Lesson 9.7: Integrating NLP Signals into Trading Models
We've successfully built a pipeline to turn messy text into clean, numerical signals. Now for the final step: how do we actually use these signals? This lesson covers the practical strategies for incorporating text-based features, like sentiment scores, into a quantitative trading model alongside traditional price-based features.
Part 1: The Goal - Augmenting, Not Replacing
It's very rare for a trading model to be based *only* on text data. A more robust and common approach is to use NLP-derived signals to **augment** or **enhance** a model that is already using traditional quantitative features (like momentum, value, or volatility from past prices).
Our goal is to add new columns to the feature matrix () that our trading model (e.g., an XGBoost model from Module 4) sees. This gives the model a new, orthogonal source of information to learn from.
Part 2: Feature Engineering from Raw NLP Signals
Our NLP pipeline might generate a stream of raw sentiment scores for a stock every time a news article is published. We can't use this raw, irregular stream directly. We need to convert it into a regular, time-aligned feature that matches our price data (e.g., one value per day).
This is a feature engineering task. Common techniques include:
1. Rolling Averages and Aggregations
This is the most common approach. It smooths out the noisy, high-frequency sentiment data.
- `sentiment_24h_avg`: The average sentiment score of all articles published in the last 24 hours.
- `sentiment_7d_vol`: The standard deviation of sentiment scores over the last 7 days (a measure of news "dispersion" or disagreement).
- `news_volume_24h`: The total number of articles published about the stock in the last 24 hours (a measure of attention).
2. Exponentially Weighted Moving Averages (EWMA)
An EWMA of sentiment gives more weight to recent news and less weight to older news. This is often more realistic than a simple rolling average.
3. Event-Based Features
Create binary features that flag specific events.
- `had_earnings_call_last_week`: A 1 if the company had an earnings call, 0 otherwise.
- `has_negative_news_burst`: A 1 if the rolling sentiment average just dropped below a certain threshold (e.g., -0.5).
4. Topic Model Features
If you have run an LDA model, you can use the document's topic proportions as features. For example:
- `topic_ai_weight`: The weight of the "Artificial Intelligence" topic in the company's latest 10-K filing. A rising weight over time could be a bullish signal.
Part 3: The Combined Model Workflow
The End-to-End Quant Workflow with NLP
- Step 1: Get Price Data.Download daily price data for your universe of stocks.
- Step 2: Generate Traditional Quant Features.From the price data, calculate features like 21-day momentum, 6-month momentum, 21-day realized volatility, etc.
- Step 3: Run the IE Pipeline.Run your Information Extraction pipeline (from Lesson 9.6) on a massive corpus of news/filings to generate a time-stamped database of NLP signals (e.g., per-article sentiment).
- Step 4: Engineer NLP Features.Aggregate the raw NLP signals into daily features that align with your price data (e.g., `sentiment_24h_avg`, `news_volume_7d`).
- Step 5: Combine Feature Sets.Merge your traditional quant features and your new NLP features into a single, wide feature matrix, .
- Step 6: Train the Final Model.Train a powerful, non-linear model like XGBoost on this combined feature set to predict a target variable (e.g., next week's return).
- Step 7: Evaluate Feature Importance.After training, use techniques like SHAP values or the built-in feature importance plot to see if your NLP features were actually useful. Does `sentiment_24h_avg` rank as an important predictor alongside traditional factors like momentum?
What's Next? Our Final NLP Capstone
We've done it. We have the full, end-to-end blueprint for a modern quantitative trading model that incorporates the rich information from unstructured text.
It's time to put all this theory into practice.
In our final lesson of Module 9, we will undertake a capstone project: **Sentiment Analysis of Earnings Call Transcripts to Predict Stock Returns**. We will build the features, train a model, and analyze the results to see if the tone of management's voice can predict the market's reaction.