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

Supervised regression–based machine learning is a predictive form of modeling in which the goal is to model the relationship between a target and the predictor variable(s) in order to estimate a continuous set of possible outcomes. These are the most used machine learning models in finance.

One of the focus areas of analysts in financial institutions (and finance in general) is to predict investment opportunities, typically predictions of asset prices and asset returns. Supervised regression–based machine learning models are inherently suitable in this context. These models help investment and financial managers understand the properties of the predicted variable and its relationship with other variables, and help them identify significant factors that drive asset returns. This helps investors estimate return profiles, trading costs, technical and financial investment required in infrastructure, and thus ultimately the risk profile and profitability of a strategy or portfolio.

With the availability of large volumes of data and processing techniques, supervised regression–based machine learning isn’t just limited to asset price prediction. These models are applied to a wide range of areas within finance, including portfolio management, insurance pricing, instrument pricing, hedging, and risk management.

In this chapter we cover three supervised regression–based case studies that span diverse areas, including ...

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Machine Learning and Data Science Blueprints for Finance

by Hariom Tatsat, Sahil Puri, Brad Lookabaugh

Chapter 5. Supervised Learning: Regression (Including Time Series Models)

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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