The widespread adoption of AI and machine learning is revolutionizing
many industries today. Once these technologies are combined with the
programmatic availability of historical and real-time financial data,
the financial industry will also change fundamentally. With this
practical book, you'll learn how to use AI and machine learning to
discover statistical inefficiencies in financial markets and exploit
them through algorithmic trading.
Author Yves Hilpisch shows practitioners, students, and academics in
both finance and data science practical ways to apply machine learning
and deep learning algorithms to finance. Thanks to lots of
self-contained Python examples, you'll be able to replicate all results
and figures presented in the book.
In five parts, this guide helps you:
- Learn central notions and algorithms from AI, including recent
breakthroughs on the way to artificial general intelligence (AGI) and
superintelligence (SI)
- Understand why data-driven finance, AI, and machine learning will have
a lasting impact on financial theory and practice
- Apply neural networks and reinforcement learning to discover
statistical inefficiencies in financial markets
- Identify and exploit economic inefficiencies through backtesting and
algorithmic trading--the automated execution of trading strategies
- Understand how AI will influence the competitive dynamics in the
financial industry and what the potential emergence of a financial
singularity might bring about
