Lesson 9.8: Capstone - Sentiment Analysis of Earnings Calls
This is the final exam for Module 9. We will apply our NLP toolkit to a classic 'quantamental' problem: Can we use the sentiment of an earnings call to predict the stock's post-announcement drift? This project synthesizes text processing, sentiment analysis, and feature engineering into a testable trading signal.
Part 1: The Problem and The Hypothesis
The Business Problem: An earnings announcement contains two pieces of information: the "hard" numbers (EPS, Revenue) and the "soft" information in the management discussion and Q&A session. The numbers are priced into the stock instantly. But does the *tone* of the call contain predictive information about future returns? This slow drift is what we want to capture.
The Quant Hypothesis: Companies whose earnings calls have an unusually positive sentiment will, on average, outperform companies whose calls have an unusually negative sentiment over the next 1-3 months. We will build a model to test this.
Part 2: The End-to-End Workflow
The NLP Signal Creation Pipeline
- Data Acquisition: Gather a large corpus of historical earnings call transcripts. For each transcript, we need the company ticker and the date of the call.
- Text Processing: For each transcript, parse the text. It's often useful to separate the "Prepared Remarks" section from the "Q&A" section, as they can have different tones.
- Sentiment Scoring: For each section of each transcript, calculate a sentiment score using the lexicon-based approach (from Lesson 9.2) with a financial dictionary like Loughran-McDonald.
- Feature Engineering: Create a "surprise" feature. The absolute sentiment score isn't as important as its deviation from the norm. For each company, calculate its average sentiment score over the past 2 years. Our feature will be:
- Target Variable Creation: Our target is the stock's forward return. For each earnings call, calculate the stock's total return over the next month ().
- Analysis: Create a simple "quintile spread" analysis. Rank all companies each month by their `Sentiment_Surprise` score. Go long the top 20% and short the bottom 20%. Does this long-short portfolio generate positive returns?
Part 3: The Complete Python Implementation (Conceptual)
Below is a conceptual Python implementation of this workflow. Acquiring a large corpus of transcripts is a major data challenge, so we will use a simulated DataFrame for this example.
import pandas as pd
import numpy as np
# --- Let's assume we have run our pipeline and have this starting DataFrame ---
# In reality, this would come from a massive data collection and NLP process.
np.random.seed(42)
dates = pd.to_datetime(pd.date_range('2018-01-01', '2023-12-31', freq='Q'))
tickers = ['AAPL', 'MSFT', 'GOOG', 'AMZN', 'META']
data = []
for date in dates:
for ticker in tickers:
# Simulate a base sentiment and add noise
base_sentiment = 0.05 + tickers.index(ticker) * 0.02
sentiment = base_sentiment + (np.random.rand() - 0.5) * 0.1
# Simulate forward returns, slightly correlated with sentiment
fwd_return = sentiment * 0.1 + (np.random.rand() - 0.5) * 0.05
data.append([date, ticker, sentiment, fwd_return])
df = pd.DataFrame(data, columns=['date', 'ticker', 'sentiment', 'fwd_return_1m'])
df.set_index('date', inplace=True)
# --- Step 4: Feature Engineering (Sentiment Surprise) ---
# Calculate the rolling 2-year (8 quarters) average sentiment for each stock
df['avg_sentiment_2y'] = df.groupby('ticker')['sentiment'].transform(lambda x: x.rolling(window=8, min_periods=4).mean().shift(1))
df['sentiment_surprise'] = df['sentiment'] - df['avg_sentiment_2y']
df.dropna(inplace=True)
# --- Step 6: Quintile Spread Analysis ---
# For each quarter, rank stocks into 5 quintiles based on sentiment surprise
df['sentiment_quintile'] = df.groupby('date')['sentiment_surprise'].transform(
lambda x: pd.qcut(x, 5, labels=False, duplicates='drop') + 1
)
# Calculate the returns of the long-short portfolio
long_portfolio = df[df['sentiment_quintile'] == 5]
short_portfolio = df[df['sentiment_quintile'] == 1]
# Calculate the mean return for each quintile for each period
quintile_returns = df.groupby(['date', 'sentiment_quintile'])['fwd_return_1m'].mean().unstack()
# The Long-Short strategy return is the return of the top quintile minus the bottom quintile
long_short_returns = quintile_returns[5] - quintile_returns[1]
# --- Analyze the results ---
print("--- Long-Short Portfolio Performance ---")
mean_return = long_short_returns.mean()
std_dev = long_short_returns.std()
sharpe_ratio = (mean_return / std_dev) * np.sqrt(4) # Annualize quarterly returns
print(f"Average Quarterly Return: {mean_return:.4%}")
print(f"Quarterly Volatility: {std_dev:.4%}")
print(f"Annualized Sharpe Ratio: {sharpe_ratio:.2f}")
# Plot the cumulative returns of the strategy
cumulative_strategy_return = (1 + long_short_returns).cumprod()
cumulative_strategy_return.plot(figsize=(12, 6), title='Cumulative Return of Sentiment Long-Short Strategy')
plt.ylabel('Cumulative Return')
plt.grid(True)
plt.show()
- A positive and statistically significant Sharpe Ratio would provide strong evidence that our NLP-derived sentiment signal has genuine predictive power (alpha).
- The quant would then proceed to integrate this `sentiment_surprise` feature into their main multi-factor trading models, alongside traditional factors like momentum and value.
- Further research would involve using more sophisticated NLP models (like fine-tuning a FinBERT model for sentiment) to see if a more accurate sentiment score can lead to a more profitable strategy.
A quant would look at the final Sharpe Ratio of the long-short portfolio:
Congratulations! You Have Completed Module 9
You have now completed a comprehensive tour of applying Natural Language Processing to financial data. You've learned how to represent text numerically, calculate sentiment, discover topics, and extract named entities. Most importantly, you've completed a capstone project that outlines a real-world workflow for turning this unstructured data into a testable alpha signal.
What's Next in Your Journey?
You are now equipped with an incredibly powerful and modern toolkit. In our final module, we will explore the frontiers of quantitative machine learning.
In **Module 10: The Modern Quant: Frontiers & Specializations**, we will cover advanced topics like model explainability (SHAP & LIME), sophisticated backtesting techniques, and an introduction to using Reinforcement Learning for trading.