Quantitative investing uses mathematical models, statistical analysis, and systematic rules to make investment decisions. It replaces subjective judgment with data-driven processes.
Beginner
What It Means
Quantitative (or “quant”) investing uses computers and data to make investment decisions instead of gut feelings or qualitative analysis. The rules are explicit, testable, and consistently applied.
How It Works
- Identify patterns in historical data
- Build models that capture those patterns
- Test rigorously on out-of-sample data
- Implement systematically without emotion
- Monitor and refine continuously
Quant vs. Traditional
| Aspect | Quant | Traditional |
|---|
| Decision Making | Rules-based, algorithmic | Judgment-based, intuitive |
| Data | Massive datasets, many signals | Company-specific research |
| Holdings | Hundreds to thousands | Typically 30-100 |
| Emotion | Removed by design | Human element |
| Scalability | High | Limited |
Why It Matters
Quant investing removes emotional biases (fear, greed, overconfidence) and enables processing of far more information than humans can handle manually. It brings scientific rigor to investment management.
Advanced
Quant Strategy Types
| Type | Description | Example |
|---|
| Factor-Based | Target proven return drivers | Value, momentum, quality |
| Statistical Arbitrage | Exploit price relationships | Pairs trading |
| Machine Learning | Pattern recognition | Neural networks |
| High-Frequency | Speed-based strategies | Market making |
| Risk Parity | Allocate by risk contribution | Bridgewater All Weather |
The Quant Process
1. Hypothesis Generation
- Academic research
- Market observations
- Economic theory
2. Data Collection
- Price data
- Fundamental data
- Alternative data
3. Signal Construction
- Factor definitions
- Composite scores
- Timing signals
4. Backtesting
- Historical simulation
- Out-of-sample testing
- Robustness checks
5. Portfolio Construction
- Optimization
- Risk constraints
- Transaction costs
6. Execution
- Algorithmic trading
- Market impact minimization
7. Monitoring
- Performance attribution
- Model decay detection
Data Sources
| Data Type | Examples | Use |
|---|
| Market Data | Prices, volumes, order book | Trading signals |
| Fundamental | Financials, estimates | Value, quality factors |
| Alternative | Satellite, sentiment, web | Unique insights |
| Macro | GDP, rates, inflation | Regime detection |
Backtesting Pitfalls
Backtesting can be misleading. Common errors:
- Overfitting: Finding patterns that don’t persist
- Look-Ahead Bias: Using data not available at decision time
- Survivorship Bias: Testing only on stocks that survived
- Transaction Costs: Ignoring realistic trading costs
Machine Learning in Quant
| Technique | Application |
|---|
| Regression | Return prediction |
| Classification | Buy/sell signals |
| Clustering | Regime detection |
| NLP | Sentiment analysis |
| Neural Networks | Complex pattern recognition |
Machine learning requires even more caution about overfitting. More complex models are easier to overfit to historical noise.
Quant vs. Discretionary
| Factor | Quant | Discretionary |
|---|
| Capacity | Higher | Lower |
| Consistency | Very high | Variable |
| Adaptability | Slower | Faster |
| Transparency | High | Lower |
| Crowding Risk | Higher | Lower |
Challenges
| Challenge | Description |
|---|
| Alpha Decay | Signals lose power as they become known |
| Crowding | Too many quants trading same signals |
| Regime Changes | Models built on past may not work in future |
| Data Quality | Garbage in, garbage out |
| Black Swan Events | Models fail in unprecedented conditions |
Parallax Approach
Parallax combines quantitative methods with investment insight:
- Factor-based stock selection
- Systematic risk management
- Transparent, rules-based process
- Continuous model monitoring
- Multi-factor integration
