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.
arXiv:2607. 23370v1 Announce Type: new Abstract: Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance.
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.
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.
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).
arXiv:2607. 00856v1 Announce Type: cross Abstract: In recent years, large language models have achieved remarkable success and have seen growing adoption in financial applications.
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.
arXiv:2311. 06273v1 Announce Type: cross Abstract: The rise of ChatGPT has brought a notable shift to the AI sector, with its exceptional conversational skills and deep grasp of language.
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.
This paper presents an empirical comparison of lexicon-based and Large Language Model (LLM)-based sentiment analysis for extracting market-relevant signals from social media discourse in highly volatile equity markets. Using Reddit data from r/WallStreetBets and focusing on meme stocks (GME, AMC, NOK), we construct time-aligned sentiment indicators and evaluate their relationship with market returns, with particular attention to extreme positive return events in the upper tail of the return distribution.
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.
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.
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.