Bayesian Decision Making for Traders & CFOs

Probabilistic Thinking, Hierarchical Models, and Real-World Risk Applications with Python & Stan

Produkt bewerten

Alice Schwartz

Verlag:

Independently Published

Unsere-Artikel-Nr.: P35725564

EAN: 9798184645643

Erhältlich:

Nicht auf Lager

Zustellung: Di, 08.09.2026

Versand: Kostenlos

CHF 52.–

Beschreibung

Reactive Publishing. Bayesian Decision Making for Traders & CFOs. delivers a practical, code-first framework for applying Bayesian inference to real-world financial problems. You'll move beyond basic probability to build and deploy hierarchical models that capture complex market dynamics, investor behavior, and organizational risk. What You'll Discover Inside:. How to apply Bayesian thinking to trading strategies, portfolio construction, and risk management. Building hierarchical models that account for varying market regimes and individual heterogeneity. Real-world applications in volatility forecasting, credit risk, option pricing, and capital allocation. Full Python implementations using Stan (CmdStanPy) for efficient Bayesian computation. Techniques for model checking, posterior predictive checks, and decision analysis under uncertainty. Practical workflows for integrating Bayesian outputs into trading systems and executive dashboards. Written for quantitative traders, risk managers, and forward-thinking CFOs, this book bridges theory and practice with clear explanations, complete code examples, and reproducible case studies. Whether you're refining high-frequency signals or making multi-million-dollar capital decisions, you'll gain the probabilistic tools needed to navigate uncertainty with confidence. Perfect for practitioners who want to move past black-box models and develop transparent, adaptable decision frameworks.

Spezifikationen

Sprache

Englisch

Autor

James Preston
Vincent Bisette
Alice Schwartz

Erscheinungsjahr

2026

Format

Buch (Softcover)

Anzahl Seiten