The Financial Data Playbook is a compendium of thoughtful business lessons from a career financial machine learning engineer. It offers insights on how to navigate the business expectations and theoretical challenges of modern applied finance.
Each argument is presented as a careful mathematical discussion that illustrates how investors, executives, and engineers think about and allocate resources to high-risk high-reward research and development projects.
Readers will learn about:
- Measuring the business value of improving model accuracy
- Improving the accuracy and validity of investment performance simulations
- Estimating the amount of data required to improve a model
- Choosing the right model for the data
- Comparing the performance of modern and legacy methods
- Accounting for counter-party expectations to improve accuracy
- And more …
This book is written for finance and data business managers who want to maximize their financial data science R&D investment by understanding the opportunities and constraints presented by recent advances in financial machine learning.
Where to Buy
Available at Amazon.