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.

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