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Natural Language Processing (NLP) for Quant Trading

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*🪐 Jupyter Notebook*
https://github.com/romanmichaelpaolucci/Quant-Guild-Library/blob/main/2025%20Video%20Lectures/65.%20Natural%20Language%20Processing%20for%20Quant%20Finance/nlp.ipynb

**TL;DW Executive Summary**
– There is often an inverse relationship between data accessibility and opportunity in subsequent alphas
– Alternative data (like text) can be rich of signal but is not without noise (i.e. DOGE vs DOGE)
– Other quant fields like natural language processing (NLP) are concerned in many cases with extracting signal from noise
– Doc-Term Matrices are just one quantitative representation of the qualitative information we see in text
– Similarly scores and machine learning techniques can be applied directly to the Doc-Term matrix but largely become data science problems
– Other techniques for signal extraction exist, the finance literature has long considered dictionary based approaches to produce sentiment scores for example
– Once we have a signal, we still need to know what to trade, this then becomes a named entity recognition problem to determine what to buy and sell
– Again, this named entity recognition problem is not without noise and can impact our trading decisions
– Once we have established means of producing a signal and instrument we can observe the signal in the cross-section of a basket of equities (not just one document but many documents, not just one firm but many firms) and go long the highest signal quantile and short the lowest signal quantile to produce a market-neutral portfolio aiming to profit from a structural inefficiency we observed in this alternate data

I hope you enjoyed!

– Roman
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*📖 Chapters:*
00:00 – Alternative Data Sources and Alpha
03:12 – Evidence Text Data Influences Prices
05:39 – Corpora, Documents, and Tokens
08:24 – Tokenization and Vector Representations
11:26 – Visualizing Text Data in 3D
12:16 – Measuring Document Similarity
13:44 – Cosine (Document) Similarity
19:38 – Trading Signals using a Target Vector
21:25 – Natural Language Processing (NLP)
23:09 – Other Ways to Extract Signal from Text
26:34 – N-Grams and Negation Rules
30:18 – Machine Learning Approaches
31:50 – Named Entity Recognition (NER)
34:38 – What About LLMs?
35:48 – Cross-Sectional Equity Trading Strategies
40:20 – TL;DW Executive Summary
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*🗣️ Shout Outs*

A special thank you to my members on YouTube for supporting my channel and enabling me to continue to create videos just like this one!

*⭐ Quant Guild Directors*
Dr. Jason Pirozzolo
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*📝 Related Article*
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https://medium.com/quant-guild/how-to-build-a-neural-network-for-nlp-tasks-with-pytorch-and-gpu-da542591dbe1

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*🗂️ Resources*

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