Artificial intelligence in finance : a Python-based guide / Yves Hilpisch.

Format:

Book

Author/Creator:

Hilpisch, Yves J., author.

Language:

English

Subjects (All):

Financial services industry--Information technology.

Financial services industry.

Physical Description:

1 online resource (475 pages)

Edition:

1st edition

Place of Publication:

Beijing : O'Reilly, [2021]

System Details:

text file

Summary:

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

Contents:

Part 1. Machine intelligence. 1. Artificial intelligence

2. Superintelligence

Part 2. Finance and machine learning. 3. Normative finance

4. Data-driven finance

5. Machine learning

6. AI-first finance

Part 3. Statistical inefficiencies. 7. Dense neural networks

8. Recurrent neural networks

9. Reinforcement learning

Part 4. Algorithmic trading. 10. Vectorized backtesting

11. Risk management

12. Execution and deployment

Part 5. Outlook. 13. AI-based competition

14. Financial singularity

Part 6. Appendixes.

Notes:

Includes bibliographical reference and index.

Description based on print version record.

ISBN:

9781492055389

1492055387

9781492055402

1492055409

9781492055426

1492055425

OCLC:

1202814895