Driving smarter investment strategies with data and AI
Author: Rajiv Thillainathan
The investment landscape is witnessing a transformative shake up as the potential to leverage vast amounts of differentiated data opens the way for new investing techniques. Investments based on traditional factors such as cashflow yield and price to earnings ratio are starting to lose their predictive market power amid shorter economic cycles. These rapid cycles are placing particular pressure on fundamental managers who are struggling to adjust to the new investment landscape.
As investment managers seek new sources of alpha, one approach that is gaining traction is quantamental investing, which combines fundamental techniques – such as information based on balance sheets and income statements – with quantitative investment, which uses complex mathematical and computer models for trading decisions.
In fact, some institutions have completely changed their business strategy and adopted a quantamental investment strategy. Globally some of the largest hedge funds – Renaissance Capital, DE Shaw and Tudor Investment, to name a few -- have embraced quantamental investing, and many big asset management companies, such as Goldman Sachs and Credit Suisse, are beginning to put quantamental at the centre of investment strategies. In the Australia and New Zealand region, even the larger superannuation funds are developing in-house platforms for these types of strategies.
To better identify and manage risk, these institutions are using advanced computational techniques to validate strategies and build rigorous statistical models. They are tapping into alternative sources of data to uncover additional insights into concurrent trends and future developments and they are adopting new methods of analysis by deploying machine learning techniques. The expectation is that quantamental investing enhanced by alternative data sources will give institutions an information advantage, which in turn should generate additional alpha without significant time loss, thanks to the real-time potential of artificial intelligence.
What data and AI mean for a quantamental approach
A quantamental approach is by definition data-rich. It involves analysing vast amounts of financial and non-financial data to identify a group of shares that exhibit certain characteristics with promising returns, based on a combination of factors.
It involves skillful use of statistics, financial econometrics and AI techniques to analyse huge quantities of both structured and unstructured data in order to capture behaviour, trends and patterns relevant to a group of shares. This helps to uncover the investment potential of these shares.
When constructing a quantamental strategy, it’s important to combine fundamental and quantitative metrics.