Non-Linearity in Financial Modeling - Vol 3

Practical Guide to Machine Learning in Financial Applications
Erscheinungsjahr 2026
ISBN: 978-3-032-33035-2
Verlag: Springer Nature Switzerland AG

Buch, Englisch, 238 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Dynamic Modeling and Econometrics in Economics and Finance

Financial markets are inherently nonlinear, noisy, and full of hidden structure. This book provides a clear and practical pathway for mastering machine learning in such an environment, guiding readers from foundational understanding to confident application. Rather than overwhelming the reader with an encyclopedic list of algorithms, it focuses on a carefully selected set of methods that offer the best balance of performance, interpretability, and robustness. By concentrating on these core approaches, the book equips you to understand not only how to use machine learning models, but also why they behave as they do and how to improve them thoughtfully.

Through an accessible and disciplined approach, the text shows how to build solutions that avoid the common traps of financial modeling—overfitting, misapplied validation, and the temptation to chase patterns that vanish upon closer inspection. Readers learn how to design workflows that respect temporal dependencies, handle imbalanced data responsibly, and translate theoretical concepts into models that deliver meaningful and trustworthy results.

This book serves quantitative finance professionals seeking modern tools with statistical rigor, data scientists transitioning into finance who must adapt to domain-specific challenges, and students or researchers who require practical guidance alongside their theoretical training. It also supports practitioners versed in Python or other analytical platforms who want to enhance their capabilities in financial contexts, as well as engineers responsible for deploying machine learning systems in production environments. For readers with an interest in the role of machine learning in finance, it provides a clear and intuitive introduction to advanced techniques without unnecessary complexity.

This book offers a grounded and practical guide for anyone aiming to turn machine learning concepts into reliable, high-value applications in the financial world.

Zielgruppe

Graduate

Autoren/Hrsg.

Maitra, Sarit

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensfinanzen
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
• Wirtschaftswissenschaften Wirtschaftswissenschaften Wirtschaftswissenschaften: Allgemeines
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie

Inhaltsverzeichnis

Chapter 01. Introduction to Machine Learning.- Chapter 02. Anomaly Detection: Application of Machine Learning.- Chapter 03. Financial Econometrics with Machine Learning.- Chapter 04. Decision Tree & Random Forest.- Chapter 05. Machine Learning - Overcoming Real-World Challenges.- Chapter 06. Theory-to-Production: Building ML Pipeline.- Chapter 07. Introduction to Deep Learning and Neural Networks.- Chapter 08. Statistical Foundations & ML for Trading Strategies.

Über Autor(innen)

Sarit Maitra received his Ph.D. in information technology from Universiti Teknologi PETRONAS, Malaysia. He is currently affiliated with Alliance School of Business, Alliance University, Bengaluru, India, as Professor, Business Analytics. He comes with nearly three decades of industry experience, specialized in data/big data and business analytics. With deep expertise in data strategy and decision science, he leverages both linear and nonlinear modeling approaches to power simulation, optimization, and decision-support systems consistently translating complex data into measurable business outcomes. He leverages his industry to transform data into actionable insights, lead high-performing teams, and align analytics initiatives with organizational goals. He has contributed to several scholarly works and publications in leading academic journals. He plays a key role in multiple consulting engagements, spearheading analytics strategy and data-driven business decisions to deliver business strategy and success.