abreu Quevedo, Giancarlo Lucca, Rafael A. Berri, Eduardo Borges, Bruno L. Dalmazo

safety

arXiv AI

Jun 10

A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis

arXiv:2606. 10412v1 Announce Type: new Abstract: The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously.

By Fanrong Liu, Zhang Yuwei, Mingni Luo

llms rag nlp reinforcement-learning multimodal benchmarks

arXiv AI

Jun 17

LLM-Powered Multi-Agent System for Automated Crypto Portfolio Management

arXiv:2501. 00826v3 Announce Type: replace-cross Abstract: Cryptocurrency portfolio management requires the fusion of heterogeneous multi-modal signals, including structured price and on-chain time series, unstructured news text, and technical indicators, under high-volatility and real-time constraints.

By Yichen Luo, Yebo Feng, Jiahua Xu, Paolo Tasca, Yang Liu

llms rag agents multimodal benchmarks

arXiv Machine Learning

Jun 2

Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting

arXiv:2606. 00060v1 Announce Type: cross Abstract: This paper investigates whether machine learning forecasts of hourly BTC-USDT returns can be converted into economically meaningful trading performance after transaction costs.

By Andrei Bysik, Robert \'Slepaczuk

arXiv Machine Learning

Jul 21

Volatility-Aware Extreme Event Detection in High-Frequency Financial Markets

arXiv:2607. 17555v1 Announce Type: new Abstract: Predicting extreme price movements in high-frequency financial markets is a challenging task due to non-stationarity, heavy-tailed return distributions, and severe class imbalance.

By Maorufa Zaman, Haris Md Sahed

Hugging Face Trending Papers

Jul 20

Volatility-Aware Extreme Event Detection in High-Frequency Financial Markets

Predicting extreme price movements in high-frequency financial markets is a challenging task due to non-stationarity, heavy-tailed return distributions, and severe class imbalance. In particular, rare but impactful events are often difficult to detect using conventional modeling approaches, which typically treat extreme movements as isolated observations.

arXiv Machine Learning

Jul 20

CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approach

arXiv:2607. 16028v1 Announce Type: new Abstract: This paper presents our system for Task 3 of the CLEF 2026 FinMMEval Lab, which requires daily long, flat, or short trading decisions for Bitcoin (BTC) and Tesla (TSLA) using news and historical market data.

By Andrei Neagu, Eeham Khan, Leila Kosseim

llms agents reinforcement-learning

arXiv Machine Learning

Aug 4

Enhancing Regime Shift Detection Using Unstructured Data: A Study on the Treasury Market

arXiv:2605. 30363v2 Announce Type: replace-cross Abstract: Regime shifts in financial markets reorganise the joint dynamics of asset prices and macro variables, breaking any single-regime calibration.

By Mingxuan Yi, Vidal Mehra, Jing Chen, John Cartlidge

llms

arXiv Machine Learning

Aug 11

RA-FinBERT: Rule-aware LoRA adaptation for low-resource financial sentiment classification

arXiv:2608. 09834v1 Announce Type: cross Abstract: Financial sentiment analysis converts unstructured financial news into quantitative signals that can support market analysis and decision-making.

By Fan Zhang, Jiaming Li

llms nlp fine-tuning

arXiv Machine Learning

22h ago

Deep Learning Based on Generative Adversarial and Convolutional Neural Networks for Financial Time Series Predictions

arXiv:2008. 08041v3 Announce Type: replace-cross Abstract: In the big data era, deep learning and intelligent data mining technique solutions have been applied by researchers in various areas.

By Wilfredo Tovar

diffusion safety

arXiv Machine Learning

Jul 31

FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning

arXiv:2607. 28127v1 Announce Type: cross Abstract: Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs).

By Giorgos Iacovides, Wuyang Zhou, Danilo Mandic

llms nlp reinforcement-learning benchmarks

Related stories