But how exactly can we integrate AI signals into already existing investment processes?
There are at least two ways to integrate artificial intelligence into an existing investment process.
It all starts from predictive relative performance rankings generated by an AI model updated daily. These rankings identify predicted outperformers and underperformers within a given investment universe and time horizon.
The first way to integrate AI into an existing process is directly trading a fully-fledged AI-based strategy. To build such a strategy, an investor could transform the daily ranking into target portfolio weights that guide the portfolio allocation. For example, a portfolio manager could choose to create a long-only strategy that puts equal weight on each of the top 30 assets of the AI-generated ranking every week.
A different alternative to integrating AI into an existing process is treating AI signals and rankings as an alternative alpha-generating data source. This approach can be illustrated using predictive rankings for overweight or underweight assets already traded by discretionary asset managers.
Another example is using rankings to filter predicted underperformers out of an existing list of stock picks. Furthermore, a quantitative investor could combine AI signals with custom proprietary investment factors to create enhanced quantitative strategies.
The choice between these alternatives depends on the characteristics of the portfolio management team, the investment needs, and the constraints an investor has to comply with.