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Feature Engineering for a Stock Return Prediction Neural Network

Feature engineering is a crucial step in building effective neural networks for stock return prediction. It involves transforming raw financial data into a set of meaningful features that capture relevant patterns, trends, and signals, enabling the model to learn more accurately and make better predictions. Since stock markets are complex, noisy, and influenced by many factors, feature engineering helps distill useful information that highlights the underlying structure of the data, improving the neural network's ability to forecast returns.

Understanding the Problem

Predicting stock returns typically means forecasting the percentage change in price over a future time period, rather than simply predicting the next price value. Returns are often more stationary and exhibit statistical properties that make them better suited for modeling. However, stock returns are influenced by a mix of historical price behavior, volume, market sentiment, macroeconomic indicators, and other external factors. Proper feature engineering aims to encapsulate these influences into a numerical representation the neural network can process.

Raw Data Sources

The starting point for feature engineering is the raw data, which usually includes:

• Price Data: Open, High, Low, Close (OHLC) prices for each trading interval (daily, intraday, etc.).
• Volume: Number of shares or contracts traded.
• Fundamental Data: Earnings, dividends, or other company-specific financial information.
• Market Data: Indices, sector performance, or broader economic indicators.
• Sentiment Data: News sentiment scores, social media trends.

While raw price and volume data provide the foundation, these values alone are rarely sufficient for accurate prediction due to their volatility and noise.

Common Feature Engineering Techniques for Stock Return Prediction

1. Technical Indicators

Technical indicators summarize price and volume information into metrics that reflect market trends, momentum, volatility, or mean reversion tendencies. Some widely used indicators include:

• Moving Averages (SMA, EMA): Smooth price data over fixed windows to identify trend direction.
• Relative Strength Index (RSI): Measures speed and change of price movements to indicate overbought or oversold conditions.
• Moving Average Convergence Divergence (MACD): Shows relationship between two EMAs, highlighting momentum changes.
• Bollinger Bands: Uses moving averages and standard deviations to measure volatility and potential price breakouts.
• Average True Range (ATR): Quantifies market volatility.

These indicators convert raw price series into more interpretable signals. By calculating them over different lookback periods, you can capture short-, medium-, and long-term dynamics.

2. Return Calculations

Since the goal is to predict returns, engineering features based on returns themselves is natural. Examples include:

• Log Returns: Logarithmic price changes are often preferred as they approximate continuously compounded returns and have nice statistical properties.
• Lagged Returns: Returns from previous days or time steps as input features to capture autocorrelation.
• Rolling Statistics: Moving averages, standard deviations, skewness, or kurtosis of returns over a rolling window can highlight volatility and distributional changes.

3. Time Features

Temporal features help the model learn seasonality and periodic patterns:

• Day of the Week / Month: Market behavior often varies by day or month.
• Trading Session: For intraday data, features indicating pre-market, regular, or after-hours.
• Holiday Flags: Certain holidays or events can influence market behavior.

Encoding these features cyclically (e.g., sine/cosine transforms of day-of-year) avoids artificial discontinuities.

4. Volume-based Features

Volume indicates trading intensity and can signal strength or weakness of price moves:

• Volume Moving Averages: Smoothed volume levels to detect unusual spikes.
• Volume-Price Correlations: Combining volume and price changes can reveal divergence or confirmation patterns.
• On-Balance Volume (OBV): Cumulative volume adjusted by price direction.

5. Cross-Asset or Market Features

Stocks do not move in isolation; incorporating features from related assets or indices helps capture market-wide effects:

• Market Index Returns: S&P 500 or relevant index returns as explanatory variables.
• Sector or Industry Returns: To account for sector-specific trends.
• Volatility Indices: Like the VIX, indicating market fear or complacency.

6. Sentiment and Alternative Data

If available, sentiment analysis on news or social media can provide valuable leading indicators:

• Sentiment scores, tweet volumes, or news counts can be engineered into features to capture crowd psychology.

Data Preprocessing Considerations

Before feeding features into the neural network, careful preprocessing is necessary:

• Scaling and Normalization: Features often have different scales; normalization (e.g., Min-Max, Z-score) ensures stable and efficient training.
• Handling Missing Data: Financial datasets can have gaps; strategies include forward-filling, interpolation, or discarding incomplete samples.
• Feature Selection: Redundant or noisy features can hurt performance. Techniques such as correlation analysis, principal component analysis (PCA), or model-based selection help prune the feature set.

Challenges and Best Practices

• Avoid Data Leakage: Ensure that feature calculation uses only historical data available at prediction time. For example, moving averages must be computed using past prices, never future prices.
• Balance Complexity: Over-engineering can lead to overfitting, especially on limited data. Focus on features grounded in domain knowledge.
• Evaluate Feature Importance: Use explainability tools or model-agnostic methods to understand which features contribute most.