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With the rise of computers, accessible data, and processing power, analysing information to make data-informed decisions has become increasingly popular as a tool to support or guide investment decisions.

Quantitative investing (QI) is one such strategy that has gained increasing traction and popularity. It relies on mathematical and statistical analysis, as well as software, to make investment decisions.

As with all investment strategies, the aim of QI is to achieve the best possible risk-adjusted returns. Quantitative analysts use models and algorithms to identify opportunities and trends in the market and make trades accordingly. This style of investing benefits from the exclusion of emotional bias. Decisions are based on data rather than gut feeling or experience alone. That is where algorithms and models come in: they identify and process data sets to potentially make better-informed decisions resulting in a better alpha (i.e. the excess return of an investment relative to the return of a benchmark).

In this whitepaper, we will explore what quantitative investing is, the potential risks, and how it improves investment performance.