The Last Stand: How Fundamental Investors Must Evolve to Survive in the Age of AI
The investment landscape is on the cusp of a transformation that will fundamentally redefine the role of discretionary investors. Artificial Intelligence (AI) is not just another tool in the investor’s toolkit; it is a catalyst for a seismic shift that will obliterate the traditional practices that have defined fundamental investing for decades. The days of relying solely on management meetings, conference calls, and Excel models are numbered. If fundamental investors do not adapt swiftly, they risk being rendered obsolete.
The Slow Evolution of Fundamental Investing
Despite all outside views of Wall Street being incredibly fast moving and data driven, in the world of fundamental investing, this has been the exception far more than the rule. I spent about half of my career as a fundamental investor, playing both the analyst and portfolio manager role. I’ll never forget when I started my first job on the buy-side in 2004, I was told to read two books to understand how to be a fundamental investor, The Intelligent Investor by Benjamin Graham and Value Investing by Bruce C. N. Greenwald, Judd Kahn, Paul D. Sonkin, and Michael Van Biema. The Intelligent Investor was first published in 1949 and since then, the process of researching companies and industries hasn’t changed much.
In the early days of my investment career, I spent most of my time meeting with company management teams, attending investment conferences, building financial models and speaking with customers/suppliers/partners and other industry professionals surrounding a company. While there have been a host of suppliers come to market to make these processes simpler, the overall approach has not changed much.
Why Data Adoption Has Lagged for Fundamental Investors
When data started to rise in popularity around 10 years ago, it was set to replace the way fundamental investors researched companies. They could now gain access to much more granular data on companies and industries, and with this they could surely better see how companies and industries were performing. This new type of research would change the skillset of the investment analyst. This wasn’t how it played out in reality. Instead of opting for granular data and putting in the elbow grease to wring out unique insights, most buy side analysts farmed that work out to the new breed of sell side research firms, those powered by datasets rather than surveys and channel checks. Even though sell-side research has existed for decades, it’s extremely rare for a buy-side firm to not do their own research as well. But when it comes to data, that’s what nearly all have chosen to do.
I could write a book on all the reasons why fundamental investors have largely ignored the data opportunity. One of the reasons for this lack of adoption is the general skillsets sought after by most discretionary firms and their overall DNA. Most fundamental analysts view anyone technical as separate from the investment team. As a result, most rock star technologists choose not to work in this environment. Those stars end up going to quant firms where they are valued as core to the investment machinery. This has led to a bifurcation of talent on Wall St. The vast majority of people with skillsets understanding companies and industries go to fundamental firms and those with deep technical talent go to quant firms. This has created two very different moats for these firms to play in, with little if any overlap in investment strategy. Machines have historically been very bad at understanding company strategy and pattern matching it to historic examples. With the introduction of AI the wall that has been protecting fundamental investors is about to crumble.
AI’s Potential to Revolutionize Investment Research
Generative AI is incredibly powerful at taking raw, unstructured text and turning it into data. Libraries of textual data from earnings calls to corporate filings to meeting notes are about to go from incredibly challenging to mine value out of to incredibly simple.
In my days as an analyst I remember thinking that one of the biggest challenges for quants at replicating what a human does is that they don’t understand context well and there are many key sources of data that they don’t have access to. A computer couldn’t sit in my group meeting with a CEO to understand what was being said, how the analysts felt about the company or what area of the company’s performance they were focused on. For those with resources, that data is now simple to both get access to as well as to analyze.
This doesn’t mean the end of fundamental investing, but it does mean that the time to evolve is now. The wave of data coming to market didn’t lead to the end of fundamental research mainly because quants could only work with a small subset of this type of data because of their history and coverage breadth requirements. This isn’t true of textual data. These libraries of text and meeting transcripts are decades old and cover every public company on earth. The walls protecting the remaining vestiges of fundamental investors are crashing down. Now is the time to rethink how they can combine their own unique skillsets with the power of AI to remain competitive.
So what can fundamental investors do? The first step is to use AI to help them see more of what’s going on across the global market. AI Agents can read every earnings call transcript, listen to every single company event, track every court filing and track every online discussion board. It can also be used on your own data. Years of meeting notes, investment decisions and outcomes can be mined to avoid similar mistakes throughout time. Fundamental investors must use this technology to always be listening for nuggets that they can use to spot new investment opportunities or potential changes to investment thesis they hold about current holdings.
How Nomad Data Can Help
At Nomad Data I’ve focused on leaning into tools that can help investors harness this power in ways that they can still create competitive advantage. When I was investing, one of the biggest challenges I faced was that I could never get a complete picture of what was going on across industries. I could only listen to a handful of earnings call at best a day, and would read recaps of a handful more that I missed. In all I would listen or read transcripts to roughly 150 companies a quarter out of the more than 10,000 that were reporting. With only 1.5% coverage at best, it was easy to miss things like a slowdown in a certain region of the world, or a certain key government spending less or a supply chain constraint developing. I knew what I wanted to know, but I had no way to get at that data.
Nomad Data is about to release Transcript Chat to address this exact issue. The product allows you to extract information across ten thousand transcripts a minute. You can finally “see” across all publicly reporting companies. Need a list of every product which is supply constrained globally? Want to know which companies are reporting economic weakness or slowdowns in government spending? Need to know which companies are seeing double and triple ordering, possibly the beginning of a bubble? This can now be done. The key ingredient is the creativity to know what to ask, which is still what human analysts are better at.
Combining Human Creativity with AI for a Competitive Edge
Humans are far more creative than machines and at least for now have a far better ability to see new patterns that haven’t been seen before. We’ve used this tool for idea generation for new investments as well as to research demand for certain products or to measure economic health. The powerful thing about Transcript Chat is that unlike prepacked research reports based on data, an analyst can be creative in how they use the tool to come up with highly unique analysis and insights, actually giving them an edge. The ultimate output of the product is to turn thousands of unstructured documents into a highly structured data feed which can be quantified, graphed and tracked over time.
The future of fundamental investing hinges on the ability to embrace and integrate AI into its core processes. The barriers that once separated human insight from machine efficiency are crumbling, and the traditional ways of gathering and analyzing information are no longer sufficient. Fundamental investors must evolve, leveraging AI to sift through vast amounts of data and uncover insights that were previously inaccessible. The time for change is now, and the tools to drive this change are already within reach. As the walls protecting the old ways of investing fall, a new era of investing is emerging—one where the creative human mind, empowered by AI, can unlock unprecedented opportunities and maintain a competitive edge in an increasingly data-driven world. Funds that still believe they can focus on what they’ve always been doing will see an increasingly more challenged environment where they always feel like they’re the last one to know what’s going on.