The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic:
- The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence.
- The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance.
The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author’s two decades of professional experience as a technologist, quant and academic.
2. The Role of Data in Trading and Investing
3. Artificial Intelligence – Between Myth and Reality
4. Computational Intelligence – A Principled Approach for the Era of Data Exploration
5. How to Apply the Principles of Computational Intelligence in Quantitative Finance
6. Case Study 1: Optimizing Trade Execution
7. Case Study 2: The Dynamics of the Limit Order Book
8. Case Study 3: Applying Machine Learning to Portfolio Management
9. Case Study 4: Applying Machine Learning to Market Making
10. Case Study 5: Applications of Machine Learning to Derivatives Valuation
11. Case Study 6: Using Machine Learning for Risk Management and Compliance
12. Conclusions and Future Directions