We are interested, even fascinated, in how much the analysis in this book can be mechanized. Spreadsheets, we know, help and engines can work a few things through once the inputs are given to them. Though open-minded, we are skeptical that a computer can come to the judgments from understanding the business, bringing in the accounting to represent the business, and finally coming up with an investment decision. Machines like formulas, but we have emphasized that valuation formulas are for fools. Can a machine come up with the analysis to understand the market’s pricing of growth then reach the assessment whether that growth…risky growth…is growth to buy? Can a machine incorporate all the thinking about value in this book (and more that is yet to come)? We wait to see.
A 2024 paper asks whether a LLM can do financial statement analysis and, from that analysis, develop credible earnings forecasts. It feeds financial statements (without companies’ names) to GPT4 and finds the artificial intelligence predicts earnings with more accuracy than analysts. Analysts are a good benchmark for they are not only human intelligence but supposedly have expert systems. Of course, it is well known that analysts also have biases and institutional incentives that affect their forecasts.
See Kim, A., M. Muhn, and V. Nilolaev. 2024. Financial Statement Analysis with Large Language Models. Chicago Booth Research Paper. At https://ssrn.com/abstract=4835311.
Of course, earnings prediction is only part of the task. One has to convert the forecast into a trading strategy.
In valuation, the investor asks: What will future financial statements look like? The investor then uses those predictions to challenge the market price: Can the market price, particularly the pricing of growth expectations, be justified by pro forma future financial statements? The following paper applies AI to the task:
Geertsema. P., H. Lu, and G. Ma. 2024. Projecting Financial Statements with Artificial Intelligence. At https://ssrn.com/abstract=5039433.