Big Data Science in Finance (Wiley, 2021)

by Irene Aldridge and Marco Avellaneda

The book is now available on Amazon.com!


Marco Avellaneda


Dr. Marco Avellaneda

Consultant and Professor of Mathematics, NYU Courant

More: http://www.math.nyu.edu/faculty/avellane, Marco-Avellaneda.com

Irene Aldridge


Irene Aldridge

Quant and Big Data Finance researcher, AbleMarkets, and Adjunct Professor at Cornell University

More: IreneAldridge.com



Reviews

"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


Table of Contents

For a more detailed outline, please see lesson plans

  1. Why Big Data?
  2. The changes in market structure, technology and mathematical innovation driving the trend.
  3. Neural Networks in Finance
  4. How Neural Networks work, and how to build a profitable neural network from scratch. Applications: daily price data.
  5. Supervised Learning
  6. Supervised Learning Models, step-by-step. Applications: foreign exchange, equities, fixed income pricing.
    • Ridge Regression, LASSO, Elastic Nets
    • K Nearest Neighbors (K-NN)
    • Decision Trees, Random Decision Forests and Extra Trees
    • Support Vector Machines (SVMs)
  7. Modeling Human Behavior with Semi-Supervised Learning
  8. Modeling expert researchers' decision-making process to enhance your current research framework. Applications: predicting equity ratings.
    • Performance Evaluation via Cross-Validation
    • Generative Models
    • Discriminative Models
    • Graph-based Models
  9. Letting the Data Speak with Unsupervised Learning
  10. How the artificial intelligence unsupervised learning models work, step-by-step, and why they are the future of Finance.
    • Dimensionality Reduction in Finance
    • Dimensionality Reduction with Unsupervised Learning
    • Singular Value Decomposition
    • Deconstructing Financial Returns
    • Singular Vectors as Portfolio Weights
    • Principal Component Regression
    • Key Big Data Tools: SVD and PCA in Detail
  11. Big Data Factor Models
  12. POET and other optimal factor models. Applications: Optimal portfolio management.
    • Optimal Factorization
    • Eigenportfolios
    • Factor Discovery
    • Approximate Factor Models, Unknown Factors (POET), Instrumented PCA, The Three Pass Model, Risk-Premium PCA, Nonlinear Factorization, Projected PCA
  13. Data as a Signal vs. Noise
  14. How to determine the optimal number of factors and how to treat missing observations with the power of Big Data.
    • Random data show in Eigenvalue distribution
    • What's in a data bag?
    • The Marcenko-Pastur Theorem
    • Spike Model
    • Dealing with Highly Correlated Data
    • The Karhunen-Loeve Transform
    • Data Imputation
    • Missing Eigenvalues
    • The Tracy-Widom Distribution
    • Id Missing Streaming Data (the Johnson-Lindenstrauss Lemma)
  15. Applications: Unsupervised Learning in Option Pricing and Stochastic Modeling
  16. Volatility surface modeling and predicting credit rating migration.
  17. Data Clustering
  18. Applications: Optimal Portfolio Management for Cryptocurrencies and Commodities.


Code

Download Python code profiled in the book (in addition to the code at the end of each chapter), free of charge.

Chapter 1, Intro to Data Science in Python

  1. Download Python from Python.org/downloads (click here to be redirected to Python.org)
  2. Download historical daily data for, say, SPY from Yahoo Finance by accessing https://finance.yahoo.com/quote/SPY/history?p=SPY. To download daily data for other symbols, replace SPY with the ticker of your choice. For example, to download IBM data, access https://finance.yahoo.com/quote/IBM/history?p=IBM A screenshot of the Yahoo Finance page is shown below.
  3. Download relevant Python libraries:
    • 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
  4. Open IDLE, VS Code or another Python editor you prefer.
  5. 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

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Lesson Plans

Download free lesson plans:


Test/Interview Questions

Download questions to use on your homeworks, tests, interviews and more, all free of charge.

Chapter 1, Intro to Data Science in Python

Coming soon

Chapter 2, Coding Neural Networks

Coming soon

Chapter 3, Supervised Models

Coming soon

Chapter 4, Semi-Supervised Models

Coming soon

Chapter 5, Unsupervised Models -- Intro

Coming soon

Chapter 6, Unsupervised Factor Models

Coming soon

Chapter 7, Unsupervised Signal vs. Noise

Coming soon

Chapter 8, Unsupervised Applications

Coming soon

Chapter 9, Clustering

Coming soon

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Media


Irene Aldridge discusses Ch 2 code on this website: how to build a successful 1-day ahead multi-layer neural network predictor with PyTorch


Irene Aldridge and Stacey Mankoff on the future of AI in Finance

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