arXiv AI

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction

arXiv:2606. 29347v1 Announce Type: cross Abstract: Adaptive Financial Transformer (AFT) is proposed for stock return prediction under non-stationary financial markets.

arXiv AI

Aug 11

Hybrid Neural-Classical Correction for Frozen Time Series Foundation Models: A Comprehensive Ablation Study on High-Frequency Stock Prediction

arXiv:2608. 08825v1 Announce Type: cross Abstract: Foundation models for time series forecasting demonstrate impressive zero-shot generalization but often underperform on specialized domains such as high-frequency finance.

By Kasun Dewage, Suranadi De Silva, Shankhadeep Mondal

benchmarks

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

arXiv Machine Learning

Jun 2

FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting

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

benchmarks data-engineering

arXiv Machine Learning

Jul 16

VAIOM: Continuous-Input, Discrete-Output Decoder-Only Financial Sequence Modeling

arXiv:2607. 13929v1 Announce Type: new Abstract: Financial observations are continuous, heterogeneous, and noisy, whereas decoder-only next-token models are usually built around discrete symbolic inputs.

By Yiming Ma, Xinyu Chen

llms

arXiv Machine Learning

Jun 3

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

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

llms multimodal benchmarks safety

arXiv Machine Learning

Jun 16

Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentation

arXiv:2606. 15701v1 Announce Type: new Abstract: Transformers have shown remarkable success in sequence modeling, yet their direct application to financial time series remains challenging due to noisy signals, short-memory dynamics, and distributional shifts.

By Tien Thanh Thach

llms benchmarks

arXiv Machine Learning

Jun 5

Is attention truly all we need? An empirical study of asset pricing in pretrained RNN sparse and global attention models

arXiv:2508. 19006v2 Announce Type: replace-cross Abstract: This study investigates the pre-trained RNN attention models with the mainstream attention mechanisms, such as additive attention, Luong's three attentions, global self-attention and sliding window sparse attention, for the empirical asset pricing research on the top 420 large-cap US stocks.

By Shanyan Lai

llms efficiency benchmarks

arXiv AI

Jul 7

KAN vs LSTM Performance in Time Series Forecasting

arXiv:2511. 18613v2 Announce Type: replace-cross Abstract: This study presents a controlled comparison of baseline Kolmogorov-Arnold Networks (KAN), implemented via PyKAN, and Long Short-Term Memory (LSTM) networks for the forecasting of stochastic, non-stationary financial time series.

By Tabish Ali Rather, S M Mahmudul Hasan Joy, Nadezda Sukhorukova, Federico Frascoli

benchmarks safety

arXiv Machine Learning

Jul 23

STN-TGAT: Top-K Portfolio Construction via Prior-Guided Graph Attention with Learnable Soft-Threshold Sparsification

arXiv:2607. 19385v1 Announce Type: new Abstract: This paper tackles the problem of stock ranking and portfolio construction under realistic investment settings by jointly modeling temporal dynamics and cross-sectional dependencies.

By Haoran Guo, Yutong Lu, Li Zhang

llms benchmarks

arXiv Machine Learning

Jun 10

One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data

arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.

By Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers, M. M. Lutfe Elahi, Sifat Momen, Nabeel Mohammed, Shafin Rahman

llms benchmarks 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

Jul 17

A Comparative Analysis of Machine Learning Models for Long and Short-Term Forecasting of the Egyptian Stock Market: A Focus on EGX30

arXiv:2607. 14391v1 Announce Type: new Abstract: This study concentrates on predicting stock prices in the Egyptian market, focusing on the EGX30, an influential financial hub in the Middle East.

By Muhammed Walid, Ahmed El-Naeimy, Hosam Moubarak, Walid Gomaa

Related stories