Machine Learning Models in Quantitative Finance

Reactive Publishing

Machine Learning Models in Quantitative Finance: A Practical Guide to Forecasting, Pricing, and Signal Generation

By Vincent Bisette

Unlock the power of machine learning in financial markets-without needing a PhD in data science.

This hands-on guide delivers a focused, tactical approach to integrating machine learning into quantitative finance. Designed for analysts, traders, and finance professionals, this book demystifies the process of applying ML to real-world financial data for forecasting, pricing models, and signal generation.

Inside, you'll discover:

• Practical ML models tailored for time series, options pricing, and strategy development

• Step-by-step implementation using Python and Excel

• Techniques to engineer features, reduce overfitting, and optimize model performance

• Case studies on using random forests, XGBoost, and neural networks for alpha generation

• How to build ML pipelines that integrate seamlessly with existing quant workflows

You won't find generic theory or fluff-just battle-tested tools and frameworks that work in volatile markets. Whether you're building your first predictive model or fine-tuning your algo trading stack, this book gives you the edge.

Finance moves fast. So should your models.