Workbook for Volume 1 – Part IV - Section #1: AI, ML & LLMs as Proxies for the State-of-the-Art in the Application of the “Tools” of the Logic & Statistics Program to Financial Economics
For new readers: Please read the “Pinned Post” at the top of this Substack’s Home Page, and titled Why Use Public Peer-Review to Write a Book? - “See for Yourself”.
For returning readers and subscribers: This post introduces a Revised Version for Volume 1 – Part IV - Section #1: AI, ML & LLMs as Proxies for the State-of-the-Art in the Application of the “Tools” of the Logic & Statistics Program to Financial Economics
Summary:
Volume 1 – Part IV - Section #1: AI, ML & LLMs as Proxies for the State-of-the-Art in the Application of the “Tools” of the Logic & Statistics Program to Financial Economics - This section starts Part IV – Making Good Investments Decisions with a review of Artificial Intelligence (AI), Machine Learning (ML), and Large Language Models (LLMs) applications as a proxy for benchmarking the state-of-the-art in the application of the “Tools” of the Logic & Statistics Program to Financial Economics. Two papers - Buczynski, et al. (2021) and Gautier, et al (2020) - that reviewed the state-of-the-art in the application of AI/ML to Financial Economics over the last 25 years found that this level of effort has not yet created observable improvements in the Financial Industry’s “Predictions” and investment outcomes. In 2023, LLMs attracted the largest number users in the shortest amount of time in the history of the adoption of computer-based innovations. LLMs give us the ability to communicate with computers with ambiguous plain English instead of having to communicate with them with precise computer languages. This rapid increase in end-user power comes at a cost, as illustrated by the July 19, 2023 paper “Challenges and Applications of Large Language Models” by Jean Kaddour, et al., and it seems too early to know what value LLMs will bring to the Financial Industry. This state of affairs means that the development of AI/ML/LLM applications should start with Ole Peters’ methodological insight. Researcher and entrepreneurs will benefit from going back to the earliest, foundational papers - as shown in Part II – Section #14: Ergodicity Economics: A Meaningful “Repair Program” of both the Logic & Statistics Program and the Heuristics & Bias Program – in order to build up automated complexity from an explicit understanding of the “Axioms, Assumptions & Hypotheses” that drive each and every Financial Model.
Developing…
”CTRI by Francois Gadenne” writes a business book in three volumes, published serially on Substack for public peer-review. The book connects the dots of life-enhancing practices for the next generation, free of controlling algorithms, based on the lifetime experience of a retirement age entrepreneur, & continuously updated with insights from reading Wealth, Health, & Statistics research papers on behalf of large companies as the co-founder of CTRI.