Kanungo D.K. Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python

O’Reilly Media, 2023. - 265 p. - ISBN: 1492097675.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. 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. 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. Unlike conventional AI, these systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. 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 book shows you how.Who Should Read This Book?The primary audience of this book is the thinking practitioner in the finance and investing discipline. A thinking practitioner is someone who doesn’t merely want to follow instructions from a manual or cookbook. They want to understand the underlying concepts for why they must adopt a process, model, or technology. Generally, they are intellectually curious and enjoy learning for its sake. At the same time, they are not looking for onerous mathematical proofs or tedious academic tomes. I have provided many scholarly references in each chapter for readers who are looking for the mathematical and technical details underlying the concepts and reasoning presented in this book. A thinking practitioner could be an individual investor, analyst, developer, manager, project manager, data scientist, researcher, portfolio manager, or quantitative trader. These thinking practitioners understand that they need to learn new concepts and technologies continually to advance their careers and businesses. A practical depth of understanding gives them the confidence to apply what they learn to develop creative solutions for their unique challenges. It also gives them a framework to explore and learn related technologies and concepts more easily. In this book, I am assuming that readers have a basic familiarity with finance, statistics, machine learning, and Python. I am not assuming that they have read any particular book or mastered any particular skill. I am only assuming that they have a willingness to learn, especially when ChatGPT, Bard, and Bing AI can easily explain any code or formula in this book.True PDF