Systematic investing strategy refers to rule-based investing approaches that rely on algorithms, models and data analysis to make investment decisions. Unlike discretionary investing where decisions are made based on human judgement, systematic strategies remove emotions and behavioral biases from the investing process. Some key benefits of systematic investing include diversification, risk management, transparency and potential to outperform the market. This approach is becoming increasingly popular among institutional investors and retail investors. Some common systematic strategies include quantitative equity strategies, trend following strategies, risk parity strategies etc. This article will provide an overview of systematic investing and discuss its core components like backtesting, portfolio optimization, execution algorithms and risk management.
Algorithms and models form the core of systematic strategies
The algorithms and models used in systematic investing encode the investment philosophy, ideas and rules designed by the investment manager. These models ingest various data inputs like fundamentals, prices, macroeconomic indicators, sentiment signals etc and generate buy/sell signals for trades. Great amount of research goes into formulating these models to identify inefficiencies and patterns in the market. The models also need to be robust, adaptive to changing market conditions and have sound theoretical underpinnings based on financial research.
Backtesting validates the strategy before real money deployment
Backtesting refers to applying a trading strategy on historical data to evaluate how it would have performed. It helps to estimate expected returns, risks, drawdowns, volatility etc associated with the strategy. This step validates if the identified patterns or inefficiencies hold up historically. It also instils confidence in the investment manager to deploy real money into the strategy by demonstrating effective historical performance.
Portfolio optimization and execution algorithms align with investment objectives
The trading signals from models need to be converted into a portfolio of stocks/instruments to buy and sell. Portfolio optimization refers to this process of constructing an optimal portfolio aligned to investment objectives, risks, constraints etc. Execution algorithms are the engines that take the target portfolio and execute the trades efficiently with minimal market impact and cost.
Robust risk management frameworks guard against large losses
No investing strategy performs well in all market conditions. Uncertainty and volatility are inherent in financial markets. Hence it is critical for systematic investors to implement strong risk management systems like trade sizing based on volatility, stop losses, hedging etc. This helps in managing risks and containing drawdowns during unexpected market dislocations.
In summary, systematic investing represents a rules-based, disciplined approach to investing by utilizing technology, research and data analysis. Though initial research and infrastructure requires efforts, it can lead to strategies that are scalable, sustainable and less prone to behavioral biases.