Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python

by Hariom Tatsat, Sahil Puri, Brad Lookabaugh

Overview

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

Product Details

ISBN-13:	9781492073055
Publisher:	O'Reilly Media, Incorporated
Publication date:	12/15/2020
Pages:	429
Product dimensions:	7.00(w) x 9.19(h) x (d)

About the Author

Hariom Tatsat currently works as a Vice President in the Quantitative Analytics division of an investment bank in New York. Hariom has extensive experience as a Quant in the areas of predictive modelling, financial instrument pricing, and risk management in several global investment banks and financial organizations. He completed his MS at UC Berkeley and his BE at IIT Kharagpur (India). Hariom has also completed FRM (Financial Risk Manager), CQF (Certificate in Quantitative Finance) and is a candidate for CFA Level 3.

Sahil Puri works as a Quantitative Researcher in the Analytics Division at P.I.M.C.O. His work involves testing model assumptions and finding strategies for multiple asset classes. Sahil has applied multiple statistical and machine learning based techniques to a wide variety of problems; examples include: generating text features, labeling curve anomalies, non-linear risk factor detection, and time series prediction. He completed his MS at UC Berkeley and his BE at Delhi College of Engineering (India).

Table of Contents

Preface ix

Part I The Framework

1 Machine Learning in Finance: The landscape 1

Current and Future Machine Learning Applications in Finance 2

Algorithmic Trading 2

Portfolio Management and Robo-Advisors 2

Fraud Detection 3

Loans/Credit Card/Insurance Underwriting 3

Automation and Chatbots 3

Risk Management 4

Asset Price Prediction 4

Derivative Pricing 4

Sentiment Analysis 5

Trade Settlement 5

Money Laundering 5

Machine Learning, Deep Learning, Artificial Intelligence, and Data Science 5

Machine Learning Types 7

Supervised 7

Unsupervised 8

Reinforcement Learning 9

Natural Language Processing 10

Chapter Summary 11

2 Developing a Machine Learning Model in Python 13

Why Python? 13

Python Packages for Machine Learning 14

Python and Package Installation 15

Steps for Model Development in Python Ecosystem 15

Model Development Blueprint 16

Chapter Summary 29

3 Artificial Neural Networks 31

ANNs: Architecture, Training, and Hyperparameters 32

Architecture 32

Training 34

Hyperparameters 36

Creating an Artificial Neural Network Model in Python 40

Installing Keras and Machine Learning Packages 40

Running an ANN Model Faster: GPU and Cloud Services 43

Chapter Summary 45

Part II Supervised Learning

4 Supervised Learning: Models and Concepts 49

Supervised Learning Models: An Overview 51

Linear Regression (Ordinary Least Squares) 52

Regularized Regression 55

Logistic Regression 57

Support Vector Machine 58

K-Nearest Neighbors 60

Linear Discriminant Analysis 62

Classification and Regression Trees 63

Ensemble Models 65

ANN-Based Models 71

Model Performance 73

Overfitting and Underfitting 73

Cross Validation 74

Evaluation Metrics 75

Model Selection 79

Factors for Model Selection 79

Model Trade-off 81

Chapter Summary 82

5 Supervised Learning: Regression (Including Time Series Models) 83

Time Series Models 86

Time Series Breakdown 87

Autocorrelation and Stationarity 88

Traditional Time Series Models (Including the ARIMA Model) 90

Deep Learning Approach to Time Series Modeling 92

Modifying Time Series Data for Supervised Learning Models 95

Case Study 1 Stock Price Prediction 95

Blueprint for Using Supervised Learning Models to Predict a Stock Price 97

Case Study 2 Derivative Pricing 114

Blueprint for Developing a Machine Learning Model for Derivative Pricing 115

Case Study 3 Investor Risk Tolerance and Robo-Advisors 125

Blueprint for Modeling Investor Risk Tolerance and Enabling a Machine Learning-Based Robo-Advisor 127

Case Study 4 Yield Curve Prediction 141

Blueprint for Using Supervised Learning Models to Predict the Yield Curve 142

Chapter Summary 149

Exercises 150

6 Supervised Learning: Classification 151

Case Study 1 Fraud Detection 153

Blueprint for Using Classification Models to Determine Whether a Transaction Is Fraudulent 153

Case Study 2 Loan Default Probability 166

Blueprint for Creating a Machine Learning Model for Predicting Loan Default Probability 167

Case Study 3 Bitcoin Trading Strategy 179

Blueprint for Using Classification-Based Models to Predict Whether to Buy or Sell in the Bitcoin Market 180

Chapter Summary 190

Exercises 191

Part III Unsupervised Learning

7 Unsupervised Learning: Dimensionality Reduction 195

Dimensionality Reduction Techniques 197

Principal Component Analysis 198

Kernel Principal Component Analysis 201

t-distributed Stochastic Neighbor Embedding 202

Case Study 1 Portfolio Management: Finding an Eigen Portfolio 202

Blueprint for Using Dimensionality Reduction for Asset Allocation 203

Case Study 2 Yield Curve Construction and Interest Rate Modeling 217

Blueprint for Using Dimensionality Reduction to Generate a Yield Curve 218

Case Study 3 Bitcoin Trading: Enhancing Speed and Accuracy 227

Blueprint for Using Dimensionality Reduction to Enhance a Trading Strategy 228

Chapter Summary 236

Exercises 236

8 Unsupervised Learning: Clustering 237

Clustering Techniques 239

k-means Clustering 239

Hierarchical Clustering 240

Affinity Propagation Clustering 242

Case Study 1 Clustering for Pairs Trading 243

Blueprint for Using Clustering to Select Pairs 244

Case Study 2 Portfolio Management: Clustering Investors 259

Blueprint for Using Clustering for Grouping Investors 260

Case Study 3 Hierarchical Risk Parity 267

Blueprint for Using Clustering to Implement Hierarchical Risk Parity 268

Chapter Summary 277

Exercises 277

Part IV Reinforcement Learning and Natural Language Processing

9 Reinforcement Learning 281

Reinforcement Learning-Theory and Concepts 283

RL Components 284

RL Modeling Framework 288

Reinforcement Learning Models 293

Key Challenges in Reinforcement Learning 298

Case Study 1 Reinforcement Learning-Based Trading Strategy 298

Blueprint for Creating a Reinforcement Learning-Based Trading Strategy 300

Case Study 2 Derivatives Hedging 316

Blueprint for Implementing a Reinforcement Learning-Based Hedging Strategy 317

Case Study 3 Portfolio Allocation 334

Blueprint for Creating a Reinforcement Learning-Based Algorithm for Portfolio Allocation 335

Chapter Summary 344

Exercises 345

10 Natural Language Processing 347

Natural Language Processing: Python Packages 349

NLTK 349

TextBIob 349

SpaCy 350

Natural Language Processing: Theory and Concepts 350

1 Preprocessing 351

2 Feature Representation 356

3 Inference 360

Case Study 1 NLP and Sentiment Analysis-Based Trading Strategies 362

Blueprint for Building a Trading Strategy Based on Sentiment Analysis 363

Case Study 2 Chatbot Digital Assistant 383

Blueprint for Creating a Custom Chatbot Using NLP 385

Case Study 3 Document Summarization 393

Blueprint for Using NLP for Document Summarization 394

Chapter Summary 400

Exercises 400

Index 401