book

Machine Learning and Data Science Blueprints for Finance

by Hariom Tatsat, Sahil Puri, Brad Lookabaugh

November 2020

Beginner to intermediate

429 pages

10h 40m

English

O'Reilly Media, Inc.

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Preface
Who This Book Is ForHow This Book Is OrganizedPart I: The FrameworkPart II: Supervised LearningPart III: Unsupervised LearningPart IV: Reinforcement Learning and Natural Language ProcessingConventions Used in This BookUsing Code Presented in the BookPython LibrariesO’Reilly Online LearningHow to Contact UsAcknowledgmentsSpecial Thanks from HariomSpecial Thanks from SahilSpecial Thanks from Brad
I. The Framework
1. Machine Learning in Finance: The Landscape
Current and Future Machine Learning Applications in FinanceAlgorithmic TradingPortfolio Management and Robo-AdvisorsFraud DetectionLoans/Credit Card/Insurance UnderwritingAutomation and ChatbotsRisk ManagementAsset Price PredictionDerivative PricingSentiment AnalysisTrade SettlementMoney LaunderingMachine Learning, Deep Learning, Artificial Intelligence, and Data ScienceMachine Learning TypesSupervisedUnsupervisedReinforcement LearningNatural Language ProcessingChapter Summary
2. Developing a Machine Learning Model in Python
Why Python?Python Packages for Machine LearningPython and Package InstallationSteps for Model Development in Python EcosystemModel Development BlueprintChapter Summary
3. Artificial Neural Networks
ANNs: Architecture, Training, and HyperparametersArchitectureTrainingHyperparametersCreating an Artificial Neural Network Model in PythonInstalling Keras and Machine Learning PackagesRunning an ANN Model Faster: GPU and Cloud ServicesChapter Summary
II. Supervised Learning
4. Supervised Learning: Models and Concepts
Supervised Learning Models: An OverviewLinear Regression (Ordinary Least Squares)Regularized RegressionLogistic RegressionSupport Vector MachineK-Nearest NeighborsLinear Discriminant AnalysisClassification and Regression TreesEnsemble ModelsANN-Based ModelsModel PerformanceOverfitting and UnderfittingCross ValidationEvaluation MetricsModel SelectionFactors for Model SelectionModel Trade-offChapter Summary
5. Supervised Learning: Regression (Including Time Series Models)
Time Series ModelsTime Series BreakdownAutocorrelation and StationarityTraditional Time Series Models (Including the ARIMA Model)Deep Learning Approach to Time Series ModelingModifying Time Series Data for Supervised Learning ModelsCase Study 1: Stock Price PredictionBlueprint for Using Supervised Learning Models to Predict a Stock PriceCase Study 2: Derivative PricingBlueprint for Developing a Machine Learning Model for Derivative PricingCase Study 3: Investor Risk Tolerance and Robo-AdvisorsBlueprint for Modeling Investor Risk Tolerance and Enabling a Machine Learning–Based Robo-AdvisorCase Study 4: Yield Curve PredictionBlueprint for Using Supervised Learning Models to Predict the Yield CurveChapter SummaryExercises
6. Supervised Learning: Classification
Case Study 1: Fraud DetectionBlueprint for Using Classification Models to Determine Whether a Transaction Is FraudulentCase Study 2: Loan Default ProbabilityBlueprint for Creating a Machine Learning Model for Predicting Loan Default ProbabilityCase Study 3: Bitcoin Trading StrategyBlueprint for Using Classification-Based Models to Predict Whether to Buy or Sell in the Bitcoin MarketChapter SummaryExercises
III. Unsupervised Learning

7. Unsupervised Learning: Dimensionality Reduction
Dimensionality Reduction TechniquesPrincipal Component AnalysisKernel Principal Component Analysist-distributed Stochastic Neighbor EmbeddingCase Study 1: Portfolio Management: Finding an Eigen PortfolioBlueprint for Using Dimensionality Reduction for Asset AllocationCase Study 2: Yield Curve Construction and Interest Rate ModelingBlueprint for Using Dimensionality Reduction to Generate a Yield CurveCase Study 3: Bitcoin Trading: Enhancing Speed and AccuracyBlueprint for Using Dimensionality Reduction to Enhance a Trading StrategyChapter SummaryExercises
8. Unsupervised Learning: Clustering
Clustering Techniquesk-means ClusteringHierarchical ClusteringAffinity Propagation ClusteringCase Study 1: Clustering for Pairs TradingBlueprint for Using Clustering to Select PairsCase Study 2: Portfolio Management: Clustering InvestorsBlueprint for Using Clustering for Grouping InvestorsCase Study 3: Hierarchical Risk ParityBlueprint for Using Clustering to Implement Hierarchical Risk ParityChapter SummaryExercises
IV. Reinforcement Learning and Natural Language Processing
9. Reinforcement Learning
Reinforcement Learning—Theory and ConceptsRL ComponentsRL Modeling FrameworkReinforcement Learning ModelsKey Challenges in Reinforcement LearningCase Study 1: Reinforcement Learning–Based Trading StrategyBlueprint for Creating a Reinforcement Learning–Based Trading StrategyCase Study 2: Derivatives HedgingBlueprint for Implementing a Reinforcement Learning–Based Hedging StrategyCase Study 3: Portfolio AllocationBlueprint for Creating a Reinforcement Learning–Based Algorithm for Portfolio AllocationChapter SummaryExercises
10. Natural Language Processing
Natural Language Processing: Python PackagesNLTKTextBlobspaCyNatural Language Processing: Theory and Concepts1. Preprocessing2. Feature Representation3. InferenceCase Study 1: NLP and Sentiment Analysis–Based Trading StrategiesBlueprint for Building a Trading Strategy Based on Sentiment AnalysisCase Study 2: Chatbot Digital AssistantBlueprint for Creating a Custom Chatbot Using NLPCase Study 3: Document SummarizationBlueprint for Using NLP for Document SummarizationChapter SummaryExercises
Index

Content preview from Machine Learning and Data Science Blueprints for Finance

Chapter 10. Natural Language Processing

Natural language processing (NLP) is a subfield of artificial intelligence used to aid computers in understanding natural human language. Most NLP techniques rely on machine learning to derive meaning from human languages. When text has been provided, the computer utilizes algorithms to extract meaning associated with every sentence and collect essential data from them. NLP manifests itself in different forms across many disciplines under various aliases, including (but not limited to) textual analysis, text mining, computational linguistics, and content analysis.

In the financial landscape, one of the earliest applications of NLP was implemented by the US Securities and Exchange Commission (SEC). The group used text mining and natural language processing to detect accounting fraud. The ability of NLP algorithms to scan and analyze legal and other documents at a high speed provides banks and other financial institutions with enormous efficiency gains to help them meet compliance regulations and combat fraud.

In the investment process, uncovering investment insights requires not only domain knowledge of finance but also a strong grasp of data science and machine learning principles. NLP tools may help detect, measure, predict, and anticipate important market characteristics and indicators, such as market volatility, liquidity risks, financial stress, housing prices, and unemployment.

News has always been a key factor in investment decisions. ...

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Machine Learning and Data Science Blueprints for Finance

by Hariom Tatsat, Sahil Puri, Brad Lookabaugh

Chapter 10. Natural Language Processing

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