"Irene Aldridge and Marco Avellaneda are articulate enthusiasts for Big Data Finance. They have a deep knowledge of neural networks, artificial intelligence, machine learning, and many other tools―and they are excited to share their skills. Each chapter of this wonderful book entices the reader with a broad overview, and then shows how these new concepts can be applied in financial markets. The authors are Big Data visionaries whose book belongs on your desk, not on your bookshelf."
―Elroy Dimson, Professor of Finance, Cambridge Judge Business School
"A timely, engaging, satisfying read told in a clear and lively style that wins access to a host of complex ideas. Big Data Science in Finance reaches for a broader audience than the usual subject-matter experts―and succeeds."
―Bruce Ells, VP and Director, Infrastructure Investments, TD Greystone Asset Management
"Asset managers and hedge funds are acutely aware that delivering alpha is becoming simultaneously more important and difficult. Given this background, Big Data and machine learning have become essential sources of new differentiating alpha. This much needed timely text on Big Data in finance is a refreshingly hands-on introduction to this essential subject matter that should advance the understanding of these methods and their application in modern portfolio management."
―Bernd Wuebben, Global Head, Fixed Income Quantitative Research and Systematic Investing, AllianceBernstein
"WOW! My first glance reminds me of the tried and true approach―provide theoretical background, then show implementable examples. I am actually thinking of using the book for a 'Data in Finance' offering I am working on."
―John Paul Broussard, Professor of Finance, Rutgers University and Estonian Business School
"Two of the most important figures in AI Finance have come out with a must-read Tour de Force! Soon to be a stable textbook in all of our top MBA programs."
―Jim Kyung-Soo Liew, Professor, Johns Hopkins Carey Business School
Open shell or cmd prompt (on windows, search for "cmd")
Use "python -m pip install" command to download numpy, pandas and matplotlib to start, for instance:
python -m pip install numpy
Open IDLE, VS Code or another Python editor you prefer.
You are off to a successful start!
Chapter 2, Coding Neural Networks
In Chapter 2 of the book, we develop a neural network
from scratch to illustrate the principles step-by-step
and help the reader really understand the underlying
process. Here, we show how to save considerable time
by deploying prepackaged neural networks/deep learning tools.
We show a specific step-by-step example of neural network
development on the U.S. market data.
Chapter 3, Supervised Models
Chapter 4, Semi-Supervised Models
Chapter 5, Unsupervised Models -- Intro
Chapter 6, Unsupervised Factor Models
Chapter 7, Unsupervised Signal vs. Noise
Chapter 8, Unsupervised Applications
Chapter 9, Clustering
Registration and passwords are required. Please Register