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#quantitative-finance News & Analysis

8 articles tagged with #quantitative-finance. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

8 articles
AINeutralarXiv – CS AI · May 97/10
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A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective

A comprehensive review examines how large language models are being applied to stock price forecasting in quantitative finance, with particular emphasis on practical challenges often overlooked in academic literature. The analysis, framed from a hedge-fund perspective, addresses critical implementation issues including sentiment analysis fragility, data leakage risks, and market friction constraints that affect real-world trading performance.

AIBullisharXiv – CS AI · Mar 37/104
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A Learnable Wavelet Transformer for Long-Short Equity Trading and Risk-Adjusted Return Optimization

Researchers developed WaveLSFormer, a wavelet-based Transformer model that directly generates market-neutral long/short trading portfolios from financial time series data. The AI system achieved a 60.7% cumulative return and 2.16 Sharpe ratio across six industry groups, significantly outperforming traditional ML models like LSTM and standard Transformers.

AINeutralarXiv – CS AI · 3d ago6/10
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AlphaForgeBench: Benchmarking End-to-End Trading Strategy Design with Large Language Models

Researchers introduce AlphaForgeBench, a new evaluation framework that addresses critical instability issues in Large Language Models deployed as trading agents. Rather than having LLMs generate discrete trading actions, the framework redefines their role as quantitative researchers producing alpha factors and strategies, enabling deterministic, reproducible evaluation aligned with real-world financial workflows.

AINeutralarXiv – CS AI · 4d ago6/10
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High-Quality Synthetic Financial Time-Series using a GAN-Diffusion Framework

Researchers present CoMeTS-GAN, a hybrid generative framework combining GANs and diffusion models to create realistic synthetic financial time-series data that accurately reproduce stock market stylized facts and inter-asset correlations. The approach addresses data scarcity challenges for financial institutions while improving upon existing general-purpose generative architectures.

AIBullisharXiv – CS AI · Mar 27/1015
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Portfolio Reinforcement Learning with Scenario-Context Rollout

Researchers developed a new portfolio reinforcement learning method called macro-conditioned scenario-context rollout (SCR) that addresses market regime shifts and distribution changes. The approach generates plausible return scenarios under stress events and improves portfolio performance by up to 76% in Sharpe ratio and reduces maximum drawdown by 53%.

AIBullisharXiv – CS AI · Mar 27/1016
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TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure

Researchers introduced TradeFM, a 524M-parameter generative AI model that learns from billions of trade events across 9,000+ equities to understand market microstructure. The model can generate synthetic market data and generalizes across different markets without asset-specific calibration, potentially enabling new applications in trading and market simulation.

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