Risk glossary

Quantitative investing, also known as systematic investing, is an investment approach that uses advanced mathematical modelling, computer systems and data analysis to calculate the optimal probability of executing a profitable trade. Examples include high-frequency trading, algorithmic trading and statistical arbitrage.

Historically, quantitative trading has been the domain of sophisticated hedge funds, but as computational power and data storage have become commoditised, traditional institutional investors and fundamental funds have begun to borrow quantitative techniques and tools, such as machine learning, advanced mathematical modelling, factor investing and the use of alternative data, to make trading and portfolio allocation decisions.

Some funds opt to take a purely quantitative approach whereas others use quantitative methods to supplement the human decision-making process.

Quantitative traders start by building a mathematical model of their proposed trading strategy, then they backtest it using historical market data. This process is susceptible to overfitting whereby the model is constructed to work well in the specific time period or market conditions it is tested against but underperforms when it is made live.

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