Professional and Marketing Summary

Authors: Matthew F. Dixon, Igor Halperin, Paul Bilokon
Publisher: Springer Nature Switzerland AG (2020)
Available with lifetime access at: www.bigdataconsult.fr

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General Overview

This reference work explores the integration of Machine Learning into quantitative finance, combining academic rigor with real-world applications. Written by three leading experts from top institutions (NYU, Imperial College, Illinois Tech), it provides an in-depth understanding of how Artificial Intelligence is transforming financial markets through data-driven innovation.

Objective and Scope

The book offers a clear and structured approach—bridging the gap between algorithmic theory and practical implementation in finance.
Its core ambition is to demonstrate how Machine Learning, Deep Learning, and Reinforcement Learning are reshaping risk modeling, derivatives pricing, and automated trading strategies.

Structured Content and Key Highlights

The book is organized into three major parts:

1. Foundations of Machine Learning Applied to Finance

• Regression, SVM, Random Forests, and Gradient Boosting for predicting defaults and market events.

• Introduction to neural architectures (CNN, LSTM, RNN) tailored to financial time series.

2. Practical Applications and Use Cases

• Neural network-based pricing models for financial derivatives.

• Risk management and Value-at-Risk (VaR) estimation using Machine Learning.

• Sentiment analysis from financial text data through Natural Language Processing (NLP).

3. Emerging Technologies and Automation

• Reinforcement Learning for autonomous trading systems.

• Integration and deployment of ML models in production environments.

• Financial performance optimization through the Sharpe Ratio and other advanced metrics.

Target Audience

This book is designed for:

• Quantitative Analysts (Quants) seeking to enhance their models with AI techniques.

• Financial Data Scientists working with complex, high-dimensional data.

• Graduate and Doctoral Students in finance, applied mathematics, or financial engineering.

• Trading System Developers aiming to design self-learning algorithms.

Strengths and Added Value

• High-level expertise: Authored by internationally recognized researchers.

• Direct applicability: Every concept is supported by source code and practical examples.

• Free online resources: Access to code samples and additional materials via official links.

• Results-oriented methodology: Techniques adapted to the realities of the financial sector.

Benefits for the Reader

• Master AI tools specifically applied to finance.

• Develop high-performance models for risk management, price forecasting, and trading automation.

• Understand and implement Reinforcement Learning techniques in a professional setting.

• Anticipate technological shifts in the FinTech and Financial Data Science sectors.

Legal Notice and BIG DATA CONSULT Guarantee

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Every purchase made via www.bigdataconsult.fr grants lifetime access to the digital content and its educational resources.
Learners thus benefit from a complete, reliable, and lasting resource, fully integrated into our Quantitative Finance and Data Science training programs.