AI Insights - Ep.4: Can AI Pick Stocks by Reading the News?
From Cisco Podcast Network by Cisco
May 5, 2026 · 22 min
About this episode
The episode discusses sentiment analysis in finance and the training of large language models for better interpretation of financial language.
In this month’s episode of The Cisco AI Insights Podcast, hosts Rafael Herrera and Sónia Marques explore the evolving landscape of sentiment analysis with Cisco machine learning engineer Joan Rossello. The conversation centers on the research paper from Imperial College London, “FinDPO: Financial Sentiment Analysis for Algorithmic Trading Through Preference Optimization of LLMs,” which proposes a new way to train large language models to better interpret nuanced human language. By comparing output quality, this preference-based training improved generalization and helped models interpret complex financial language without relying on simple memorization. Additionally, the discussion explored how sentiment scores derived from model probabilities were used to rank companies based on the strength of their news coverage, yielding promising results. A special thank you to the team that developed this month's paper. If you are interested in reading the paper yourself, please visit this link: https://arxiv.org/abs/2507.18417
People in this episode
Hosts: Rafael Herrera, Sónia Marques
Guest: Joan Rossello
Topics covered
- sentiment analysis
- AI in finance
- algorithmic trading
- large language models
- financial language interpretation
Keywords
- AI
- stock picking
- sentiment scores
- algorithmic trading
- financial news
Mentioned in this episode
Organizations: Imperial College London
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