“This book clarifies many of the mysteries of Neural Networks and related optimization techniques for researchers in both economics and finance. It contains many practical examples backed up with computer programs for readers to explore.”

— Blake LeBaron, Professor of Finance, Brandeis University

“An important addition to the select collection of books on financial econometrics, serving as an important reference on neural network models of nonlinear dynamics as a practical econometric tool for better decision-making in financial markets.”

— Roberto S. Mariano, Dean of School of Economics, Singapore Management University

1. Foundation Building: Begin with mathematical prerequisites and neural network fundamentals
2. Financial Context: Study market dynamics and econometric applications
3. Implementation Phase: Practice with provided code examples and datasets
4. Advanced Techniques: Explore optimization methods and performance evaluation
5. Real-World Application: Apply learned concepts to actual financial problems

I purchased this book after struggling with traditional forecasting models that consistently underperformed in volatile markets. McNelis provides exactly what practitioners need – clear explanations without overwhelming mathematical jargon, plus immediately usable code examples. The evolutionary computational tools section alone justified the investment. After implementing several techniques from Chapter 7, our portfolio’s risk-adjusted returns improved significantly. The book bridges academia and practical application better than any finance text I’ve encountered. For anyone serious about quantitative finance, this is essential reading that delivers measurable results.