There was quite a bit of hubbub over narratives as a way to manipulate people five to ten years ago. This hubbub was mainly among the credentialed class who are themselves more susceptible to the charms of a good story well told. The high point was probably the use of the word in the title of Klaus Schwab's book “The Great Narrative” in 2022.
The working person doesn't read the news for the "narrative", they want the scores of their preferred sport and whether Timmy was rescued from the well he fell into. In this sense they are more fact-based than the been-to-college crew.
Another group, one that I fall into, are the semi-numerate, good enough at math to be facile but lacking the deep understanding that a real master effortlessly exhibits. This group is prone to overestimating their ability to manipulate numbers which leads to a weak point those who are smarter can use to manipulate. Because of this I have to constantly remind myself to ask: "What if I'm wrong." and then to attempt to discern what signs to look for that would indicate error.
All this double-checking cuts into one's cat video time.
So here we go with both the alpha [not that sort of alpha] and the numeric, combined.
From the University of Chicago's Booth School of Business, Chicago Booth Review, December 5:
Large Language Models Can Improve Stock Market Forecasts
In investing as in life, numbers are important—but context matters too. Research using large language models and deep learning could help investors squeeze more value out of company financial statements while also advancing the application of machine learning, write Chicago Booth PhD student Alex Kim and Booth’s Valeri Nikolaev.
The US Securities and Exchange Commission requires publicly traded companies to include a management discussion and analysis of quarterly and annual financial statements. The researchers focused on analyzing what value the MD&A narrative content adds to the raw numbers.
Kim and Nikolaev used BERT, a large language model developed by Google and designed to capture the context-based meaning of words and sentences, to analyze thousands of annual MD&As from 1995 to 2020. By then using an artificial neural network to combine the extracted narrative context with the numbers from the company financial statements, the researchers were able to quantify how important the interactions between the numbers and the context were for projecting earnings, cash flows, and returns.
Using the numbers on their own to make these predictions had value, find the researchers, who also found value in projections that incorporated only the contextual information. But a model they developed that allows the full interaction between context and numbers achieved a higher accuracy, significant enough to move the needle for investors. This model, says Kim, is analogous to how the interconnected neurons in our brain process information. And the model’s superior performance, he explains, confirms that considering numbers with their corresponding contextual information—as many investors may already do, albeit in a more manual and less sophisticated way—is worthwhile in prediction tasks.
