Introduction to Systematic Investing

The Emergence of Hedge Funds

Hedge funds emerged as an alternative investment vehicle in the mid-20th century, offering active portfolio management strategies.

Early funds relied on macroeconomic models and fundamental analysis, but the 1980s and 1990s saw a shift towards quantitative trading.

Renaissance Technologies, DE Shaw, and Two Sigma pioneered algorithmic trading by leveraging statistical methods and high-frequency trading models.

These funds significantly outperformed traditional asset managers, leading to increased adoption of systematic strategies across the industry.

The AI Revolution in Trading

The integration of artificial intelligence in trading has transformed systematic investing by making data-driven strategies more efficient and adaptive.

Key developments include:

Big Data Analytics:

AI processes vast amounts of structured and unstructured data, including financial statements, news articles, and social media sentiment.

Machine Learning Models:

Algorithms identify complex patterns, improving prediction accuracy for price movements and volatility.

Natural Language Processing (NLP):

AI-driven sentiment analysis extracts insights from news reports and earnings calls.

Deep Learning & Reinforcement Learning:

These advanced models continuously improve decision-making by learning from past trades and optimising portfolio allocations.

Automation & Execution:

AI enhances trade execution through real-time monitoring and algorithmic order placement, reducing slippage and maximising returns.

The AI revolution has democratised systematic investing, making hedge fund-level strategies accessible to retail investors through platforms like eToro. By leveraging AI, traders can construct robust portfolios that adapt to market conditions in real time, achieving superior risk-adjusted returns, although investors should remain mindful of inherent risks such as algorithmic unpredictability and inaccuracies that could impact future performance.