A systematic strategy is a rules-based investment approach where decisions follow predetermined criteria. If conditions A, B, and C are met, action X is taken - no discretion, no emotion.
Beginner
What It Means
Systematic strategies operate on explicit rules. Every decision - what to buy, when to sell, how much to hold - follows predefined logic that a computer can execute.
Simple Example
Systematic Value Strategy:
1. Every quarter, rank all stocks by P/E ratio
2. Buy the 50 cheapest stocks
3. Sell any stock that rises above median P/E
4. Equal weight all positions
Systematic vs. Discretionary
| Aspect | Systematic | Discretionary |
|---|
| Decisions | Rules-based | Judgment-based |
| Emotion | Removed | Present |
| Consistency | Very high | Variable |
| Backtesting | Easy | Difficult |
| Adaptability | Slower | Faster |
Why It Matters
Systematic strategies eliminate behavioral biases that hurt returns: panic selling, greed buying, overconfidence, and inconsistency. They enforce discipline when emotions would otherwise take over.
Advanced
Types of Systematic Strategies
| Type | Description | Example |
|---|
| Factor-Based | Target return drivers | Value, momentum |
| Trend Following | Follow price trends | Managed futures |
| Mean Reversion | Bet on return to normal | Pairs trading |
| Risk Parity | Allocate by risk | Equal risk contribution |
| Rules-Based Indexing | Enhanced indexing | Smart beta |
Building a Systematic Strategy
1. Define Investment Universe
- Which securities to consider
- Liquidity and size filters
2. Specify Signal Generation
- What data to use
- How to calculate signals
- Ranking methodology
3. Set Portfolio Construction Rules
- Position sizing
- Sector constraints
- Risk limits
4. Establish Rebalancing Rules
- Frequency
- Threshold triggers
- Transaction cost considerations
5. Define Risk Management
- Stop-loss rules
- Maximum position sizes
- Correlation limits
Benefits of Systematic Approach
| Benefit | Description |
|---|
| Discipline | Rules prevent emotional decisions |
| Consistency | Same process every time |
| Scalability | Can manage large amounts |
| Transparency | Clear reasoning for every trade |
| Backtesting | Can test before risking capital |
| Diversification | Can hold many positions |
Challenges
| Challenge | Description |
|---|
| Overfitting | Rules that worked historically may not persist |
| Crowding | Many systematic investors use similar rules |
| Regime Changes | Rules may fail in new market conditions |
| Model Decay | Signals lose power over time |
| Black Swans | Unprecedented events break models |
Past performance of backtested strategies often overstates future performance. Always stress test for scenarios not in historical data.
Systematic vs. Algorithmic vs. Quant
| Term | Meaning |
|---|
| Systematic | Rules-based decisions (any speed) |
| Algorithmic | Computer-executed (often fast) |
| Quantitative | Data-driven, mathematical |
Rebalancing Approaches
| Approach | Description | Trade-off |
|---|
| Calendar | Fixed schedule (monthly, quarterly) | Simple but may miss signals |
| Signal-Based | When signals change significantly | Responsive but higher turnover |
| Threshold | When positions drift beyond bands | Balances responsiveness and costs |
Risk Management in Systematic Strategies
| Technique | Description |
|---|
| Position Limits | Maximum weight per holding |
| Sector Limits | Maximum weight per sector |
| Volatility Targeting | Adjust exposure to target vol |
| Stop-Losses | Exit rules on losses |
| Correlation Monitoring | Avoid concentrated bets |
- Lower volatility than concentrated stock picking
- More consistent (but perhaps smaller) alpha
- Capacity constraints as assets grow
- Factor exposure embedded in returns
Pure systematic strategies may underperform during transitions or unprecedented events, but outperform over full cycles by avoiding behavioral errors.
