Advances in Financial Machine Learning

Synopsis

Learn to understand and implement the latest machine learning innovations to improve your investment performance

Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that - until recently - only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.

In the book, readers will learn how to:

• Structure big data in a way that is amenable to ML algorithms
• Conduct research with ML algorithms on big data
• Use supercomputing methods and back test their discoveries while avoiding false positives

Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting.

Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Summary

Chapter 1: Introduction to Financial Machine Learning

This chapter provides an overview of the field of financial machine learning, including its history, key concepts, and applications. It also discusses the challenges and opportunities in using machine learning techniques for financial data analysis.

Real-world example: A hedge fund uses machine learning to predict stock market returns. The fund trains a model on historical stock data to identify patterns that can help predict future price movements. By leveraging these insights, the fund can make more informed investment decisions.

Chapter 2: Supervised Learning Techniques for Financial Data

This chapter covers supervised learning techniques used in financial machine learning, such as regression, classification, and time series forecasting. It explains how to select and train appropriate models for different financial prediction tasks.

Real-world example: A bank uses a supervised learning model to assess credit risk. The model is trained on data about borrowers' credit histories, loan applications, and other relevant factors. The bank uses the trained model to predict the probability of default for new loan applicants, helping them make more informed lending decisions.

Chapter 3: Unsupervised Learning Techniques for Financial Data

This chapter discusses unsupervised learning techniques, including clustering and dimensionality reduction, and their applications in financial data analysis. It explains how to use these techniques to identify patterns and extract useful insights from large and complex datasets.

Real-world example: A financial data analytics company uses unsupervised learning to identify groups of similar customers. By understanding the different customer segments, the company can develop targeted marketing campaigns and improve customer satisfaction.

Chapter 4: Feature Engineering for Financial Data

This chapter focuses on feature engineering techniques for financial data. It covers feature selection, transformation, and creation methods that enhance the predictive performance of machine learning models.

Real-world example: A wealth management firm uses feature engineering to improve the accuracy of its stock prediction model. By selecting relevant features, such as historical prices, earnings, and economic indicators, and transforming them using logarithmic or exponential functions, the firm significantly boosts the predictive power of its model.

Chapter 5: Evaluation and Validation of Financial Machine Learning Models

This chapter discusses methods for evaluating and validating financial machine learning models. It covers metrics for performance assessment, such as accuracy, precision, recall, and F1-score. It also explains techniques for model selection, hyperparameter tuning, and cross-validation to ensure the robustness and generalizability of the models.

Real-world example: An investment firm evaluates the performance of its portfolio optimization model using historical data. The firm calculates the Sharpe ratio, a measure of risk-adjusted return, for its model's suggested portfolios. By comparing the model's performance to benchmarks and alternative models, the firm validates its effectiveness in generating superior returns.