Statistical Decision Theory for Finance. Utility, Loss, and Choice Under Uncertainty: A Comprehensive Guide - Alice Schwartz;Hayden Van Der Post

Statistical Decision Theory for Finance introduces the mathematical logic behind financial decisions made under uncertainty. Rather than treating finance as a collection of fixed formulas, this book focuses on how analysts, investors, researchers, and quantitative practitioners can evaluate choices when outcomes are uncertain, information is incomplete, and trade-offs are unavoidable.

The book examines core ideas from statistical decision theory and connects them to financial reasoning, including utility, loss, risk preferences, Bayesian thinking, expected value, model uncertainty, and decision rules. Readers are guided through the conceptual foundations that shape portfolio choice, forecasting, risk assessment, and model-based financial analysis.

Inside, readers will explore:

Written for finance students, analysts, quants, researchers, and technically minded readers, this book provides a structured introduction to the decision frameworks behind modern financial analysis.

Statistical Decision Theory for Finance is not a trading system or investment advice manual. It is a technical guide to understanding how financial choices can be modeled, evaluated, and compared under uncertainty.