Artificial Intelligence in Finance: A Python-Based Guide

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

Many industries have been revolutionized by the widespread adoption of AI and machine learning. The programmatic availability of historical and real-time financial data in combination with techniques from AI and machine learning will also change the financial industry in a fundamental way. This practical book explains 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 how machine and deep learning algorithms can be applied to finance. Thanks to lots of self-contained Python examples, you’ll be able to replicate all results and figures presented in the book.

  • Examine how data is reshaping finance from a theory-driven to a data-driven discipline
  • Understand the major possibilities, consequences, and resulting requirements of AI-first finance
  • Get up to speed on the tools, skills, and major use cases to apply AI in finance yourself
  • Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets
  • Delve into the concepts of the technological singularity and the financial singularity
Title: Artificial Intelligence in Finance A Python-Based Guide
Author:
ISBN: 1492055433,9781492055433
Publisher: O’Reilly Media, Incorporated
Genre: Business & Economics / Finance / General
Date Published:
Pages: 474
Preview Link: Google Preview Link

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