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Full Description
Apply machine learning and AI to real financial challenges, from data analysis to strategy automation, and gain practical skills to thrive in investment, risk management, and quantitative finance roles
Key Features
Based on the author's Cornell MFE course and proven classroom success
Cutting-edge material in both finance and machine learning
Includes exclusive access to a website with additional learning resources
Book DescriptionIn a world where machine learning and AI are becoming increasingly prevalent, it is crucial not to be left behind. This book goes beyond the typical machine learning and Python books by incorporating in-depth finance content to provide readers with a unique understanding of how these technologies intersect in the financial realm.
Starting with a review of the basics of financial and econometric analyses and coding, readers will quickly and intuitively progress to more sophisticated techniques. The book equips readers with the necessary knowledge to successfully apply machine learning and AI to various finance-related problems. Instead of solely focusing on the intricacies of machine learning algorithms, this book emphasizes the strategic use of machine learning and python as enablers for solving real-world finance problems.
By the end of the book, readers will not only have a solid grasp of different types of advanced AI mechanisms, but they will also possess the ability to compare various machine learning techniques and select the most appropriate one for the specific problem at hand. Bridging the gap between theory and practice, readers will be able to build their own efficient and effective machine learning models to tackle the challenges and complexities of the finance industry.
What you will learn
Learn the fundamentals of financial analysis with Python
Understand different types of advanced machine learning and AI mechanisms and how they fit into today's quantitative research frameworks
Select the best machine learning techniques for the financial problem at hand
Analyze financial data and structure sound forecasts
Build backtests and reports required by investors
Develop in-house AI models that automate the processes
Who this book is forThis book is ideal for aspiring quants, financial engineers, data scientists, and investment professionals looking to apply machine learning and AI in real-world finance. It also serves quantitative finance students and fintech practitioners aiming to build their own data-driven trading strategies or hedge funds. A basic understanding of Python, probability, and linear algebra is helpful but not required
Contents
Table of Contents
Machine Learning and Modern Financial Landscape
Prices and Returns
Investment Performance Measures
Data Cleaning
Risk-Return Tradeoffs and Efficient Frontier
Model Performance, Linear Regression and Factor Models
Linear Regression, Statistical Arbitrage and Market-Neutral Strategies
Penalized Regressions and Portfolio Optimization
K-Nearest Neighbors and Support Vector Machines
Bayesian Learning
Decision Trees
Random Forests
Semi-supervised Learning
Neural Networks
Transformers
Unsupervised Learning
Explaining returns
Advanced Portfolio Strategies
Microstructure Investing
Options Pricing
Build Your Own Hedge Fund
