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arXiv Machine Learning

ResNLS: An Improved Model for Stock Price Forecasting

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.

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arXiv Machine Learning
Aug 19

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

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
diffusionsafety
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arXiv Machine Learning
Sep 14

VertiFuseX: Generalizable Financial Forecasting via Multi-Stream Temporal Fusion

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
efficiencybenchmarks
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arXiv Machine Learning
Sep 23

Financially Guided Deep Portfolio Optimization

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
rag
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Hugging Face Trending Papers
Aug 27

Tabular Deep Learning for Algorithmic Trading: Cross-Regime Bayesian Optimisation for Equity Signal Generation

The paper explores tabular deep learning for equity signal generation, training five model classes on daily data from about 300 large‑cap US stocks over eleven years. By using Bayesian optimisation that targets trading performance across three distinct market regimes, the authors achieve regime‑robust hyperparameter selection, yielding out‑of‑sample signal precision above random and a Hybrid ensemble (XGBoost + TabNet) with an annualised return of 51.26% and a Sharpe ratio of 2.44. The study also finds that alternative data adds limited value beyond technical and fundamental features, and that the ensemble’s outperformance is driven by stock selection rather than market exposure.

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arXiv AI
Sep 7

EXAONE Forecast for Finance

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
benchmarks
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