Three ways artificial intelligence is disrupting investment management
TSAI
Investment management is about to enter another phase of transformation as we enter the era of artificial intelligence. AI’s revolutionary potential for the industry is huge, promising to streamline processes, generate unique insights, and optimize portfolios. Here, we’ll look at AI’s most disruptive use cases for investment managers.
AI can save time measured in days and weeks, not hours
AI will continue to change the landscape of equity research. The most obvious benefit of using AI for research, whether from a micro or macro perspective, for fundamental managers or quantitative analysts, is time savings.
Fundamental managers don’t have the time, inclination, or sometimes even the resources (AI can be expensive, both from a manpower perspective and from a data and computing power perspective) to build their own AI solutions. For fundamental managers, off-the-shelf AI tools offer a dual benefit: greatly increasing the speed of adoption by using an effective AI solution (versus the R&D involved in building their own), and also using their advanced processing power to comb through data to isolate interesting trends and opportunities faster than an individual could.
For quantitative analysts, using multiple AI tools to quickly build complex AI strategies that fit their firm’s investment preferences can provide an uncorrelated source of alpha.
In theory, much of what AI does can be done by enough people, enough calculators, and enough access to data. As mentioned above, the benefit of AI is that it can do this work and dramatically reduce the time it takes to do it. Investment managers used to have to create detailed Excel spreadsheets to simulate their portfolios, including possible risks. Now, these managers can upload their portfolios to AI software via the cloud and have the software do this work for them. They are still in the driver's seat when it comes to decision making, but AI can help them isolate trends faster, easier, and more effectively than they could on their own.
Now, it takes just minutes, not days or hours, to run experiments or test investment theses. Whereas before, investment managers had to focus on one or two ideas at a time, poring over spreadsheets, reports, and newspapers, now they can test dozens of ideas simultaneously. Clients we spoke to believe that implementing AI has enabled them to more than quadruple the number of ideas they can test simultaneously.
TSAI's "Ideas" tab provides in-depth research and ideas for every stock that an investment manager is investing in
AI Helps Investment Managers Isolate Portfolio Risk
In order for investment managers to get the most out of their AI tools, they should provide information about their current portfolio holdings and investment preferences. This enables the AI to be optimized for the user's specific needs. By analyzing an investment manager’s portfolio holdings and taking into account their investment preferences and constraints, AI tools can provide personalized insights and alerts. These alerts can signal that a portfolio is over- or under-weighted in certain areas, helping investment managers maintain a balanced and diversified portfolio.
AI can now answer common questions investment managers ask:
“What stock should I buy and why?”
“What’s wrong with this stock and the broader market?”
“What risks am I taking in my portfolio and how can I position myself to take advantage of market trends?”
AI does this by combining macro insights from news and text sources (such as a company’s 10-K and 10-Q reports) with financial data such as stock pricing and interest rates, using financial-specific algorithms and portfolio construction techniques to generate a personalized portfolio that is appropriate for risk. In addition, AI can continuously assess portfolio risk and alert managers when they exceed certain risk tolerance thresholds, allowing them to act quickly to mitigate potential negative impacts.
TSAI’s portfolio integration feature provides asset managers with recommendations based on their specific portfolio and investment style
AI gives an overall view of an investment manager’s portfolio so they can make better data-driven decisions
By looking at their portfolio through AI, investment managers can make better data-driven decisions. They can look at their portfolio from a micro perspective – using AI to decide at the individual stock level what stocks they should buy and whether this is a good fit for their investment style. They can look at the portfolio from a macro perspective – understanding what is happening in the world and how they can prepare for it. They can even understand at a portfolio level what risks they are taking and how to improve their book.
AI designed specifically for investment management gives asset managers extreme confidence in their decision-making process because it integrates all of these perspectives into one coherent story. It also collects market data (stock prices, Fed data, ESG ratings, other alternative data, etc.) and combines it to help investors truly understand what is driving the market.