Machine Learning and Data Science Blueprints for Finance

Machine Learning and Data Science Blueprints for Finance

by Hariom Tatsat, Sahil Puri, Brad Lookabaugh
Released November 2020
Publisher(s): O'Reilly Media, Inc.
ISBN: 9781492073055

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Book description

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).

Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples.

This book covers:

  • Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management
  • Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies
  • Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction
  • Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management
  • Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management
  • NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

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Table of contents

  1. Preface
    1. Who This Book Is For
    2. How This Book Is Organized
      1. Part I: The Framework
      2. Part II: Supervised Learning
      3. Part III: Unsupervised Learning
      4. Part IV: Reinforcement Learning and Natural Language Processing
    3. Conventions Used in This Book
    4. Using Code Presented in the Book
    5. Python Libraries
    6. O’Reilly Online Learning
    7. How to Contact Us
    8. Acknowledgments
      1. Special Thanks from Hariom
      2. Special Thanks from Sahil
      3. Special Thanks from Brad
  2. I. The Framework
  3. 1. Machine Learning in Finance: The Landscape
    1. Current and Future Machine Learning Applications in Finance
      1. Algorithmic Trading
      2. Portfolio Management and Robo-Advisors
      3. Fraud Detection
      4. Loans/Credit Card/Insurance Underwriting
      5. Automation and Chatbots
      6. Risk Management
      7. Asset Price Prediction
      8. Derivative Pricing
      9. Sentiment Analysis
      10. Trade Settlement
      11. Money Laundering
    2. Machine Learning, Deep Learning, Artificial Intelligence, and Data Science
    3. Machine Learning Types
      1. Supervised
      2. Unsupervised
      3. Reinforcement Learning
    4. Natural Language Processing
    5. Chapter Summary
  4. 2. Developing a Machine Learning Model in Python
    1. Why Python?
    2. Python Packages for Machine Learning
      1. Python and Package Installation
    3. Steps for Model Development in Python Ecosystem
      1. Model Development Blueprint
    4. Chapter Summary
  5. 3. Artificial Neural Networks
    1. ANNs: Architecture, Training, and Hyperparameters
      1. Architecture
      2. Training
      3. Hyperparameters
    2. Creating an Artificial Neural Network Model in Python
      1. Installing Keras and Machine Learning Packages
      2. Running an ANN Model Faster: GPU and Cloud Services
    3. Chapter Summary
  6. II. Supervised Learning
  7. 4. Supervised Learning: Models and Concepts
    1. Supervised Learning Models: An Overview
      1. Linear Regression (Ordinary Least Squares)
      2. Regularized Regression
      3. Logistic Regression
      4. Support Vector Machine
      5. K-Nearest Neighbors
      6. Linear Discriminant Analysis
      7. Classification and Regression Trees
      8. Ensemble Models
      9. ANN-Based Models
    2. Model Performance
      1. Overfitting and Underfitting
      2. Cross Validation
      3. Evaluation Metrics
    3. Model Selection
      1. Factors for Model Selection
      2. Model Trade-off
    4. Chapter Summary
  8. 5. Supervised Learning: Regression (Including Time Series Models)
    1. Time Series Models
      1. Time Series Breakdown
      2. Autocorrelation and Stationarity
      3. Traditional Time Series Models (Including the ARIMA Model)
      4. Deep Learning Approach to Time Series Modeling
      5. Modifying Time Series Data for Supervised Learning Models
    2. Case Study 1: Stock Price Prediction
      1. Blueprint for Using Supervised Learning Models to Predict a Stock Price
    3. Case Study 2: Derivative Pricing
      1. Blueprint for Developing a Machine Learning Model for Derivative Pricing
    4. Case Study 3: Investor Risk Tolerance and Robo-Advisors
      1. Blueprint for Modeling Investor Risk Tolerance and Enabling a Machine Learning–Based Robo-Advisor
    5. Case Study 4: Yield Curve Prediction
      1. Blueprint for Using Supervised Learning Models to Predict the Yield Curve
    6. Chapter Summary
    7. Exercises
  9. 6. Supervised Learning: Classification
    1. Case Study 1: Fraud Detection
      1. Blueprint for Using Classification Models to Determine Whether a Transaction Is Fraudulent
    2. Case Study 2: Loan Default Probability
      1. Blueprint for Creating a Machine Learning Model for Predicting Loan Default Probability
    3. Case Study 3: Bitcoin Trading Strategy
      1. Blueprint for Using Classification-Based Models to Predict Whether to Buy or Sell in the Bitcoin Market
    4. Chapter Summary
    5. Exercises
  10. III. Unsupervised Learning
  11. 7. Unsupervised Learning: Dimensionality Reduction
    1. Dimensionality Reduction Techniques
      1. Principal Component Analysis
      2. Kernel Principal Component Analysis
      3. t-distributed Stochastic Neighbor Embedding
    2. Case Study 1: Portfolio Management: Finding an Eigen Portfolio
      1. Blueprint for Using Dimensionality Reduction for Asset Allocation
    3. Case Study 2: Yield Curve Construction and Interest Rate Modeling
      1. Blueprint for Using Dimensionality Reduction to Generate a Yield Curve
    4. Case Study 3: Bitcoin Trading: Enhancing Speed and Accuracy
      1. Blueprint for Using Dimensionality Reduction to Enhance a Trading Strategy
    5. Chapter Summary
    6. Exercises
  12. 8. Unsupervised Learning: Clustering
    1. Clustering Techniques
      1. k-means Clustering
      2. Hierarchical Clustering
      3. Affinity Propagation Clustering
    2. Case Study 1: Clustering for Pairs Trading
      1. Blueprint for Using Clustering to Select Pairs
    3. Case Study 2: Portfolio Management: Clustering Investors
      1. Blueprint for Using Clustering for Grouping Investors
    4. Case Study 3: Hierarchical Risk Parity
      1. Blueprint for Using Clustering to Implement Hierarchical Risk Parity
    5. Chapter Summary
    6. Exercises
  13. IV. Reinforcement Learning and Natural Language Processing
  14. 9. Reinforcement Learning
    1. Reinforcement Learning—Theory and Concepts
      1. RL Components
      2. RL Modeling Framework
      3. Reinforcement Learning Models
      4. Key Challenges in Reinforcement Learning
    2. Case Study 1: Reinforcement Learning–Based Trading Strategy
      1. Blueprint for Creating a Reinforcement Learning–Based Trading Strategy
    3. Case Study 2: Derivatives Hedging
      1. Blueprint for Implementing a Reinforcement Learning–Based Hedging Strategy
    4. Case Study 3: Portfolio Allocation
      1. Blueprint for Creating a Reinforcement Learning–Based Algorithm for Portfolio Allocation
    5. Chapter Summary
    6. Exercises
  15. 10. Natural Language Processing
    1. Natural Language Processing: Python Packages
      1. NLTK
      2. TextBlob
      3. spaCy
    2. Natural Language Processing: Theory and Concepts
      1. 1. Preprocessing
      2. 2. Feature Representation
      3. 3. Inference
    3. Case Study 1: NLP and Sentiment Analysis–Based Trading Strategies
      1. Blueprint for Building a Trading Strategy Based on Sentiment Analysis
    4. Case Study 2: Chatbot Digital Assistant
      1. Blueprint for Creating a Custom Chatbot Using NLP
    5. Case Study 3: Document Summarization
      1. Blueprint for Using NLP for Document Summarization
    6. Chapter Summary
    7. Exercises
  16. Index

Product information

  • Title: Machine Learning and Data Science Blueprints for Finance
  • Author(s): Hariom Tatsat, Sahil Puri, Brad Lookabaugh
  • Release date: November 2020
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781492073055