Recently I was the “protagonist” at Harvard Business School’s class on Business Analysis and Valuation (BAV). The class focused on the HBS Case Study: “New Constructs: Disrupting Fundamental Analysis with Robo-Analysts”. The case is about our Robo-Analyst Process (RAP), the AI technology that enables us to do such sophisticated research at scale. The class usually lasts about 90 minutes. The professor kicks off the discussion with “after reading the case, would you invest in New Constructs”?
There are many opinions on both sides of the question. Usually, the naysayers speak up first. They have a lot of reasons, a few of which are reasonable. But, the one answer that always lets me (and the professor) know that the student has not done his/her homework is: “there’s no moat, the data that New Constructs collects is public so anyone can do it.” Haha. Clearly, dear student, you have not done it because if you had, you would know how hard it is.
Since the bulk of the class is spent on breaking down all the adjustments we apply to analyzing a particular company, it becomes very clear just how hard what we do is to everyone in attendance. Shoot, it is hard for many of the students to even understand some of the adjustments we make, why we make them and how to make them. There’s often many arguments over the way to make the adjustments.
But, there is no argument over the need to make them nor the material impact they have.
However, there remains the very difficult question – do the adjustments matter enough that investors MUST care? This question reminds me of the classic: does a tree falling in the woods make any noise if no one hears it?
In other words, the adjustments might be critical to getting to the truth about profitability and valuation of companies, but do they matter to the stock price? Can we use the adjustments research to pick stocks and make money? Or, another way to pose the question is: if everyone is relying on bad research, then does it do an investor any good to use better research?
The obvious answer is: “Yes! Of course, an investor armed with New Constructs’ superior research will out-perform those relying on the bad research.” Some might even say that relying on inferior research is a violation of an investor’s fiduciary duty to care for his/her clients.
These days, being the protagonist for the case study on the company I founded is much easier because we have proof in the form of several published papers that our research does, indeed, deliver idiosyncratic alpha.
However, the first time I joined the author of the HBS case, Charles Wang, for the inaugural teaching of the case, we did not have those papers. Back then, as it was for many of the early years at New Constructs, I had to have faith that the markets would eventually care about the truth about profits and valuation. And, having that faith was not always easy. There are quotes in the case that I shared with Charlie from institutional investors, who said: “yeah David, your research is probably superior, but as long as everyone else is relying on inferior research, I am ok with inferior research.” The first time I heard that my head nearly exploded. Another classic is: “David, I think what you’re doing is amazing, but I do not want you to be successful because you’re taking away my competitive advantage.” That one blew me away, too. These quotes come from people who managed $50 billion and more.
I wondered how could these folks ever get to where they are with such a short-sighted attitude? Didn’t they realize that at some point, even if it was not New Constructs, a technology that could analyze the 200 to 2,000 page financial filings would emerge? So, why fight the inevitable? Get on board with the next big technology and get ahead of your competition.
Eventually, I realized that these money managers were like the student naysayers. They had never done the work; so they didn’t realize how difficult and time-consuming it was. Sure, they may have read a few 10-Ks, maybe a couple hundred or even a thousand 10-Ks. But, they had never done the work to pull all of the important data out of the filings and organize it all in a way that it could produce superior research. In other words, they had no idea of the magnitude of the problem that New Constructs was solving.
Over the next few weeks, we are doing a couple of webinars that will show everyone just how big the problem that New Constructs solves is.
We’re going to share research that is uniquely prescient in identifying stocks that outperform. And, let me tell you, it has everything to do with the footnotes. With the footnotes, we’re able to identify stocks with super profits, better than what any other analyst can see. And, we have proof that it works.
Many of you might remember one of our top Long Ideas, AutoZone (AZO). It’s been a very successful pick for us. You can get our original Long Idea report for free here. If you read closely, you might pick up on why the footnotes lead us to feature AZO as early as we did.
Figure 1: AutoZone Performance Over Time
Sources: Yahoo Finance
I am deeply humbled that Charles Wang called me back in 2017 to ask for help with a problem with his BAV class. He said, “David, people don’t want to take my class anymore because they don’t think analyzing financial statements matters any more. There’s not enough value in the footnotes to justify the time required to read and analyze them.”
My response: “Well, they’re right. It’s not worth the time for an individual to do that work. But, what if I told you that there was a technology that could do the work for them, and they could have their cake and eat it, too? They could enjoy all the benefits of footnotes analysis without having to spend the time to do the analysis. Is that of interest to you?”
Charlie said “yes”, of course.
And, the rest is history.
Have a great week.
David
This article was originally published on March 7, 2024.
Disclosure: David Trainer, Kyle Guske II, and Hakan Salt receive no compensation to write about any specific stock, style, or theme.
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