Artificial Intelligence in Finance: A Python-Based Guide

Artificial Intelligence in Finance: A Python-Based Guide
PDF, ePUB
  • eBook:
    Artificial Intelligence in Finance: A Python-Based Guide
  • Author:
    Yves Hilpisch
  • Edition:
    1 edition
  • Categories:
  • Data:
    November 3, 2020
  • ISBN:
    1492055433
  • ISBN-13:
    9781492055433
  • Language:
    English
  • Pages:
    478 pages
  • Format:
    PDF, ePUB

Book Description
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

Content

I. Machine Intelligence
1. Artificial Intelligence
2. Superintelligence

II. Finance and Machine Learning
3. Normative Finance
4. Data-Driven Finance
5. Machine Learning

III. Statistical Inefficiencies
7. Dense Neural Networks
8. Recurrent Neural Networks
9. Reinforcement Learning

IV. Algorithmic Trading
10. Vectorized Backtesting
11. Risk Management
12. Execution and Deployment

V. Outlook
13. AI-Based Competition
14. Financial Singularity

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