23 Market Features That Influence Stock Prices
Understanding which features influence stock prices is fundamental to building effective AI forecasting models. Modern machine learning systems can process hundreds of features, but knowing which ones actually matter—and why—separates successful quantitative trading from expensive data science experiments. This comprehensive guide explores 23 critical market features across technical indicators, sentiment data, and fundamental metrics.
Technical Indicators: Price and Volume Patterns
Technical indicators derive from historical price and volume data, capturing momentum, volatility, and trend information.
1. Simple Moving Average (SMA)
The average closing price over a specified period (e.g., 20-day, 50-day, 200-day). SMAs smooth out noise and reveal underlying trends.
Why it matters: When price crosses above/below major moving averages, it often signals trend changes. The 50-day/200-day crossover ("golden cross" or "death cross") is widely watched by traders.
Implementation note: Use multiple timeframes (short, medium, long) to capture different momentum patterns.
2. Exponential Moving Average (EMA)
Similar to SMA but gives more weight to recent prices, making it more responsive to new information.
Why it matters: More reactive than SMA, EMAs can signal trend changes earlier but generate more false signals during choppy markets.
3. Relative Strength Index (RSI)
Measures momentum on a 0-100 scale, comparing the magnitude of recent gains to recent losses.
Why it matters: RSI identifies overbought (>70) and oversold (<30) conditions. Divergences between RSI and price often precede reversals.
Key insight: RSI works best in ranging markets and can give false signals during strong trends.
4. Moving Average Convergence Divergence (MACD)
Shows the relationship between two exponential moving averages (typically 12-day and 26-day), along with a signal line (9-day EMA of MACD).
Why it matters: MACD crossovers and histogram patterns signal momentum shifts. The indicator combines trend following and momentum in a single metric.
5. Bollinger Bands
Plot standard deviation bands (typically 2σ) around a moving average, creating dynamic support and resistance levels.
Why it matters: Price touching or exceeding bands indicates extreme conditions. "Bollinger Band squeeze" (narrowing bands) often precedes volatility breakouts.
6. Average True Range (ATR)
Measures market volatility by calculating the average range between high and low prices over a specified period.
Why it matters: ATR helps with position sizing and stop-loss placement. High ATR suggests increased risk and wider stop distances.
7. Volume Weighted Average Price (VWAP)
The average price weighted by volume throughout the trading day.
Why it matters: Institutional traders use VWAP as a benchmark. Price action relative to VWAP indicates intraday strength or weakness.
8. On-Balance Volume (OBV)
Cumulative indicator that adds volume on up days and subtracts volume on down days.
Why it matters: OBV shows whether volume is flowing into or out of a security. Divergences between OBV and price can signal potential reversals.
9. Stochastic Oscillator
Compares a security's closing price to its price range over a specific period, generating values between 0-100.
Why it matters: Like RSI, identifies overbought and oversold conditions. The %K and %D line crossovers provide trading signals.
10. Average Directional Index (ADX)
Measures trend strength on a 0-100 scale, without indicating direction.
Why it matters: ADX above 25 indicates a strong trend (good for trend-following strategies), below 20 suggests a range-bound market (good for mean-reversion strategies).
Volatility Features: Risk and Uncertainty Measures
11. Historical Volatility
Standard deviation of returns over a specified period, typically annualized.
Why it matters: Volatility clusters—high volatility periods tend to persist. Forecasting volatility is often easier than forecasting direction and crucial for risk management.
12. VIX (Implied Volatility)
The CBOE Volatility Index measures market expectations of near-term volatility based on S&P 500 options prices.
Why it matters: Often called the "fear index," VIX spikes during market stress. High VIX values often coincide with market bottoms, while extreme lows can precede corrections.
Key relationship: VIX typically has a negative correlation with stock prices (around -0.7), making it valuable for predicting market direction during volatile periods.
13. Realized Volatility
Calculated from intraday returns rather than daily closing prices, providing a more precise volatility measure.
Why it matters: Captures intraday price movements that daily closing prices miss, especially important for short-term predictions.
Momentum and Mean Reversion Features
14. Rate of Change (ROC)
Measures the percentage change in price between the current price and the price N periods ago.
Why it matters: Simple but effective momentum indicator. Extreme ROC values often precede reversals due to mean reversion.
15. Money Flow Index (MFI)
Volume-weighted RSI that incorporates both price and volume data.
Why it matters: Identifies whether money is flowing into or out of a security. Divergences between MFI and price can signal upcoming reversals.
Market Sentiment Features
16. News Sentiment Score
Natural language processing analysis of financial news articles, generating positive, negative, or neutral scores.
Why it matters: News sentiment provides early signals before information is fully reflected in prices. Multiple studies show news sentiment has predictive power for 1-5 day returns.
Implementation approaches:
- Pre-trained financial sentiment models (FinBERT, StockBERT)
- Count of positive vs. negative words from financial lexicons
- Topic modeling to identify relevant vs. irrelevant news
17. Social Media Sentiment
Sentiment analysis of Twitter, Reddit (especially r/wallstreetbets), and StockTwits discussions.
Why it matters: Social sentiment can drive retail trading activity and create short-term price movements, especially for smaller-cap stocks.
Caution: Social sentiment is noisy and easily manipulated. Use in combination with other features, not isolation.
18. Analyst Ratings and Target Prices
Consensus buy/sell/hold ratings and average price targets from sell-side analysts.
Why it matters: Rating changes (especially upgrades/downgrades) often trigger significant price movements. However, analyst forecasts are frequently optimistic and slow to adjust.
19. Options Market Data
Put/call ratios, implied volatility skew, and open interest patterns from options markets.
Why it matters: Options traders are often sophisticated investors making directional bets. Unusual options activity can signal upcoming moves before they occur in the underlying stock.
Key metrics:
- Put/Call Ratio: High ratios indicate bearish sentiment (often contrarian bullish)
- Implied Volatility Skew: Difference in IV between puts and calls reveals directional bias
- Gamma Exposure: High gamma can amplify price moves in either direction
Fundamental Features
20. Price-to-Earnings Ratio (P/E)
Stock price divided by earnings per share.
Why it matters: P/E ratios indicate whether stocks are expensive or cheap relative to earnings. However, "cheap" stocks can get cheaper (value traps) and "expensive" stocks can get more expensive (growth stocks).
Variations: Forward P/E (using estimated future earnings) and PEG ratio (P/E adjusted for growth rate) provide additional context.
21. Earnings Surprises
The difference between reported earnings and analyst consensus estimates.
Why it matters: Positive surprises typically drive prices higher, and vice versa. Post-earnings announcement drift (PEAD) shows that prices continue moving in the direction of surprises for weeks after the announcement.
Key insight: The magnitude of surprise matters, but so does the consistency. Companies that consistently beat estimates often command valuation premiums.
22. Insider Trading Activity
Purchases and sales of stock by company executives, directors, and large shareholders.
Why it matters: Insiders have superior information about their companies. Significant insider buying (especially by multiple insiders) can signal undervaluation.
Important distinction: Insider buying is more informative than selling (executives sell for many reasons unrelated to company prospects, but typically only buy when optimistic).
Macroeconomic Features
23. Interest Rates and Yield Curve
Federal funds rate, 10-year Treasury yield, and the spread between long and short-term rates.
Why it matters: Interest rates affect stock valuations through discount rates and compete with stocks for investment capital. An inverted yield curve (short rates > long rates) has preceded most recessions.
Sector-specific impacts:
- Banks benefit from steeper yield curves (higher net interest margins)
- Growth/tech stocks suffer when rates rise (future earnings discounted more heavily)
- Utilities and REITs compete with bonds for yield-seeking investors
Feature Engineering Best Practices
Normalization and Scaling
Different features have vastly different scales (RSI: 0-100, price: $1-$1000, volume: millions). Normalize features to make them comparable:
- Z-score normalization: (value - mean) / std dev
- Min-max scaling: (value - min) / (max - min)
- Rank transformation: Convert values to percentile ranks
Lag Features
Include lagged versions of features (yesterday's RSI, last week's return) to capture temporal patterns:
- Yesterday's features for daily predictions
- Last week's features for weekly predictions
- Rolling statistics (e.g., 5-day average RSI)
Interaction Features
Create features that combine multiple indicators:
- RSI relative to its moving average (is momentum accelerating?)
- Price position within Bollinger Bands (near upper/lower band?)
- Volume as a multiple of average volume
Avoiding Look-Ahead Bias
Critical mistake: using information that wouldn't have been available at prediction time.
- Wrong: Using today's closing price to predict today's return
- Right: Using yesterday's closing price to predict today's return
- Wrong: Calculating moving averages using data from the test period
- Right: Calculating moving averages using only historical training data
Feature Selection Strategies
Start Broad, Then Narrow
- Initial exploration: Include all potentially relevant features
- Correlation analysis: Remove highly correlated features (correlation > 0.9)
- Feature importance: Use tree-based models to rank feature importance
- Sequential elimination: Remove lowest-importance features and retest
- Domain knowledge: Keep features with strong theoretical justification even if importance is moderate
Domain-Specific Considerations
Different strategies require different features:
- Day trading: Focus on volume, intraday patterns, and real-time sentiment
- Swing trading: Technical indicators, short-term momentum, earnings events
- Long-term investing: Fundamentals, valuation metrics, macroeconomic factors
- Volatility trading: VIX, historical volatility, options market data
Common Pitfalls
1. Too Many Features (The Curse of Dimensionality)
Problem: Including hundreds of features leads to overfitting, especially with limited training data.
Solution: Use dimensionality reduction (PCA, autoencoders) or aggressive feature selection. Generally, aim for 10-30 high-quality features rather than 100+ mediocre ones.
2. Ignoring Data Quality
Problem: Missing values, outliers, and errors in features poison model training.
Solution: Implement robust data cleaning pipelines. For missing values, use forward-fill (carry forward the last known value) rather than interpolation, which can create look-ahead bias.
3. Static Feature Sets
Problem: Features that worked historically may lose predictive power as markets evolve.
Solution: Regularly reassess feature importance and be willing to add/remove features as market dynamics change.
Putting It All Together
Building an effective feature set requires:
- Diversity: Combine technical, sentiment, and fundamental features
- Relevance: Match features to your prediction horizon (intraday vs. daily vs. weekly)
- Quality: Prioritize high-quality data sources over feature quantity
- Engineering: Create derived features that capture domain knowledge
- Validation: Test feature importance on out-of-sample data
- Iteration: Continuously evaluate and refine your feature set
Conclusion
These 23 market features represent the foundation of most successful AI stock prediction systems. Technical indicators capture price and volume patterns, sentiment features incorporate market psychology and information flow, and fundamental metrics anchor predictions in economic reality. The art of feature engineering lies not in using all available features, but in selecting the right combination for your specific prediction task.
Remember that features are just one piece of the puzzle. Even with perfect features, prediction is inherently difficult due to market noise and efficiency. Use features to tilt the odds in your favor, but always maintain realistic expectations about what AI can achieve in the notoriously challenging domain of stock market forecasting.