VertiFuseX is a hybrid LSTM architecture that fuses multi‑scale temporal representations at the penultimate layer, stacking features from LSTM, Bi‑LSTM, and St‑LSTM branches and a parallel DNN stream. On 15 years of global equity index data, it reduces MAPE by 30‑54% and improves MAE and RMSE by over 40% compared to LSTM baselines, outperforming seven state‑of‑the‑art models across 33 metric‑dataset comparisons. The model is lightweight (675k parameters, 2.6 MB footprint) with 1.5 ms/sample inference latency and demonstrates robust, interpretable forecasting with reduced drawdowns in algorithmic trading simulations.
By Aashish Bohra, Vivek Vijay
arXiv:2605.28853v2 Announce Type: replace-cross
Abstract: Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction cos...
By Rahul Fernandes, Travis Desell
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
EXAONE Forecast for Finance (EXAONE Finance) is a financial time‑series foundation model designed to overcome the limitations of existing models that rely on self‑attention and assume fully observed data. It replaces self‑attention with a causal 1D convolution for temporal mixing and a group‑aware pooling MLP for variate mixing, achieving linear‑time complexity. The model is pretrained on a large, diverse financial corpus and, through masked context augmentation, learns to handle missing data, ultimately topping the FinVerse benchmark across accuracy, ranking, and profitability metrics.
By Seunghan Lee, Jaehoon Lee, Jun Seo, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Minjae Kim, Sungdong Yoo, Junhyeok Kang, Sangjun Han, Soonyoung Lee, Wonbin Ahn
arXiv:2609.04239v2 Announce Type: replace
Abstract: This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series foundation model (TSFM) tailored to financial...
By Seunghan Lee, Jaehoon Lee, Jun Seo, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Minjae Kim, Sungdong Yoo, Junhyeok Kang, Sangjun Han, Soonyoung Lee, Wonbin Ahn
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
The paper proposes a hybrid generative adversarial network (GAN) that combines a bi-directional LSTM and a CNN (Bi‑LSTM‑CNN) to generate synthetic financial data aligned with real market data. By preserving stock trend features, the model predicts future stock price movements across multiple markets (TSX, SHCOMP, S&P 500). Experiments show that this hybrid approach outperforms existing machine‑learning prototypes, and the study highlights gaps between investors and technical researchers.
By Wilfredo Tovar
ResNLS is a hybrid neural model combining ResNet and LSTM to forecast stock prices by emphasizing dependencies between adjacent prices. The model uses the closing prices of the previous five trading days as input, achieving optimal performance and outperforming state‑of‑the‑art baselines by at least 20%. In back‑testing, a trading strategy based on ResNLS‑5 predictions mitigated losses during market declines and generated profits during upturns.
By Yuanzhe Jia, Ali Anaissi, Basem Suleiman
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
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
arXiv:2609.23703v1 Announce Type: cross
Abstract: Financial language models can transform unstructured firm-specific news into structured decision signals, but financial AI research lacks an integrat...
By Kemal Kirtac
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