AI in fundamental investing
Machine learning has been rapidly growing in the public markets field and I figure that in the future it will help analyze big data and be used as a better, more advanced screener. Essentially, AI will be an assistant researcher for the portfolio manager of 2030 and the investing professionals of the future will need to have data science skills in addition to the fundamentals. But I can be wrong, so I wanted to know what everyone else thinks about the relation that AI and fundamental investing will have in the future.
It's already being used, just not in the way non-practitioners think it is.
Some places where AI is more of a buzzword than anything else include:
The distinction I want to draw is that data science does not imply AI. You'd be surprised that, even at quant firms, most of the data science being done is different forms of linear regression. At fundamental firms, it's usually just clever data cleaning, group-by, and sum. There are several reasons for this, but ultimately, the data scientists need to convince the analyst, PM, and CIO of their conclusions. Machine learning won't convince them, because they won't understand it.
With that, I do agree that financial analysts and data scientists will converge toward each other. Given how prevalent data has become in fundamental investing, the most successful data scientists can speak the language of finance, understand what drives a business, and deliver a useful result while abstracting the messy details away. And the most successful analysts won't mind getting their hands dirty with a BI tool or even SQL. They'll start munging through the data to find interesting ideas that the data scientists can spend more time fleshing out.
So basically, analysts of the future will have to know how to analyze large data and understand it but will not be required to write any code since that is going to be the job of the data scientist who will have to present the data to to pm.
I’m actually in the process of writing a research paper about this topic so maybe I can chime in. Data science is already used as an “advanced screener” for fundamental analysis, so I don’t expect anything new or groundbreaking in that regard. The real draw of ML applied to fundamentals (at least for me) would be improved predictive accuracy of credit defaults, rating changes, bankruptcies, etc.
As another poster stated, most firms don’t understand how to apply ML to fundamental analysis or have unrealistic expectations about what can be predicted and the accuracy of the models. As a side note, I think object oriented programming will become standardized sometime in the future and will be as ubiquitous as excel.
When will your research paper be done, once it is can you send it to me? I want to learn more about how data science is used in fundamental investing and figure that reading you research paper can only help.