That means focusing on impactful themes and finding financial instruments that might be affected in unobvious ways. In other words, the AI investing brain should be able to determine which stocks, bonds and currencies have not yet been priced into the effects of events. Validating the timing and quantifying the expected alpha requires highly nonlinear, multidimensional and cross-asset ML models.
NLP with a Difference
Many investment teams use NLP technology to process texts and create sentiment signals. But since these daily signals are quickly priced into securities, they tend to be correlated with momentum strategies, in our view. For an AI investing brain to find alpha potential, it must identify more persistent patterns, with longer-term effectiveness that leads to alpha potential uncorrelated from other asset classes.
That requires training a model that covers a specialized yet diverse set of topics to identify subjects, including geopolitics, technology, finance and macroeconomics, that could impact asset prices. The AI brain is fed a regular diet of information from credible sources that we believe are unbiased—from the International Monetary Fund to the World Trade Organization and global central banks. It is constantly evolving and now has tens of millions of nodes (i.e., words and combinations of words) in its knowledge graph.
Utilizing the key themes, this evolving knowledge graph taps into current events in real time. It draws on a select group of reliable media sources and ranks the news based on what is more likely to move financial markets in a persistent way versus short-lived hype. The top-ranked, potentially most influential news items are then used to identify the closest financial assets to invest in the theme.
The Matchmaking Algorithm: Identifying Assets with Alpha Potential
The next step is to connect the dots between influential themes and investment opportunities by gauging the potential impact of each identified theme on financial assets across a broad universe, including stocks, equity indices, government bond indices, commodities and currencies.
When a human investor identifies a theme, it can’t always see the first-, second- and third-order effects. For example, tighter carbon regulations would obviously affect carmakers and key raw materials used to reduce emissions. But the ripple effect goes much further and would be harder to see; it could extend from substitutes for palladium (used in catalytic convertors in vehicle exhausts), to the electric vehicle value chain, to other industries that depend on lithium from technology companies producing chips and hardware, to renewable energy storage systems.
An ML model can extract patterns and quantify multi-asset interdependencies to help forecast returns by providing the direction, long or short, and size for each asset. This model estimates the expected alpha for each security identified as a candidate from a relevant theme. Of course, not every security identified should be selected to be added to the portfolio. Quantitative tools can also help construct a strategy that aims to maximize the alpha potential of short-listed securities while applying multiple levels of risk control.
Case Study 3: Sharpening Equity Research and Portfolio Processes
Equity portfolios that are rooted in fundamental processes might not seem the most obvious candidates for the deployment of AI technology. After all, stock pickers use deep research, sector and company expertise, and most importantly, human judgement to identify alpha opportunities.
We don’t think AI will replace human analysts for active equity portfolios anytime soon. Yet the need for sophisticated tools to manage risk and create portfolios with strong risk-adjusted return potential has become more important than ever. In our view, AI tools can help improve equity investing processes in three main ways: efficiency, managing risk/portfolio construction and generating signals.
Efficiency: Saving Time on the Boring Stuff
For analysts to be most productive, they must focus on asking the big questions with the best information. Yet in the past, collecting the best information in a data-saturated world was labor intensive and flawed. News reports, official filings and earnings calls generate an endless stream of information. This forced investment teams to focus deep research on a limited number of higher-priority holdings or strong investment candidates.
Now, ChatGPT can be used to summarize transcripts of earnings calls and events that analysts wouldn’t normally be able to attend. When implemented correctly, it’s like the analyst has 10,000 interns to help accelerate the discovery of new information. Internal chatbots can be used to help analysts become more efficient at finding information from an immense pool of notes and reports; chatbots can also be used to help quantitative analysts write code, speeding up important data discovery processes.
Managing Portfolio Risks and Construction
Risk-management portfolio construction is another area where AI can be very helpful.
Cluster analysis is a form of AI that’s been used in our risk-management arsenal for several years. It aims to detect unknown risks that may be lurking in unseen market patterns, which may go undetected by standard risk models. This sophisticated ML technique segments stocks into groups whose returns have been moving closely together over a defined period. For example, it can help separate groups of stocks that relate to a new sub-theme or risk, which can guide the portfolio manager to hedge the unintended positions.
In addition to raw data like stock returns, unstructured text can be used to discover relationships between companies and trends. NLP and word embeddings allow us to source thematic stocks or identify portfolio risks by systematically analyzing materials, including company transcripts, 10-K and 10-Q forms, and Wikipedia data. This provides a scalable way to find stocks that are related to broad themes such as demographics and aging, AI or urbanization, which could be important drivers of risk and return.
Generating Sentiment Signals
AI can also support equity research by generating signals. We believe that these shouldn’t be used in isolation, but they can be effective when paired with fundamental analysis.
Using large language models (LLMs) such as BERT and finBERT, we have long used NLP tools to generate signals such as positive or negative sentiment from CEOs. These tools can also be used to estimate a Gunning fog index, a gauge of the complexity of words and sentences, which may indicate that a company is trying to confuse investors. LLMs are much better than traditional tools at picking up these nuances.
Another set of NLP signals drills into words embedded in corporate filings to identify large changes in real time. By systematically and comprehensively finding sections that have changed, we may be able to discover potential risks earlier than others. Fundamental analysts can then review the 10-Ks of companies flagged to validate the risks detected.
When strategically deployed, AI can help active equity investors make better investment decisions. However, we believe that AI is a tool that must be used in conjunction with human expertise and judgment. While AI can help us process vast amounts of data quickly and efficiently, we believe that for long-term equity investors, it cannot replace the insights and skill that come from years of industry experience.
