Generative Finance Without LLMs: Applying Probabilistic ML


Generative AI, and Chat GPT-4 in particular, is all the rage these days. Probabilistic machine learning (ML) is a type of generative AI that is ideally suited for finance and investing. Unlike deep neural networks, on which ChatGPT is based, probabilistic ML models are not black boxes. These models also enable you to infer causes from effects in a fairly transparent manner. This is important in heavily regulated industries, such as finance and healthcare, where you have to explain the basis of your decisions to many stakeholders.

There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual reasoning. Probabilistic ML also lets you systematically encode personal, empirical, and institutional knowledge into ML models. Unlike conventional AI, these systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. You won’t get such quantified doubts from ChatGPT’s confident hallucinations, more commonly known as fibs and lies.

Whether they're based on academic theories or ML strategies, all financial models are subject to modeling errors that can be mitigated but not eliminated. Probabilistic ML systems treat uncertainties and errors of financial and investing systems as features, not bugs. And they quantify uncertainty generated from inexact inputs and outputs as probability distributions, not point estimates. This makes for realistic financial inferences and predictions that are useful for decision-making and risk management.

By moving away from flawed statistical methodologies and a restrictive conventional view of probability as a limiting frequency, you’ll move toward an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty. This tutorial will introduce you the fundamental concepts, processes and technologies so that you can get started using this powerful generative AI framework.

Session Outline:

Module 1
Learning objective: The trifecta of errors in all financial models
Python code review: Errors in estimating interest rates on credit cards

Module 2
Learning objective: Three types of uncertainty and the meaning of probability
Exercise: Deriving the inverse probability rule - mistakenly known as Bayes' theorem and applying it to the Monty Hall problem
Python code review: Simulating the solution to the Monty Hall problem

Module 3
Learning objective: Why conventional statistical inferences are deeply flawed
Exercise: How Null Hypothesis Significance Testing is guilty of the Prosecutor's fallacy and p-value commits the inverse fallacy
Python code review: Why confidence intervals should not be applied to market data analysis

Module 4
Learning objective: Learning and applying the probabilistic ML framework
Python Code review: Estimating the earnings expectations of a company

Background Knowledge:

Basic Python and High School mathematics/statistics


Deepak K. Kanungo is an algorithmic derivatives trader, educator, inventor, author and CEO of Hedged Capital, an AI-powered proprietary trading company he founded in 2009. Since 2019, Deepak has taught tens of thousands of O’Reilly Media subscribers worldwide the concepts, processes, and machine learning technologies for algorithmic trading, investing, and finance with Python. He is the author of the award-winning book Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python.

In 2005, long before machine learning was an industry buzzword, Deepak invented a probabilistic machine learning method and software system for managing the risks and returns of project portfolios. It is a unique probabilistic framework that has been cited by IBM, Fujitsu and Accenture, among others. See his patent filing here.

Previously, Deepak was a financial advisor at Morgan Stanley during the Great Financial Crisis, a Silicon Valley fintech entrepreneur, a director in the Global Planning Department at Mastercard International, and a senior analyst with Diamond Technology Partners. He was educated at Princeton University (astrophysics) and the London School of Economics (finance and information systems).

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