Large Language Models and NLP’s have moved from labs to trading desks—shaping the way we trade:

1.    Can you give real-world applications of LLMs in quant finance—from sentiment analysis to automated document parsing?

2.    How are you using NLP to accelerate research, generate alpha, and improve operational efficiency?

3.    How have you grasped the strengths, limitations, and responsible deployment of LLMs in production environments?

As foundation models like GPT, Claude, and Gemini enter finance, the next quant frontier is extracting latent signals from massive unstructured datasets. This masterclass explores how LLMs can be fine-tuned and interrogated to extract market-moving insights from earnings calls, filings, news flow, and more. Participants will prototype alpha pipelines using LLM outputs and debate their interpretability, drift, and compliance viability.

Structure:

Part 1: Applied LLM Briefing

Walkthrough of practical use cases: fine-tuned models for sentiment extraction, risk signal detection, and meta-feature creation

Part 2: Group Alpha Sprint

Teams receive an unstructured dataset (e.g., snippets from 10-Ks, call transcripts, macro headlines) and a mission: generate 1–2 usable features or signals using a foundation model output (pre-provided or simulated)

Part 3: Debate Round

Teams justify their signal’s validity, interpretability, and potential drift exposure; the group votes on robustness

Part 4: Expert Close

Discussion on prompt engineering, retraining pipelines, and alpha lifecycle for LLM signals

Takeaways:

• Practical LLM integration in quant workflows
• Feature engineering from unstructured sources
• Risk assessment of opaque models in a high-compliance context