Introduction to Systematic and Discretionary Trading
The choice between systematic trading and discretionary trading is a central debate among investors and traders, especially in France where regulation and market expectations are evolving rapidly. Systematic trading relies on precise and automated rules, while discretionary trading depends on human judgment in real time. This article compares these two approaches based on real data, academic studies, and best practices observed in financial markets.
Advantages of Systematic Trading: Eliminating Emotions and Ensuring Discipline
The main advantage of systematic trading is the elimination of emotional biases that often affect human decisions. According to a study by the Banque de France (2022), nearly 65% of trading errors are linked to emotional reactions such as fear or greed. By automating decisions, rule-based trading enforces strict discipline, limiting behavioral mistakes.
Moreover, systematic trading allows for fast and consistent execution of strategies, especially in high-frequency markets where latency is critical. This is particularly true for quantitative strategies based on proven technical indicators.
Backtests vs Forward Tests: Reliability and Limitations
A cornerstone of systematic trading is backtesting, which involves testing a strategy on historical data. For example, the 12-1 momentum strategy (which consists of buying assets that outperformed over the past 12 months excluding the last month) generated an estimated annualized return of 10.5% on European equities between 1990 and 2020 (source: Bloomberg).
However, backtesting carries a major risk: overfitting, meaning an excessive adaptation of the strategy to historical data, which reduces its robustness in real-world conditions.
To address this issue, forward tests (real-time testing on unseen future data) are essential. A study by the AMF (2021) highlights that out of 100 backtested strategies, only 23 maintained positive and stable performance during forward testing. This step is therefore crucial to validate a strategy’s viability.
Overfitting: A Common Pitfall in Systematic Trading
Overfitting occurs when a strategy is too complex, incorporating too many parameters, and ends up modeling noise rather than signal. A report by INSEE (2020) on financial models shows that over 70% of complex quantitative strategies fail in real conditions due to this phenomenon.
To minimize this risk, it is recommended to adopt simple and robust strategies with a limited number of parameters, and to use statistical techniques such as cross-validation or shrinkage.
Simple Strategies That Work: 12-1 Momentum and Mean Reversion
Two simple approaches have proven effective in academic literature and markets:
12-1 Momentum: This strategy involves investing in assets that have had positive performance over the past 12 months, excluding the last month to avoid short-term reversal effects. In the European market, this strategy generated an annualized alpha of 5.2% after risk adjustment between 1990 and 2020 (source: Bloomberg).
Mean Reversion: This strategy aims to exploit the tendency of prices to revert to a historical mean. For example, on French indices, a 20-day mean reversion strategy yielded an annualized return of 6.1% with a Sharpe ratio of 1.1 over the 2000-2022 period (source: Banque de France).
Comparative Table: Systematic vs Discretionary Trading
Criterion
Systematic Trading
Discretionary Trading
Decision
Automated, based on defined rules
Based on human judgment and experience
Emotions
Eliminated, strict discipline
Present, frequent source of errors
Backtesting
Essential, but risk of overfitting
Not directly applicable
Adaptability
Less flexible, requires recalibration
Flexible, can adapt instantly
Complexity
Can be high, but simpler is preferable
Variable depending on experience
Execution
Fast, unbiased
Slower, subject to human errors
Cost
Initial investment in development
Lower technical costs, but high time investment
Discretionary Trading: Flexibility and Intuition
Discretionary trading allows the trader to adapt their strategy based on market conditions, macroeconomic events, or unexpected news. This flexibility is an advantage in highly volatile or structurally changing markets.
However, this approach heavily depends on the trader’s experience and temperament. According to an AMF survey (2023), 57% of French retail traders suffer significant losses due to emotional or impulsive decisions.
Discretionary trading requires deep market knowledge as well as rigorous risk management to limit losses.
Which Approach for the French Investor?
For French retail investors, several factors should guide the choice:
Technical knowledge: Systematic trading requires programming and statistical skills, often lacking among retail investors.
Time availability: Discretionary trading is time-consuming, whereas systematic trading can be automated.
Performance objectives and risk tolerance: Systematic momentum and mean reversion strategies offer a good balance between return and risk, with controlled volatility (Sharpe ratio around 1).
Access cost: Systematic trading often requires an initial investment to develop or acquire algorithms, while discretionary trading can start with little capital.
Conclusion: Clear Verdict for the Investor
Systematic trading, based on simple and robust rules such as the 12-1 momentum or mean reversion strategies, offers better control over emotional risks and strict discipline that translate into more stable long-term performance. The elimination of behavioral biases and rigorous use of backtests and forward tests help limit classic investment errors.
Conversely, discretionary trading remains relevant for experienced investors capable of managing their emotions and quickly adapting their strategy to changing market conditions, but it is often riskier and less consistently profitable on average.
For French investors looking to engage in active trading, the recommendation is clear: favor a simple systematic approach, validated by robust testing, to maximize the probability of success while limiting emotion-driven errors.
Sources: Banque de France (2022), AMF (2021, 2023), INSEE (2020), Bloomberg (1990-2022).
