لایب سان

Author(s): Hariom Tatsat, Sahil Puri , Brad Lookabaugh

Publisher: O'Reilly Media, Year: 2020

ISBN: 1492073059,9781492073055

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 iii 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 iv | Table of Contents 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 Table of Contents | v 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 vi | Table of Contents 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 TextBlob 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 Table of Contents | vii

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