Modeling Earnings Surprises with Ensemble Methods

Overview

Earnings surprises—deviations between actual and expected earnings—can significantly impact stock prices. Predicting these surprises accurately is a challenging task due to the influence of various factors, including macroeconomic conditions, sector performance, and company-specific metrics. Ensemble methods, such as boosting and bagging, offer a robust approach by combining multiple models to improve prediction accuracy.

This article will cover:

1. The basics of earnings surprises and their importance.
2. How ensemble methods enhance predictive modeling.
3. A Python implementation using boosting and bagging techniques to predict earnings surprises.

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1. What Are Earnings Surprises?

1.1 Definition

An earnings surprise occurs when a company’s reported earnings differ from analysts' consensus estimates.

[
\text{Earnings Surprise (%)} = \frac{\text{Actual EPS} - \text{Expected EPS}}{\text{Expected EPS}} \times 100
]

1.2 Market Impact

• Positive Surprises: Often lead to stock price increases.
• Negative Surprises: Typically cause stock price declines.

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2. Why Use Ensemble Methods?

2.1 Limitations of Single Models

• Overfitting: Single models often memorize patterns in training data rather than generalizing.
• Bias-Variance Tradeoff: Individual models may struggle to balance accuracy and stability.

2.2 How Ensemble Methods Help

Ensemble methods improve performance by combining multiple models:

1. Bagging (Bootstrap Aggregating): Reduces variance by training models on different subsets of data and averaging their predictions.

• Example: Random Forest.

2. Boosting: Focuses on correcting errors made by previous models, iteratively improving performance.

• Example: Gradient Boosting, XGBoost, LightGBM.

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3. Python Implementation

3.1 Import Libraries

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, r2_score

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3.2 Load and Prepare Data

Example Dataset

Columns:

• Actual_EPS: Reported earnings per share.
• Expected_EPS: Analyst consensus estimate.
• Features: Company metrics (e.g., revenue growth, debt ratio), sector performance, and macroeconomic indicators.

# Load earnings data
data = pd.read_csv('earnings_data.csv')

# Calculate earnings surprise as target
data['Earnings_Surprise'] = (data['Actual_EPS'] - data['Expected_EPS']) / data['Expected_EPS'] * 100

# Select features and target
X = data.drop(['Actual_EPS', 'Expected_EPS', 'Earnings_Surprise'], axis=1)
y = data['Earnings_Surprise']

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3.3 Split Data into Training and Test Sets

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

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3.4 Model 1: Bagging with Random Forest

# Train Random Forest
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)

# Predict
y_pred_rf = rf_model.predict(X_test)

# Evaluate
print("Random Forest - Mean Squared Error:", mean_squared_error(y_test, y_pred_rf))
print("Random Forest - R-Squared:", r2_score(y_test, y_pred_rf))

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3.5 Model 2: Boosting with Gradient Boosting

# Train Gradient Boosting
gb_model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42)
gb_model.fit(X_train, y_train)

# Predict
y_pred_gb = gb_model.predict(X_test)

# Evaluate
print("Gradient Boosting - Mean Squared Error:", mean_squared_error(y_test, y_pred_gb))
print("Gradient Boosting - R-Squared:", r2_score(y_test, y_pred_gb))

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3.6 Visualize Predictions

import matplotlib.pyplot as plt

# Plot actual vs predicted for Random Forest
plt.figure(figsize=(10, 6))
plt.scatter(y_test, y_pred_rf, label='Random Forest', alpha=0.7)
plt.scatter(y_test, y_pred_gb, label='Gradient Boosting', alpha=0.7, color='red')
plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='black', linestyle='--')
plt.title('Actual vs Predicted Earnings Surprise')
plt.xlabel('Actual Earnings Surprise (%)')
plt.ylabel('Predicted Earnings Surprise (%)')
plt.legend()
plt.show()

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4. Key Insights

4.1 Model Comparison

• Random Forest (Bagging): Handles large feature sets well and provides stability through averaging.
• Gradient Boosting: Captures non-linear relationships and performs better on complex datasets with smaller sample sizes.

4.2 Practical Applications

• Use ensemble models to screen stocks for potential positive surprises.
• Combine predictions with sentiment analysis or technical indicators for more robust trading strategies.

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5. Limitations and Enhancements

Limitations

• Feature Engineering: The quality of predictions heavily depends on the relevance of input features.
• Data Availability: Accurate analyst estimates and macroeconomic data are essential.
• Overfitting Risk: Boosting models can overfit if hyperparameters are not tuned properly.

Enhancements

• Stacking Ensembles: Combine bagging and boosting models to leverage their strengths.
• Automated Hyperparameter Tuning: Use GridSearchCV or Bayesian optimization for optimal parameter selection.
• Deep Learning Models: Explore neural networks for large, high-dimensional datasets.

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6. Conclusion

Ensemble methods, including bagging and boosting, provide powerful tools for predicting earnings surprises. By leveraging these techniques, traders and analysts can improve the accuracy of their forecasts and develop strategies to capitalize on earnings-driven price movements. While these models require careful design and validation, their flexibility and performance make them invaluable in financial modeling.

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References

• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning.
• Scikit-learn Documentation: https://scikit-learn.org/
• Gradient Boosting for Regression: https://xgboost.readthedocs.io/
• Investopedia: Earnings Surprise: https://www.investopedia.com/