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How to drive digital alpha using AI

How to drive digital alpha using AI

Over the past 20 years, investment management has undergone an aggregation revolution, with increasingly sophisticated information portals speeding up the depth and breadth of information reaching investors.

In the next five years, investment management will go through an analytical revolution, through the ability of AI to make a step change in the speed, cost and ergonomics of distilling valuable insights that enhance investors’ skill, conviction, patience and therefore performance.

This will change the face of investment management ― not only will professional investors be able to make better informed investment decisions faster, for the first time private investors will have access to the sophisticated same stock, portfolio, and market insights as the professionals.

At the heart of this revolution is augmented intelligence, harnessing the power of AI combined with human decision making. As Paul Tudor Jones famously said, ‘no human is better than a machine, but no machine is better than a human with a machine’.

The impact of AI
For the first time, artificial intelligence can now bring a whole new perspective to investment decision making. The power of AI is its ability to tirelessly look for, combine, and distil signals from masses of noisy data already available in the marketplace. By bringing out ‘interesting’ insights, whether to confirm or enhance a suspected salient point or by identifying one that might have been overlooked otherwise, AI is the humble ‘idiot-savant’ that can usefully take on the tedious data-intensive work that humans are not best suited for.

By deploying artificial intelligence to analyse investment information, the investor can perform advanced stock screening, instantly identifying and triaging the handful of stock opportunities within global markets that both fit one’s investment preference and carry a higher than average chance of outperforming in the future.

This can happen instantly, with investors presented with a visual representation of the key signals extracted by the AI engine and, just like in a video game, can rapidly absorb and weigh the relevant insights to decide on an appropriate course of action, such as where best to conduct deep-dive research. This ultimately translates into better-informed stock selection and better returns.

Using AI, investors can also supplement slow, complex legacy portfolio management systems by being able to visualise and control real-time the active risks embedded in a portfolio, as well as access suggestions to mitigate unwanted exposures, all without the need for a PhD in data sciences. Investors will now be able to easily target and monitor the specific risks they know something about and intend to profit from, without unknowingly over-exposing their portfolio to sources of uncertainty they know nothing about. The key benefit of such risk control and transparency is better returns and more learning. 

Warning signs
The AI engine can perform smart monitoring as markets, portfolios and holdings’ alerts get derived from an array of signals, not simply a share price decline. The investor is then alerted, often before most of a price decline, to consider selling all or part of a holding, or reducing exposure to a risk factor, or indeed to tactically change market exposure. Such a development vastly contributes to reducing investor biases with less vigilance required (less decision fatigue), fewer false alarms (less over-trading), and more balanced perspectives (reduced overconfidence), all contributing to better investor performance.

By using AI, investors benefit from the limitless scalability and versatility of the technology. Modern user interfaces and data analytics facilitate the delivery of complex combinations of data sources, and cloud-based technology makes it cheap to do so. All of the above can also be applied to inform investors’ fund selection and performance monitoring. So rather than relying on top-down backward-looking analysis, AI can deliver instant forward-looking insights based on a bottom-up analysis of the funds’ holdings.

A step-change for professional investors
Asset managers are under ever growing pressure to deliver more with less. On the one hand, the supply of active funds is not decreasing, but on the other hand, demand for their services has been declining in favour of passive strategies.

Faced with such a productivity challenge, active funds can embrace the AI analytical revolution, delegating the more systematic data-intensive task to an AI engine, freeing analysts and managers’ time to focus on provocative research and high-conviction portfolio decisions, and exploiting its computing power to contribute an extra informational edge. All for a fraction of the cost of traditional research.

McKinsey recently highlighted that a handful of asset management firms are currently creating significant value (what they call digital alpha) by digitising their front-office operations to deliver improved investment performance. This correlation between digital leadership and improved overall performance is no accident and, according to McKinsey, asset managers must now become digital leaders in order to succeed in the future. 


Raphael Fiorentino is chief executive of Butterwire, which markets its AI equity analyst to private investors, and bespoke versions of it to professional investors.


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